Qualitative Methods And Approaches In Logistics : Part 1 9781845446475, 9780861767168

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International Journal of

ISSN 0960-0035

Physical Distribution & Logistics Management

Volume 32 Number 6 2002

Qualitative methods and approaches in logistics: part 1 Editor Professor James R. Stock Paper format The International Journal of Physical Distribution & Logistics Management includes ten issues in traditional paper format. The contents of this issue are detailed below.

Internet Online Publishing with Archive, Reference Linking, Emerald WIRE, Key Readings, Research Register, Institution-wide Licence, E-mail Alerting Service and Usage Statistics. Access via the Emerald Web site: http://www.emeraldinsight.com/ft See overleaf for full details of subscriber entitlements.

Access to International Journal of Physical Distribution & Logistics Management online ________ 398 Editorial advisory board ___________________________ 399 Abstracts and keywords ___________________________ 400 French abstracts___________________________________ 401 Spanish abstracts __________________________________ 403 Japanese abstracts_________________________________ 405 Editorial __________________________________________ 407 Identifying sources of uncertainty to generate supply chain redesign strategies Jack G.A.J. van der Vorst and Adrie J.M. Beulens _____________________

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Implementing collaborative forecasting to improve supply chain performance Teresa M. McCarthy and Susan L. Golicic ___________________________

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A qualitative examination of factors affecting reverse logistics systems for end-of-life computers A. Michael Knemeyer, Thomas G. Ponzurick and Cyril M. Logar _________

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Linking logistics to strategy in Argentina Octavio Carranza, Arnold Maltz and Juan Pablo Antu´n ________________

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CONTENTS

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EDITORIAL ADVISORY BOARD Dr Prabir Bagchi Professor of Logistics & Management, George Washington University, USA Dr Ronald H. Ballou Professor of Operations, Case Western Reserve University, USA Rick D. Blasgen Vice President Supply Chain, Nabisco Inc., USA Dr Joseph L. Cavinato Senior Vice President, National Association of Purchasing Management, USA Dr Garland Chow Associate Professor of Logistics, University of British Columbia, Canada Dr Martin Christopher Professor of Marketing and Logistics, Cranfield School of Management, UK Dr David J. Closs Professor of Marketing and Logistics, Michigan State University, USA Dr Jacques Colin Institut Universitaire Technologie, France Dr Rajiv P. Dant Associate Professor of Marketing, Boston University, USA Dr Patricia Daugherty Siegfried Professor of Marketing, Division of Marketing, University of Oklahoma, USA David A. Durtsche TranzAct Technologies, Inc., USA Dr Margaret A. Emmelhainz Associate Professor of Marketing, University of Georgia, USA Graham A. Ewer Chief Executive, Institute of Logistics, UK Patrick Forsyth Oklahoma State University-Tulsa, USA Frances Fowler Miami University, Ohio, USA Thomas L. Freese Principal, Freese & Associates, Inc., USA Dr Jerry Goolsby Associate Professor of Marketing, University of South Florida, USA Dr Bernard J. Hale Logistics Consultant, USA Dr Anthony F. Han Professor of Transportation Management, National Chiao Tung University, Taiwan, Republic of China Dr James L. Heskett UPS Foundation Professor of Business Logistics, Harvard University, USA Herbert Hodus Consultant, IFM Logistics, USA Dr Daniel E. Innis Associate Dean, Ohio University, USA Dr Zahir Irani Senior Lecturer of Information Systems, Brunel University, UK Olof Johansson University of Umea, Sweden Dr Andrew Kerr Managing Director, Griffin Corporate Services, NSW, Australia Dr Bernard J. La Londe Professor Emeritus, Ohio State University, USA Dr Douglas M. Lambert Raymond E. Mason Professor of Transportation & Logistics, Ohio State University, USA Dr Richard A. Lancioni Professor of Marketing & Logistics, Temple University, USA

Dr C. John Langley Jr Professor of Supply Chain Management, Georgia Institute of Technology, USA Dr Michael Levy Charles Clarke Reynolds Professor of Marketing, Babson College, USA Dr Arvinder P.S. Loomba Associate Professor of Organization and Management, San Jose State University, USA Clifford F. Lynch President, C.F. Lynch & Associates, USA John McCormick University of New South Wales, Australia Professor Alan McKinnon Logistics Research Centre, Heriot-Watt University, Edinburgh, UK Norman E. Marr Division of Marketing, University of Huddersfield, UK Dr G.C. Meeuse Rotterdam, The Netherlands Dr John Thomas Mentzer The Bruce Excellence Chair of Business Policy, University of Tennessee, USA Dr Alan Mercer Professor of Operations Research, Lancaster University, UK Dr Paul Murphy Professor of Marketing and Logistics, John Carroll University, USA Dr Bruce Murtagh Professor of Management, Graduate School of Management, Macquarie University, Australia Dr Pieter Nagel Partner, Burns Bridge Nagel Pty Ltd, Australia Dr R. Mohan Pisharodi Associate Professor of Marketing, Oakland University, USA Dr Cees J. Ruijgrok Professor Logistics Section, INRO-TNO, The Netherlands Dr Jay Sankaran Senior Lecturer, University of Auckland, New Zealand Dr Philip B. Schary Professor Emeritus, Oregon State University, USA Dr Arun Sharma Associate Professor of Marketing, University of Miami, USA Dr Tage Skjott-Larsen Professor, Institute for Logistics and Transport, Copenhagen Business School, Denmark Alan Slater Director, Added Value Logistics Consulting Limited, Manchester, UK Amrik Sohal Director, Monash University, Australia Dr Mark Speece Nanyang Technological University, Singapore Dr Thomas W. Speh Professor of Marketing and Logistics, Miami University, USA Dr Jay U. Sterling Associate Professor of Marketing and Logistics, University of Alabama, USA Dr Diana Twede Associate Professor, Michigan State University, USA Dr Hans van der Hoop Logistics International, Rotterdam, The Netherlands Dr Hugo T.Y. Yoshizaki Assistant Professor of Production Engineering, University of Sa˜o Paulo, Brazil Dr Paul H. Zinszer Associate Professor of Marketing, Syracuse University, USA

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International Journal of Physical Distribution & Logistics Management, Vol. 32 No. 6, 2002, Abstracts and keywords. # MCB UP Limited, 0960-0035

Identifying sources of uncertainty to generate supply chain redesign strategies Jack G.A.J. van der Vorst and Adrie J.M. Beulens Keywords Supply chain management, Uncertainty, Case studies, Food industry Dynamic demands and constraints imposed by a rapidly changing business environment make it increasingly necessary for companies in the food supply chain to cooperate with each other. The main questions individual (food) companies face are whether, why, how and with whom they should start supply chain management activities. Presents a qualitative research method for analyzing a supply chain network and for identifying effective chain redesign strategies. Presents a generic list of supply chain redesign strategies based on a multi-disciplinary literature review. Proposes that in order to identify the most effective strategies in a specific chain scenario one should focus on the identification and management of the sources of uncertainties in the supply chain’s decision-making processes. The application of the research method in three food supply chains resulted in a valuable tool that can be used in supply chain redesign projects, as it indicates potentially effective redesign strategies when a specific source of uncertainty is encountered in a supply chain. Implementing collaborative forecasting to improve supply chain performance Teresa M. McCarthy and Susan L. Golicic Keywords Case studies, Product development, Sales, Forecasting Sales forecasting and collaboration are two business phenomena that have independently been recognized as contributing to improved organizational performance. The present research employs case study methodology to explore the synergies to be gained from combining the two processes. Depth interviews were conducted with executives at three firms currently engaged in collaborative forecasting with supply chain partners. Results revealed unique approaches to collaborative forecasting that circumvent the inhibitors of collaborative planning, forecasting, and replenishment adoption, and yield substantial improvement in company and supply chain performance including

increased responsiveness, product availability assurance, optimized inventory and associated costs, and increased revenues and earnings. Seven guidelines to implementing interfirm collaborative forecasting are presented. A qualitative examination of factors affecting reverse logistics systems for end-of-life computers A. Michael Knemeyer, Thomas G. Ponzurick and Cyril M. Logar Keywords Reverse logistics, Qualitative techniques, Computers, Recycling The current study demonstrates the value of utilizing qualitative research methods to analyze logistics problems. Specifically, the study utilizes a qualitative methodology to examine the feasibility of designing a reverse logistics system to recycle and/or refurbish end-of-life computers that are deemed no longer useful by their owners. The qualitative methodology is a modified version of a customer visit program in which the in-depth interviews were used to identify the special needs of stakeholders who could potentially participate in the proposed system. The qualitative interviews were structured and implemented using a standardized approach set forth in the literature. The results indicate that this qualitative technique proved valuable in obtaining industry-sensitive stakeholder data, which allowed the researchers to more thoroughly analyze the feasibility of the proposed reverse logistics system. Linking logistics to strategy in Argentina Octavio Carranza, Arnold Maltz and Juan Pablo Antu´n Keywords Logistics, Strategy, Argentina, Benchmarking, Customer service Qualitative results of a benchmarking process in logistics areas between companies operating in Argentina are presented. A description of the main logistics processes reengineered by these companies is done and some inferences are taken from the study. The companies are finally analyzed according to another benchmarking process generated in Michigan State University, which leads to a discussion on how companies can be characterized as world class in emerging countries.

French abstracts Identifier les sources d’incertitude pour engendrer des strate´gies de reconception de la chaıˆne d’approvisionnement Jack G.A.J. van der Vorst et Adrie J.M. Beulens Mots-cle´s Gestion de la chaıˆne d’approvisionnement, Incertitude, Etudes de cas pratiques, Industrie alimentaire En raison des demandes et contraintes dynamiques impose´es par un environnement e´conomique en changement rapide, il est de plus en plus ne´cessaire que les socie´te´s, qui se trouvent dans la chaıˆne d’approvisionnement alimentaire, coope`rent l’une avec l’autre. Les questions principales qui se posent aux entreprises (alimentaires) individuelles sont les suivantes: Devraient-elles entamer des activite´s de gestion de la chaıˆne d’approvisionnement? Pourquoi devraient-elles le faire? Comment le feraient-elles et avec qui ? L’article pre´sente une me´thode de recherche qualitative permettant d’analyser un re´seau de chaıˆnes d’approvisionnement et d’identifier des strate´gies efficaces de reconception des chaıˆnes. Il donne une liste ge´ne´rique des strate´gies de reconception de la chaıˆne d’approvisionnement, fonde´es sur un examen de publications multidisciplinaires. Il propose comme suit: afin d’identifier les strate´gies les plus efficaces dans un sce´nario spe´cifique concernant une chaıˆne d’approvisionnement, il faudrait s’efforcer d’identifier et de ge´rer les sources d’incertitude pre´sentes dans les processus de prise de de´cisions dans la chaıˆne d’approvisionnement. L’application de la me´thode de recherche dans trois chaıˆnes d’approvisionnement alimentaires a produit un outil pre´cieux, qui peut eˆtre utilise´ dans des projets de reconception de la chaıˆne d’approvisionnement, car il indique des strate´gies de reconception qui peuvent eˆtre efficaces, lorsque l’on se heurte a` une source d’incertitude spe´cifique dans une chaıˆne d’approvisionnement.

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Mise en oeuvre de la pre´vision en collaboration afin d’ame´liorer la performance de la chaıˆne d’approvisionnement Teresa M. McCarthy et Susan L. Golicic Mots-cle´s Etudes de cas pratiques, Mise au point des produits, Ventes, Pre´visions La pre´vision des ventes et la collaboration sont deux phe´nome`mes e´conomiques qui ont e´te´ reconnus, se´pare´ment, comme contribuant a` l’ame´lioration de la performance organisationnelle. La recherche que voici se sert de la me´thodologie fonde´e sur l’e´tude de cas pratiques pour explorer les synergies que l’on peut obtenir en combinant ces deux processus. Des interviews approfondis furent mene´s aupre`s des cadres dans trois firmes actuellement implique´es dans la pre´vision en collaboration, avec des partenaires dans leur chaıˆne d’approvisionnement. Les re´sultats re´ve´le`rent des approches uniques, utilise´es pour la pre´vision en collaboration, qui permettent d’e´viter les inhibiteurs de l’adoption de CPFR, et qui produisent de nettes ame´liorations dans la performance de l’entreprise et dans celle de la chaıˆne d’approvisionnement, notamment une sensibilite´ accrue, une garantie quant a` la disponibilite´ des produits, une ame´lioration de l’inventaire et des couˆts associe´s, ainsi qu’un accroissement des revenus et des salaires. L’article pre´sente sept re`gles ge´ne´rales pour la mise en pratique de la pre´vision en collaboration entre entreprises. Un examen qualitatif des facteurs affectant les syste`mes de logistique inverse pour les ordinateurs ayant atteint la fin de leur cycle de vie A. Michael Knemeyer, Thomas G. Ponzurick et Cyril M. Logar Mots-cle´s Logistique inverse, Techniques qualitatives, Ordinateurs, Recyclage L’e´tude que voici de´montre la valeur que l’on peut retirer, lorsque l’on utilise des me´thodes de recherche qualitatives pour analyser les proble`mes logistiques. Plus particulie`rement, l’e´tude se sert d’une me´thodologie qualitative pour examiner la faisabilite´ de la conception d’un syste`me de logistique inverse, pour recycler et/ou remettre a` neuf les ordinateurs ayant atteint la fin de leur cycle de vie (EOL - end-of-life) et qui sont conside´re´s, par leur proprie´taires, comme n’e´tant plus utiles. La me´thodologie qualitative est une version modifie´e d’un programme de visite aux

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clients, dans lequel les interviews approfondis furent utilise´s pour identifier les besoins particuliers des parties prenantes, susceptibles de participer au syste`me propose´. Les interviews qualitatifs furent structure´s et mis en oeuvre au moyen d’une approche uniformise´e, de´taille´e dans les publications sur le sujet. Selon les re´sultats, cette technique qualitative s’est ave´re´e pre´cieuse pour obtenir des donne´es sur les parties prenantes, qui sont sensibles a` l’industrie, et qui ont permis aux chercheurs d’analyser plus minutieusement la faisabilite´ du syste`me de logistique inverse propose´. Relier la logistique a` la strate´gie en Argentine Octavio Carranza, Arnold Maltz et Juan Pablo Antu´n Mots-cle´s Logistique, Strate´gie, Argentine, Evaluation des performances, Service au client L’article pre´sente les re´sultats qualitatifs d’un processus d’e´valuation des performances dans des domaines de logistique, entre diverses socie´te´s exploitant en Argentine. Il donne une description des principaux processus de logistique, re´invente´s par ces socie´te´s, et tire certaines conclusions de l’e´tude. Pour terminer, les socie´te´s sont analyse´es selon un autre processus d’e´valuation des performances, mis au point a` l’Universite´ de l’Etat de Michigan, qui donne lieu a` une discussion sur la manie`re dont les socie´te´s peuvent eˆtre caracte´rise´es d’entreprises de classe mondiale dans les pays faisant leur apparition.

Spanish abstracts Identificacio´n de fuentes de incertidumbre para generar estrategias de redisen˜o de cadenas de suministro Jack G.A.J van der Vorst y Adrie J.M. Beulens Palabras clave Gestio´n de la cadena de suministro, Incertidumbre, Estudios de casos, Industria de la alimentacio´n Las exigencias y restricciones dina´micas impuestas por un entorno comercial de cambio ra´pido hacen que cada vez resulte ma´s necesario que las empresas de la cadena de suministro de alimentos cooperen entre sı´. Las cuestiones principales a las que se enfrentan las empresas (alimenticias) individuales son: si, por que´, co´mo y con quie´n deberı´an iniciar actividades de gestio´n de la cadena de suministro. Presenta un me´todo cualitativo de investigacio´n para analizar una red de cadenas de suministro y para identificar estrategias eficaces de redisen˜o de las cadenas de suministro. Asimismo, presenta una lista gene´rica de estrategias de redisen˜o de las cadenas de suministro basada en una revisio´n de bibliografı´a multidisciplinaria. Propone que con motivo de identificar las estrategias ma´s eficaces en un a´mbito de cadenas especı´ficas, uno deberı´a enfocarse en la identificacio´n y gestio´n de las fuentes de incertidumbre en los procesos de toma de decisiones de la cadena de suministro. La aplicacio´n del me´todo de investigacio´n en tres cadenas de suministro de alimentos resulto´ en una herramienta valiosa que puede utilizarse en proyectos de redisen˜o de cadenas de suministro, ya que indica estrategias de redisen˜o potencialmente eficaces cuando se localiza una fuente especı´fica de incertidumbre en una cadena de suministro.

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Implementacio´n de la pronosticacio´n en colaboracio´n para mejorar el rendimiento de las cadenas de suministro Teresa M. McCarthy y Susan L. Golicic Palabras clave Estudios de casos, Desarrollo de productos, Ventas, Pronosticacio´n La pronosticacio´n de ventas y la colaboracio´n son dos feno´menos comerciales que se han reconocido independientemente como factores contribuyentes a un mejor rendimiento organizacional. La investigacio´n actual emplea metodologı´a de estudios de casos para explorar las sinergias que se pueden obtener al combinar los dos procesos. Se realizaron entrevistas en profundidad con ejecutivos de tres empresas involucradas actualmente en la pronosticacio´n en colaboracio´n con socios de la cadena de suministro. Los resultados revelaron planteamientos singulares de la pronosticacio´n en colaboracio´n que evitan a los inhibidores de la adopcio´n de CPFR y resultan en una mejora sustancial del rendimiento de la empresa y la cadena de suministro, incluyendo un aumento de la capacidad de respuesta, aseguramiento de la disponibilidad de productos, inventario optimizado y costes asociados, ası´ como en un incremento de los ingresos por ventas y las ganancias. Se presentan siete guı´as para la implementacio´n de la pronosticacio´n en colaboracio´n entre empresas. Un examen cualitativo de factores que afectan a los sistemas de logı´stica inversa para los ordenadores ‘‘end-of-life’’ A. Michael Knemeyer, Thomas G. Ponzurick y Cyril M. Logar Palabras clave Logı´stica inversa, Te´cnicas cualitativas, Ordenadores, Reciclado El estudio actual demuestra el valor de emplear me´todos de investigacio´n cualitativa para analizar problemas de logı´stica. Especı´ficamente, el estudio utiliza una metodologı´a cualitativa para examinar la viabilidad de disen˜ar un sistema de logı´stica inversa para reciclar y/o renovar ordenadores ‘‘end-of-life’’ (EOL) cuyos propietarios ya no consideran u´tiles. La metodologı´a cualitativa es una versio´n modificada de un programa de visita de clientes en el que las entrevistas en profundidad se utilizaron para identificar la necesidades especiales de grupos con

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intereses que podı´an participar potencialmente en el sistema propuesto. Las entrevistas cualitativas se estructuraron e implementaron empleando un planteamiento estandarizado que se explica en la bibliografı´a. Los resultados indican que esta te´cnica cualitativa resulto´ valiosa para obtener datos sensibles dentro de la industria sobre los grupos con intereses, que permitieron a los investigadores analizar ma´s exhaustivamente la viabilidad del sistema propuesto de logı´stica inversa.

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Conexio´n de la logı´stica con la estrategia en Argentina Octavio Carranza, Arnold Maltz y Juan Pablo Antu´n Palabras clave Logı´stica, Estrategia, Argentina, Puntos de referencia, Servicio al cliente Se presentan los resultados cualitativos de un proceso de establecimiento de puntos de referencia en a´reas logı´sticas entre empresas que operan en Argentina. Se describen los procesos logı´sticos principales redisen˜ados por estas empresas, y se hacen inferencias del estudio. Finalmente, las empresas se analizan segu´n otro proceso de establecimiento de puntos de referencia generado en la Michigan State University, que conduce a una discusio´n sobre co´mo las empresas pueden caracterizarse como de clase mundial en paı´ses emergentes.

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Editorial This is the first of two special issues on ‘‘Qualitative methods and approaches in logistics.’’ Logistics, as well as most other business fields, have long been dominated by the use of quantitative methods. In recent years, the use of various qualitative methods and approaches have been increasing in their usage, although their inclusion in logistics has always been present. For example, executive interviews, case studies, and content analysis have been represented in many logistics books, monographs and articles. Many of these articles have appeared in the pages of IJPDLM. Therefore, their use is not so new, although becoming much more widespread. Additionally, there has always been a tendency for research to utilize qualitative methods and approaches in non-North American environments, particularly in Europe and Asia, where case studies of companies and industries have been common over the years. Even in North America, some qualitative research has been conducted, although not as common. In this first of two special issues, four articles are included that utilize or examine qualitative methods and approaches in a variety of logistics settings. The first paper by van der Vorst and Beulens, ‘‘Identifying sources of uncertainty to generate supply chain redesign strategies,’’ presents a qualitative research method for analyzing a supply chain network and for identifying effective supply chain redesign strategies. The method is applied in a food industry utilizing three supply chains. The second article by McCarthy and Golicic, ‘‘Implementing collaborative forecasting to improve supply chain performance,’’ employs case study methodology to examine the impact of sales forecasting and collaboration on organizational performance. Collective planning, forecasting and replenishment adoption is evaluated utilizing information obtained from indepth interviews with company executives. As a result of the research, seven guidelines for implementing interfirm collaborative forecasting are proposed. The third article by Knemeyer, Ponzurick and Logar, ‘‘A qualitative examination of factors affecting reverse logistics systems for end-of-life computers,’’ uses a qualitative methodology to examine the design of a reverse logistics system to recycle and/or refurbish end-of-life computers. Indepth interviews of customers were utilized to obtain stakeholder data that allowed the researchers to analyze the feasibility of a proposed reverse logistics system. The final article by Carranza, Maltz and Antu´n, ‘‘Linking logistics to strategy in Argentina,’’ presents qualitative results of a benchmarking process

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of Argentinean companies. The study is a first attempt to understand how advanced logistics practices are implemented in emergent countries, specifically Argentina. It is hoped that this, and the second special issue, on qualitative methods and approaches will stimulate their further use in logistics research. Quantitative and qualitative approaches, if done with rigor and quality, can only benefit logistics thought and practice. James R. Stock

The research register for this journal is available at http://www.emeraldinsight.com/researchregisters

The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/0960-0035.htm

Identifying sources of uncertainty to generate supply chain redesign strategies Jack G.A.J. van der Vorst Department of Management Research, Wageningen University, Wageningen, The Netherlands, and

Adrie J.M. Beulens

Supply chain redesign strategies 409 Received June 2001 Revised November 2001 Accepted March 2002

Department of Information Technology, Wageningen University, Wageningen, The Netherlands Keywords Supply chain management, Uncertainty, Case studies, Food industry Abstract Dynamic demands and constraints imposed by a rapidly changing business environment make it increasingly necessary for companies in the food supply chain to cooperate with each other. The main questions individual (food) companies face are whether, why, how and with whom they should start supply chain management activities. Presents a qualitative research method for analyzing a supply chain network and for identifying effective chain redesign strategies. Presents a generic list of supply chain redesign strategies based on a multi-disciplinary literature review. Proposes that in order to identify the most effective strategies in a specific chain scenario one should focus on the identification and management of the sources of uncertainties in the supply chain’s decision-making processes. The application of the research method in three food supply chains resulted in a valuable tool that can be used in supply chain redesign projects, as it indicates potentially effective redesign strategies when a specific source of uncertainty is encountered in a supply chain.

Introduction Companies increasingly see themselves as part of a supply chain that has to compete against other supply chains, rather than as a single firm competing against other individual firms (Christopher, 1998). This holds true especially in food supply chains because of shelf life constraints of food products and increased consumer attention for safe and environment/animal-friendly production methods (Boehlje et al., 1995). Recent events have increased interest in supply chain management (SCM) as a means of improving the strength of supply chains. Examples are the BSE crisis in the UK, classical swine fever in The Netherlands and the recent spread of foot-and-mouth disease in Europe. The tracking and tracing of the whereabouts of the animals and the activities undertaken in the whole supply chain proved to be essential in preventing the further spread of diseases and in gaining consumer trust. These crises made managers aware that incorrect actions at one stage in the supply chain affected the performance of the complete supply chain and that there was a need for integrated control and intensified cooperation in the supply chain. Furthermore, the increased interest in SCM has been spurred by intensified competition due to open EU-markets and developments in information and communication technology (ICT) that

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enable the frequent exchange of huge amounts of information for coordination purposes. Since the 1980s, literature on SCM has stressed the need for collaboration among successive actors, from primary producer to final consumers, to better satisfy consumer demand at lower costs. As defined by the Global Supply Chain Forum, SCM integrates business processes from end user to original suppliers; and it provides products, services and information that add value for customers and stakeholders (Lambert et al., 1998). A driving force behind SCM is the recognition that sub-optimization occurs if each organization attempts to optimize its own results rather than optimize the performance of the chain by integrating its goals and activities with other organizations (Cooper et al., 1997). Stevens (1989) refers to the interdependency of activities: If one activity fails, the chain is disrupted, creating poor performance and destabilizing the workload in other areas, thereby jeopardizing the effectiveness of the supply chain.

