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Purpose This paper explains the development stages of a generic decision support system to leverage supply chain performance (GLE). The purpose of this paper is to identify and trade off the critical supply chain measures which are interrelated and in contradiction with each other. Design/methodology/approach The GLE was developed as an extension of the supply chain performance assessment tool proposed by Banomyong and Supatn (2011). It contained nine measures covering key activities along the supply chain under dimensions of cost, time and reliability. Their interrelations were figured out by causal linkages, whereas their contradictions were traded off as multi-objective optimization. It is solved using fuzzy goal programming along with a weighted max-min operator in order to acquire the Pareto-optimal solution. Findings The results from the GLE showed there were two critical supply chain measures including supply chain cost per sales and average order cycle time. They contradictorily influenced by a root-cause, namely product lot size. Its Pareto-optimal value was provided to achieve the minimized values of supply chain cost per sales and average order cycle time which were consistent with their relative weights. Research limitations/implications As generic features, the GLE needs further validation in several industries under various supply chain strategies. The further validation may contribute the GLE to include multiple decision variables, multiple types of product and multiple periods of time. In addition, the GLE may consider a dimensional measure of environmental impact along the supply chain activities. Originality/value The GLE is a unique decision support system to identify and trade off the critical, interrelated and contradicting supply chain measures. More uniqueness is obtained when the GLE offers an option of inputting a set of relative weights for the interrelated supply chain measures.

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