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Supply chain performance measurement system: a Monte Carlo DEA-based approach

Author

Listed:
  • Wai Peng Wong
  • Wikrom Jaruphongsa
  • Loo Hay Lee

Abstract

A supply chain operates in a dynamic platform and its performance efficiency measurement requires intensive data collection. The task of collecting data in a supply chain is not trivial and it often faces with uncertainties. This paper develops a simple tool to measure supply chain performance in the real environment, which is stochastic. Firstly, it introduced the Data Envelopment Analysis (DEA) supply chain model to measure the supply chain performance. Next, it enhanced the model with Monte Carlo (random sampling) methodology to cater for efficiency measurement in stochastic environment. Monte Carlo approximations to stochastic DEA have not been practically used in empirical analysis, despite being an important tool to make statistical inferences on the efficiency point estimator. This method proves to be a cost saving and efficient way to handle uncertainties and could be used in other relevant field other than supply chain, to measure efficiency.

Suggested Citation

  • Wai Peng Wong & Wikrom Jaruphongsa & Loo Hay Lee, 2008. "Supply chain performance measurement system: a Monte Carlo DEA-based approach," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 3(2), pages 162-188.
    • Handle: RePEc:ids:ijisen:v:3:y:2008:i:2:p:162-188
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    Citations

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    Cited by:

    1. Zohreh Sadeghi & Reza Farzipoor Saen & Mahdi Moradzadehfard, 2022. "RETRACTED ARTICLE: Developing a network data envelopment analysis model for appraising sustainable supply chains: a sustainability accounting approach," Operations Management Research, Springer, vol. 15(3), pages 809-824, December.
    2. repec:cor:louvrp:-2393 is not listed on IDEAS
    3. Javad Gerami & Reza Kiani Mavi & Reza Farzipoor Saen & Neda Kiani Mavi, 2023. "A novel network DEA-R model for evaluating hospital services supply chain performance," Annals of Operations Research, Springer, vol. 324(1), pages 1041-1066, May.
    4. Illi Kim & Changhee Kim, 2018. "Supply Chain Efficiency Measurement to Maintain Sustainable Performance in the Automobile Industry," Sustainability, MDPI, vol. 10(8), pages 1-16, August.
    5. Chiang, Nai-Yuan & Lin, Yiqing & Long, Quan, 2020. "Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods," Operations Research Perspectives, Elsevier, vol. 7(C).
    6. Grigoroudis, Evangelos & Petridis, Konstantinos & Arabatzis, Garyfallos, 2014. "RDEA: A recursive DEA based algorithm for the optimal design of biomass supply chain networks," Renewable Energy, Elsevier, vol. 71(C), pages 113-122.
    7. Alireza Karimi & Saeed Jafarzadeh-Ghoushchi & M. A. Mohtadi-Bonab, 2020. "Presenting a new model for performance measurement of the sustainable supply chain of Shoa Panjereh Company in different provinces of Iran (case study)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(1), pages 140-154, February.
    8. Wai‐Peng Wong & Qiang Deng & Ming-Lang Tseng & Loo‐Hay Lee & Chee‐Wooi Hooy, 2014. "A Stochastic Setting To Bank Financial Performance For Refining Efficiency Estimates," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(4), pages 225-245, October.
    9. Olawale Ogunrinde & Ekundayo Shittu, 2023. "Benchmarking performance of photovoltaic power plants in multiple periods," Environment Systems and Decisions, Springer, vol. 43(3), pages 489-503, September.
    10. Mehmet Ceyhan & James Benneyan, 2014. "Handling estimated proportions in public sector data envelopment analysis," Annals of Operations Research, Springer, vol. 221(1), pages 107-132, October.
    11. HATAMI-MARBINI, Adel & TAVANA, Madjid & EMROUZNEJAD, Ali & SAATI, Saber, 2012. "Efficiency measurement in fuzzy additive data envelopment analysis," LIDAM Reprints CORE 2393, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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