Cooper et al. (1997) extend SCM beyond logistics. Based on a review of the literature and management practices, the authors conclude that there is a need for some level of coordination of activities and logistical as well as other business processes within and between organizations in the supply chain. An example is designing products for SCM (e.g. Lee and Sasser, 1995). Considering these definitions of SCM and those of, among others, Ellram (1991), Bechtel and Jayaram (1997) and Lambert and Cooper (2000), we define SCM as follows: SCM is the integrated planning, co-ordination and control of all business processes and activities in the supply chain to deliver superior consumer value at minimum cost to the end-consumer while satisfying requirements of other stakeholders.

In a literature review on SCM, Beamon (1998) found that a number of issues have not yet been adequately addressed in the literature. This conclusion was supported by Lambert and Cooper (2000), who see a need for building theory and developing normative tools and methods for successful SCM practice. The following questions, among many others, were identified by Lambert and Cooper (2000) as potential research opportunities: . How should a firm decide which internal process to link with which supplier(s) and customer(s)? . What decision criteria determine whose internal business processes prevail across all or part of the supply chain? . How should a firm analyze the network to determine if there is a better configuration? . What are potential barriers to implementation and how should they be overcome? Our research aims to fill in part of these knowledge gaps concerning the redesign process of SCM. The main questions individual (food) companies face are whether, why, how and with whom they should start SCM activities. Companies should be able to analyze what SCM can do for them and find out

what the consequences might be if a supply chain view is taken together with one or more supplier and/or customer. The aim of this paper is to present a qualitative research method for analyzing supply chains and for identifying effective chain redesign strategies. In this paper we will: (1) introduce the concept of ‘‘sources of uncertainty in supply chain decision making processes’’ as a key driver for chain redesign; (2) present a generic list of SCM redesign strategies based on a multidisciplinary literature review; (3) present a generic list of sources of uncertainty indicating opportunities for SCM based on three case studies; and (4) conclude with a tool for supply chain redesign, whereby the identification of the sources of uncertainty supports the selection of the relevant SCM redesign strategies. This paper is organized as follows: first, a literature review is presented followed by a discussion of the research model. To identify relevant redesign strategies the concept of supply chain uncertainty is elaborated upon and an overview is given of supply chain redesign strategies. The main section of the paper describes the case study methodology and the main case study results, i.e. a generic list of sources of uncertainty that indicate opportunities for supply chain management. Next, a tool for chain redesign is presented. The paper concludes with a brief discussion and summary of this contribution. Literature review Around 1990, academics first described SCM from a theoretical standpoint to clarify how it differed from more traditional approaches to managing the flow of materials and the associated flow of information (Ellram and Cooper, 1990). Initially, according to Bechtel and Jayaram (1997), the emphasis was on facilitating product movement and coordinating supply and demand between a supplier and buyer. Logistics managers in retail, grocery, and other high inventory industries began to see that a significant competitive advantage could be derived through the management of materials through inbound and outbound channels. Although at the beginning SCM was mainly discussed in purchasing literature, the emphasis now lies on the process of supplying goods to consumers to fulfil their needs. SCM literature provides little information about complete methodologies that could provide guidelines on how to redesign supply chains and evaluate these redesigns qualitatively and quantitatively (Beamon, 1998; Lambert and Cooper, 2000). Most approaches focus on parts of such an integrated methodology. Process mapping techniques, for example, have received particular attention (see Hines and Rich (1997) for an overview). Stern et al. (1996) propose the most generic chain redesign method. They lay out a marketing channel planning approach that permits the reorientation of distribution systems so that they are more responsive to customer needs. Best

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practice distribution systems are designed by bringing together information on the following elements: . Existing system. An accurate description is needed of the current distribution system, the market coverage it provides, the value-added activities it performs, and the present and future challenges it faces. . Ideal distribution system. Starting from a blank sheet the ideal system is designed. This calls for thorough research on end-user wishes in order to segment markets before actually delivering the service outputs. . Constraints. Current and future biases, objectives, constraints, and threats imposed by internal and external factors are identified. Although very useful at the strategic level, the main weakness of this redesign approach is its lack of attention to operational aspects. From a strategic perspective, end-user wishes are identified and translated into chain requirements. But how these requirements are translated into relevant settings for all strategic and operational redesign variables is not clear. ‘‘Do what you think best’’ is the only guiding principle in this more detailed part of the approach. From a more operational perspective, Handfield and Nichols (1999) present an approach for cycle-time reduction, which comprises six steps. They use as a starting point two or more organizations that have agreed to set up a supply chain; but, unlike Stern et al. they focus solely on cycle-time reduction. Beamon and Ware (1998) developed a process quality model that, according to them, can be used to assess the performance of a supply chain system and its subsystems, assist in identifying quality problem areas, and provide a framework for continuous improvement. However, they do not give a methodology on how to establish the improvement other than to ‘‘identify and prioritize improvement plans’’. In this paper we aim to present a more integrated approach to supply chain redesign. Uncertainty and SCM In the literature, uncertainties in supply, process and demand are recognized to have a major impact on the manufacturing function (cf. Wilding, 1998). We agree with Davis (1993), who believes that the real problem in managing and controlling complex networks is ‘‘the uncertainty that plagues them’’. Uncertainty propagates throughout the network and leads to inefficient processing and non-value adding activities. This uncertainty is expressed in questions such as: what will my customers order, how many products should we have in stock, and will the supplier deliver the requested goods on time and according to the demanded specifications? ‘‘The more uncertainty related to a process, the more waste there will be in the process’’ (Persson, 1995). The presence of uncertainty stimulates the decision maker to create safety buffers in time, capacity or inventory to prevent a bad chain performance. These buffers will restrict operational performances and suspend competitive

advantage. We agree with Mason-Jones and Towill (1998), who state that ‘‘those companies which cope best with uncertainty are most likely to produce internationally competitive bottom-line performances’’. But what is ‘‘uncertainty’’? Our definition of supply chain uncertainty is based on the five requirements for effective system management by de Leeuw (2000). If one or more of these requirements is not fulfilled, decision makers in the supply chain will experience uncertainty resulting in ineffectiveness (i.e. not realizing planned objectives): (1) The managing system should have an objective and corresponding performance indicators to manage the supply chain in the right direction. (2) To estimate future system states one has to have information on the environment and current supply chain state. (3) There should be enough information processing capacities to process information on the environment and supply chain state. (4) In order to direct the managed system in the right direction one should be able to estimate the impact of alternative actions. This requires a model of the system, presenting the relationships between available redesign variables and performance indicators. (5) There should be enough potential control actions. Each environmentsupply chain state combination requires one or more different control actions to manage the system in the direction of the objectives. We define supply chain uncertainty as follows: Supply chain uncertainty refers to decision making situations in the supply chain in which the decision maker does not know definitely what to decide as he is indistinct about the objectives; lacks information about (or understanding of) the supply chain or its environment; lacks information processing capacities; is unable to accurately predict the impact of possible control actions on supply chain behavior; or, lacks effective control actions (noncontrollability).

Partnerships with key suppliers and customers may reduce uncertainty and complexity in an ever-changing global environment and minimize risk while maintaining flexibility (Handfield and Nichols, 1999). By breaking down the walls between supply chain stages (and thus enlarging the system), SCM provides the opportunity to reduce decision-making uncertainties within the system, which have been considered by management up to now to be unchangeable external ‘‘givens’’ (Silver et al., 1998; Van der Vorst, 2000). More information and control actions will become available to the decision makers in each stage since additional coordination activities can be employed with suppliers and customers. Hence, they will be able to manage the system in the direction of the organizational and/or supply chain objectives. This process is depicted in Figure 1. After internal integration, uncertainty can be further reduced through coordination with the environment (SCM). The remaining

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Figure 1. Decision-making uncertainty as a design variable and its implications

uncertainty results in emergency measures (e.g. an additional delivery to a customer for only a few products) or deviations from required performance (e.g. lower delivery reliability). The SCM literature discusses a lot of different supply chain redesign strategies that can be used to reduce supply chain uncertainty and, as a result, improve chain performance. However, it is unclear what strategy should be used in what particular situation and, furthermore, a complete list of chain redesign strategies has not been reported up to now. Supply chain redesign strategies Based on an extended multi-disciplinary multi-industry literature review (in SCM, logistics management, business process re-engineering, marketing and operational research journals), we developed a generic list of supply chain redesign strategies mentioned in literature that focus on chain performance improvement. The use of one or several of these redesign strategies will alter the logistical chain scenario, i.e. the design of and logistical way of working in the supply chain. In detail, a chain scenario can be described by four elements: (1) Chain configuration: the structure, facilities and means, the parties involved and the roles to be performed in the supply chain. (2) Chain control structure: the set of decision functions (located at multiple decision layers with different decision horizons) that govern the execution of operational activities aimed at realizing logistical objectives within the constraints set by the chain configuration and strategic objectives (e.g. delivery frequency, order acceptance policy, production planning structure, etc.). (3) Chain information systems: the systems (with their characteristics) that support decision making and/or are required to perform operations (e.g. EDI, ERP, APS, etc.).

(4) Chain organization and governance structures, which assign tasks (along with the corresponding responsibilities and authorities) to organizations and persons in the supply chain.

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Table I presents an overview of redesign strategies, categorized by element of the chain scenario; and it refers to the most relevant articles on each strategy (note that some authors may suggest more redesign strategies).

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Sources of supply chain uncertainty Our research model is based on the following proposition: P. To identify effective chain redesign strategies for a particular established supply chain, one should focus on the identification and management of the sources of uncertainties in decision-making processes that hinder optimal chain performance. According to this proposition, potentially effective redesign strategies can be recognized via the analysis and identification of sources of decision-making uncertainties in the supply chain in the light of chain performance requirements. Sources of uncertainty are characteristic features of the system that are responsible for causing uncertainty for a particular decision maker. These sources of supply chain uncertainty can be categorized as: (1) Inherent characteristics that cause more or less predictable fluctuations (which have stochastic occurrence patterns). Uncertainty may take the form of high variability in demand, process or supply, which in turn creates problems in planning, scheduling and control that jeopardize delivery performance (Fisher et al., 1997). Because of the specific product and process characteristics in food supply chains, such as perishability of end products, variable harvest and production yields and the huge impact of weather conditions on consumer demand, these chains are especially vulnerable to this type of uncertainty. (2) Characteristic features of the chain that result in potential disturbances of system performance (non-optimality): .

chain configuration (e.g. inflexible capacities);

.

chain control structure (e.g. wrong decision rules applied);

.

chain information system (e.g. information delays); and/or

.

chain organization and governance structure (e.g. misjudgment by a decision maker).

(3) Exogenous phenomena that disturb the system, such as changes in markets, products, technology, competitors and governmental regulations. As Van der Heijden (1996) states: Forecasts may work very well for a while, but forecasters need to be aware of the variables that could suddenly break historic patterns and create new trends.

Table I. Supply chain redesign strategies Champy (1995); Christopher (1998); Quinn (1999)

Houlihan (1985); Davenport and Short (1990); Lewis and Naim (1995); Bowersox et al. (1998)

Lee et al. (1997); Garg and Lee (1999)

Womack et al. (1990); Kurt Salmon Associates (1993); Persson (1995)

Stalk and Hout (1990); Hoekstra and Romme (1992); Jordan and Graves (1995); Christopher (1998); Handfield and Nichols (1999)

Stern et al. (1996); Thomas and Griffin (1996); Lee and Tang (1997); Van Hoek (1998)

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Supply chain configuration 1. Redesign the roles and processes in the supply chain a) Change or reduce the number of parties involved b) Change the location of facilities c) Re-allocate the roles actors perform and related processes d) Eliminate non-value-adding activities Supply chain control structure 2. Reduce customer order lead times a) Change position of chain decoupling point b) Implement ICT systems for information exchange and decision support c) Reduce waiting times d) Create parallel administrative and logistical processes e) Increase manufacturing flexibility f) Improve reliability of supply and production quantity and quality 3. Synchronize all logistical processes to customer demand a) Increase number of events per time unit (frequency) for all processes b) Decrease the lot sizes applied in the supply chain 4. Coordinate and simplify logistical decisions a) Coordinate and redesign policies (especially batch sizes) b) Eliminate or reduce human interventions c) Differentiate to products, systems and processes d) Simplify structures, systems, processes and products Supply chain information system 5. Create information transparency in the supply chain a) Establish an information exchange infrastructure in the supply chain and exchange demand, supply, inventory or WIP information b) Increase information timelines by implementing real-time information systems c) Develop a common database and standardize bar coding Supply chain organizational structure 6. Jointly define chain objectives and performance indicators a) Jointly define logistical chain objectives and corresponding chain PIs b) Agree on how to measure logistical performances c) Align employee’s incentives with chain objectives

Main references

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The management task is first to redesign the supply chain for optimal performance in line with chain objectives while reducing the potential for randomness. Then to protect (i.e. redesign) the system against the remaining random probabilistic events and exogenous events that disturb the system. Since this paper focuses on supply chain redesign to achieve optimality within the supply chain, we will leave out the environmental impacts. A potentially effective chain scenario can be established by selecting one or more of the redesign strategies. Therefore, we are interested in the relationship between sources of uncertainty and effective supply chain redesign strategies. When an effective scenario is implemented in a supply chain, this should reduce uncertainties and, as a result, improve chain performance. These relationships are depicted in our research model (Figure 2). The underlying assumption here is that if there were no uncertainties the chain scenario and chain performance in a given environment would be optimal. The next section presents the research methodology that was used to test our proposition. This methodology focuses on the identification of sources of uncertainty in food supply chains and the relationship between these sources and potential SCM redesign strategies.

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Research methodology According to Yin (1994), the case study is the preferred strategy in exploratory research, because: . ‘‘how’’ questions are posed to identify operational links, which have to be traced over time; . the investigator has little control over events (unlike in an experiment); and . the focus is on a contemporary phenomenon within some real-life context. In our research, we tried to explain causal links in real-life interventions that are too complex for the survey method or for experimental strategies. Yin (1994) adds that the results of case studies can be generalized to support theoretical propositions, but they do not apply automatically to populations or universes. Three cases were selected. First, a supply chain for fresh vegetables and fruits in The Netherlands comprising multiple growers, auctions, importers, a focal export firm, and multiple foreign retailers. Second, a supply chain for

Figure 2. Research model

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chilled salads comprising multiple suppliers, an industrial chilled salad producer, one retailer distribution center and about 100 retail outlets. And third, a supply chain for cheese products comprising two cheese factories, a cheese processor, one retail distribution center and 30 retail outlets. All three case studies aimed at identifying feasible chain scenarios that would achieve an integral chain performance improvement. In each case study, project teams were formed consisting of key decision makers in all supply chain stages: managers responsible for logistics (purchasing, warehousing, distribution) and information management, and the managing directors. The project teams were used for expert testing purposes to validate the results obtained. Case study protocol According to Yin (1994), the quality of a research project and its case study design can be tested in four areas. Table II presents the results of such an evaluation indicating the suitability of our research design. In each case study, we adhered to three principles of data collection: (1) Use of multiple sources of data collection (triangulation): . repeated semi-structured interviews with key representatives concerning current system structure and their opinions about current and past system states; . direct observations during field visits. We spent a great deal of time with those familiar with (a particular part of) the supply chain

Table II. Evaluation of the research design

Criteria

Definition

Main case study tactics used

Construct validity

Establishing correct operational measures for the concepts being studied

Using multiple sources of evidence: literature, discussions with key participants in project teams and with other researchers, observations

Internal validity

Establishing causal relationships between research variables (certain conditions are shown to lead to other conditions)

Constructing cause-effect models and discussing these with key chain participants; comparing empirically based relationships with predicted ones (customer claim analysis etc.)

External validity

Establishing the domain to which a study’s findings can be generalized

Replication logic applied to multiple case studies

Reliability

Demonstrating that the operations of a study can be repeated with the same results

Establishing a case study protocol that is used in each case study; detailed reporting of all assumptions and relations identified and data used

system in order to identify all elements of the real system that could have a significant impact upon chain performance; and . mapping of all business processes, including discussions in project team meetings with key managers and employees to verify the maps and identify redesign opportunities for performance improvements. (2) Creation of a case study database (with narratives, notes, computerized files, etc.) so that all information can be retrieved later. In our research, all notes, interview reports, and other findings were transformed into computerized files, which were subsequently verified by the interviewees. (3) Maintenance of a chain of evidence, such that an external observer could follow the derivation of any evidence from initial research questions to ultimate case study conclusions. This was accomplished by presenting our ‘‘chain of evidence’’ in the group discussions, where it could be evaluated and criticized. In each case study, the following research questions were discussed and answered in project team meetings: . What are the supply chain objectives and performance indicators? . What chain scenario is currently being implemented (referring to the four elements)? . What uncertainties are present? Do they restrict chain performance? If so, how? . What are the sources of these supply chain uncertainties? . What supply chain redesign strategies could be effective for each source of uncertainty? To assist in answering these questions, two methods were applied in the case studies: process mapping techniques and cause-effect modeling. To redesign chain processes, one has to describe them thoroughly and analyze their relationships with other processes and chain performance. The inter- and intra-company value-adding processes have to be mapped to fully understand the value streams in which an organization currently operates (Turner, 1993). We have found that, in supply chains with an emphasis on time management, a combination of two different mapping tools can fulfil these requirements: (1) Organization description language for describing in detail the inputs, transformation (including procedures and responsibilities) and outputs of each business process in the supply chain (Uijttenbroek et al., 1995). For example, for the process ‘‘order picking’’: input is the order picking list with demanded quantities per product specification per customer order; transformation concerns the picking procedure (including

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rationing policies if shortages occur); and outputs refer to a confirmation to administration (indicating shortages), corrected warehouse inventory records and (partially or fully) picked orders (see Figure 3). (2) Event process chain (EPC) modeling (Kim, 1995) for describing the dynamic behavior of the supply chain processes. This approach is specifically focused on time and place (i.e. the organizational unit where the process takes place) to visualize and reduce throughput times. By explicit modeling of time, we can identify bottlenecks in both administrative and physical logistical activities. Graphic depiction of all relevant processes in the supply chain offers, in our experience, an excellent tool for discussing relationships between processes and for defining redesign strategies that reduce throughput times. Figure 4 presents an example of a simple EPC model. The second method used is the formulation of cause-effect models, starting with customer complaints in close cooperation with the problem owners, i.e. the key decision makers in the supply chain. Via these models the main sources of uncertainties can be identified and typified. Figure 5 depicts a simplified example of such a cause-effect model for the supply chain of chilled salads.

Figure 3. Example of an ODL map

Figure 4. Example of a timerelated EPC model

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Figure 5. Overview of part of a cause-effect model concerning stock outs in retail outlets

A generic list of sources of supply chain uncertainty The application of the case study protocol in each case study resulted successively in: . the definition of chain objectives and relevant performance indicators (including norms); . a detailed description of the current chain scenario; . a typology of decision-making uncertainties currently experienced by decision makers in the supply chain (see Table III); and . a list of factors that created these supply chain uncertainties. When the identified sources in all three case studies are gathered and compared, a generic list of sources of supply chain uncertainty becomes apparent (see Table IV). We will now discuss each of the sources and present some examples from the case studies. Next we will focus on effective redesign strategies. Quantity aspects

Quality aspects

Time aspects

Supply

Supply quantities

Supply qualities

Supplier lead time

Demand and distribution

Customer demand for product quantities

Customer demand for product specifications

Customer order distribution lead time

Process

Production yield and scrap; write-offs

Produced product quality; product quality after storage

Production throughput times; storing time

Information availability

Information accuracy

Information throughput times

Planning and control

Table III. Typology of sources of supply chain uncertainty and the aspects they concern

Table IV. Generic sources of uncertainty linked to supply chain redesign strategies

Order sales period

Supply chain control structure Information lead time and decision process time Supply, manufacturing and distribution lead time

Chain facilities

Parallel interaction

2c) Implement ICT systems for information exchange and decision support 2d) Reduce waiting times 1a) Change or reduce the parties involved 1b) Change the location of facilities 1c) Re-allocate the roles actors perform and related processes 1d) Eliminate non-value-adding activities 2a) Change position of chain decoupling point 2d) Reduce waiting times 2e) Create parallel administrative and logistical processes 2f) Increase manufacturing flexibility 4a) Coordinate and redesign policies 3a) Increase the number of events per time unit (frequency) for all processes 3b) Decrease the lot sizes applied (continued)

1a) Change or reduce the parties involved 1b) Change the location of facilities 1a) Change or reduce the parties involved 3a) Increase the number of events per time unit (frequency) for all processes 5a) Establish an information exchange infrastructure in the chain and exchange demand, supply, inventory or work-in-process information 6a) Jointly define logistical chain objectives 1c) Re-allocate the roles actors perform and related processes

1c) Re-allocate the roles actors perform in the chain and related processes 1d) Eliminate non-value-adding activities 2g) Improve the reliability of supply and production quantity and quality 4a) Coordinate and redesign policies 5b) Exchange demand, supply, inventory or work-in-process information

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Supply chain configuration Chain infrastructure

Inherent characteristics Product, demand, process and supply characteristics

Generic sources

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Supply chain organization structure Authority/responsibility Human behavior

Information availability

Data and definition accuracy and applicability

Supply chain information system Data timelines

6c) Align employees’ incentives with chain objectives 4b) Eliminate or reduce human interventions 6c) Align employees’ incentives with chain objectives

2c/5b) Implement real-time ICT systems for information exchange 2d) Reduce waiting times 2c) Implement real-time ICT systems for information exchange and decision support 5c) Develop a common database and standardize bar-coding 6a) Jointly define logistical chain objectives and chain performance indicators 6b) Agree on how to measure logistical performances in the supply chain 5a) Establish an information exchange infrastructure in the supply chain and exchange demand, supply, inventory or work-in-process information

2c) Implement ICT systems for decision support 4a) Coordinate and redesign policies 1a) Change or reduce the parties involved 1c) Re-allocate the roles actors perform and related processes 1d) Eliminate non-value-adding activities 4c) Differentiate to products, systems and processes 4d) Simplify structures, systems, processes and products

Administrative and decision procedure

Decision complexity

Supply chain redesign strategies

Generic sources

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Table IV.

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Inherent characteristics In the three case studies, chain performance was hampered by the following inherent characteristics causing fluctuations in time, quality and quantity: . Demand. In all cases, consumer demand fluctuated, due in part to seasonal patterns and changes in consumer preferences. For example, chilled salads are sold most frequently when it is barbecuing weather. Inherent changes in consumer preference resulted in requests for different products (larger assortments), which impacted the need for shelf space in retail outlets. . Product. The perishability of products led to a need for air-conditioned transportation and restricted storage time to prevent quality decay. On the other hand, cheese requires long storage times (weeks or up to a year) to mature before it can be processed further or delivered. Furthermore, packaging characteristics (such as materials used and the number of products packed together) influence product handling time. . Process. The producer of chilled salads and the cheese processor had to deal with fluctuations in process outcomes and production times, which were mainly due to variable process yield and scrap-rates. . Supply. Food products are characterized by natural variations in quality, seasonal patterns and yield. The supply of goods in the case studies was sometimes hampered by bad weather conditions or traffic congestion, resulting in uncertainty concerning the timing, quantity and quality of supply. Supply chain configuration Identified sources of uncertainty related to the configuration refer to the chain infrastructure (long distances between suppliers and customers), parallel interaction and the available facilities. Sometimes, capacity shortages resulted in longer throughput times (e.g. picking products or filling retail shelves). Parallel interaction concerns the interaction of supply chains with each other (Wilding, 1998). For example, in the cheese supply chain a truck filled with products had to wait for cross-docking at a distribution center until another supplier’s products arrived. Supply chain control structure Here two main elements can be distinguished that are can cause uncertainty. The first is the order forecast horizon; the second is decision policies and complexity. Order forecast horizon When an order is generated one usually counts stock and forecast demand to calculate the required amount of products. The length of the order forecast horizon (the number of days one looks ahead to forecast total demand) depends on two factors, namely:

(1) Order leadtime. The time that elapses from the moment an order is placed to the moment ordered goods are received. This time period comprises five elements: . information lead time (i.e. the time needed for the order to be received and processed by the supplier); . administration or decision process time (i.e. the time needed to generate a production plan, picking lists and distribution schedules); . product manufacturing time (if applicable); . distribution lead time (i.e. the time needed to pick, load and transport the products); and . waiting times between these processes. (2) Order sales period. The time that elapses from the moment the ordered goods are received to the moment the goods of the next order are delivered. The ordered quantity should be large enough to suffice for all sales during the order sales period, because it is only at the time of the next delivery that product availability is increased again.

