Abstract
A model is developed using fuzzy probability to screen survey data across relevant criteria for selecting suppliers based on fuzzy expected values. The values are derived from qualitative variables and expert opinion of membership in these variables found in industry survey data. The application is made to a supply chain management decision of supplier selection based upon delivery performance which is further divided into attributes that comprise this criterion. The algorithm allows multiple criteria to be considered for each decision parameter. Large sets of survey data regarding six suppliers in the electronic parts industry are gathered from over 150 purchasers and are analyzed through spreadsheet modeling of the fuzzy algorithm. The resulting decision support system allows supply chain managers to improve supplier selection decisions by applying fuzzy measures of criteria and associated beliefs across the dataset. The proposed model and method are highly adaptable to existing survey datasets, including datasets that have incomplete data, and can be implemented in organizations with low decision support resources, such as small and medium sized organizations.
Highlights
Selecting suppliers has become an area of increasing study due to its importance for establishing long-term channel relationships
The question becomes whether this fuzzy set-based model is useful in identifying supplier performance and is a better supplier selection approach
The fuzzy set-based method for ranking suppliers is shown to be more robust than other methods
Summary
Selecting suppliers has become an area of increasing study due to its importance for establishing long-term channel relationships. Of importance is that the firm be able to identify suppliers through an effective evaluation process [1]. Selection criteria and scoring can occur at various levels of the organization, which leads to conflicts in scoring, in systems built to reflect qualitative criteria. Assessors operating in these systems often experience inherent uncertainty regarding supplier performance due to lack of a proper anchoring or definition of numerical scores. This ambiguity means that a supplier’s score on a criterion may not be determinant. Assessors rely on statistical analysis that provides the highest probability of achieving a qualitatively defined membership, yet membership of a supplier in the category of “good,” for example, may vary in opinion over time and by different assessors
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