Resilient Supplier Selection in Electronic Components Procurement: An Integration of Evidence Theory and Rule-Based Transformation into TOPSIS to Tackle Uncertain and Incomplete Information

Abstract

The experience of disruptive events causing supply chain vulnerability and business downturns has motivated manufacturing purchasers to consider resilience capability when selecting suppliers. However, this problem is complex, mainly due to difficulties in obtaining precise data on supplier performance. Disruptions are viewed as low-possibility events, leading to incomplete or insufficient evidence to support assessment. A literature review presented in this paper identifies a list of prospective criteria for resilient supplier selection, within the electronics market, considering both quantitative and qualitative aspects in a symmetrical way. A new hybrid methodology, able to handle various forms of uncertain and incomplete data, is proposed to facilitate the supplier selection process. Evidence theory, which suggests the assignment of degrees of belief, instead of traditional probabilities, to expected results, is adopted to construct a decision matrix. The rule-based transformation technique is then employed to transform various forms of the assessment results into a unified format before further aggregation by the modified Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The proposed methodology is tested with a case of resilient supplier selection in a company producing computer hardware components. The proposed decision-making methodology can be applied not only by electronics purchasers but also by practitioners in other industries to logically and straightforwardly model the uncertainty and incompleteness of the available information.