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The total order forecast horizon equals the order lead time plus the order sales period (see Figure 6). The longer this horizon, the further one has to look ahead and the greater the inaccuracy of the forecast (because of inherent demand characteristics and imperfect weather forecast reliability), resulting in extensive inventory, non-value-added activities and/or stock out costs. For example, in the supply chain for chilled salads we found that the retail outlet’s order forecast horizon was six days (order lead time was three days and delivery frequency of the distribution center was two times/week). Decision policies and complexity Decision policies applied in a supply chain may result in bad performances. In the supply chain for fresh fruits and vegetables, the purchasing department of the exporting firm aggregated customer orders over time to be able to buy large batches, thus reducing responsiveness. In the supply chain for cheese and

Figure 6. Time windows in the order cycle

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desserts, the retailer’s delivery policy required delivery to the cross-dock distribution center before 10 o’clock in the morning, even though the next activity at that distribution center (DC) did not start until hours later (see Appendix, example 1). Ignoring or aggregating information in administrative or decision policies may create uncertainty. Furthermore, customers demand many different products in one delivery, but each product may have a different lead time. Hence, decision complexity is a major source of supply chain uncertainty. Supply chain information system A lot of uncertainty found in all case studies was related to a lack of correct, accurate and up-to-date information. Data timeliness and data applicability are prerequisites when exchanging information. If information is not up-to-date and well managed in order to provide current information on stock levels and stock availability, the total time frame of consideration, i.e. the order forecast horizon, becomes even larger (see Figure 6). Uncertainty due to a lack of accuracy in recording inventory levels was experienced in all three case studies (see also Inger et al., 1995). For example, if the computer indicated that there should be 100 items in stock, planning was based on this number even if there were actually no more than 60 available. In all three cases the inventory levels were not known at all times, nor were product qualities. Order specifications were understood incorrectly over the telephone or order forms were unreadable (see Appendix, example 2). Furthermore, there was a lack of information on demand, work-in-process and to-be-supplied goods. And if data was presented concerning consumer demand, it was often difficult to translate it into the right format. Another source of uncertainty was found in data definitions; e.g. product quality was defined differently by the participants in the fresh produce supply chain. Supply chain organization structure The final sources of uncertainty were identified in the company culture and division of responsibilities and authority. Specific human behavior in decisionmaking processes resulted in different outcomes because of cognitive or political influences. For example, in the supply chain for fruits and vegetables, the exporting firm suspected that customers sometimes deliberately ordered too many products, and when these could not be sold they were returned claiming quality faults. Of course, this problem is also due to the lack of a product batch registration system, which would make it possible to refute such claims. Tool for supply chain redesign Now that we have drawn up a generic list of sources of uncertainty, we can link them to the list of supply chain redesign strategies identified earlier. In each case study, workshops were conducted to discuss and link each source of uncertainty to effective redesign strategies that can eliminate or reduce the corresponding

uncertainty and improve operational performance (using a detailed and specified version of Table I; Van der Vorst, 2000). Table IV is the generic result of this exercise. It represents a valuable tool that can be used in supply chain redesign projects as it indicates potentially effective redesign strategies when a specific source of uncertainty is encountered in a supply chain.

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Discussion Although the results we obtained proved to be useful to the participating organizations, our research is nevertheless subject to a number of limitations. In this section we will focus on the degree of generalization of our approach. By describing and discussing the relevant processes and variables in the food supply chains using process models, we assume to have captured the critical variables in each supply chain within the demarcation area of our research. We recognize that the specific detailed findings in each case study might differ significantly if other types of supply chains were investigated (since these are highly dependent on the characteristics of the supply chain scenario and the objectives of participating companies). However, we believe that the sources of uncertainty and the supply chain redesign strategies identified are of a generic nature. This is particularly true for the list of redesign strategies, since it is based on literature from many sectors and disciplines (see Table II). Of course, it is possible that additional sources of uncertainty may yet be found that could be linked to our list of redesign strategies, thereby further completing our methodology. Preliminary studies show that our approach to chain redesign is also applicable to larger chain networks, which incorporate more interacting supply chains. Focusing on the chain network will give more insight into the functioning of supply chains, since performance improvements made in one supply chain might result in a performance decline in another.

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Conclusions This paper presented a qualitative research approach to supply chain redesign. Case studies showed that the presence of uncertainties in supply chain decision-making situations results in the establishment of several non-valueadding activities that reduce profitability. By using process-mapping techniques, an accurate and detailed description of the current chain can be obtained. This facilitates discussions with key employees in the supply chain and helps to identify uncertainties and, more importantly, sources of supply chain uncertainty. In all three cases, the identification of uncertainties and especially their sources led to the recognition of effective chain redesign strategies. The list of supply chain redesign strategies assisted in this process by providing a complete overview of possible redesign strategies. We therefore endorse our initial proposition: to identify effective supply chain redesign strategies one should focus on the identification and management of the sources of uncertainties in supply chain decision-making processes.

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Kim, Y.G. (1995), ‘‘Process modeling for BPR: event-process chain approach’’, Proceedings of the 16th International Conference on Information Systems, Amsterdam, pp. 109-21. Kurt Salmon Associates (1993), Efficient Consumer Response; Enhancing Consumer Value In The Grocery Industry, Food Marketing Institute, Washington, DC.

Supply chain redesign strategies

Lambert, D.M. and Cooper, M.C. (2000), ‘‘Issues in supply chain management’’, Industrial Marketing Management, Vol. 29, pp. 65-83. Lambert, D.M., Cooper, M.C. and Pagh, J.D. (1998), ‘‘Supply chain management: implementation issues and research opportunities’’, International Journal of Logistics Management, Vol. 9 No. 2, pp. 1-19. Lee, H.L. and Sasser, M.M. (1995), ‘‘Product universality and design for supply chain management’’, Production Planning and Control, Vol. 6 No. 3, pp. 270-7. Lee, H. and Tang, C.S. (1997), ‘‘Modeling the costs and benefits of delayed product differentiation’’, Management Science, Vol. 43 No. 1, pp. 40-53. Lee, H.L., Padmanabhan, V. and Whang, S. (1997), ‘‘Information distortion in a supply chain: the bullwhip effect’’, Management Science, Vol. 43 No. 4, pp. 546-58. Lewis, J.C. and Naim, M.M. (1995), ‘‘Benchmarking of aftermarket supply chains’’, Production Planning and Control, Vol. 6 No. 3, pp. 258-69. Mason-Jones, R. and Towill, D.R. (1998), ‘‘Shrinking the supply chain uncertainty circle’’, Control, September, pp. 17-22. Persson, G. (1995), ‘‘Logistics process redesign: some useful insights’’, International Journal of Logistics Management, Vol. 6 No. 1, pp. 13-25. Quinn, F.J. (1999), ‘‘Re-engineering the supply chain: an interview with Michael Hammer’’, Supply Chain Management Review, Spring, pp. 20-6. Sheombar, H.S. (1995), ‘‘Understanding logistics co-ordination – a foundation for using EDI in operational (re)design of dyadical value adding partnerships’’, Dissertation KUB, Tutein Bolthenius, ‘s Hertogenbosch, Tilburg University, Tilburg. Silver, E.A., Pyke, D.F. and Peterson, R. (1998), Inventory Management and Production Planning and Scheduling, 3rd ed., John Wiley & Sons, New York, NY. Stalk, G.H. and Hout, T.M. (1990), Competing Against Time: How Time Based Competition is Reshaping Global Markets, Free Press, New York, NY. Stern, W.L., El-Ansari, A.I. and Coughlan, A.T. (1996), Marketing Channels, 5th ed., Prentice-Hall, London. Stevens, G.C. (1989), ‘‘Integrating the supply chain’’, International Journal of Physical Distribution & Materials Management, Vol. 19 No. 8, pp. 3-8. Thomas, D.J. and Griffin, P.M. (1996), ‘‘Coordinated supply chain management’’, European Journal of Operational Research, Vol. 94, pp. 1-15. Turner, J.R. (1993), ‘‘Integrated supply chain management: what’s wrong with this picture?’’, Industrial Engineering, December, pp. 52-5. Uijttenbroek, A.A., Dijk, A. and Verroen, P.P. (1995), Procesbeschrijving met ODL; Organization Description Language, Lansa, Leidschendam (in Dutch). Van der Heijden, K. (1996), Scenarios: The Art of Strategic Conversation, Wiley, New York, NY. Van der Vorst, J.G.A.J. (2000), ‘‘Effective food supply chains; generating, modelling and evaluating supply chain scenarios’’, PhD-thesis, Wageningen University, Wageningen. Van Hoek, R.I. (1998), ‘‘Reconfiguring the supply chain to implement postponed manufacturing’’, International Journal of Logistics Management, Vol. 9 No. 1, pp. 95-110.

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Wilding, R. (1998), ‘‘The supply chain complexity triangle: uncertainty generation in the supply chain’’, International Journal of Physical Distribution & Logistics Management, Vol. 28 No. 8, pp. 599-616. Womack, J., Jones, D. and Roos, D.T. (1990), The Machine that Changed the World, Rawson Associates, New York, NY. Yin, R.K. (1994), Case Study Research: Design and Methods, 2nd ed., Sage Publications, Thousand Oaks, CA. Appendix Example 1. Waiting times in the supply chain A producers is obliged to deliver the retailer distribution center before 10:00 hrs each Thursday. When the supplier arrives, shipment papers are immediately sent to the accounts department to confirm the retail orders. When the supplier is late, administration calls him demanding an explanation. However, the process flow analyses showed that the goods are not required until the end of the day when they are cross-docked. They are not distributed to the retail outlets until the next day. A confrontation of both parties with this issue revealed that the time restriction was based on last year’s distribution schedule. The changes were mistakenly not passed through to the accounts department and the supplier. Example 2. Data accuracy and information throughput times in the cheese supply chain Orders are generated at retail outlets and punched into the outlet information system. They are processed automatically at the retailer distribution center and then sent to the supplier by fax. Consecutively, the order is entered into the supplier’s information system manually. Then picking lists are generated and during order picking the number of picked items is entered into the information system again, after which a check is made on delivery reliability. Thus, each order is processed (typed over) three times; this makes the system prone to data errors requiring many hours.

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Implementing collaborative forecasting to improve supply chain performance Teresa M. McCarthy and Susan L. Golicic Department of Marketing, Logistics and Transportation, The University of Tennessee, Knoxville, Tennessee, USA

Implementing collaborative forecasting 431 Received May 2001 Revised December 2001

Keywords Case studies, Product development, Sales, Forecasting Abstract Sales forecasting and collaboration are two business phenomena that have independently been recognized as contributing to improved organizational performance. The present research employs case study methodology to explore the synergies to be gained from combining the two processes. Depth interviews were conducted with executives at three firms currently engaged in collaborative forecasting with supply chain partners. Results revealed unique approaches to collaborative forecasting that circumvent the inhibitors of collaborative planning, forecasting, and replenishment adoption, and yield substantial improvement in company and supply chain performance including increased responsiveness, product availability assurance, optimized inventory and associated costs, and increased revenues and earnings. Seven guidelines to implementing interfirm collaborative forecasting are presented.

Introduction The strategic competitive advantages to be gained by adopting a supply chain management approach to business are widely recognized (Cooper and Ellram, 1993; La Londe and Masters, 1994; Mentzer et al., 2001). Supply chain management is defined as: The systemic, strategic coordination of the traditional business functions within a particular company and across businesses within the supply chain, for the purposes of improving the long-term performance of the individual companies and the supply chain as a whole (Mentzer et. al., 2001, p. 22).

Nix (2001) explains that a managed supply chain environment begins with forming collaborative relationships initially with immediate trading partners, then eventually with additional tiers in the supply chain. Intuitively, focusing collaborative efforts on strategic sources of disruption between trading partners can result in improved performance for the supply chain. Ireland and Bruce (2000) suggest that forecasting is a pivotal business function that, when not strategically, systematically coordinated between firms, can contribute to disruption of activities at the point between trading partners where product is planned, ordered, and replenished. As such, collaborative forecasting provides a substantial opportunity for improved supply chain performance and should be viewed as a priority for firms adopting a supply chain management approach (Helms et al., 2000). Collaboration and sales forecasting are two phenomena that have each been extensively discussed in the literature, and have been independently identified

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as contributing to corporate performance. The purpose of the current research is to explore how trading partners combine the two practices to create a collaborative forecasting effort. The existing literature on collaborative forecasting falls into two categories. The first explores intra-firm collaborative forecasting efforts among functional business units within a firm (Diehn, 2000/2001; Lapide, 1999; Reese, 2000/2001; Wilson, 2001). The second category addresses interfirm collaborative forecasting among trading partners, but largely focuses on one specific approach to integrated collaborative forecasting – collaborative planning, forecasting, and replenishment (CPFR) (Ackerman, 2000; Andraski, 1999; Barratt and Oliveira, 2001; Ireland and Bruce, 2000; VICS, 1999). Despite promising initial results and the detailed and comprehensive nature of the CPFR process model, a number of barriers have prohibited its anticipated widespread adoption. Among the barriers of CPFR implementation are the provision of adequate technology and software, difficulties of real-time coordination of information exchange, substantial investment of time and personnel for set-up, the process intensive nature of maintaining the efforts across several suppliers and products, lack of scalability from the pilot stage, and the required synchronous changes in corporate culture for both firms in the collaborative relationship (Barratt and Oliveira, 2001; Girard, 1999; Suleski, 2000). In light of these barriers to implementation, Barratt and Oliveira (2001) call for a re-examination of the CPFR process model. If many firms are disinclined to implement the CPFR process due to the aforementioned barriers, we believe these firms would be interested in knowing if alternative approaches to collaborative forecasting are being adopted, and if they result in improved performance. Research in this particular area is lacking in both the academic and practitioner literature. This paper addresses this gap by specifically asking the question – How do firms engage in interfirm collaborative forecasting, and how do these approaches to collaborative forecasting impact supply chain performance, and thus, company performance? The present research explores collaborative forecasting in general, rather than the specific application of CPFR. Collaborative forecasting cannot be effectively studied outside its context of business to business relationships. Therefore, an inductive research methodology – which logically progresses from naturally occurring, largely uncontrollable observations toward theoretical generalizations – is most appropriate (Bonoma, 1985; Yin, 1994). Case study methodology best meets these requirements and was consequently chosen for our research. The following section offers a review of the supply chain collaboration literature and sales forecasting literature. Subsequently, we describe our case study methodology and present results of interviews with three organizations currently engaged in collaborative forecasting. Following presentation of results, we then return to and review the literature for support and triangulation of our findings on responsiveness, product availability assurance, optimized inventory, and increased revenues and earnings. Conclusions

and implications offer seven guidelines for firms seeking to implement collaborative forecasting initiatives. Finally, limitations and future research are discussed. Collaboration among supply chain partners Collaboration among organizations on the management of various supply chain activities is a current trend believed by some company executives to lead to a competitive advantage over other supply chains (La Londe and Masters, 1994; Mentzer et al., 2000). Supply chain collaboration has been described in the literature in many ways – as a business tool that builds sales (Andraski, 1999); as an interaction among peers sharing a common set of goals and measures (Citera et al., 1995); as a process for parties to jointly search for solutions (Haeckel, 1998); and as a relationship in which trading parties develop a long-term cooperative effort (Sriam et al., 1992). Common to many of these descriptions is a long-term relationship between supply chain parties that work together. In interviews conducted with executives responsible for their organization’s supply chain, Mentzer et al. (2000) asked respondents to offer their interpretation of supply chain collaboration. Respondents largely reiterated concepts previously mentioned from the literature, but added that the parties should work as one entity toward common objectives (Mentzer et al., 2000). Therefore, we adopt the Mentzer et al. (2000) definition of supply chain collaboration as a long-term relationship among organizations actively working together as one toward common objectives. One area in which collaboration is taking place in the supply chain is forecasting. Sales forecasting process and collaborative forecasting Before a company can successfully engage in collaborative forecasting, it must establish its own internal forecasting process. Consistent, systematic and appropriate forecasting processes positively impact performance through decreased operations costs, improved customer service, increased sales, and reductions in inventory. These improvements have positively affected return on shareholder value (Mentzer, 1999). Models of the forecasting process offered in the literature provide inclusive guidelines to be followed by companies when creating their forecast (Lawless, 1990; Murdick and Georgoff, 1993; Reid, 1985). In general, researchers agree on the course of action for developing forecasts, although they may emphasize some steps more than others. Mentzer and Bienstock (1998) offer a comprehensive model of the sales forecasting management process, which categorizes the forecasting process into four components: (1) management; (2) systems; (3) techniques; and (4) performance measurement.

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Organizations must recognize these four dimensions as part of their internal forecasting process before they can successfully enter into an interfirm collaborative forecasting effort. Interfirm collaborative forecasting extends the process beyond the four walls of an enterprise to include trading partners, whether face-to-face or electronically, when building the forecast (Burgin et al., 2000). When this occurs, the most appropriate and most accurate information is used to develop the forecast. The result is a higher quality forecast driving business decisions that are made in the supply chain. The ultimate goal is to have this information exchange occurring at all levels of the supply chain in the development of a single forecast. The benefits that come from collaborative forecasting are similar in type, but greater in magnitude than those that come from single-firm, intra-enterprise forecasting such as that described above. Much of the existing popular press and scholarly literature on collaborative forecasting focuses on a tool referred to as CPFR. The voluntary interindustry commerce standards association (VICS) committee established CPFR in 1998 to help companies co-manage processes and share information. The roadmap developed by VICS (1998) instructs companies to: . develop an agreement on the target(s) and metrics; . create a joint plan to meet the target(s); . jointly create a forecast; . identify any exceptions; . jointly address the exceptions; and . create and fill the orders. In total, CPFR process mapping involves four sub-processes, 26 functions, and a total of 51 outputs (Barratt and Oliveira, 2001), and requires trading partners to have a synchronous collaborative vision, the required technology, and resources to implement and execute successfully (Ireland and Bruce, 2000). According to VICS, the expected outcomes include improved efficiencies, increased sales, reduced assets and working capital, and decreased inventory. It must be noted that CPFR is only the tool that helps facilitate collaborative forecasting between supply chain partners. As with any other tool, the use of CPFR alone will not result in successful collaborative efforts unless internal forecasting processes have been established, and solid relationships among partners have been forged. In other words, relationships must evolve from being traditional, adversarial, and self-serving in nature to relationships characterized by sharing information and working together toward common goals with the focus on the end-use consumer. Collaborative forecasting involves reliance on supply chain partners to provide accurate, detailed and timely demand information. It requires trust in that information as well as in the partners that provide it. Thus, we define collaborative forecasting as the purposive exchange of specific and timely

information (e.g. quantity, level, time horizon, location, probability of new business, etc.) between trading partners to develop a single shared projection of demand. We now describe the case study methodology used to determine how collaborative forecasting is implemented and what impact it has on supply chain performance.

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Methodology Case studies are appropriate for exploratory research when answering a ‘‘how’’ question such as ours (Yin, 1994). This methodology deals with a variety of evidence – the primary resources being systematic interviewing and direct observation. According to Yin (1994), the case study is an empirical inquiry that investigates a contemporary phenomenon within its real-life context. The business to business relationship context is highly pertinent to our phenomenon of interest. We use a multiple-case holistic design which is more robust for replication of results. Design of the study – including data collection, analysis, and quality – follows procedures recommended by Yin (1994).

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Sample The unit of analysis for our case study is the organization. Within the context of the business to business relationship, either a supplier or customer is the focal organization that participates in collaborative forecasting. These companies were chosen based on desired replication of findings; that is, we selected companies that were known a priori to engage in collaborative forecasting. A review of the literature, which consistently attributed specific performance outcomes to supply chain collaboration in general, led us to believe we would find similar performance outcomes without much variation across organizations in the more specific collaborative forecasting consequences. Hence a small number of cases is acceptable as results should illustrate replication of findings (Yin, 1994). Three different industries – chemicals, consumer goods, and apparel manufacturing, each having different positions in a variety of supply chains – were selected in order to explore both similar and contrasting situations. Research design Our research started with a preliminary theory that collaborative forecasting efforts other than CPFR exist, and that these efforts impact supply chain performance. This is based on the positive performance contributions that both forecasting (Fildes and Beard, 1992; Makridakis and Wheelwright, 1977; Mentzer and Bienstock, 1998; Reid, 1985) and collaboration (Burt and Pinkerton, 1996; Ellinger et al., 1999) have exhibited. The next step was to select cases in which the phenomenon was present and design a data collection protocol. The protocol included the open-ended questions that would be asked of our informants and plans for collecting other sources of evidence such as company documents and records. Each case study was conducted and analyzed individually. We used an iterative nature of comparing, explaining,

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and revising the data interpretation to develop our theoretical model of collaborative forecasting. We then drew across-case conclusions and modified the initial theory as necessary. From this, implications were developed including guidelines for implementing interfirm collaborative forecasting. This research process is very similar to the four stages of draft, design, prediction, and disconfirmation recommended by Bonoma (1985) for case studies. Data quality Our research relies on all relevant evidence as recommended for quality by Yin (1994). There are four specific tests to ensure that a case study produces quality results. The first, internal validity, is not applicable for exploratory case studies such as the present research; causal relationships are not tested at this stage, but are proposed as a result of findings, and addressed in recommendations for future research. Construct validity ensures that correct measures are used for the research concepts. This is demonstrated in case study research through the convergence of multiple data sources (triangulation), a chain of evidence, and key informant reviews. The research uses interviews, field notes, company documents, and records to develop interpretations. All data were documented and tracked to maintain a verifiable chain of evidence. All informants involved in this research conducted member checks, reviewing and approving notes and reports pertaining to their company. External validity is supported through replication of findings. One goal of the study was analytic generalization – that is replicable results from which theoretical implications could be inferred. We used multiple cases and relevant literature as data sources to address this. The final test for quality is reliability, which ensures that the same results can be reached if the research is repeated. Reliability can be established by using a protocol and ‘‘database’’ (common location) for data collection and analysis. To address this, the research team followed a protocol for interviews and documented all data that were stored in a database. Outside reviewers were used to review the chain of evidence created which further supported reliability of the research. Case study results Table I offers profiles of the three companies involved in the case studies including the impetus for collaborative forecasting within each company, with whom they have entered into collaborative forecasting relationships (i.e. customers or suppliers), and how collaborative forecasting is integrated into their companies. It must be noted that the decision to implement collaborative forecasting in each of the three companies was subsequent to an internal forecasting process audit resulting in improved forecasting practices within the enterprise. When comparing the impetus for collaborative forecasting across the three firms, one consistent theme that emerged was recognition by senior management of the strategic competitive advantage to be gained by engaging in interfirm collaborative forecasting. Thus, management was committed to fostering an environment open to collaboration with trading partners.

