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Data envelopment analysis for a supply chain

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Abstract

Data envelopment analysis (DEA) is a method for evaluating the management efficiency of decision-making units (DMUs). This article proposes a DEA model for supply-chain management. Traditional studies focused on the selection of partners and the construction of the supply chain. Therefore, this study considers how to optimize the supply chain itself in order to maximize the benefit by DEA. In addition, a significant matter is that supply chains have sometimes unbalanced business processes. This means that some particular DMUs on the supply chain have a superiority which maintains efficiency. That is why the other DMUs on the supply chain need to operate in unfavorable conditions. As a result, their operations badly affect the total efficiency of the supply chain. Therefore, the proposed method introduces an adjustment variable to calculate the optimum operation of the supply chain. The utility and effectiveness of the proposed method are shown by numerical experiments.

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References

  1. Srinivas T, Baker RC (2002) A multi-phase mathematical programming approach for effective supply-chain design. Eur J Operational Res 141:544–558

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Author information

Authors and Affiliations

  1. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuencho, Naka-ku Sakai, Osaka, 599-8531, Japan

    Shingo Aoki, Akio Naito, Ryota Gejima, Kazushige Inoue & Hiroshi Tsuji

Authors
  1. Shingo Aoki
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  2. Akio Naito
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  3. Ryota Gejima
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  4. Kazushige Inoue
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  5. Hiroshi Tsuji
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Corresponding author

Correspondence to Shingo Aoki.

Additional information

This work was presented in part at the 15th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2010

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Cite this article

Aoki, S., Naito, A., Gejima, R. et al. Data envelopment analysis for a supply chain. Artif Life Robotics 15, 171–175 (2010). https://doi.org/10.1007/s10015-010-0787-6

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  • DOI: https://doi.org/10.1007/s10015-010-0787-6

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