Application of Fuzzy Logic on Understanding of Risks in Supply Chain and Supplier Selection

Abstract

The aim of this research is firstly to determine the key risk factors of Supply Chain Management (SCM) and developing an efficient model to assess them. In this work, first the risks involved in SCM has been identified and arranged in a systematic hierarchical structure. Questionnaire surveys have been used for data collection from a managerial decision-making group of a case industry. Next, based on the obtained linguistic data, a fuzzy logic based assessment module has been designed for the evaluation of aggregated SC risks. Finally, various risk factors have been categorized; then ranked using ‘fuzzy maximizing and minimizing fuzzy set theory’ in order to identify/assess the major risk factors that need to be managed or controlled. The present trend in the market is no longer the competition among the enterprises but the supply chain. Supplier selection is the most critical decision of the whole procuring department. Selection of supplier is a complicated decision involving many criteria to take into consideration. In later part, this study tries to rank the suppliers centred on different risks and draw a compromise solution. In order to achieve this, understanding risks is of utmost important. In this work, risks associated with the supplier selection have been recognized and analyzed to rank candidate suppliers based on their affinity to risk using fuzzy based VIKOR method. These risks have varied probability of occurrence and impact on the supply chain. Risks have been represented by linguistic variables and then parameterized by Triangular Fuzzy Number (TFN). Fuzzy risk extent has been calculated and thereby Fuzzy Best Value (FBV) and Fuzzy Worst Value (FWV) have been determined. Fuzzy Utility value has been calculated and utilizing this, ranking has been made by closeness to FBV and farness to FWV. Best alternative has been preferred by maximizing utility group and minimizing regret group.