Co. B Consumer goods company selling to both general and specialty retailers of over $2 billion annually. They have facilities throughout North America and employ 18,000 employees

Co. A International chemical company with sales offices in over 30 countries, and annual sales volume approaching $5 billion

Company profile

Independent forecasting business unit was established in response to management directives and key account requests to develop closer ties with customers to gain competitive advantage

Customers: three years Suppliers: just beginning

Customers: two The search for solutions to customers’ years satisfaction issues Recognition that differentiation on product or service alone was no longer sufficient to remain competitive Results from a survey of customers approximately three years prior revealing the primary customer satisfaction issue was product availability assurance Customers were looking to reduce the number of suppliers with whom they sourced inventory

Impetus for collaborative forecasting

Collaborative forecasting partners and length of time

Prior to regularly scheduled monthly meetings with each key account, company B sends each account a system-generated forecast for that customer to review prior to their meeting. The two companies then work together to incorporate any new information that affects the forecast such as promotions, pricing changes, store expansions, product offering changes, or product obsolescence (continued)

Salesforce was trained in CF and makes monthly ‘‘forecasting calls’’ (vs sales calls) to customers specifically for the purpose of reviewing the sales forecast. Forecasting meeting fosters conversation revealing actionable, qualitative market intelligence. Salesperson has live version of forecast on laptop – revisions are made and sent to corporate on real-time basis Customers have been encouraged to immediately communicate any changes in demand that would affect the forecast rather than waiting for the monthly meeting

Execution

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Table I. Summary of company collaborative forecasting information

Table I. Senior management directives approximately three years ago to reduce costs and improve process efficiencies Decision to assess supplier performance, and reduce supplier base to those that that were most effective Process mapping of planning, forecasting, and replenishment processes between company C and largest customer. Outcome was recognition of the strategic value in sharing information with supplier to derive one consensus forecast for the dyad

Impetus for collaborative forecasting

Customers: two years Suppliers: three years

Company C sends 12 week rolling forecast to supplier on a weekly basis. Weekly conference-call meetings involving company C’s purchasing and production planning representatives and suppliers sales staff are conducted to arrive at a collaborative forecast

Execution

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Co. C Manufacturer and marketer of basic apparel with some seasonal and fashion products. The majority of product is sold in large discount retail chains under the company’s nationally recognized brand name, with a small percentage sold under various private labels

Company profile

Collaborative forecasting partners and length of time

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All three firms focused on committing resources to train boundary-spanning personnel in collaborative forecasting methods. In particular, those collaborating with customers focused their efforts on training the salesforce, and for those collaborating with suppliers, purchasing was the focus of training efforts. These boundary-spanning personnel are in the most advantageous position to gather intelligence from the trading partner and to engage the partner in collaborative forecasting efforts. Intelligence gathering conversations with trading partners can elicit information from trading partners on decisions involving pricing, promotion, advertising, new store or plant openings, discontinued items or new product introductions, as well as other factors that can impact demand and forecast accuracy. In each case, training consisted of teaching the focal firm’s personnel to: . ask questions that would render quality; . attain timely market intelligence from the trading partner; . educate the trading partner about the advantages to be gained by both firms as a result of improved forecast accuracy due to collaboration; . encourage the trading partner to communicate information that might impact forecast accuracy on a timely basis; and . act as a ‘‘forecasting consultant’’ for the trading partner by teaching them how to improve their own forecasting skills. This approach requires considerably less investment of time and personnel than that required by CPFR. An additional common activity adopted by all three firms is institution of regularly scheduled meetings between the sales and purchasing departments for the sole purpose of discussing the forecast. Companies A and B established monthly meetings with their customers, and company C established weekly meetings with their supplier with the goal of developing a single shared projection of demand. Collaborative forecasting efforts were initially targeted at A-level accounts, but eventually adopted with many B-level accounts as the firms began to realize the benefits to be gained by expanding the scope of their efforts with minimal additional investment. Regarding technology requirements, none of the three firms found it necessary to make substantial investments. Each of the firms had already assessed and upgraded their own internal forecasting system as a result of their enterprise forecasting process audit. Therefore, without joint electronic real-time access to the forecast, alternative methods of information sharing had to be established. Company A shares their forecast with customers during monthly ‘‘forecasting calls’’ where the salesperson displays a live version of the forecast on their laptops, and revisions are entered with the customer based on customer intelligence. Subsequent information exchange throughout the month is in the form of e-mail spreadsheet attachments, which is the primary method of forecasting information exchange for companies B and C. While it is recognized that this form of information exchange is far less efficient than the

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integrated technology required by CPFR, the technology requirements of CPFR are cost prohibitive for many companies, rendering less formal information exchange an acceptable alternative. These less systematized methods of collaborative forecasting are not characterized as scaleable across trading partners. However, manufacturers do not perceive CPFR to be scalable as a firm’s network of trading partners involved in CPFR increases (Girard, 1999). As can be seen from the above results, barriers to implementation of CPFR can be lowered or circumvented by adopting less formalized methods of collaborative forecasting. The three firms in our case study clearly developed unique alternatives to CPFR, but with some common elements. However, the true test of the effectiveness and efficiency of these methods of collaborative forecasting can only be measured by assessing the performance outcomes. The following describes results of performance outcomes that emerged from the case study interviews. Case study results (company A) Over the last two and a half years, company A and their customers have recognized several mutually beneficial outcomes of the collaborative forecasting process. Among the benefits mentioned are increased responsiveness, increased product availability assurance, and optimized inventory levels and associated costs – all of which contribute to increased revenues and earnings for both partners in the collaborative relationship. Specific examples are offered below. Increased responsiveness Company A found that the more timely flow of information related to changes in demand allowed them to be more responsive to customers’ needs. For example, during a collaborative forecasting meeting, a customer informed the salesperson of their plans to change the formula for a particular product which would involve substituting orders of a particular chemical – for which they were the only customer – with another chemical. Access to this information allowed company A to gradually decrease and cease production of the original product, and substantially ramp-up production of the new product in order to meet demand when the customer was ready to convert. Without collaborative forecasting, the salesperson would not have learned of the switch until the orders were placed, resulting in an overstock of the original chemical for which there were no other customers, and an inability to fulfill demand for the new product. Increased lead-time due to collaborative forecasting resulted in reduced product obsolescence and increased responsiveness. Product availability assurance Company A found that information garnered during collaborative forecasting meetings allows for improved production and distribution network planning, resulting in increased product availability assurance. For

example, when company A initiated collaborative forecasting with one key customer, that customer was only purchasing 30 per cent of their 16 million pound requirement from company A. As a result of collaboration, confidence in the forecast allowed company A to dedicate work in process (WIP) supply for this customer, and exhibit a consistent track record for product availability. Furthermore, collaboration lead to a reassessment and realignment of the existing distribution network to better accommodate the customers’ needs. Ultimately, company A became the sole supplier for this customer. In addition, through collaborative forecasting, salespeople are better able to understand what product availability means from the customers’ point of view. For example, conversations with customers give salespeople better insights into what ‘‘on-time’’ delivery means to each customer, and thus they are able to assure availability of product when the customer needs it rather than when company A thinks they need it. Finally, in times of materials shortage, collaborative partners’ forecasts are given priority for order fulfillment while other customers might be placed on product allocation. Optimized inventory and associated costs As previously mentioned, increased confidence in customers’ forecasts has allowed company A to dedicate WIP supply for collaborative customers, and is therefore able to produce smaller, more frequent shipments. Consequently, customers are able to lower their overall inventory levels and are less compelled to carry excessive safetystock. Moreover, company A has significantly reduced their North American warehouse inventories and all costs associated with storing inventory (e.g. insurance, obsolescence, shortage, and other logistics costs) because dedicated WIP is being directly shipped to customers rather than being placed in storage awaiting shipment. Company A has also eliminated the need for safety stock for collaborative partners due to increased understanding of and trust in the mutual forecast. Increased revenues and earnings The examples of increased responsiveness, product availability assurance, and optimized inventory and associated costs described above all resulted in increased revenues or earnings, e.g. reduced obsolescence, sole-supplier status, and reduced warehouse inventories. Several other examples were offered by company A, including the following. In his first collaborative forecasting meeting with a long-term customer from India who consistently ordered 50 barrels of a particular chemical from company A, the salesperson learned that this customer was ordering 40 per cent of his total demand from a competitor. When asked why they were not ordering 100 per cent of the product from company A, the customer responded that several years ago when they attempted to order more they were informed that company A only had the capacity to fulfill 50 barrels, which the customer has continued to order over the years. However, company A’s production capacity for that product had

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since increased, but the customer’s unfulfilled demand from years ago had not been recognized until collaborative forecasting began. In that initial meeting, company A was able to capture the remaining 40 per cent of demand for what was a very high margin product. Increased responsiveness, product availability assurance, and optimized inventory and associated costs consistently emerged as outcomes of collaborative forecasting from company documents and the interviews with company A. These outcomes resulted in the improvement of supply chain performance through increased revenues and earnings. Case study results (company B) Feedback regarding collaborative forecasting at company B from both external and internal customers has been positive since the process was implemented. Information exchange has increased in both frequency and quality, which has positively affected the relationship between the company and its customers. The improvements have prompted company B to develop better measures to consistently capture the benefits and quantify the results of collaborative forecasting. Forecast accuracy at the customer and SKU levels have improved 1 per cent and 10 per cent respectively. Due to this improvement, company B has avoided excess inventory in the approximate amount of $8 million. In addition, they have begun a similar process with their upstream suppliers which involves sharing the same forecast that was created in collaboration with customers. The primary benefits experienced by company B and their customers that became apparent during the interviews include increased responsiveness and optimized inventory, both of which will be described. Increased responsiveness Through the increased exchange of demand information, company B has improved their responsiveness to customers. Having more accurate predictions of customer demand has allowed the company to better anticipate and react to changes in demand. One respondent called this, ‘‘catching the winners and the losers [products] earlier.’’ This has helped them better manage the number of expedited shipments to customer locations. Another example of increased responsiveness concerns seasonal products. Information concerning the introduction and termination of seasonal products flows between the companies in a more timely fashion than in the past. This contributes to increased service levels and decreased obsolete inventory (discussed in the next section). One specific example of increased responsiveness occurred with one retailer two weeks after the introduction of a new product. During the collaborative forecasting meeting, the retailer communicated early indication of a trend in sales for the new product that was substantially higher than originally forecasted. Company B was able to respond to the increase in demand, and the customer expanded the number of stores in which the product was offered from 100 to 800.

Optimized inventory and associated costs Company B has been able to reduce their safety stock and excess inventory while maintaining the appropriate levels of inventory to meet their customers’ needs through collaborative forecasting. Fill rates for company B’s products are 95 per cent. Advanced notice of product changes has permitted the company to place approximately 10 per cent of their SKUs into ‘‘B status’’ in their production system, which automatically adjusts production requirements to remove all safety stock for that SKU. Specifically, one customer provided advanced notice of the discontinuance of a particular product style, which was produced in runs of 2500 pieces at $125/piece. Company B was able to immediately place the SKU into B status saving over $300,000 by eliminating production of safety stock for remaining runs, and reducing the number of remaining runs to allow sell-thru of existing safety stock. Overall, company B has seen a $5 million decrease in inventory for the business units participating in collaborative forecasting. Increased revenues and earnings The preceding are just a few examples pertaining to increased responsiveness and optimized inventory levels that company B and their customers have realized as a result of collaborative forecasting. While each of the above examples resulted in increased earnings or revenues, the specific benefits have been difficult to quantify thus far. Therefore, in addition to expanding this process to suppliers, improving performance measures and running forecasting on an exception basis is the next step that company B plans to take in continuously improving their collaborative forecasting. Case study results (company C) For company C, the primary outcome of a functional process mapping with their key supplier was recognition of the value in sharing information with their supplier to derive one consensus forecast for the dyad. Such a collaborative process allows both companies to recognize and record true unconstrained demand, differentiate that unconstrained demand from the constrained demand plan based on the supplier’s capability to fulfill demand, and ultimately reduce the gap between the two. Through the collaborative forecasting process, company C and their suppliers and customers have experienced benefits such as increased responsiveness, product availability assurance, optimized inventory and associated costs, and increased revenues and earnings. Increased responsiveness Company C believes that, by collaboratively forecasting with their suppliers, both partners have become more responsive to each other’s needs. Prior to engaging in collaborative forecasting with their fabric suppliers, the communication process involved providing suppliers with a purchase order four to six weeks in advance of anticipated demand, and revising the requested

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delivery quantity as actual demand was incurred. If actual demand fell short of the quantity ordered, surplus inventory was warehoused by the supplier for up to 60 days, at which time the inventory would convert to bill and hold status (i.e. company C would be billed for the inventory warehoused by the supplier). As a result of collaborative forecasting, company C now provides their supplier with a 12-week forecast on a weekly basis. The supplier is able to see changes in this forecast on a timelier basis and adjust their production cycles accordingly. In one specific example, company C dramatically increased their forecast for four weeks out in response to a retailer’s request to run a promotion. During the supplier forecasting meeting, the supplier communicated their inability to meet that demand in four weeks, needing five weeks to supply the fabric. Company C discussed the issue with the retailer, who agreed to push the promotion back one week. Prior to collaborative forecasting, the supplier would have been unable to meet the demand the week it was requested, and company C would not have been able to deliver product in time for the promotion, ultimately resulting in lost sales and dissatisfied customers. However, due to collaboration, all three members within the supply chain were allowed the opportunity to be responsive to each other’s needs, resulting in an optimal solution. Product availability assurance For company C, product availability assurance emerged as an internal tool to convince their salesforce on the benefits of collaborative forecasting. As part of the company’s initiative to reduce costs and improve efficiencies, the salesforce was expected to increase customer service levels while decreasing inventory levels. Although company C’s salesforce had assured their customers of product availability prior to collaborative forecasting, the mindset was to carry large inventory levels to meet this assurance. However, the combination of high forecasting error and large inventories often meant overstocks in slowerturning SKUs and out-of-stocks in faster-turning SKUs. Company C was able to illustrate to the salesforce how improved forecast accuracy resulting from collaborative forecasting, combined with substantially reduced lead times, raw materials inventory, and WIP inventory (discussed in next section), would allow the salesforce to continue product availability assurance for their customers. Optimized inventory and associated costs Several examples of optimized inventory and associated costs have been evidenced by company C and their suppliers and customers as a result of collaborative forecasting. For example, by regularly sharing information with suppliers to derive one collaborative forecast, company C now receives insights they consider to be invaluable. As one company C executive stated: The perspective of a different party many times offsets our biases or brings an unbiased perspective . . . We feel it has brought a balance and sense of reality that is not there when

you try to find a more accurate time series method or just get a different qualitative opinion from an internal executive.

Collaborative forecasting with suppliers and customers has allowed these supply chain partners to ‘‘take the wiggles out’’ of the problematic bullwhip effect experienced so often between trading partners by reducing demand forecast variability. Company C estimates that sharing 12-week forecasts on a weekly basis has removed approximately four weeks of inventory out of their supplier’s warehouse, and out of their raw materials, WIP, and finished goods inventories. In large part, optimized inventory and associated costs between company C and one of their primary suppliers is attributable to innovative methods of inventory warehousing, delivery, and billing that resulted from collaborative forecasting negotiations. The result is approximately 60 per cent shorter lead times. For goods shipped from one of company C’s manufacturing plants in central America, lead times have been reduced to 40 days from 120 days prior to collaborative forecasting. Company C has also managed to reduce retailers’ inventories, thus increasing inventory turnover and allowing retailers to ‘‘get more out of the investment of space’’ dedicated to company C’s product. Increased revenues and earnings Each of the above examples for company C resulted in increased earnings and/ or revenues for all partners in the collaborative forecasting relationship. Due to significantly reduced lead times, company C is able to move more production to off-shore facilities, taking advantage of reduced labor costs and passing this cost savings along to customers while still experiencing improved lead times. In another example, as previously mentioned, a customer adjusted their scheduled in-store promotion as a result of collaborative forecasting which lead to very effective supply chain demand planning. By adjusting and coordinating promotional activities with upstream trading partners, the retailer was able to maximize sales during the promotion, and company C did not incur chargebacks for inventory that otherwise would have been delivered postpromotion. Finally, because one of company C’s suppliers experienced substantially reduced costs associated with reduced inventory levels, the supplier did not pass along a price increase that had been a commitment in a contractual agreement. These benefits are directly attributable to the collaborative nature of the forecasting process, and would not have been achieved without sharing timely and accurate information between the two supply chain partners. Theoretical model The results across all cases involved in this study show compelling evidence of replication of findings. All three cases offered several examples of increased responsiveness and optimized inventory and related costs, some of which were presented above. In addition, companies A and C both offered examples of product availability assurance. In each case, the benefits of collaborative

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forecasting directly resulted in increased earnings and/or revenues. Clearly, the barriers to implementation of CPFR can be overcome with alternative methods of collaborative forecasting that result in improved company and supply chain performance. Thus, based on these findings, we present our model of collaborative forecasting (see Figure 1).

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Discussion of literature related to findings When conducting exploratory research such as with the present study, Strauss and Corbin (1998) explicate the value of returning to the literature after data collection has been completed to confirm the findings, thus validating the relationships proposed among the constructs emerging from qualitative data analysis (Yin, 1994). As such, the literature is employed as an additional source of data providing triangulation. Thus, results from this study directed us back to the literature to address construct validity by providing multiple sources of evidence for the same phenomena. The following is a review of the literature related to the outcomes of collaborative forecasting. Increased responsiveness In today’s fast paced environment, companies are seeking ways to establish time-based strategies to achieve competitive advantage including building responsiveness into operations (Bowersox and Daugherty, 1995). One way to accomplish this is by collaboratively sharing information with preferred suppliers. A preferred supplier will possess the capability to respond to unpredicted needs such as fluctuations in demand or sudden need for a new product (Leenders et al., 1985). Company B’s more timely access to their customers’ demand data allowed them to be more responsive to large fluctuations in demand for highly seasonal products, thereby decreasing product obsolescence and stock-outs. In company A’s responsiveness example, information gathered during collaborative forecasting allowed them to be responsive to their customer’s needs for a new product in a timely fashion.

Figure 1. Theoretical model of collaborative forecasting

One method of creating flexibility in logistics systems is compressed order cycles, which facilitate the ability to be more responsive to changing customer requirements and exploit new opportunities (Bowersox and Daugherty, 1995; La Londe and Masters, 1994). Another method of developing flexibility involves identifying and suggesting creative new ways to serve customers needs, and customizing delivery of those needs for key accounts (Leenders et al., 1985). Company C worked with their main supplier to create innovative methods for delivery of goods which contributed to shorter cycle times, allowing them to be more responsive to their customers needs. Davis and Manrodt (1991) discuss the importance of responding to customers on a request-by-request basis, and to perform during the ‘‘moment of truth’’ when a critical need arises. In establishing responsiveness strategies, Nix (2001) indicates the importance of understanding how customers will respond to product stock-outs. Customer service levels should take these reactions into account, which can range from a one-time lost sale to permanent loss of the customer to a competitor. For company C, collaborative forecasting with one retailer and supplier thwarted what would have been an unsuccessful and costly retail promotion. Product availability assurance The current customer-side trend toward proactive procurement involves reducing the supplier base in order to maximize results from remaining vendors through increased supplier commitment, thereby securing improved customer service, and mitigating uncertainty and risk of stock outages (Bitner 1995; Smeltzer and Siferd, 1998). A primary factor in selecting a supplier is the ability to assure availability of product (Burt and Pinkerton, 1996; Heinritz et al., 1991; Leenders et al., 1985). Confidence in a suppliers’ ability to deliver product typically develops over time with continued performance, and requires extensive communication and cooperation between trading partners (Bitner, 1995). Due to collaborative forecasting efforts resulting in detailed information sharing between supply chain partners, companies A and C were able to confidently assure their internal and external customers of product availability. In turn, company A’s customers were confident in their ability to follow through on that assurance, and ultimately established them as a preferred supplier. Leenders et al. (1985) suggest that an additional benefit to developing a close relationship with selected vendors emerges in times of materials shortage when suppliers establish priorities of customers on vital requirements based on the quality and commitment of their relationship. Company A extended that promise to each of their collaborative forecasting partners. In today’s proactive procurement environment in which customers are reducing their supplier base, long-term product availability assurance is a critical factor in selecting suppliers, and a collaborative relationship is one way in which to provide this assurance.

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Optimized inventory and associated costs ‘‘Inventory is an asset for which less rather than more should be the desired goal’’ (Logistics Focus, 1998, p. 19). Inventory managers are regularly challenged with this inventory management paradox. The inventory management objective is to minimize channel-wide inventory and related costs while avoiding undesired stock-outs resulting in loss of sales, given established customer service levels. Decreased uncertainty of demand can lead to reduced need for safety stocks and stockpiling of inventory (Cooper and Ellram, 1993) as stock levels begin to reflect true customer demand (La Londe and Masters, 1994). Company B has completely eliminated production of safety stock levels for certain SKUs due to consistent and timely communication of demand fluctuations from their customer. Similarly, company A’s dedicated WIP for collaborative customers has reduced the need for their customers to carry excessive safety stock, and has reduced company A’s warehousing levels because WIP is being directly shipped to customers rather than placed in storage. Stalk and Haut (1990) suggest that by communicating projected fluctuations in demand to upstream partners on a real-time basis, manufacturers can better plan their production cycles to avoid overhead expenses incurred by allowing factory output to ramp up and down to meet unanticipated demand that was not communicated in a timely fashion. By collaborating on the forecast, company C and their supplier were able to remove demand amplification from their consensus forecast, resulting in more efficient production cycles. Collaborative forecasting allows participating companies access to more accurate demand information, which then permits those companies to optimize their inventory levels. Increased revenues and earnings By far, the most often cited outcome of collaboration is an improvement in financial performance, including increased sales and profits, and decreased costs (Andraski, 1999; Kalwani and Narayandas, 1995; Mentzer et al., 2000; Sriam et al., 1992; VICS, 1998). Specifically, the literature offers abundant support for the notion that increased responsiveness (Berry, 1995, Nix, 2001), product availability assurance (Heinritz et al., 1991; Leenders et al., 1985), and optimized inventory and associated costs (Logistics Focus, 1998) result in increased revenues and earnings. Company C’s substantial decreases in lead time have afforded them the flexibility to shift production to off-shore locations, thus reducing costs in the supply chain. Sole- or preferred-provider status is often granted to suppliers that provide responsiveness and product availability assurance to their customers (Burt and Pinkerton, 1996; Heinritz et al., 1991; Leenders et al., 1985). As a result, customers do not incur exorbitant switching costs (Logistics Focus, 1998), and suppliers are able to retain and grow their existing customer base rather than the more costly alternative of new customer acquisition (Fornell and Wernerfelt, 1987), ultimately producing increased earnings for both customers and suppliers. Company A cited specific examples

of customers that had chosen them as their preferred provider directly due to outcomes of collaborative forecasting, thus increasing the company’s revenues. Returning to the literature clearly provides support for the findings of our exploratory case study research and offers validation for the construct relationships proposed in our theoretical model. The findings presented here are exploratory, and the resultant theoretical model of collaborative forecasting presents several stakeholder implications and opportunities for future research. Conclusions and implications Many firms recognize the supply chain efficiencies and competitive advantage to be gained by implementing interfirm collaborative forecasting. CPFR is the primary tool discussed by practitioners in the popular press when referring to collaborative forecasting. While CPFR has shown great promise for improved supply chain performance in pilot studies, several barriers exist prohibiting widespread adoption (Barratt and Oliveira, 2001; Girard, 1999; Suleski, 2000). Results of the present study reveal alternative approaches to interfirm collaborative forecasting that do not require the substantial investment in human and technological resources required by CPFR. Moreover, results show these alternative approaches can result in increased responsiveness and product availability assurance, optimized inventory and associated costs, and increased revenues and earnings for the individual firms as well as the supply chain. In assessing the common themes and practices emerging from case study interviews of three companies currently engaged in interfirm collaborative forecasting, we offer the following seven guidelines to be employed by firms interested in implementing collaborative forecasting with their trading partners. First, companies must begin by auditing their internal forecasting processes. Before considering collaborating with trading partners, firms must assess each of the four forecasting process components – management, systems, techniques, and performance measurement (see Figure 1) – to ensure they have consistent, systematic and appropriate internal forecasting processes (Mentzer and Bienstock, 1998). The second guideline emerging from the case studies is to gain senior management support for the collaborative forecasting initiative. It appears likely that interfirm collaborative forecasting will be taken more seriously and more effectively managed and integrated into the internal forecasting process if supported at senior levels. The third step involves selecting and training the appropriate boundaryspanning personnel in interfirm collaborative forecasting techniques. The salespeople, purchasing managers, and buyers are in the most advantageous positions to gather market intelligence such as marketing mix activities from customers and suppliers and to best understand the impact they will have on shaping demand. In addition to gathering market intelligence, these boundaryspanning personnel should be trained to educate their trading partners of the benefits of collaborative forecasting and engage them in the process. Information sharing between partners reduces both demand and supply

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uncertainty resulting in improved forecast accuracy and, thus, improved operational decisions stemming from the forecast such as production and logistics planning for both firms. Realizing initial benefits will allow the collaborative relationship to grow and more benefits to occur, such as more efficient and effective interfirm demand planning and demand management. The notion of training your trading partners to collaboratively forecast contradicts the recommendation that, to successfully pilot the CPFR process, a trading partner must meet or exceed your own supply chain capabilities and be ready for CPFR (Ireland and Bruce, 2000). Each of the three firms in our study gradually phased their sales and/or buying staff into collaborative forecasting. This leads us to our fourth guideline: initially target key companies and subsequently target the lower levels. Companies A and B began by targeting several key accounts, and company C began the process with one primary supplier. By initially focusing on key accounts, our respondents realized quick returns on their efforts in the form of performance improvement. Quantifiable, measurable improvement directly resulting from collaborative forecasting can be used to ‘‘sell’’ the concept to skeptical internal personnel as well as customers and suppliers. These initial results reinforce management’s commitment to the process and provide further incentive to expand the efforts with additional trading partners. The fifth guideline suggests establishing regularly scheduled meetings with the sole purpose of discussing the forecast. Topics addressed during typical sales calls, such as negotiations over price, quantity, and shipping terms, are not to be discussed during the forecasting meeting. Conversation should address broader issues related to accurately estimating demand. The timing, accountability, and deliverables for each of these meetings should be clearly understood by both parties. However, these meetings are not the only time information exchange related to demand should take place. The sixth guideline suggests that firms determine an appropriate method of on-going, timely information exchange. For some firms, this may involve EDI or Internet linkages, for others it may include e-mail or telephone calls. Whatever the method chosen, the information exchange will be most beneficial if executed on a timely basis. The final guideline is to create one single shared projection of demand between the trading partners. Developing separate forecasts duplicates efforts and undermines the potential benefits to be gained by collaborative efforts. Results from this research clearly illustrate that firms wishing to pilot collaborative forecasting efforts but unable to surmount the barriers to implementing CPFR can successfully adopt alternative methods resulting in increased responsiveness and product availability assurance, optimized inventory and associated costs, and increased revenues and earnings for both firms. The findings from this research present theoretical implications as well. While intra-firm forecasting has received a great deal of attention in academic literature, theory on supply chain collaboration is in the early stages.

