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The efficiency of joint decision making in buyer-supplier relationships

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  • Markus Biehl
  • Wade Cook
  • David Johnston

Abstract

This paper examines the effectiveness of joint decision making within 87 pairs of buyer-supplier relationships in manufacturing. Joint decision making is an important attribute of a more cooperative supply chain relationship that may ultimately result in a better performance. Efficiency is modeled as a multiple criteria problem using Data Envelopment Analysis (DEA). Inputs of five kinds of joint decision making activity are examined relative to two measures of output based on the assessment of the buying firm. Three contingent constructs (product customization and innovation, media richness of the communication between buyer and supplier, and continuity in the relationship) are then examined for their impact on the relative performance of each pair. The implications for the management of supply chain relationships and benchmarking of best practice are then discussed. Copyright Springer Science+Business Media, LLC 2006

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  • Markus Biehl & Wade Cook & David Johnston, 2006. "The efficiency of joint decision making in buyer-supplier relationships," Annals of Operations Research, Springer, vol. 145(1), pages 15-34, July.
    • Handle: RePEc:spr:annopr:v:145:y:2006:i:1:p:15-34:10.1007/s10479-006-0023-x
    DOI: 10.1007/s10479-006-0023-x
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    References listed on IDEAS

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    1. Jeffrey H. Dyer, 1997. "Effective interim collaboration: how firms minimize transaction costs and maximise transaction value," Strategic Management Journal, Wiley Blackwell, vol. 18(7), pages 535-556, August.
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    3. Kleinsorge, Ilene K. & Schary, Philip B. & Tanner, Ray D., 1992. "Data Envelopment Analysis for monitoring customer-supplier relationships," Journal of Accounting and Public Policy, Elsevier, vol. 11(4), pages 357-372.
    4. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    5. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
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    Cited by:

    1. Ke Ma & Lichuan Wang & Yan Chen, 2017. "A Collaborative Cloud Service Platform for Realizing Sustainable Make-To-Order Apparel Supply Chain," Sustainability, MDPI, vol. 10(1), pages 1-21, December.
    2. Thuzar Linn & Broos Maenhout, 2019. "The impact of environmental uncertainty on the performance of the rice supply chain in the Ayeyarwaddy Region, Myanmar," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 7(1), pages 1-29, December.
    3. Zhongbao Zhou & Mei Wang & Hui Ding & Chaoqun Ma & Wenbin Liu, 2013. "Further study of production possibility set and performance evaluation model in supply chain DEA," Annals of Operations Research, Springer, vol. 206(1), pages 585-592, July.
    4. Yen Sheng Tsai & Wei-Hsi Hung, 2023. "A low-cost intelligent tracking system for clothing manufacturers," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 473-491, February.
    5. Chunguang Bai & Joseph Sarkis, 2016. "Supplier development investment strategies: a game theoretic evaluation," Annals of Operations Research, Springer, vol. 240(2), pages 583-615, May.
    6. Ming-Miin Yu & Bo Hsiao, 2016. "Measuring the technology gap and logistics performance of individual countries by using a meta-DEA--AR model," Maritime Policy & Management, Taylor & Francis Journals, vol. 43(1), pages 98-120, January.
    7. Oliveira, Fernando S. & Ruiz, Carlos & Conejo, Antonio J., 2013. "Contract design and supply chain coordination in the electricity industry," European Journal of Operational Research, Elsevier, vol. 227(3), pages 527-537.

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