DIGITAL SUPPLIER SELECTION FOR A GARMENT BUSINESS USING INTERVAL TYPE-2 FUZZY TOPSIS

OpenAlex · English

Recent advances in digital technology and manufacturing have changed the industry dramatically. Fundamentally, Industry 4.0 has begun to affect many areas, aiming at improving production and engineering processes, improving the quality of products and services, optimizing the relationship between customers and organizations, bringing new business opportunities and providing economic benefits. One of those areas is the supply chain management, which greatly affects business productivity. Industry 4.0 has provided a highly efficient digital supply chain that has established a smart connection between supply, production, logistics and customers. This has promoted the digitization of suppliers resulting in an increase in the performance of parent companies, which, therefore, wish to identify and use the suppliers that best use digital technologies. However, it is an uncertain decision problem which has many criteria in order to determine these suppliers. Therefore, this problem can be solved by multi-criteria decision-making methods that can best model uncertainties. In this study, it is aimed to select the best one among the suppliers which are digitalized by using the industry 4.0 technologies from the suppliers of a parent firm operating in the garment industry. In order to solve the selection problem, the interval type-2 fuzzy TOPSIS method, which includes a interval of type-2 fuzzy sets and which can model the uncertainties very well in solving fuzzy multi-criteria decision making problems, was used. At the end of the study, alternatives were listed according to closeness indexes and the best digital supplier selection was made according to the results of sensitivity analysis and necessary evaluations were made. As a result, the selection model used in this paper, which is the first study in the literature on the selection of digital suppliers and which is not included in the selection of traditional suppliers, can contribute to researchers and practitioners by using them for other industries.