Collaborative forecasting presents an opportunity to contribute to this theory building effort and to extend theory on forecasting and its impact on entire supply chains. Limitations and future research Case study methodology is primarily qualitative; as such there are several limitations which must be acknowledged and addressed in future research. This study relied primarily on interviews with personnel from three companies as data. Theoretical relationships were derived from rich interpretations; however this inductive method is only used to build theory. The literature was used to provide support for the developed theoretical relationships, but validation is necessary. That must be accomplished through further empirical investigation using a research design for theory testing. Sampling procedures also limit this study’s contributions. This study relied on purposive theoretical sampling; therefore, findings cannot be generalized to large populations. The entire population of companies involved with collaborative forecasting did not have an equal probability of being selected as study participants. The sample consisted of a few carefully chosen managers within the three organizations known by the researchers to participate in interfirm collaborative forecasting. At the most limited level, the findings can be generalized to the supply chains of the study participants. Again, empirical investigation with a larger sample will address these concerns. Our research was exploratory and needs to be taken to the next step to test and explain causal linkages, and thus internally validate the model. More companies, whether through additional case studies or quantitative surveys, should be examined. Although the three companies we studied are diverse and we believe the results to be analytically generalizable, additional companies’ outcomes would provide more support for external validity. The research described here along with the literature could be used to develop measures for this phase of the research. Nonfinancial outcomes such as relationship quality that are not addressed in this study should be researched and added to the model to determine their effect on the operational or financial outcomes and corporate performance. Furthermore, due to the short-term nature of the collaborative forecasting efforts of our case studies (i.e. two to three years), the ability to maintain the improved performance outcomes should be explored in a longitudinal study. In addition to completing the outcome portion of the model, studies should be conducted exploring the processes companies use to begin achieving collaborative forecasting. In other words, the front end of the model (or antecedents) needs to be added. Qualitative and quantitative methods can be combined to determine the relational and operational factors that need to be in place as well as any moderators or mediators to the process. Hypotheses can then be developed in order to test the overall model. Improved forecast accuracy and reduced uncertainty have increased the quality of business decisions that are based on the forecast. Collaborating with

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supply chain partners on one consensus forecast helps to improve this accuracy and reduce uncertainty. Companies that implement collaborative forecasting will likely achieve the benefits of increased responsiveness, increased product availability assurance, optimized inventory and associated costs, which are expected to lead to increased earnings and improved corporate performance.

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Haeckel, S.H. (1998), ‘‘About the nature and future of interactive marketing’’, Journal of Interactive Marketing, Vol. 12, Winter, pp. 63-71. Heinritz, S., Farrell, P.B., Giunipero, L. and Kolchin, M. (1991), Purchasing: Principles and Applications, 8th ed., Prentice Hall, Englewood Cliffs, NJ. Helms, M.M., Ettkin, L.P. and Chapman, S. (2000), ‘‘Supply chain forecasting – collaborative forecasting supports supply chain management’’, Business Process Management Journal, Vol. 6 No. 5, pp. 392-407. Ireland, R. and Bruce, R. (2000), ‘‘CPFR: only the beginning of collaboration’’, Supply Chain Management Review, September/October, pp. 80-8. Kalwani, M.U. and Narayandas, N. (1995), ‘‘Long-term manufacturer-supplier relationships: do they pay off for supplier firms?’’, Journal of Marketing, Vol. 59, January, pp. 1-16. La Londe, B.J. and Masters, J.M. (1994), ‘‘Emerging logistics strategies: blueprints for the next century’’, International Journal of Physical Distribution & Logistics Management, Vol. 24 No. 7, pp. 35-47. Lapide, L. (1999), ‘‘New developments in business forecasting’’, The Journal of Business Forecasting Methods & Systems, Vol. 18 No. 3, Fall, pp. 24-5. Lawless, M.J. (1990), ‘‘A forecasting approach to operating profit’’, Journal of Business Forecasting, Vol. 9, Summer, pp. 6-10. Leenders, M.R., Fearon, H.E. and England, W.B. (1985), Purchasing and Materials Management, 8th ed., Irwin, Homewood, IL. Logistics Focus (1998), ‘‘Taking stock to keep customers happy’’, Logistics Focus, Vol. 6 No. 3, pp. 19-20. Makridakis, S. and Wheelwright, S.C. (1977), ‘‘Forecasting: issues and challenges for marketing management: a framework for relating the available techniques to specific situations’’, Journal of Marketing, October, pp. 24-38. Mentzer, J.T. (1999), ‘‘The impact of forecasting on return on shareholder’s value’’, Journal of Business Forecasting, Vol. 18, Fall, pp. 8-12. Mentzer, J.T. and Bienstock, C.C. (1998), Sales Forecasting Management, Sage Publications, Thousand Oaks, CA. Mentzer, J.T., Foggin, J.H. and Golicic, S.G. (2000), ‘‘Supply chain collaboration: enablers, impediments, and benefits’’, Supply Chain Management Review, Vol. 4, September-October, pp. 52-8. Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D. and Zacharia, Z.G. (2001), ‘‘What is supply chain management?’’, in Mentzer, J.T. (Ed.), Supply Chain Management, Sage Publications, Thousand Oaks, CA, pp. 5-24. Murdick, R.G. and Georgoff, D.M. (1993), ‘‘Forecasting: a systems approach’’, Technological Forecasting and Social Change, Vol. 44, August, pp. 1-16. Nix, N. (2001), ‘‘Customer service in a supply chain management context’’, in Mentzer, J.T. (Ed.), Supply Chain Management, Sage Publications, Thousand Oaks, CA, Ch. 13, pp. 347-69. Reese, S. (2000/2001), ‘‘The human aspects of collaborative forecasting’’, The Journal of Business Forecasting Methods & Systems, Vol. 19 No. 4, Winter, pp. 3-9. Reid, R.A. (1985), ‘‘How to set up a forecasting process’’, Journal of Business Forecasting, Vol. 4 No. 4, pp. 9-10. Smeltzer, L.R. and Siferd, S.P. (1998), ‘‘Proactive supply management: the management of risk,’’ International Journal of Purchasing & Materials Management, Winter, pp. 38-45. Sriam, V., Krapfel, R. and Spekman, R. (1992), ‘‘Antecedents for buyer-seller collaboration: an analysis from the buyer’s perspective’’, Journal of Business Research, Vol. 25, December, pp. 303-20.

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Stalk, G.H. Jr and Haut, T.M. (1990), Competing Against Time: How Time-Based Competition Is Reshaping Global Markets, Free Press, New York, NY. Strauss, A. and Corbin, J. (1998), Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory, 2nd ed., Sage Publications, Thousand Oaks, CA. Suleski, J. (2001), ‘‘Beyond CPFR: retail collaboration comes of age’’, The Report on Retail Business, April, AMR Research Inc., Boston, MA. Voluntary Interindustry Commerce Standards Association (VICS) (1998), ‘‘Collaborative planning, forecasting, and replenishment (CPFR)’’, available at: www.cpfr.org Wilson, N. (2001), ‘‘Game plan for a successful collaborative forecasting process’’, The Journal of Business Forecasting Methods & Systems, Vol. 20 No. 1, Spring, pp. 3-6. Yin, R.K. (1994), Case Study Research: Design and Methods, 2nd ed., Sage Publications, Thousand Oaks, CA. Further reading Bowersox, D.J., Mentzer, JT. and Speh, T.W. (1995), ‘‘Logistics leverage’’, Journal of Business Strategies, Vol. 12, Spring, pp. 36-49. Mentzer, J.T., Bienstock, C.C. and Kahn, K.B. (1999), ‘‘Benchmarking sales forecasting management’’, Business Horizons, Vol. 42, May-June, pp. 48-56.

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A qualitative examination of factors affecting reverse logistics systems for end-of-life computers A. Michael Knemeyer John Carroll University, Boler School of Business, University Heights, Ohio, USA

Reverse logistics systems for EOL computers 455 Received June 2001 Revised December 2001, April 2002

Thomas G. Ponzurick and Cyril M. Logar West Virginia University, College of Business and Economics, Morgantown, West Virginia, USA Keywords Reverse logistics, Qualitative techniques, Computers, Recycling Abstract The current study demonstrates the value of utilizing qualitative research methods to analyze logistics problems. Specifically, the study utilizes a qualitative methodology to examine the feasibility of designing a reverse logistics system to recycle and/or refurbish end-of-life computers that are deemed no longer useful by their owners. The qualitative methodology is a modified version of a customer visit program in which the in-depth interviews were used to identify the special needs of stakeholders who could potentially participate in the proposed system. The qualitative interviews were structured and implemented using a standardized approach set forth in the literature. The results indicate that this qualitative technique proved valuable in obtaining industry-sensitive stakeholder data, which allowed the researchers to more thoroughly analyze the feasibility of the proposed reverse logistics system.

Introduction Reverse logistics is the process of moving goods from their typical final destination for the purpose of capturing value, or proper disposal (Rogers and Tibben-Lembke, 1998). A reverse logistics system incorporates a supply chain that has been redesigned to manage the flow of products or parts destined for remanufacturing, recycling, or disposal and to effectively use resources (Dowlatshahi, 2000). Moving goods from their point of origin toward their final destination has long been the focus of logistics systems. Producers manufacture goods then distribute them toward targeted user segments. The users take these goods and consume them until the time they are deemed no longer useful. With obsolescence rates on the rise (Blumberg, 1999), the question as to what the user ultimately does with this end-of-life (EOL) product becomes an issue that has both environmental and economic implications. Possible user alternatives for handling these EOL products include keeping the product and storing it temporarily, disposing of the product via landfills, or recycling the product (Jacoby et al., 1977). Unfortunately, too many EOL products are finding their way into landfills. According to the EPA’s Municipal Solid Waste Factbook, 29 states have ten years or more of landfill capacity

International Journal of Physical Distribution & Logistics Management, Vol. 32 No. 6, 2002, pp. 455-479. # MCB UP Limited, 0960-0035 DOI 10.1108/09600030210437979

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remaining, 15 states have between five and ten years of landfill capacity remaining, and six states have less than five years of landfill capacity remaining (Rogers and Tibben-Lembke, 1998). If we are to offset the increasing demand for landfills, enhanced efforts for recycling are needed and this directly requires reverse logistics activities (Barnes, 1982). An important consideration at this point is the availability of reverse logistics systems that efficiently and effectively deal with EOL products. It has been suggested that reverse logistics systems are similar to forward logistics systems with the exception being the roles are reversed. In fact, Murphy and Poist (1989, p. 12) assert that reverse logistics or reverse distribution is the ‘‘movement of goods from a consumer towards a producer in a channel of distribution.’’ The user (taking the role of the producer) has a finished good to move toward a buyer (the recycler/refurbisher taking the role of the consumer). The logistical flows would follow but again in reverse order (Zikmund and Stanton, 1971). This view of reverse logistics appears to provide a simple solution to the problems associated with developing a reverse logistics system. While there is support for a connection between overall logistics competence and the implementation of environmentally responsible logistics (ERL) (Goldsby and Stank, 2000), most logistics systems remain ill equipped to handle product movement in a reverse channel (Stock and Lambert, 2001). Indeed, recyclable material does not necessarily flow backwards through the same channel (Pohlen and Farris, 1992). Carter and Ellram (1998) and Dowlatshahi (2000) identify specific internal and external factors that impact the ability to successfully design and implement such a system. Some examples of these factors include the ability to ensure an adequate supply of inputs to the system, regulatory issues impacting the system, and the potential demand markets for the outputs of the system. Although the reverse logistics literature provides an excellent framework for developing a reverse logistics system and its subsequent policies (e.g. Stock, 1992; Rogers and Tibben-Lembke, 1998; Carter and Ellram, 1998; Stock, 1998 and Dowlatshahi, 2000), it fails to adequately describe the challenges that exist when dealing with these factors in the ‘‘real world.’’ That is, an in-depth understanding of the complexity of the factors affecting the reverse logistics activities that must be addressed and put in motion to make these systems operative is lacking in the logistics literature. The current research study attempts to address this shortcoming by examining how these key factors impact the successful design and implementation of a reverse logistics system for EOL computers. In particular, by utilizing qualitative methods to examine a proposed reverse logistics system for EOL computers, the study attempts to demonstrate a process for utilizing qualitative research methods to obtain in-depth information concerning the factors affecting the reverse logistics activities for these specific goods. The application of qualitative research methods to this particular reverse logistics implementation problem should provide an example for practitioners and

researchers as to the possible benefits of using these methods when addressing Reverse logistics other logistics issues. Additionally, the findings should provide a clear systems for EOL understanding for companies and policy makers as to the true degree of computers complexity that exists when dealing with EOL products. Why examine EOL computers? Even with the growth in the numbers of EOL computers, there is currently no mandatory take-back of computers in the USA. Every year an electronic trash heap nearly as tall as Mount Everest is tossed into garbage cans, stashed in garages or forgotten in closets. Some 500 million computers will be rendered obsolete by 2007 in the USA alone (Hamilton, 2001). According to a report by the National Safety Council, computers are ranked as the nation’s fastestgrowing category of solid waste by the Environmental Protection Agency (Hamilton, 2001). However, there are many opportunities to reuse and create some value out of this nearly omnipresent asset (Rogers and Tibben-Lembke, 1998). Several alternatives exist for disposing of these computers. These alternatives include temporary storage, passing it on to someone else (e.g. friend, charity), recycling, refurbishing or landfilling. Eventually, as the computers become older and less capable of performing to increasingly higher requirements, it appears that recycling or disposal are the only long-term options for these products. Unfortunately, the literature exploring these options indicates that recycling is not the option of choice. It has been reported that only slightly more than 10 per cent of the computers taken out of service in the USA each year are recycled (Platt and Hyde, 1997). This is troublesome since it is estimated that by 2002 EOL computers will exceed new purchases by 3.4 million units annually (National Safety Council’s Environmental Health Center, 1999). From these results, it appears that landfill usage may be the short-term solution to this problem. However, states like Massachusetts, Minnesota and Wisconsin have either banned or are considering banning the dumping of computer-related equipment in their landfills (Stough and Benson, 2000). Based on these findings, it would appear that landfilling EOL computers might not be a viable long-term solution to this issue. If landfilling is not the long-term solution, it makes sense to look for alternative methods for disposing of EOL computers. A recent study by Murphy and Poist (2000) revealed that recycling materials, reducing consumption, and reusing materials are the three most commonly utilized green logistics strategies. Thus, one possible alternative is to develop a reverse logistics system to collect and then either refurbish or recycle the computers that are no longer useful. In this case, refurbished is defined to mean collecting the EOL computers and then upgrading those that still have some market usefulness albeit to a target market that is less demanding of technological requirements. While this extends the life cycle for this equipment, ultimately this equipment will no longer be refurbishable.

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Recycling, on the other hand, is collecting and totally demanufacturing/ dismantling the EOL computers in order to recover the basic commodities comprising the computer. For computers these commodities generally fall into three categories – glass, metal and plastic (e.g. polyurethane, polystyrene). Thus, the reverse logistics flow of EOL computers would consist of two possible demand outlets for the process output, those parties interested in purchasing refurbished computers and those interested in buying recycled commodity materials. Factors affecting reverse logistics activities Conceptual model The majority of literature dealing with reverse logistics is both descriptive and anecdotal. Moreover, Carter and Ellram (1998) found that most of the academic literature only examines relatively narrow aspects of reverse logistics, such as recycling. Stock (1992, 1998), Carter and Ellram (1998), Rogers and Tibben-Lembke (1998) and Dowlatshahi (2000) are exceptions to this finding. These studies took a more holistic view towards reverse logistics, and it is this view towards reverse logistics that serves as the foundation for this study. In particular, the study utilizes a conceptual model of factors affecting reverse logistics systems to develop a process model specifically for EOL computers. Dowlatshahi (2000) discusses the concept of reverse logistics and its importance as a profitable and sustainable business strategy. He identifies and describes the reverse logistics systems literature and outlines keys to successful design and use of these systems. In particular, his research focuses on establishing internal strategic and operational issues that may require consideration in reverse logistics systems. Similarly, Carter and Ellram (1998) conduct an extensive overview of the reverse logistics literature. They integrated this material with a framework for comparative analysis established in the marketing literature (see Achrol et al., 1983) to develop a model of external factors affecting reverse logistics. Their study utilizes the general literature on reverse logistics to develop propositions and a model of the external drivers and constraints to reverse logistics programs. It is this model, combined with the material developed by Dowlatshahi (2000), which serves as the basis for the current examination of reverse logistics issues facing EOL computers (see Figure 1). Stock (1992, 1998), Rogers and Tibben-Lembke (1998) and Rogers and Tibben-Lembke (2001) provide additional clarity for the proposed model through their comprehensive and multifunctional review of reverse logistics activities. In particular, they provide a thorough discussion of the macro environment surrounding the task environment for EOL computers and provide additional details concerning many of the factors examined in this study. The proposed model shown in Figure 1 is comprehensive in that it shows all the distinct types of tangible, external factors that affect a firm’s reverse

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Figure 1. Conceptual model of factors affecting reverse logistics systems

logistics activities as well as examining internally focused strategic and operational factors to consider when implementing a reverse logistics system (Stock, 1992, 1998; Rogers and Tibben-Lembke, 1998; Carter and Ellram, 1998; Dowlatshahi, 2000). This examination of both internal and external factors directly responds to Carter and Ellram’s (1998) assertion that both should be examined in future research focusing on reverse logistics. This conceptual model suggests that the external environment is comprised of four sectors: (1) input; (2) regulatory; (3) output; and (4) competitive.

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Within these sectors are key interorganizational partnerships including suppliers, competitors, government agencies and interested aggregators, such as consumer and lobbying groups, which influence the government and regulatory bodies. Internally, the firm must examine key strategic factors consisting of: . strategic costs; . overall quality; . customer service; . environmental concerns; and . legislative concerns. Also, key operational factors needing to be addressed consist of cost-benefit analysis, transportation, warehousing, supply management, remanufacturing and recycling, and packaging. The next section integrates these perspectives into a proposed model of the factors impacting the design and implementation of a reverse logistics system specifically for EOL computers. Proposed model for EOL computers If recycling is a preferred alternative to landfilling, the question of structure and process still remains. What should be the design of a reverse logistics system to handle EOL computers? How should such a reverse logistics system be implemented? To answer these questions one must examine both the factors that affect the implementation of such a reverse logistics program and the economic viability of such a process (Walker, 2000). Stock (1998) asserts that a critical factor for success is to map or flow chart the reverse logistics process in order to understand the components and their interrelationships. To this end, the authors propose a model outlining the various nodes and flows that would be part of a reverse logistics system for EOL computers. This model pays particular attention to the specific activities needed to handle EOL computers and considers how the internal and external factors may impact the economic viability of the system (see Figure 2). The model outlines both external and internal factors that can affect the reverse logistics process in general and for EOL computers, in particular. Consistent with Carter and Ellram (1998), the proposed model incorporates both competitive and regulatory factors affecting the external environment. It goes on to examine other factors affecting the success of the operation from a self-contained perspective as well. These internal factors include identification and acquisition of product supply (EOL computers) from various entities including both the private and commercial sector as well as the residential sector. The model then looks at the inbound processing aspect of reverse logistics. In this case, a decision whether the products should be recycled, (dismantled) or refurbished needs to be made. Operational issues are then considered including value-added processing as well as outbound processing of the output. The processed output can then be

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Figure 2. Propoesed model of reverse logistics system for EOL computers

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sold to targeted customer segments or disposed of as waste. This proposed model represents a hybrid of the basic reverse supply chain models established in Walker (2000). A key premise of the proposed process model is the ability of the system to aggregate the necessary supply of recyclable material (in this case EOL computers), transport that material in a cost-effective and efficient manner to a central point for processing and then sell the output of this process to targeted markets where demand exists. A comprehensive qualitative research process was utilized to both examine the factors affecting the design and implementation of such a reverse logistics system and given these factors assess the economic viability of this proposed system for EOL computers. Methodology A qualitative research approach was utilized to examine the factors surrounding the proposed reverse logistics model. Historically, qualitative research has been given less than a fair sense of appreciation and has been criticized for lack of scientific rigor, small samples, subjectivity and nonreplicable efforts (Goodyear, 1990). What has merited less attention in the academic and business literature is the important role which qualitative research plays in accessing and generating discussions with key decision makers in organizations and with industry experts (Wright, 1996). Qualitative measures serve a useful purpose when one is attempting to understand the world from the perspective of the potential customer (Calder, 1977; Dougherty and Hardy, 1996). The qualitative method utilized for this study is a modified version of a customer visit program in which the qualitative interview is used to identify the special needs of stakeholders who could potentially participate in a reverse logistics system for EOL computers. McQuarrie (1991) establishes this qualitative method as a viable research technique and provides a detailed discussion as to how researchers can best utilize this method. The visitation program was especially useful in that it allowed the researchers to collect information visually in addition to the interviews that were conducted. The current study utilizes this method to focus on the processes that currently exist for dealing with EOL computers and how the parties involved view this process. The research team utilized on-site stakeholder interviews to gather this information. A systematic analysis of these conversations serves as the basis for building a clearer understanding of the factors influencing a reverse logistics system for these products. The previously mentioned stakeholder interview can be a valuable research tool and plays an important role in accessing information from key organizational decision makers and industry experts (Wright, 1996). In highly competitive markets, organizations are often reluctant to disclose industrysensitive information (Crimp and Wright, 1995) but the in-depth interview is seen as an efficient, cost-effective qualitative research approach for overcoming this reluctance (Wright, 1996).

