Evaluation of artificial intelligence technology success factors for an effective supply chain using interval-valued intuitionistic fuzzy AHP method

The increasing complexity of supply chains in today's globalizing world has increased the importance of effective supply chain management. Consequently, businesses increasingly rely on new technologies to maintain flexibility, efficiency, and competitiveness. In this context, artificial intelligence (AI) technologies have the potential to offer significant opportunities at many critical junctures in supply chain management. The valuable contributions AI algorithms make to effective supply chains in areas such as supplier relationships, logistics planning, inventory management, demand forecasting, and fast and accurate decision-making demonstrate the strategic importance of this technology. However, harnessing the potential of AI technology in supply chain management requires overcoming several challenges, including technical infrastructure, human resource adequacy, data integrity, and security. In this perspective, this study is motivated by the need to identify the critical factors that will enable businesses to overcome these challenges and maximize the effectiveness of AI technologies. Therefore, this study aims to identify, model, and systematically and strategically analyze the key factors affecting the success of integrating AI into the supply chain for an effective supply chain. To this end, a model consisting of four main dimensions and 16 criteria is proposed based on a literature review and expert opinions based on Delphi method. To test the applicability and validity of the proposed model, an application was conducted using a case study in Turkey. The Analytic Hierarchy Process (AHP), one of the multi-criteria decision-making (MCDM) techniques, was used in this application. To overcome the uncertainty, intuition, and hesitancy due to human perception inherent in real- life decision-making processes, the AHP method was extended to interval-valued intuitionistic fuzzy (IVIF) sets. The analysis conducted reveals that elements such as “Strategic Vision”, “Quality of Algorithms” and “Data Security and Compatibility” are of critical importance for effective supply chain management.