A model is developed using fuzzy probability to screen survey data across relevant criteria for selecting suppliers based on fuzzy\nexpected values. The values are derived from qualitative variables and expert opinion of membership in these variables found in\nindustry survey data. The application is made to a supply chain management decision of supplier selection based upon delivery\nperformance which is further divided into attributes that comprise this criterion. The algorithm allows multiple criteria to be\nconsidered for each decision parameter. Large sets of survey data regarding six suppliers in the electronic parts industry are gathered\nfrom over 150 purchasers and are analyzed through spreadsheet modeling of the fuzzy algorithm. The resulting decision support\nsystem allows supply chain managers to improve supplier selection decisions by applying fuzzy measures of criteria and associated\nbeliefs across the dataset. The proposed model and method are highly adaptable to existing survey datasets, including datasets that\nhave incomplete data, and can be implemented in organizations with low decision support resources, such as small and medium\nsized organizations.
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