However, as noted by McQuarrie (1991), the qualitative customer interview Reverse logistics technique does have some limitations. The first limitation is a lack of coherent systems for EOL rationale for choosing which potential customers to visit. Due to the relatively computers small number of industry recognized stakeholders, conducting a census of these stakeholders offset this concern in the present study. In addition, there is sometimes a lack of organization that can result from unclear objectives and an 463 unstructured discussion guide. This limitation was overcome by following a formal six-step procedure outlined in the literature (McQuarrie, 1991). Recognizing Merriam’s (1988) assumptions for qualitative research design and following McQuarrie’s (1991) six-step procedure (see Figure 3), a series of potential stakeholder interviews were conducted. The first step in this process was to set the research objective for the study. In this case, the research objective was to determine the operational and economic feasibility of establishing an independent reverse logistics system for all makes of EOL computers. The second step was to identify, select and recruit potential stakeholders to be interviewed. Potential stakeholders were determined to be any individual or organization that might participate in the proposed system. Thus, participants were seen as potential suppliers of EOL computers, potential demanufacturers (organizations that dismantle computers into major components) and processors of the computers (organizations which take these components and extract raw materials, e.g. gold, silver, various types of plastics) and potential buyers of this output. The third step was to familiarize the interviewers (in this case the interviews were jointly conducted by the researchers) with the particulars regarding the potential stakeholder visit. In other words, this step focused on what type of

Figure 3. Six-step procedure for implementing a visitation program

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information was needed and how the interview should be structured in order to obtain that information. This led to the fourth step, which was the development of a discussion guide with a formal set of questions for conducting the interviews (see the Appendix). The fifth step was conducting the actual interview. Since the researchers conducted the interviews, the interview structure was consistent throughout the process with one researcher serving as the moderator and the other(s) serving in a listening and note-taking role. After the interviews were completed, the final step involved the researchers conducting a debriefing session in which the interview notes were analyzed and results recorded. Data collection Due to travel budget limitations, the researchers were forced to implement a combination of personal visits and telephone conference call interviews. When completed, 42 potential stakeholders were interviewed via personal visits, while six were interviewed via conference call for a total of 48 distinct interviews. Although the sample size was small, it was possible to conduct in-depth personal interviews that averaged two to four hours in duration. These interviews provide considerable advantages over quantitative surveys in facilitating in-depth responses with leading firms as well as overcoming individual interviewees’ reservations concerning confidentiality (Wright, 1996). Additionally, the sample size is consistent with other studies utilizing this type of research methodology (see Wright, 1996). The participants represent both firms whose major focus is on reverse logistics activities and firms whose primary business is impacted by reverse logistics issues. In particular, firms representing input streams (public and private), EOL computer processors (demanufacturers, separators, metal recyclers, glass recyclers, and plastics recyclers) and buyers of the outputs of this process were interviewed. Additionally, government agencies and interest groups involved in this area were interviewed. In both the personal interviews and telephone interviews, the same discussion guide structure was followed (see the Appendix). One visit/ interview team was formed to conduct the interviews. Roles were adjusted (moderator/listener) to coincide with the potential organization’s position in the proposed reverse logistics system and the research team member’s expertise. The moderator guided the discussion, while the listeners were responsible for taking written notes of the conversation. The decision to utilize written notes instead of recording the conversations was in due to respondent concerns about being recorded. As discussed previously, the interviews were designed to determine key factors influencing the design and implementation of the proposed system. In particular, identification of potential supply sources, potential demand markets, best practices for recycling and demanufacturing/dismantling as well as perceived past and future directions of this process were goals of the research.

In order to address the feasibility of the proposed reverse logistics system, Reverse logistics the discussion guide and subsequent interviews covered a wide range of topics. systems for EOL These topics included the potential stakeholders’ expectations, their interest in computers participating in the reverse logistics program, their ability to perform the required services, their current level of performance, and their level of expertise. The stakeholders were also asked for suggestions on how to make 465 this program operational. Summary reports and oral debriefings followed each interview. The research findings pinpointed strengths, weaknesses and methods for developing and implementing the proposed reverse logistics system for EOL computers. The next section provides a phenomenology of the qualitative findings concerning each of the factors influencing the proposed system. A phenomenology consists of a descriptive narrative, a synthesis of knowledge about the phenomenon under study (Creswell, 1994). Key qualitative findings The results of the analysis address factors impacting the proposed reverse logistics system. The key qualitative findings concerning the factors that impact the proposed system are discussed in this section. These findings were derived from a qualitative data analysis procedure that classified information collected during the in-depth interviews into categories. During this analysis the data were categorized, reviewed, and coded. A list of key findings was developed (as suggested by Merriam, 1988) from this information. External considerations External considerations are divided into four sectors: input, regulatory, output, and competitive. Particular to the case of reverse logistics systems for EOL computers, the input sector represents an analysis of possible suppliers of these types of products. The regulatory sector examines how government and other external groups impact the system participants. The output sector focuses on the ultimate demand markets for either refurbished computers or EOL computer commodities. The competitive sector addresses the level of competition within various parts of this proposed system. Input sector To be successful at many aspects of reverse logistics, sufficient volumes of materials are required (Stock, 1998). Eight interviews were conducted to classify the input sources for EOL computers. The organizations interviewed in this sector represented major industrial users of computers in both the private and public sector. A determination was made by the research team to focus on large industrial and governmental users (with their large volumes of centralized computers) because they were identified as the most active recycling firms (National Safety Council’s Environmental Health Center, 1999). While the residential sector and small companies provide additional sources of computers, the difficulties with supporting an economically viable system for

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collecting these inputs from these geographically dispersed supply sources was viewed as unlikely in the short run. The interviews confirmed Stock’s (1998) view that success is highly dependent on ensuring sufficient volumes of input materials. In particular, the interviews supported the view that large industrial users provide the best way to ensure a sufficient input stream for the proposed system. Additionally, the interviews established that computer manufacturers represented a viable source of input. This supply mainly consisted of components of EOL computers that failed to meet quality standards or EOL computers that were returned. The interviews also uncovered that while there seems to be an adequate supply of EOL computers, the actual location of these computers is not always easily identifiable. Those that are identifiable tend to be located at larger companies and computer manufacturers that are currently being serviced by existing demanufacturers/recyclers (see discussion of competitive sector). These findings were particularly prevalent when determining the potential input stream from large governmental agencies. The findings suggest that attempting to secure input streams from organizations that quickly depreciated their EOL computers is desirable due to the high potential for refurbishment and resale of these computers. While it may prove to be difficult to identify these companies, targeting business segments that rely heavily on technology (for example, information technology firms) may prove to be a good starting point. This strategy is more profitable than that of just demanufacturing the EOL computers for the purpose of recovering the metals, plastics and glass. Regulatory sector US environmental legislation is undergoing continuous change (Stock, 1998). In particular, individual states have promulgated a variety of laws and regulations concerning waste management (Stock, 1998). A total of 16 interviews were completed dealing with the dynamic regulatory sector. These 16 interviews consisted of eight interviews with federal and state government agencies and eight interviews with other interested aggregators including lobbyists, consultants, and recycling experts who were interested in the problem of EOL computers. Several key influencing issues in the regulatory sector were identified via the interviews. These findings indicate that the regulatory sector’s primary influence on the proposed reverse logistics system for EOL computers would be in the areas of disposal, input sourcing, and location decisions for the key system participants. The findings of the interviews concerning EOL computers were consistent with Stock’s (1998) general findings that environmental legislation is dynamic at all levels of government. In terms of disposal, the companies indicated that various computer components are currently banned from landfills, including circuit boards with high lead content. The companies also identified regulatory factors dealing with sourcing issues. These included the Presidential Executive Order 12999, Educational Technology: Ensuring Opportunity for All Children in

the Next Century (April 1996) mandating that government EOL computers Reverse logistics are to be refurbished, if possible, then given to public school systems systems for EOL demonstrating the greatest need. Other governmental computers are sent to computers pilot recycling projects, surplus property programs, or are destroyed for security reasons. Finally, several of the interviews focused on the relation of incentive programs for economic development at the state level and how they 467 might influence the formation of a reverse logistics system for EOL computers. These incentive programs involve employee training, tax incentives, financial incentives, and infrastructure support. Output sector It was initially determined that EOL computers provide two distinct yet interdependent product lines. Both of these product lines involve the collection and assessment of EOL computers. The acquired EOL computers will arrive at a demanufacturing/recycling facility and be inspected for refurbishing ‘‘value.’’ Those computer electronics deemed ‘‘valuable’’ by the inspection process will end up as refurbished EOL computers and will represent the first product line to be resold to customers wanting low-cost refurbished computers. Stock (1998) identifies this market as providing relatively high value as compared to most other products. The second product line would consist of computers deemed ‘‘not valuable’’ by the inspection process. They will be dismantled, and will result in three broad lines of recycled materials: metals, glass, and plastics. These materials will then go through intermediate processing that converts this material into a form ready for direct insertion into another manufacturing process (Pohlen and Farris, 1992). These recycled materials will then be sold to prospective buyers. Pohlen and Farris (1992) found that companies remain competitive by charging less for recycled materials than virgin materials. Interviews were conducted with eight companies that were potential buyers of these respective materials. Discussions with the purchasers of the components from dismantled computers confirmed Stock’s (1998) assertion that these buyers require a consistent and high quality stream of product. This was especially the case for plastics separators who asserted their need for a supply stream that could easily be separated into specific plastic compounds for resale to industrial manufacturers. Glass and metal separators were less stringent, but still wanted a clean supply stream so as to minimize their processing and disposal costs. Manufacturers commented on the increasing pressure to incorporate recycled materials into their production of new products. Several mentioned an interest in setting policy standards whereby a certain percentage of recycled material would be included in the production of their new products. However, the manufacturers made it clear that their decision to use recycled materials was contingent on being able to obtain a steady supply of quality, cost-effective recycled materials. Consistent with Pohlen and Farris (1992), several buyers

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mentioned their preference for virgin materials unless cost advantages existed for the recycled materials. Competitive sector As mentioned previously, Stock (1998) establishes the need to ensure sufficient volumes of materials in order to be successful. Stock (1998) also found that reverse logistics processes, in the cases where they are being managed, are managed within a single firm or between a few firms, but seldom across supply chains. This suggests that there may exist duplication of efforts in several areas of the country. To examine this issue an assessment of the competitive sector was conducted via a series of eight interviews with companies currently demanufacturing/recycling EOL computers. These companies agreed to be interviewed because they were assured that their individual responses would be held confidential and because they were being considered for participation in the proposed reverse logistics system. Competition for the proposed system was viewed from three distinct perspectives. The first perspective is competition for the supply of EOL computers available to the proposed system. The second perspective is competition for buyers of low-end refurbished computers. The third perspective is the competition for buyers of the metal, glass, and plastics. Looking at the first perspective, competition must be viewed with consideration for their geographic proximity to the members of the proposed system. Using this perspective, it is reasoned that other organizations operating within geographic proximity to the proposed system could adversely impact the system’s ability to acquire the necessary supply (quantity and quality) of EOL computers required to make the system operationally viable. The discussions with competitor firms suggest that a 500-mile radius from the proposed focal location for the reverse logistics system be examined for possible competition. The second perspective towards competition is that of other organizations attempting to retail low-end refurbished computers to the marketplace. The output from a reverse logistics system for low-end refurbished (as opposed to high-end new) computers is faced with a particular set of competitors. Our discussions indicate that while indirect competition comes from retailers of new computers, the direct competition is coming from a growing number of entrants into the refurbished computer marketplace. In fact, one of the interviewed companies has opened their own storefront retail outlets and web site in order to market their refurbished computers. The third perspective toward competition involves other organizations producing recycled metal, glass, and plastics and the competition between them and the proposed reverse logistics system for buyers of these recycled materials. The interviews indicate that for these three materials, there appears a ready demand for glass and metal. The demand for plastics is more dependent upon achieving an acceptable and consistent level of quality product at competitive market prices.

An additional finding from the interviews suggest that those companies in Reverse logistics the demanufacturing/recycling business are currently serving companies who systems for EOL depreciated their computers and have elected not to demanufacture the product computers but have an outside recycler refurbish them for resale. The recycler and supplying company would then share the profits from selling the refurbished computer. In other words, those already in the demanufacturing business have 469 identified what has been called the ‘‘low hanging fruit,’’ or most valued EOL computers, and have captured a large share of that market. This may serve as a barrier of entry to the proposed system for EOL computers because the economic viability of the system would seem to be dependent on the ability to collect and sell a substantial number of refurbished computers. In summary, it appears the companies already in the demanufacturing/ recycling business have identified the largest supply sources of post-industrial EOL computers. These companies have developed relationships and presently service the larger companies and computer manufacturers. Consistent with Stock’s (1998) findings, these relationships tend to form between small numbers of firms. None of our discussions identified coordinated efforts across entire supply chains. The demanufacturers/recyclers have also directed their attention to not only demanufacture the EOL computers but to refurbish those products for eventual resale. This strategy has proven to add more value, give the computers longer useful lives, and provide increased profits for the demanufacturing/recycling companies than only dealing with the raw materials. .

Internal considerations Both internal and external factors, acting together, impact reverse logistics activities. Building upon studies by Stock (1992) and Dowlatshahi (2000), an examination of internal considerations was also undertaken. Specifically, qualitative interviews examined both strategic and operational processing factors within an organization that would impact the proposed reverse logistics system for EOL computers. Findings from the interviews for both sets of factors are discussed in the following sections. Strategic factors Strategic planning of companies varies because of the nature of their businesses and the history of leadership and planning within the organization (Cooper et al., 1992). When examining reverse logistics systems, companies must identify their long-term goals and the broad steps necessary to achieve these goals (Cooper et al., 1992). Strategic factors in this setting consist of strategic costs, overall quality, customer service, environmental concerns, and legislative concerns (Dowlatshahi, 2000). These factors are critical and must be considered prior to operational factors (Dowlatshahi, 2000). Eight companies were interviewed to gain perspective on strategic factors. Strategic costs can include the costs of equipment for dismantling products, the costs for qualified workers to run the reverse logistics system, and the cost

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of additional warehouse facilities. These costs are considered strategic due to the need to allocate sufficient resources (capital and/or senior management effort) to these initiatives (Stock, 1998) as opposed to the resources going to other areas of the company. Minimizing strategic costs depends on effective utilization of current resources, methods, and technologies, which are essential for a successful reverse logistics system (Fuller, 1978; Stock, 1992; Kopicki et al., 1993; Kuuva and Airila, 1994; Willits and Giuntini, 1994; Thierry et al., 1995; Dowlatshahi, 2000). The company interviews established the equipment necessary to recycle and refurbish EOL computers. A key finding was that most of the equipment utilized was customized to support the processing requirements for each individual company. In relation to labor, transportation and warehouse costs, the interviewed companies indicated that these costs were dependent on both the input and output streams desired for the proposed system. Specifically, the strategic decision to place a heavier emphasis on refurbishing as opposed to recycling, the greater the costs associated with labor, transportation and warehouse facilities must be considered. The skills required by employees and the storage space requirements increase dramatically as the focus shifts to value-added EOL computer refurbishing. However, the companies also indicated that these increased costs were typically offset by an even larger increase in profits. Companies must make the commitment to provide refurbished products that are the same high quality as the corresponding virgin products (Stock, 1992; Thierry et al., 1995; Carter and Ellram, 1998). The interviewed companies strongly agreed with these assertions. In fact, many of the companies discussed their decision to set high standards for accepting input EOL computers. The companies believed that the only way to guarantee this high quality input stream was to form partnerships with select companies that could provide adequate volumes of pre-qualified EOL computers. For any type of logistics system to be effective, identifying and fulfilling customer-service requirements is essential (Murphy, 1986; Stock, 1992; Byrne and Deeb, 1993; Andel, 1993; Giuntini and Andel, 1995a, b; Witt, 1995). The major customer service requirements of strategic importance identified during the interviews included assurances of a consistent and standardized output stream of high quality products, especially for buyers of plastics. Also, manufacturers mentioned the difficulties they had experienced with obtaining a consistent flow of recycled materials to integrate back into their production process. One company even suggested that they would utilize a higher percentage of recycled material into their products if they could be assured of an acceptable supply stream. Taking environmental factors into account in reverse logistics systems can lead to cost savings and environmental improvements because reverse logistics systems retrieve resources that would not otherwise be used (Byrne and Deeb, 1993; Carter and Ellram, 1998; Dowlatshahi, 2000). Dowlatshahi (2000) contends that consumers may be willing to pay more for products that benefit their communities and the environment. This ideal was found to be non-

existent during the interviews for this study. None of the buyers indicated that Reverse logistics they would pay more for recycled materials if they could obtain virgin material systems for EOL at a lower price. Moreover, several of the companies mentioned that they would computers hesitate to use recycled materials (with acceptable quality and price characteristics) if the material impacted the aesthetics and/or perceived reliability of their products. In summary, the companies expressed a 471 willingness to use recycled/refurbished products. However, they were unwilling to accept these products for only their environmental benefits. The legislative factors primarily mentioned by computer manufacturers focused on the potential implications of not addressing the growing number of EOL computers. These companies indicated that they were making improvements with recycling/refurbishing of their most up-to-date machines, but difficulties surfaced in dealing with plastics as a recycled component when the computer stayed in circulation for an extended period of time. Several companies suggested that unless they could improve the way they handle these types of products, the federal government might become more involved. Operational factors Operational factors influencing the reverse logistics systems for EOL computers include a cost-benefit analysis, transportation, warehousing, supply management, packaging as well as refurbishing and dismantling (Dowlatshahi, 2000). Although the operational factors are not of equal importance in all situations, firms should consider each of them in terms of their relative importance to the situation being examined (Dowlatshahi, 2000). The qualitative data collected in this study suggests that supply management, processing, and transportation are the most important operational factors for the proposed system. Cost-benefit analysis involves an appraisal of returned materials, the costs of refurbishing/recycling processes, and the overall costs and benefits of the output. An analysis of the data provided during the interviews suggests that while the proposed system is economically feasible, much depends on the types of inputs obtained and outputs produced. In particular, the larger the percentage of computers refurbished, the quicker the investment costs for equipment and facilities can be recouped. Also, labor and transportation costs were found to be the key variable costs when processing EOL computers. Transportation is usually the largest reverse logistics cost, often 25 per cent or more of the total reverse logistics costs (Stock, 1998). Consistent with this finding, Murphy (1986) suggests that most firms relied on truck transportation in reverse logistics. The companies interviewed for this study confirm this finding still holds true today. One firm did utilize rail for a specific account, but this was an exception to their normal practice of using truck transportation. Another key finding in the area of transportation involved the challenges of transporting mixed input streams. Several firms discussed baling (forming bundles of crushed housings to facilitate handling) as a method for increasing the economics of transporting EOL computer housings, but this is infeasible for

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companies receiving a mixed stream of refurbishable and recyclable computers in the same shipment. Several differing views emerged concerning which party was responsible for the costs of inbound transportation, the processor or the source of the supply. In one case, the government discarders were willing to pay to have their EOL computers transported to the demanufacturing facility. However, they also desired to have the demanufacturer reimburse them a percentage of the profits acquired from the sale of this equipment. In terms of warehousing, Murphy (1986) found that private warehousing was popular for reverse logistics because of its convenience and reliability. While these findings were supported by the current research, it should also be noted that over 80 per cent of the top 100 third-party logistics providers currently offer reverse logistics services (Inbound Logistics, 2001). Similarly, Lieb and Randall (1999) reported that third-party executives viewed reverse logistics as an ‘‘opportunity’’ area. This suggests that reverse logistics activities performed by third-party providers may become more prevalent in the future. A key issue for EOL computer processors is determining the physical size of their facility. As was discussed with transportation, the type of input stream dramatically impacted the amount of space required to handle the inbound shipments. The research team observed several instances of companies not having enough space to store their inbound EOL computers. Some even decided to store the computers outside, thus eliminating the ability to refurbish the computer. Supply management for reverse logistics systems focuses on the reuse of the parts and materials of returned products to reduce the use and costs of raw materials (Stock, 1992). Much of the discussion in this area dealt with the need to establish relationships with suppliers who are able to provide a consistent input stream for processing. As mentioned previously, developing relationships with suppliers of high quality, competitively priced EOL computers was seen as a key strategic factor to the system becoming economically feasible. Operationally, these require sufficient technical expertise in order to pre-qualify the input streams received from these partners. The companies indicated that they have developed stringent requirements for what they would be willing to pre-qualify for purchase. Additionally, many of the companies maintain quality control teams that continuously monitor their input streams. Packaging for a reverse logistics system for EOL computers focuses primarily on protection issues. In general, the companies’ main concern was to protect inbound shipments of EOL computers that were potentially refurbishable. The companies suggested that with the increased profits that could be achieved by refurbishing these computers as opposed to recycling the materials, it was important that as many computers as possible reach the processing facility undamaged. Dowlatshahi (2000) suggests that a firm’s use of current manufacturing processes, standardized components, and design for refurbishing/recycling

largely determines its success with reverse logistics. The examination of the Reverse logistics reverse logistics system for EOL computers identified several key findings systems for EOL concerning the processing, standardized components, and design for computers refurbishing/recycling. The recycling/refurbishing processes were found to be similar on a macro level. However, there exist several instances of proprietary technology being utilized for processing EOL computers. In particular, 473 processes for separating the metals, plastics, and glass into their most basic form were not consistent across companies. Issues identified in the area of standardized components mainly dealt with the lack of consistency in the type of plastic used in the computer housings by various computer manufacturers. Finally, companies that refurbish a large number of the EOL computers complained of the inconsistency in the design of the computers. They believe that labor costs could be dramatically decreased with a more standardized design by computer manufacturers. Conclusions and implications The results of this study found that qualitative research measures serve a vital role in assessing the factors impacting a reverse logistics system in general and for EOL computers in particular. The qualitative approach allowed the researchers to gather hard-to-obtain, sensitive and confidential data that would not be easily acquired (if acquired at all) using traditional quantitative research methods. For example, the information concerning the use of numerous proprietary methods for separating the compound plastics would have been difficult to collect using other methods. By acquiring this industry-sensitive data, the researchers were able to analyze the various factors that affect the operational implementation of an overall reverse logistics system for EOL computers. These factors included sensitive issues regarding EOL supply sources, questions surrounding the demanufacturing operation and processing of EOL computers, location of potential buyers including their level of demand and expectations, public policy issues as well as competitive concerns and possible reactions. Moreover, the utilization of a formal structure tested and outlined in the literature minimized the limitations traditionally associated with qualitative research measures (McQuarrie, 1991). Using this industry-sensitive qualitative data, the researchers were able to analyze the various internal and external factors and determine the role they would play in operationalizing this proposed interdependent system. By having the hard-to-obtain data necessary to analyze the various components of the system, the researchers were then able to assess the overall economic viability of the proposed reverse logistics system for EOL computers. The results of this qualitative assessment suggest that while the conceptual model of factors impacting a reverse logistics system is a valuable general framework from which to examine these types of systems, the true value lies in the ability of qualitative research techniques to identify critical issues to address for the specific products flowing through these systems. For instance,

