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

Wai Peng Wong, Wikrom Jaruphongsa and Loo Hay Lee

International Journal of Industrial and Systems Engineering, 2008, vol. 3, issue 2, 162-188

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.

Keywords: supply chain efficiency; data envelopment analysis; DEA; Monte Carlo approximations; stochastic data; supply chain management; SCM; supply chain performance; performance measurement. (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:3:y:2008:i:2:p:162-188

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