A major concern of firms today is how to measure their performances and enhance the efficiency of their decision making units in satisfying customers. This research presents a customer satisfaction performance measurement methodology which helps managers to assess the efficiency of, and suggest opportunities for improvements to, closely-linked decision making units in a supply chain. For the purposes of this research, closely-linked decision making units are units that perform the same or very similar functions, i.e., a set of distribution centers that link a manufacturer with retailers; or units linked directly as source-destination pairs, like a retailer and his/her direct customers.; This methodology is applied to the decision making units in a particular environment in four major steps: Step I: Selection of appropriate customer satisfaction dimensions (CSDs) and supply chain inputs (SCIs) for the application environment, Step II: Gather performance data for each selected CSD and SCI, Step III: Performance measurement for the application, and Step IV: Sensitivity analysis for the application. The methodology uses importance-performance analysis to classify the CSDs and SCIs, and data envelopment analysis to evaluate the efficiency of each decision making unit. Application of this methodology provides answers to the questions: “How well are the decision making units performing?,” “Where are the boundaries on performance efficiency?” and “How far can these units move toward achieving improved efficiency?”; Slices of actual supply chains coupling service or product producers with their customers were used for testing the methodology. The tests were conducted in three organizational settings: education, manufacturing, and healthcare. These case studies demonstrate that the methodology provides a useful technique for evaluating the performance of linked decision making units with respect to customer satisfaction and the methodology can help to identify opportunities for improvement.
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