Fuzzy Stochastic Data Envelopment Analysis (FSDEA) Modeling for Supply Chain Performance Evaluation
Varathorn Punyangarm, Patcharaporn Yanpirat, Peerayuth Charnsethikul , Saowanee Lertworasirikul and Rawee Suwandechochai
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The concept of traditional data envelopment analysis (DEA) model has been
used for measuring relative efficiencies of a set of homogenous decision making units
(DMUs) by comparing a target DMU with other DMUs that utilize the same multiple
inputs to produce the same multiple outputs based on linear programming (LP)
techniques. Unlike the traditional DEA model, the concept of supply chain management
requires the performance of overall supply chain rather than the performance of each
individual member. This paper applies the methodology of DEA model for evaluating
supply chain performance (DEA-SC) and achieving the best practice. However, since
the traditional DEA model is in the form of a LP model, the assumption of crisp
deterministic inputs and outputs are required. This requirement is limited to applications
of the traditional DEA model in real world problems with both variation in term of
randomness and vagueness of input and output data. In this paper, the fuzzy stochastic
DEA model for evaluating supply chain performance (FSDEA-SC) is proposed to
handle simultaneously both randomness and vagueness. Two steps of transforming the
FSDEA-SC model into the crisp deterministic DEA model for evaluating supply chain
performance (CDDEA-SC) used in this paper are based on the concept of chanceconstrained
programming (CC) and the possibility approach.
