Abstract
Supply chain operates in a dynamic platform and its performance measurement requires intensive data collection from the entire value chain. The task of collecting data in 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 introduces the data envelopment analysis (DEA) supply chain model in combination with Monte Carlo simulation to measure the supply chain performance in the stochastic environment. Secondly, a GA-based heuristic technique will be presented to improve the prediction of the performance measurement. This methodology offers an alternative to handle uncertainties in supply chain efficiency measurement and could also be used in other relevant fields, to measure efficiency.
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