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Aligning supply chain collaboration using Analytic Hierarchy Process


  • Ramanathan, Usha


The significance of collaboration among supply chain members has been sufficiently stressed in the recent literature as a powerful tool for increasing accuracy of demand forecasts and for consequent cost reductions. Since it has been recognized that naïve forecasting is no longer cost efficient, Supply Chain (SC) members have found it very important to exchange relevant information that will help improve accuracy of demand forecasting. This information differs widely in terms of their characteristics. For example, some information (e.g. historic sales data) that is cheap to exchange may not contribute to a great increase in forecast accuracy. Similarly, some information may not be very reliable (e.g. demand forecast by individual SC members). In general, there is a trade-off in the kind of information required and the kind of information exchanged. This study analyses these trade-offs using an Analytic Hierarchy Process (AHP) model. The model is then implemented based on case studies conducted in two manufacturing firms. The AHP model ranks available information in terms of their contributions to improve forecast accuracy, and can provide vital clues to SC partners for preparing exchangeable data. From the case studies using AHP model, it was proved that using the preferred SC data, the firms could enhance forecasts accuracy. This in turn can help the firms to make decisions on SC collaborative arrangements for information exchange.

Suggested Citation

  • Ramanathan, Usha, 2013. "Aligning supply chain collaboration using Analytic Hierarchy Process," Omega, Elsevier, vol. 41(2), pages 431-440.
  • Handle: RePEc:eee:jomega:v:41:y:2013:i:2:p:431-440
    DOI: 10.1016/

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    References listed on IDEAS

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    Cited by:

    1. Shahid Rasheed & ChangFeng Wang & Bruno Lucena, 2015. "Risk Leveling in Program Environments—A Structured Approach for Program Risk Management," Sustainability, MDPI, Open Access Journal, vol. 7(5), pages 1-24, May.
    2. Ahn, Byeong Seok, 2017. "The analytic hierarchy process with interval preference statements," Omega, Elsevier, vol. 67(C), pages 177-185.
    3. Hariga, Moncer & Gumus, Mehmet & Daghfous, Abdelkader, 2014. "Storage constrained vendor managed inventory models with unequal shipment frequencies," Omega, Elsevier, vol. 48(C), pages 94-106.
    4. Barros, Carlos Pestana & Wanke, Peter, 2015. "An analysis of African airlines efficiency with two-stage TOPSIS and neural networks," Journal of Air Transport Management, Elsevier, vol. 44, pages 90-102.
    5. Graça, Paula & Camarinha-Matos, Luís M., 2017. "Performance indicators for collaborative business ecosystems — Literature review and trends," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 237-255.
    6. repec:eee:jomega:v:70:y:2017:i:c:p:58-76 is not listed on IDEAS
    7. repec:eee:proeco:v:191:y:2017:i:c:p:143-153 is not listed on IDEAS
    8. Bilbao-Terol, Amelia & Arenas-Parra, Mar & Cañal-Fernández, Verónica & Antomil-Ibias, José, 2014. "Using TOPSIS for assessing the sustainability of government bond funds," Omega, Elsevier, vol. 49(C), pages 1-17.
    9. Wanke, Peter & Pestana Barros, Carlos & Chen, Zhongfei, 2015. "An analysis of Asian airlines efficiency with two-stage TOPSIS and MCMC generalized linear mixed models," International Journal of Production Economics, Elsevier, vol. 169(C), pages 110-126.
    10. Peter Wanke & Carlos Barros & Nkanga Pedro João Macanda, 2016. "Predicting Efficiency in Angolan Banks: A Two-Stage TOPSIS and Neural Networks Approach," South African Journal of Economics, Economic Society of South Africa, vol. 84(3), pages 461-483, September.
    11. Choudhary, Devendra & Shankar, Ravi, 2015. "The value of VMI beyond information sharing in a single supplier multiple retailers supply chain under a non-stationary (Rn, Sn) policy," Omega, Elsevier, vol. 51(C), pages 59-70.


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