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the methodology was able to uncover several key issues that were specific to EOL computers. These issues include: . the challenge of locating and securing a sufficient and consistent supply of EOL computers that meet the quality standards needed to operate the proposed system; . the existence of customized technology within individual members of the system to process the computers; . transportation, warehousing, and handling complexities as a result of the input stream being a mix of refurbishable and non-refurbishable computers; and . the challenges of coordinating a reverse logistics system that is currently made up of several independent organizations dealing with differing aspects of the system. In conclusion, this study has responded to Stock’s (1998) suggestion that the reverse logistics process must be mapped or flow charted in order to understand the components and their interrelationships. This mapping process has demonstrated the complexity of reverse logistics systems for EOL computers and the importance of the relationships that exist to deal with these products. Additionally, the qualitative research method utilized in this study has proven to be useful in collecting industry-sensitive data needed to map and to analyze the economic viability of an overall system designed to recycle and refurbish EOL computers. By collecting the data in this manner, the researchers were able to assess a reverse logistics system which would be comprised of stakeholders who could in some cases be seen as possible competitors but would be needed to institute the overall system designed to address a common problem – what to do with EOL computers. Daugherty et al. (2001) suggest that commitment of management resources has more influence on the achievement of reverse logistics program goals than financial resource commitment. Unfortunately, many firms do not believe it is possible to justify a large investment in improving reverse logistics systems and capabilities because generally not enough analysis is completed (Meyer, 1999). It is hoped that the methodology and insights gained in this study will provide managers a template for analyzing their own reverse logistics challenges. While the study focuses on EOL computers and the findings are therefore most relevant to those types of products, it is believed that the process used to examine these products is transferable to other reverse logistics situations. The key would be identifying the relevant parties to interview. Future research in this area could focus on examining issues impacting each of the factors affecting reverse logistics systems. For example, a study focusing on consumer motivations for recycling EOL products might be beneficial. Given the importance of ensuring a consistently high quality supply stream, this could provide an important contribution to the research on reverse logistics systems for EOL products. Associated with this topic, an examination of

various policy models that support this collection might also provide valuable Reverse logistics insights into ways the government sector could support these efforts. systems for EOL Other issues that deserve examination are how product development computers decisions take into account the ultimate need to dispose of the product as it reaches EOL status. Do the benefits of product differentiation justify the increased costs of dealing with a mixed stream of EOL products? An 475 examination of the impacts of green design and manufacturing on reverse logistics activities could also be beneficial to this area of research. References Achrol, R., Reve, T. and Stern, L. (1983), ‘‘The environment of marketing channel dyads: a framework for comparative analysis’’, Journal of Marketing, Vol. 47, Fall, pp. 55-67. Andel, T. (1993), ‘‘New ways to take out the trash’’, Transportation & Distribution, Vol. 34 No. 5, pp. 24-30. Barnes, J. (1982), ‘‘A problem in reverse logistics’’, Journal of Macromarketing, Vol. 2 No. 2, pp. 31-7. Blumberg, D. (1999), ‘‘Strategic examination of reverse logistics and repair service requirments, needs, market size, and opportunities’’, Journal of Business Logistics, Vol. 20 No. 2, pp. 141-59. Byrne, P. and Deeb, A. (1993), ‘‘Logistics must meet the ‘green’ challenge’’, Transportation & Distribution, Vol. 34 No. 2, pp. 33-7. Calder, B. (1977), ‘‘Focus groups and the nature of qualitative marketing research’’, Journal of Marketing Research, Vol. 14, pp. 353-64. Carter, C. and Ellram, L. (1998), ‘‘Reverse logistics: a review of literature and framework for future investigation’’, Journal of Business Logistics, Vol. 19 No. 1, pp. 85-102. Cooper, M., Innis, D. and Dickson, P. (1992), Strategic Planning for Logistics, Council of Logistics Management, Oak Brook, IL. Creswell, J. (1994), Research Design: Qualitative and Quantitative Approaches, Sage, Thousand Oaks, CA. Crimp, M. and Wright, L. (1995), The Marketing Research Process, Prentice-Hall, Englewood Cliffs, NJ. Daugherty, P., Autry, C. and Ellinger, A. (2001), ‘‘Reverse logistics: the relationship between resource commitment and program performance’’, Journal of Business Logistics, Vol. 22 No. 1, pp. 107-23. Dougherty, D. and Hardy, C. (1996), ‘‘Sustained product innovation in large mature organizations: overcoming innovation-to-organization problems’’, Academy of Management Journal, Vol. 39 No. 5, pp. 1120-53. Dowlatshahi, S. (2000), ‘‘Developing a theory of reverse logistics’’, Interfaces, Vol. 30 No. 3, pp. 143-54. Fuller, D. (1978), ‘‘Recycling consumer solid waste: a commentary on selected channel alternatives’’, Journal of Business Research, Vol. 6 No. 1, pp. 17-31. Goldsby, T. and Stank, T. (2000), ‘‘World class logistics performance and environmentally responsible logistics practices’’, Journal of Business Logistics, Vol. 21 No. 2, pp. 187-208. Goodyear, M. (1990), ‘‘Qualitative research’’, in Birn, R., Hague, P. and Vangelder, P. (Eds), A Handbook of Market Research, Kogan Page, London. Guintini, R. and Andel T. (1995a), ‘‘Advance with reverse logistics’’, Transportation & Distribution, Vol. 36 No. 2, p. 73.

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Guintini, R. and Andel T. (1995b), ‘‘Reverse logistics role models’’, Transportation & Distribution, Vol. 36 No. 4, p. 97. Hamilton, A. (2001), ‘‘How do you junk your computer’’, Time, Vol. 157, No. 6, pp. 70-1. Inbound Logistics (2001), ‘‘Inbound logistics: top 100 3PL providers’’, Inbound Logistics, July, pp. 47-59. Jacoby, J., Berning, C. and Diettvorst, T. (1977), ‘‘What about disposition?’’, Journal of Marketing, Vol. 41 No. 4, pp. 23-8. Kopicki, R., Berg, M., Legg, L., Dasappa, V. and Maggioni, C. (1993), Reuse and Recycling: Reverse Logistics Opportunities, Council of Logistics Management, Oak Brook, IL. Kuuva, M. and Airila, M. (1994), ‘‘Conceptual approach on design for practical product recycling’’, ASME: Design for Manufacturability, DE-Vol. 16, pp. 115-23. Lieb, R. and Randall, H. (1999), ‘‘1997 CEO perspectives on the current status and future prospects of the third party logistics industry in the United States’’, Transportation Journal, Vol. 38 No. 3, pp. 28-42. McQuarrie, E. (1991), ‘‘The customer visit: qualitative research for business-to-business marketers’’, Marketing Research, Vol. 3 No.1, pp. 15-29. Merriam, S. (1988), Case Study Research in Education: A Qualitative Approach, Jossey-Bass, San Francisco, CA. Meyer, H. (1999), ‘‘Many happy returns’’, Journal of Business Strategy, July/August, pp. 27-31. Murphy, P. (1986), ‘‘A preliminary study of transportation and warehousing aspects of reverse distribution’’, Transportation Journal, Vol. 34 No. 1, pp. 48-56. Murphy, P. and Poist, R. (1989), ‘‘Management of logistical retromovements: an empirical analysis of literature suggestions’’, Transportation Research Forum, pp. 177-84. Murphy, P. and Poist R. (2000), ‘‘Green logistics strategies: an analysis of usage patterns’’, Transportation Journal, Vol. 40 No. 2, pp. 5-17. National Safety Council’s Environmental Health Center (1999), Electronic Product Recovery and Recycling Baseline Report: Recycling of Selected Electronic Products in the United States, National Safety Council, Washington DC. Platt, B. and Hyde, J. (1997), Plug Into Electronics Reuse, Institute for Local Self Reliance, Washington, DC. Pohlen, T. and Farris, T. (1992), ‘‘Reverse logistics in plastics recycling’’, International Journal of Physical Distribution & Logistics Management, Vol. 22 No. 7, pp. 35-47. Rogers, D. and Tibben-Lembke, R. (1998), Going Backwards: Reverse Logistics Trends and Practices, Reverse Logistics Executive Council, University of Nevada, Reno, NV, p. 14. Rogers, D. and Tibben-Lembke, R. (2001), ‘‘An examination of reverse logistics practices’’, Journal of Business Logistics, Vol. 22 No. 2, pp. 129-48. Stock, J. (1992), Reverse Logistics, Council of Logistics Management, Oak Brook, IL. Stock, J. (1998), Development and Implementation of Reverse Logistics Programs, Council of Logistics Management, Oak Brook, IL. Stock, J. and Lambert, D. (2001), Strategic Logistics Management, McGraw Hill, New York, NY. Stough, R. and Benson, B. (2000), Mechanisms for Promoting Recycling of Electronics Products, Community Reuse Organization of East Tennessee, Oak Ridge, TN. Thierry, M., Salomon, M., Van Nunen, J. and Van Wassenhove, L. (1995), ‘‘Strategic issues in product recovery management’’, California Management Review, Vol. 37 No. 2, pp. 114-35. Walker, W. (2000), ‘‘Rethinking the reverse supply chain’’, Supply Chain Management Review, Vol. 4 No. 3, pp. 52-9.

Willits, S. and Giuntini, R. (1994), ‘‘Helping your company ‘go green’’’, Management Accounting, Vol. 75 No. 8, pp. 43-7. Witt, C. (1995), ‘‘Distribution: a differentiator in 2000’’, Material Handling Engineering, Vol. 50 No. 11, pp. 57-77. Wright, L. (1996), ‘‘Exploring the in-depth interview as a qualitative research technique with American and Japanese firms’’, Marketing Intelligence & Planning, Vol. 14 No. 6, pp. 59-65. Further reading Wu, H. and Dunn, S. (1995), ‘‘Environmentally responsible logistics systems’’, International Journal of Physical Distribution & Logistics Management, Vol. 25 No. 2, pp. 20-38. Zikmund, W. and Stanton, W. (1971), ‘‘Recycling solid wastes: a channels of distribution problem’’, Journal of Marketing, Vol. 35 No. 3, pp. 34-9. Appendix. Interview guide for the visitation program The issues outlined below provided a general context for the questions asked during the interviews with the reverse logistics stakeholders. The topic emphasis within each of the issue categories varied according to the role the interviewed stakeholder would play in the reverse logistics system. External considerations (1) Input: .

Source (s) of supply – identification, number, and location.

.

Quality of the supply.

.

Quantity of the supply required to operate efficiently.

.

Quantity of supply by source.

.

Consistency of supply into the future by source.

.

Reliability of supply by source.

.

Future supply needs, requirements, and sources.

.

Property characteristics of the supply requirements.

.

Costs associated with transporting, handling, and storing the supply.

.

Issues associated with gathering the necessary supply including quantity, quality, characteristics, reliability and costs.

(2) Regulatory: .

Waste – characteristics, volume, disposal, costs, etc.

.

Environmental issues.

.

Policy issues related to regulations, permits, etc.

.

Policy issues related to the use of recycled material in producing newly manufactured products.

(3) Output: .

Consistency of supply into the future.

.

Problems with providing the quality and quantity of supply at competitive price – past, present, and future.

.

Demand for the product(s).

.

Customers – numbers, characteristics by business type, and location.

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.

Quantity demanded by customers – past, present, and future.

.

Quality demanded by customers – past, present, and future.

.

Special characteristics of the product demanded by customer – past, present, and future.

.

Costs associated with identifying and marketing to customers.

.

Costs associated with transporting the product to the customer.

.

Profit margins by product type.

(4) Competition: .

Who are the other competitors.

.

What other options are being implemented to dispose of the product supply.

.

Problems with obtaining the quality and quantity of supply at a competitive price – past, present, and future.

.

Assess the competition in terms of number, location, strength, and expertise as related to obtaining the necessary supply.

.

Assess the competition in terms of number, location, strength, and expertise as related to selling the output.

.

How the business has changed – dynamics of the competitive environment including strengths, weaknesses, threats and opportunities.

Internal considerations (1) Strategic factors: .

Quality of the supply.

.

Consistency of supply into the future by source.

.

Future supply needs, requirements, and sources.

.

Property characteristics of the supply requirements.

.

Turnover rates.

.

Costs associated with processing, handling, storage, transportation, packing, shipping, etc.

.

Waste – characteristics, volume, disposal, costs, etc.

.

Customer base – numbers, characteristics by business type, and location.

.

Customer service requirements.

.

Characteristics of the product produced.

.

Profit margins by product type.

.

Policy and regulatory issues (requirements for the use of recycled material in producing newly manufactured products).

.

What other options are being implemented to dispose of the product supply and the legislative implications of those options.

(2) Operational factors: .

Quantity of the supply required to operate efficiently.

.

Property characteristics of the supply requirements.

.

Policy and environmental issues – regulations, permits, etc. regarding operating the facility.

.

Space requirements.

.

Equipment requirements.

.

Employee requirements – numbers and skills.

.

Productivity level – number of shifts and volume processed per shift.

.

Turnover rates.

.

Special handling requirements.

.

On-site storage requirements.

.

Costs associated with processing, handling, storage, transportation, packing, shipping, etc.

.

Problems related to the processing, etc. – past, present, and future.

.

Waste – characteristics, volume, disposal, costs, etc.

.

Costs associated with identifying and marketing to customers.

.

Profit margins by product type.

.

Policy and regulatory issues (requirements for the use of recycled material in producing newly manufactured products).

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The research register for this journal is available at http://www.emeraldinsight.com/researchregisters

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The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/0960-0035.htm

Linking logistics to strategy in Argentina Octavio Carranza

480 Received May 2001 Revised January 2002

Universidad Panamericana, Jalisco, Me´xico

Arnold Maltz Arizona State University, Tempe, Arizona, USA, and

Juan Pablo Antu´n Universidad Nacional Auto´noma de Me´xico, Morleos, Mexico Keywords Logistics, Strategy, Argentina, Benchmarking, Customer service Abstract Qualitative results of a benchmarking process in logistics areas between companies operating in Argentina are presented. A description of the main logistics processes reengineered by these companies is done and some inferences are taken from the study. The companies are finally analyzed according to another benchmarking process generated in Michigan State University, which leads to a discussion on how companies can be characterized as world class in emerging countries.

Introduction Global competition is a reality for any firm in a sizable local market. With the advent of fast, nearly universal communications, we are approaching the time when ‘‘all become global citizens, and so must the companies that want to sell us things’’ (Ohmae, 1989). Furthermore, the biggest growth opportunities for many multinationals are in the less developed or emergent regions of the world (Dornier et al., 1998, p. 79). As logistics is recognized as an important enabler of corporate success, logistics professionals are called on for world-class performance wherever the necessary infrastructure, supplier capabilities, information systems, and human resources are available (Simchi-Levi et al., 2000, p. 162). In Argentina global competition, liberalization of the economy, and ‘‘dollarization’’ of the currency have increased pressure on local businesses to perform at world-class levels. Recent economic difficulties have made continuing improvements even more imperative. Argentine logistics managers have responded to the challenge to become internationally competitive. Sa´nchez-Chiappe and Herrero (1997) presented some of the improvements that have occurred in the food industry. This paper shows how leading Argentine companies have implemented logistics programs to support overall corporate strategy and strive for excellent performance. These initiatives have succeeded in raising performance levels significantly. However, as we show in the final section of the paper, even the best Argentine companies do not believe they have reached world-class status in key logistics competencies. International Journal of Physical Distribution & Logistics Management, Vol. 32 No. 6, 2002, pp. 480-496. # MCB UP Limited, 0960-0035 DOI 10.1108/09600030210437988

This project has been supported and executed thanks to the trust and enthusiasm of four companies and the capabilities of the top executives that participated in the project, to whom the authors want to express their sincere thanks. The authors would like to thank three anonymous referees for their comments.

Purpose of the research Linking logistics Pialog, the project that supported this research, had two purposes. First, it was to strategy in intended to bring together leading Argentine companies in a multicompany Argentina benchmarking consortium. Benchmarking is: A technique to compare their operations to those of both competitors and leading firms in related and nonrelated industries. Manufacturers in particular are using benchmarking in important strategic areas as a tool to calibrate logistics operations (Bowersox and Closs, 1996, p. 677).

The companies came together to share experiences and opinions about logistics organization, intracompany integration, and relationships with logistics service providers. These discussion topics were chosen by the companies themselves, as is customary in benchmarking consortia. The companies who came together in Pialog identified another common agenda – the need to become world class. Argentina is a moderately sophisticated logistics marketplace where companies actively strive to bring in world-class best practices. Each of these companies is a major force in its sector of the Argentine economy. All of them indicated that an effective, efficient logistics functions is crucial to continuing success. Three out of four companies are subsidiaries of major multinationals. Their corporate offices have challenged them to replicate state-of-the-art logistics practices in Argentina, and they are measured against subsidiaries from all over the world. The fourth company is an Argentine firm that dominates its market but is facing competition from Brazilian firms (thanks to MERCOSUR) and other multinationals. Research methodology Both parts of the Pialog agenda presented research opportunities. All the companies in our group have access to logistics processes and tools that have proven themselves on a worldwide basis. As they shared the application of these tools it appeared that the choice of emphasis was a function of each company’s market position, product characteristics, and strategic vision. As we followed up each company we found that we could use the case study method (Yin, 1994; Ellram, 1996) to show how company strategy, adapted to a particular national environment, drives the selection of projects and allocation of resources to the logistics area. We found the results particularly interesting because we could compare the pressures and capabilities of multinational subsidiaries to those of a national market leader. Upon considering the issue of ‘‘world class’’ we realized that the Supply Chain 2000 model validated at Michigan State University (Bowersox et al., 1999) provided an accepted method of measuring logistics capabilities and comparing companies to a global benchmark. Three of the four companies filled out the Supply Chain 2000 questionnaire. The results shed light on the logistics challenges in an emerging country such as Argentina. Thus the research reported here is intended to make two contributions to logistics knowledge. First, we show how specific national situations and company strategies determine the strategic priorities of corporate logistics

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functions. Second, we hope to illustrate the applicability of the Supply Chain 2000 model to company benchmarking and the quest for world class status. Since we are limited to a small group of Argentine companies, the research is necessarily exploratory. Nevertheless we believe there are some useful lessons which may be generalizable to other emerging markets. The remainder of the paper is divided into four sections. The next section describes the specific companies, including their markets and their position in the Argentine marketplace. The following section uses an initiative from each company to show how each firm uses logistics to support its overall strategy. Following that we share the results from the companies who completed the Supply Chain 2000 instrument, and we discuss the implications of these results. The paper concludes with a general discussion of the research findings and suggestions for further research. Case description – the Argentine companies Argentina has been classified as an emerging nation (Simchi-Levi et al., 2000, p. 161). Compared to First World or Triad nations emerging countries do not have fully developed physical or information infrastructures, and performance levels vary between companies. Argentina shares the geographic structure of many Latin American countries in that there is a high population concentration in the capital region of Buenos Aires while the rest of the country is relatively lightly populated. In the 1980s Argentina experienced hyperinflation which ended when the Argentine peso was linked to the US dollar in 1991. Other than the multinationals, Argentine retail outlets tend to be small and thinly capitalized, and credit concerns are part of the buyer/supplier relationship. Finally, physical security is an ongoing issue especially for high value goods. With this general introduction to the Argentine situation, we describe the individual companies participating in this research. Our purpose is to position each firm in the Argentine environment, where environment is ‘‘all external factors important to a firm’s management planning and implementation of a logistics strategy’’ (Bowersox and Closs, 1996, p. 458). In order to understand the interaction between the firm and its environment, it is necessary to assess the following about each company: . line of business; . market potential and geography; . competitive situation; . technological capability; . channel structure; . service industry trends (especially logistics services); and . relevant government regulations. The specific results for each company are summarized in Table I and discussed below. We also note the strategic objectives the companies stated in response to this assessment.

Company

1

2

3

4

Line of business/ product

Oil and gas

Fast food restaurant

Beer and soft drink bottler

Cigarettes

Company market position vs competition

Strong #2 in relatively stable market

Pioneer in high potential market

Leader in relatively stable market

Leader in relatively stable market

Technology/ product

Highly flammable; special equipment required

Food and supplies required together

Perishable product requires specialized equipment

Extremely high value

Vulnerability to theft

Medium

Medium

High

High

Channel structure

Independently owned service stations

Franchised outlets

Direct sales to retailers and consumers; small distributors

Small distributors; very small retailers (kiosks)

Service trends

Increasing expectations, and 24 hour delivery capability

100 per cent complete orders

National coverage, including local sales

Daily delivery to all levels of retail

Perceived profit driver

Commodity prices vs operating costs

Location level margins

Extent of distribution

Retail availability

Strategic priority

Free resources for diversification

Support continued expansion

Defend established market position

Defend established market position

Logistics objective

Minimize operating and transaction costs

Guarantee supplier delivery performance

Maximize product availability at a competitive cost

Maximize product availability ‘‘on the shelf’’

Company 1 This company produces and distributes hydrocarbon derivatives, mainly petrol and oil. The production cycle begins with the extraction of crude from the well, continues with transportation to the refinery, and ends with the distribution of the oil to gas stations throughout Argentina. For this company, ‘‘logistics’’ means moving crude oil to and through the refinery, while ‘‘Customer Service,’’ a marketing group, is responsible for distribution of finished product. Although Customer Service/physical distribution practices were the focus of this investigation, there was an awareness of the importance of the upstream (‘‘logistics’’) processes.

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Table I. Drivers of logistics strategy

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Company 1 markets what is essentially a ‘‘commodity’’ product-oil and gasoline. It is a strong number two in the Argentine market, behind the government oil company. This company believes that logistics has to support a cost leadership strategy (Porter, 1980). The largest logistics expense is transportation because the product is bulky and requires special handling. The company distributes its product through small, independently owned service stations so there are many relatively small deliveries and the financial aspects of the delivery can be very important. Since the product is highly flammable, specialized equipment and delivery drivers are required. Company 2 This company is a major fast food restaurant chain. Fast food chains are relatively new in Argentina, and this company is still expanding toward national coverage. The company has capitalized on its global brand and deep understanding of food preparation and marketing to open over 100 outlets so far with good success. At the same time the company has made some specific menu adaptations to better compete in the Argentine market. Others have observed that the key challenge for foreign restaurants is developing reliable suppliers and inbound logistics systems (Dornier et al., 1998, pp. 99-109; Hertzfeld, 1991; Ritchie, 1990). As the company opens new outlets, it must concentrate on continuing to develop both its goods suppliers and the logistics capabilities of service providers. Materials management, which is responsible for supplying all the needs of the individual restaurants (from hamburgers to plastic glasses, from toys for promotions to condiments) is the core of this company, because profit margins of each restaurant are very low and complete meal offerings are a strategic necessity. Company 3 Company 3 is the clear market leader in its sector of the beverage market – beer. It also has a significant presence in the soft drink market. Recently both US and Brazilian companies have entered the Argentine market. In response, the company has adopted a strategy designed to deny competitors a foothold in the most easily served (and most profitable) areas. One important aspect of this strategy is ‘‘blanket coverage.’’ The company is committed to product availability virtually anywhere in the country, which presents a major logistics challenge. In addition, company 3 has to be flexible enough to respond to competitor innovation, e.g. plastic bottles. This beer producer regards logistics as a strategic competence to defend its competitive position. Logistics and marketing are continually assessing how to improve the delivery network, which is a mix of small distributors and company-direct distribution. Logistics is improving distributor support in general and distributor training as well. Logistics is also working on improving operational efficiency to free resources for costly marketing and promotion programs.

