Supplier selection and evaluation in the supplier with multi-criteria decision-making method in the textile industry

In today's rapidly developing technology environment, in a globalizing world where competition is at the highest level, efficiency (competence), sustainability and efficiency in production become a must-have for businesses and enable customers to respond to their demands more flexibly, quickly and in the most appropriate way. In the lean thinking approach, the elimination of waste and the integration of this mindset into the organizational culture are regarded as fundamental requirements. In businesses, the use of lean production tools aims to increase production efficiency, adapt the lean approach to the overall functioning of the organization, and ensure the production of defect-free and high-quality products on the first attempt through improvement processes. In this context, eliminating waste is intended to enable adaptation to environmental and sectoral changes and to enhance profitability under competitive market conditions. Today, there is an increase in lean production practices to reach the targets. Supplier selection varies according to the industry and situation of each business. Therefore, there is no single way to choose a supplier. Multi-criteria decision-making methods enable the identification of the most appropriate option in supplier selection and evaluation processes by rationally considering various criteria. In the literature, there are numerous studies conducted on supplier selection across various sectors using different criteria and methods. This study aims to select the most suitable lean supplier for LC Waikiki. In this context, 55 suppliers collaborating with the company were evaluated based on 11 different criteria using the AHP and TOPSIS methods. Programs of Excel and Visual Studio were used for the evaluation. The obtained results were compared and evaluated, and the firms were ranked according to their ci values. As a result of the analysis, EFC Giyim Tekstil (ci = 0.843) was identified as the most suitable candidate.