Company 4 Linking logistics This cigarette manufacturer is a subsidiary of one of the global leaders in this to strategy in industry. Cigarette customers are brand loyal but expect extremely high levels Argentina of product availability. In fact, customers will change retailers and even brands if the first choice product is not on the shelf. At the same time cigarette distribution in Argentina is a complicated process. Both independent distributors and very small retailers (kiosks) are supplied directly by this 485 manufacturer. On the positive side, the company characterizes overall demand as very stable so that production planning is relatively simple, but daily execution of the delivery function is critical. Logistics coordinates the delivery process from the plant all the way to the smallest retailers. Summary Discussions with the four leading firms uncovered some common themes for logistics execution in Argentina (see Appendix). All four companies were dealing with many relatively small distribution outlets service stations, restaurants, retailers, and kiosks. Servicing such dispersed networks requires costly transportation operations and complicated inventory management by individual site. In addition, financial and credit issues are important when delivering to such small customers. In three out of four cases specialized equipment and procedures were necessary to insure the security of the shipment and the safety of the drivers and the public. Each of these four companies had access to advanced logistics processes either through parent companies or consultants, so they had a good sense of what was possible. Finally, logistics was recognized as important to overall strategy in each company. Company 1 operates in a commodity market with powerful competitors, so it has little or no ability to raise prices. Reducing logistics costs is critical to maintain profitability and free resources for expansion into other product lines. Company 2 is committed to expansion. But each new location can only be profitable and insure repeat business if it has everything the customer wants every time. Company 3 is defending an established leadership position. Efficient logistics translates into the ability to preempt the competition anywhere in the country while maintaining competitive prices and high levels of promotion. Company 4 also believes that coverage and product availability are crucial to keeping loyal customers, hence its commitment to high shelf-level availability through actively managed transportation. Case analysis – logistics initiatives Market conditions and the specifics of the product result in differing logistics priorities by company. Company 1 distributes to a slow growth, mature market. The priority is to reduce costs and free up cash for product diversification. In contrast, company 2 is relatively new to Argentina. It is developing suppliers and systems which can support a long period of sustained growth. Company 3’s priority is to maintain its long time position as the market leader. A selective distribution strategy and continuous reengineering of logistics processes has resulted in major improvements in customer service and

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geographic coverage. Like company 1 and company 3, company 4 faces a mature market. Once again the strategy is to minimize costs for direct and indirect customers, while guaranteeing high levels of product availability. Each company’s logistics strategy is linked to overall corporate objectives. Below we indicate how each company has operationalized this linkage in the Argentine context. Company 1 – Robust, error-free transportation Company 1 determined that improving transportation could increase cash flow in two ways: (1) Improved transportation safety would result in fewer accidents. Argentina’s accident rate is relatively high. Since company 1’s product is highly flammable any accident can be extremely serious. So there is considerable human and financial leverage in reducing accidents. (2) More reliable transportation serves as a foundation for lowering transaction costs and improving cash flow. Unattended delivery and automated customer billing should be acceptable to customers if they can trust the delivery operators. Upgrading the transportation supply base Company 1 uses third parties for its deliveries but remains actively involved in the management of the transportation operation. Company 1’s corporate parent encourages this behavior by sharing best practices from other subsidiaries all over the world. For example, the carrier instruction book is influenced by parent company standards, and route optimization software is supplied by the parent company. As a result, company 1 has a template for organizing its transportation suppliers and expecting continuous improvement based on internationally validated performance standards. Company 1’s efforts to improve supplier performance have especially borne fruit in the highway safety area. Through a specific program, it analyzed causes of possible accidents in all the facets of driving mechanical equipment. Implementing and enforcing this program has resulted in the Argentine subsidiary having one of the lowest accident levels among the parent company’s many subsidiaries. The lower accident rate has had both financial and social benefits for the company. As transportation performance became more reliable and driver professionalism increased, service station operators became more receptive to two other improvements – unattended delivery and electronic financial transactions. If unattended deliveries are allowed, drivers are entrusted with considerable responsibility and access to customer sites. Customers benefit since there is less interference with normal operations. Company 1 benefits from faster deliveries and improved equipment utilization. Electronic financial transactions are also linked to driver activity. Under company 1’s procedures the driver’s report of a completed delivery automatically debits the customer’s

account and updates their available credit. The customer has better Linking logistics information while company 1 experiences improved cash flow. to strategy in Company 1 significantly improved its transportation operation by Argentina combining parent company procedures and tools with specific knowledge of the Argentine environment. The improvements resulted in lower accident costs, more flexible delivery operations, lower transaction costs, and improved cash flow. Logistics took its status as a cost center seriously and worked 487 successfully to generate resources company 1 could use to lessen its dependence on commodity market pricing. Company 2 – Maximizing supplier performance As we noted above, the key to fast food success is an excellent value proposition combined with a reliable supply base and solid inbound logistics. Company 2 plans to grow in Argentina using the same value proposition that has succeeded in other parts of the world – ‘‘to be a single source for lunch or dinner.’’ To support this proposition, the company uses a central supply warehouse as its ‘‘value integrating platform,’’ where every component of a customer’s meal is brought together and sent on to the individual restaurants. The diversity of materials that are required to support this market offer results in a warehouse that is very different from the traditional notion of warehouses. Under this regime, one missing material ruins the product, e.g. the lack of mustard spoils the ‘‘nature’’ of a hot dog. The warehouse is the platform for coordination with suppliers using the latest point of sale data from the restaurants. To insure the success of this process, company 2 encouraged a key foreign supplier to open operations in Argentina based on expectations of market growth. This main strategic supplier provides the three highest volume materials, as well as managing the warehouse and the transportation to the restaurants. This supplier responds to company 2’s needs for innovation and new locations while operating under an ‘‘open accounts’’ policy for revenue purposes. This key supplier relationship is seen as a template for developing relationships with all the other suppliers in company 2’s network. Since company 2 is relatively new to Argentina, it does not enjoy a huge power advantage over its indigenous supply base. Therefore, company 2 bases its channel leadership on trust. Having a successful standardized relationship to point to is an important ingredient in building trust with Argentine suppliers. Also, the Argentine suppliers can interface directly with another supplier like themselves. Finally, the ‘‘first tier’’ supplier understands the job of supporting a large network from its previous work with company 2 and can help other suppliers structure their processes accordingly. Company 2 has set growth as its strategic priority. In the fast food business profitable growth is only possible if good suppliers can depend on an efficient logistics operation to get their goods into the restaurants. Company 2 circumvented the learning curve by bringing along a seasoned supplier when it came to Argentina. Using the lead logistics supplier concept frees company 2 to concentrate on marketing and identifying expansion opportunities. Company 2

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knows that its supplier can and will handle increased demands at the both the warehouse and supply network level while fostering favorable long term partnerships. Company 3 – Defending market dominance Company 3 has only one basic product and one of the biggest distribution networks of the country. It has chosen to defend its market leadership by continuously reengineering its logistic system and seeking out logistics innovations from all over the world. The company has successfully undertaken reorganization of its warehouse management structure and network, redesign of specialized delivery equipment, and major improvements in information systems, among other projects. Three interrelated initiatives are specifically designed to deny competitors easy entry into company 3’s markets. The overall objective is to have the strongest, most flexible and most cost-effectve service offering in every part of Argentina. Since markets in Argentina vary from densely populated Buenos Aires to the isolated areas of the south and northwest, company 3 has cultivated a varied set of logistics capabilities through three programs direct distribution, distributor training, and postponement. Direct distribution. Company 3 tailors its distribution strategies by geographical area. Where customer density is high or key markets are involved, the company uses third party operators that it can control directly. This direct distribution allows company 3 to minimize costs where it can attain economies of scale in delivery and warehousing, ensuring competitive prices and freeing resources for marketing and promotion. Having a direct distribution capability also allows the company to service major sporting events and other one-time opportunities without relying on distributors to cover these important chances for exposure and brand building. Distributor support. In many areas company 3 determined that using distributors was appropriate based on cost-to-serve of particular retail customers and the financial capabilities of the independent distributors. In these cases, company 3 provides specialized support and training so that each distributor can attain maximum efficiency in its logistics operations. This support can include information systems, help with designing specialized equipment as well as routine marketing support in the distributor’s area. Postponement. Argentina is a relatively lightly populated country, and nationwide coverage requires serving distant customers. At the same time there is a temptation to centralize production since a great deal of demand is in Buenos Aires. With a perishable commodity such as beer, this can be a particularly problematic strategy. Yet company 3 did not want to give up market share in the far northwest, which could be a potential entry point for international competitors. Instead, company 3 installed filling and packaging equipment close to the final demand site, effectively ‘‘postponing’’ finished goods production to better match actual demand. Special trucks were designed and used to transport intermediate material to this new finishing operation.

This postponement strategy has resulted in a decrease in inventory and Linking logistics finished goods spoilage while preserving high service levels. to strategy in Company 3 is a ‘‘home-grown’’ company which has systematically looked for Argentina and instituted logistics innovations from all over the world. Its disciplined use of direct distribution, distributors, and postponement allows company 3 maximum flexibility to respond to market opportunities and competitive threats anywhere in Argentina in a cost effective manner. 489 Company 4 – Making high value product available Consumer products companies worldwide emphasis availability they are concerned that any lost sale will damage brand loyalty and may eventually lead to a lost customer. Company 4 also has this mindset, but its approach to product availability reflects the realities of the Argentine market. In Argentina, most cigarettes are sold through very small, specialized retailers and there are literally thousands of these kiosks to be serviced in a large city. Also, as is true worldwide, cigarette deliveries are vulnerable to theft because of their high value and small size. Such ‘‘shrinkage’’ not only costs in terms of lost product but also impedes the company’s ability to provide product. Company 4 instituted two programs to strengthen its delivery network and combat ongoing theft. First, it invested in its transportation companies and distributors to implement sophisticated and extremely effective antitheft strategies. Both procedural and physical improvements were made to minimize the shrinkage associated with this company’s high value product. In addition, satellite tracking and other communications technologies were required as a cost of doing business. Although details of the new security procedures are naturally not available, this effort constituted a significant investment for company 4. Second, the company brought in a highly sophisticated routing algorithm to optimize delivery performance to thousands of very small kiosks that serve final consumers. Such a dispersed retail network requires maximum flexibility in daily delivery operations. In contrast to company 1, which organizes deliveries based on fixed areas, company 4 does not assign fixed delivery areas or guarantee a minimum number of daily deliveries to its third party services. The flexible, planned use of third parties combined with advanced security procedures results in very high availability in spite of the diffuse distribution network and high value nature of the product. Summary We held indepth discussions with four major companies serving the Argentine marketplace. Each of these companies improved logistics operations to support overall corporate objectives. Table II summarizes the various programs the four companies implemented in their logistics areas. In each case it was possible to link the specific program to an overall corporate objective. Company 1’s emphasis on cost reduction and cash flow improvement fits well with the company interest in freeing resources for diversification. Company 2 brought in a key supplier whose familiarity with the company assured support for growth

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490 Table II. Supporting strategy with logistics programs in Argentina

Company

1

2

3

4

Logistics objective

Cost reduction

Complete, reliable supply

National availability

Product availability

Programs

1. Road safety 2. Unattended delivery 3. Electronic financial transactions

1. The warehouse as a platform that integrates value 2. Lead logistics provider (and key supplier) handles transactions

1. Postponement 2. Distributor development 3. Dual distribution as competitive weapon

1. Product security 2. Flexible delivery services

and a smooth flow of supplies. Company 3 used a disciplined approach to planning service to match direct distribution, strong distributors, or postponement with suitable customer bases. This ongoing match is the best way to defend a national network from outside competition. Finally, company 4 insured high availability for its product by safeguarding it against shrinkage and bringing in systems that can plan thousands of deliveries on a daily basis. Are these companies ‘‘world class’’? Each of the four companies in this study has invested heavily in logistics and supply chain capabilities. Each has access to global expertise in this area. At least one firm (company 1) has been challenged by its home office to become ‘‘world class’’ in logistics. Another firm (company 3) has stated its objective to become world class to fend off international competition. Company 2 is executing against a standard template that its parent organization has used throughout the world in various forms. Company 4 also is a subsidiary of a well-regarded multinational, but it elected not to participate in this phase of the research. To test how these companies compare to global competitors, a survey was done employing the ‘‘Supply Chain Management 2000’’ model. This model was developed by Michigan State University to rate capabilities in relation to company performance for various supply chain competencies. Data were gathered using a questionnaire with 106 items that have common five-point Likert scales. Responses are elicited from logistics executives, hence the data can be categorized as perceptual. The capabilities rated are: . customer integration; . technology and planning; . material and service supplier integration; . relationship integration (understood as intercompany behavioral relationships); and

.

internal integration (referring to coordination of different functional Linking logistics areas of the company). to strategy in

Michigan State tested the validity of the model by computing the statistical correlation of the analyzed variables with others that measure company performance. The performance measures are subsumed in five main variables: (1) customer service; (2) cost management; (3) quality; (4) productivity; and (5) asset management. There is also an overall performance item. Performance results are based on executive ratings of their own company’s performance vs their competitors’ perceived performance. The authors of the model (Bowersox et al., 1999) recognize the possibly subjective nature of the above performance ratings. They cite the great difficulties that have been encountered in relating the economic and financial ratios of public companies to underlying nonfinancial operating factors. They suggest that multiple environmental factors and one-time incidents make it virtually impossible to use pure financial results as an objective measure of performance. Therefore the Michigan State group assumes that the performance ratings supplied by executives have reasonable validity. Using these baseline performance ratings they identify superior companies as those whose ratings significantly exceed the mean values of the overall group in any particular competence area. Companies 1, 2, and 3 completed the benchmarking questionnaire designed by Michigan State University. The performance level of the Argentine companies was compared with the performance of the ‘‘best’’ companies recorded in the Michigan State study. The specific scores of the three Argentine companies and the average of the ‘‘world-class’’ companies are shown in Table III. Typically, one might expect that company performance in emergent countries would be lower than similar company performance in developed countries. Emergent countries often lag behind advanced countries in logistics infrastructure, supplier capabilities, information system availability, and human resources. (Simchi-Levi et al., 2000, p. 162). Possible causes could be cultural differences, since emergent countries may expect and accept lower performance standards throughout the supply chain. Also, the physical system including roads, railroads, and air facilities may not be compatible with worldclass performance. Finally, information and communications systems may not be as widely available and reliable as in more advanced countries. The results for these three Argentine companies were consistent with this expectation. Overall performance ratings were up to 40 per cent lower than the ‘‘best’’ company average recorded by North American companies. By

Argentina 491

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individual capability, the best scores on supplier integration and internal integration were 5 per cent and 8 per cent, respectively, worse than the best North American companies. The gap between Argentine and North American companies was widest in the area of customer integration. Since this is the area that North American companies believe is the most related to overall performance, the size of this gap merits discussion. In the opinion of US executives, the capacity that best explains performance differences is customer integration. The low ratings from the Argentine executives may partly result from these companies’ low expectations of their own customers. All the companies that participated in the project had an international flavour (they were in one way or another related to international companies, and/or participated in foreign markets). They were probably well aware of possibilities in other countries and fully realized that their level of integration was not comparable to well-known cases such as Procter & Gamble/Walmart. More importantly, they almost certainly realized that most of their customers were not ready for that kind of tightly integrated collaboration. This was borne out in looking at individual items. The reported gap in ‘‘connectivity’’ between these companies and the North American results was one of the largest reported. Supplier integration was the second factor addressed as relevant by North American executives. Supplier integration for company 2 was one of two capabilities nearest the world-class benchmark. This company’s strength on the inbound side of the supply chain is almost certainly a function of its focus in that area and its importing of a world-class supplier to coordinate restaurant supply on a daily basis. But this cannot be assumed to work in most Argentine channel systems. Again the issue may be the the sophistication and capabilities of the available supply chain partners. Argentina, in particular, seems to suffer some cultural and economic disadvantages as well as a smaller economy overall. Consequently the operations expectations established in developed countries may not be immediately reasonable. Lower volumes per customer will naturally translate into somewhat higher unit resource requirements, while less sophisticated suppliers with lower levels of capitalization cannot provide the same partnership benefits available in countries with more developed infrastructures. Competency

Table III. Logistics competency scores – Argentina firms vs world-class benchmarks

Customer integration Technology and planning Internal integration Measurment integration Relationship integration Material/service supplier integration Overall score

Firm 1

Firm 2

Firm 3

World-class benchmark

47 51 70 51 54 46 273

32 45 60 47 50 54 234

48 49 66 58 49 48 260

60 62 77 60 61 57 370

Comparing the three Argentine companies to each other reinforces our thesis Linking logistics that logistics capabilities are aligned with corporate strategic objectives. to strategy in Paralleling the world-class results, Argentine companies are most advanced in Argentina the area of internal integration. This reflects the reality that many firms are advised to integrate internally, aided by information technology such as ERP systems, before they join with outside suppliers and customers. Company 2’s focus on suppliers and the inbound channel is clearly shown in its much higher 493 scores for supplier and relationship integration vs customer integration. Company 3’s emphasis on product availability and close monitoring of service is consistent with its very high score on measurement integration. Finally, Company 1’s project to coordinate with its dealers seems to be reflected through its relatively high score on relationship integration. Interestingly, the two producing companies reported comparable levels of technology and planning capability, while company 2 lagged. Further investigation confirmed that company 2 achieved its relatively high level of supplier synchronization without a great deal of information technology. Summary The three companies who filled out the Supply Chain 2000 survey are leaders of the Argentine economy and consciously scan the global environment for logistics best practices. Nevertheless their self-ratings are well below worldclass levels on a composite basis. As suggested by Simchi-Levi et al. (2000, p. 162), the lag in customer integration may be a function of the lack of capability in the customer base, rather than with these particular companies. These companies may not be world class, but the profile of their performance levels does reflect their strategic priorities. The highest ratings are for internal integration, reflecting the tendency of most firms to integrate internally before looking to customers and suppliers. Company 2 has its highest relative scores in material/supply integration, consistent with the importance it places on inbound supply for restaurant success. Company 3’s devotion to product availability and coverage seems to show through in its high score on measurement integration, which would allow it to monitor these important service levels. Finally, company 1’s successful effort to automate financial transfers and reduce customer transaction costs is consistent with its relatively good showing on relationship integration. Discussion and suggestions for further research This research is a first attempt to understand how advanced logistics practices can be implemented in emergent countries, specifically Argentina. As we facilitated a benchmarking seminar involving Argentine logistics leaders, we observed that First World practices were likely to succeed when they fit with the Argentine business environment and the strategic priorities of the particular firm. In some cases the project’s success depended on resources supplied by a global parent company. In other cases processes that had succeeded elsewhere could be transferred directly to Argentina, as long as the infrastructure (e.g. key suppliers or technology) was transferred as well.

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The logistics initiatives implemented by these leading companies are relatively familiar to First World observers. The benefits involved are familiar – reliable supply bases, minimized transaction costs, high levels of availability. However, these companies often had to build infrastructure taken for granted in North America and Europe before they could proceed. Two companies had to upgrade their transportation suppliers with better training and technology, and they are still actively managing daily operations. A third company imported a key supplier as part of its effort to structure a supply base that could handle rapid growth and high service simultaneously. Finally, the sole ‘‘native’’ Argentine company instituted a novel postponement operation and designed special transport equipment as part of its commitment to total national coverage. It has become commonplace to talk in terms of a ‘‘borderless world’’ and assume that best practices can be implemented anywhere given enough resources. If our observations are correct, reality is more complicated. Since most companies have limited resources it is important that the proposed initiatives tie to overall corporate strategy. The firms in our study had clearly defined strategic objectives and the logistics programs were aligned with these objectives, whether that be diversification, growth, or market defense. Even when strategic alignment is accomplished, careful attention must be paid to the local business environment. Necessary capabilities may not be available. Suppliers and technology may have to be brought in or local suppliers may have to be systematically upgraded. The time to implement programs will have to be increased if infrastructure building is required. At the same time, we wish to stress that these companies did succeed in making drastic improvements in logistics costs and service. Although they do not consider themselves ‘‘world class’’ by First World standards, these companies had generally respectable scores on most logistics capabilities. Equally important, the four firms approached world class performance in areas of strategic importance. It is highly likely that these companies are limited by their environment and potential partners, not by any shrotcomings in internal capability or knowledge. In a global business environment knowledge and vision know no borders, but implementation still depends on local conditions. Limitations and further research This research is only a first step toward understanding the special conditions faced by logistics professionals in so-called ‘‘emerging’’ economies. The limited sample of four companies represents some of the best that Argentina can offer, rather than a representative group. All of these firms are large by Argentine standards, and three are subsidiaries of successful multinational firms. They have access to resources, knowledge, and people that more typical Argentine firms cannot hope to match. Argentina itself is not necessarily a typical emerging economy. It has been in recession (and crisis as this is written) for over four years, and the hard won stability of the early 1990s is in serious jeopardy. Unlike East Asia, Eastern Europe, or even Brazil, Argentina’s exports have not been healthy for some

years and it did not participate in the worldwide boom of the late 1990s. In Linking logistics many ways Argentina is still trying to regain the prosperity it enjoyed in the to strategy in early part of the twentieth century when European immigrants made its Argentina economy the envy of South America. For all these reasons, we recommend additional research into the important issue of transferring logistics technology and processes to emergent and third world economies. Initial case studies should be undertaken in a variety of 495 geographies and economic settings. Results should be obtained from other industries, especially electronics and non-durable consumer goods which are the cornerstone of many less developed countries. We would also suggest additional research on logistics to support service industries. The single example from this study suggests there may be benefits to entire industries when a service organization builds a local supply chain. With only four companies this is clearly a limited study into an important topic. Emergent and third world countries need good logistics systems to develop their economies. As responsible academics, we should be accumulating knowledge to advise firms how to implement logistics improvements whatever the local circumstances and capabilities. References and further reading Antu´n, J.P. (1994), Logı´stica: Una Visio´n Siste´mica, Serie D-39 Instituto de Ingenierı´a, UNAM, mayo, p. 206. Antu´n, J.P. (1996) Logı´stica Empresarial: una maniobra siste´mica para la estrategia de competitividad, Disertacio´n de Ingreso a la Academia Mexicana de Ingenierı´a como Acade´mico Correspondiente en Argentina (Enero 11, 1996), Academia Mexicana de Ingenierı´a, Me´xico, D.F. Antu´n, J.P. and Bricen˜o, S. (1998),‘‘Operadores logı´sticos en Me´xico: revisio´n de sus pra´cticas y estrategias de desarrollo’’, en Seminar Proceedings of OECD Conference on Intermodal Transport Networks and Logistics, OECD-SCT, Me´xico, 1998, pp. 119-34. Bogan C. and English, M. (1994), Benchmarking for Best Practices: Winning Through Innovative Adaptation, McGraw Hill Inc., New York, NY. Bowersox, D. and Closs, D. (1996), Logistical Management, McGraw-Hill, New York, NY. Bowersox, D., Closs, D.J. and Stank, T.P. (1999), 21st Century Logistics: Making Supply Chain Integration a Reality, Council of Logistics Management, Oak Brook, IL. Bowersox, D., Daugherty, P., Droge, C., Germain, R. and Rogers, D. (1993), Logistical Excellence: It’s Not Business As Usual, Digital Press, Burlington, VT. Carranza, O. (1999), Los procesos de Benchmarking en Argentina: Un enfoque desde la Logı´stica, Documento de Trabajo, Universidad de San Andre´s. Dornier, P.-P., Ernst, R., Fender, M. and Kouvelis, P. (1998), Global Operations and Logistics: Text and Cases, Wiley, New York, NY. Ellram, L.M. (1996), ‘‘The use of the case study method in logistics research’’, Journal of Business Logistics, Vol. 17 No. 2, Fall, pp. 93-138. Hertzfeld, J. (1991), ‘‘Joint ventures: saving the Soviets from Perestroika,’’ Harvard Business Review, Vol. 69 No. 1. Lambert, D. and Stock, J. (1993), Strategic Logistics Management, 3rd ed., Irwin, Boston, MA. Ohmae, K. (1989), ‘‘Managing in a borderless world,’’ Harvard Business Review, Vol. 67 No. 4, pp. 152-61.

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O’Laughlin, K.A., Cooper, J. and Cabocel, E. (1993), Reconfiguring European Logistics Systems, Council of Logistics Management, Oak Brook, IL. Porter, M. (1980), Competitive Strategy, Free Press, New York, NY. Ritchie, P. (1990), ‘‘McDonald’s: a winner through logistics’’, International Journal of Physical Distribution & Logistics Management, Vol. 20 No. 3, pp. 21-4. Sa´nchez-Chiappe, I. and Herrero, V.A. (1997), ‘‘The status of supply chain management in Argentina’s food industry’’, International Journal of Logistics Management, Vol. 8 No. 1, pp. 87-96. Simchi-Levi, D., Kaminsky, P. and Simchi-Levi, E. (2000), Designing and Managing the Supply Chain, Irwin, Boston, MA. Waller, D., D’Avanzo, R. and Lambert, D. (1995), Supply Chain Directions for a New North America, Council of Logistics Management, Oak Brook, IL. Yin, R.K. (1994), Case Study Research: Design and Methods, 2nd ed, Sage, Thousand Oaks, CA. Appendix. Research protocol: best practices in leader companies in Argentina Key subjects (1) Corporative organization: functional structure and logistic processes. (2) Logistic processes characterization in supply, integration of delocalized production and product distribution. (3) Integration of supply chains and origin rules in MERCOSUR. (4) Strategic alliances with logistics operators: structure of the alliance, eventual ‘‘joint ventures’’, contract characteristics, formal cost procedures and ‘‘pricing’’, operative indicators, technological innovation aspects, information flow management associated with flowing goods (from the market to production to suppliers, in the management and processing of distribution orders, etc.). (5) Inventory management experiences in owned facilities and 3rd parties facilities. (6) EDI experiences. (7) Integrated logistics experiences with software (MRP, DRP, SAP, etc.). (8) Information exploitation in POS for in-house logistics management and 3rd parties logistics operators. (9) Logistics postponement strategies. (10)

Strategies in localization of owned logistics platforms (in-house operated and by 3rd parties) and of 3rd parties, as innovative aspects in infraestructure technology as in management.

(11)

Transportation management strategies (owned and from 3rd parties).

(12)

Analytical accounting systems in logistics.

(13)

Marketing-logistics interaction.

(14)

Product engineering and logistics interaction.

(15)

Customer service as an integrative logistics perspective.

(16)

Innovative design of logistics products (logistics operators core business).