Track: Decision Sciences
Artificial Intelligence (AI) offers a promising solution for fostering agile and resilient supply chains. ?Machine learning, natural language processing, and robotics are all potential enablers of supply chain transformation. Such AI technologies can be applied in different supply chain activities. However, selecting the most suitable AI technology is challenging since the features of supply chains changes for different organizations from various sectors. Accordingly, this study aims to present a fuzzy Simple Additive Weighting (SAW)-Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) methodology for assessing the success factors of AI application in Supply Chain Management (SCM). Fuzzy sets approach is applied to represent assessments of experts by handling uncertainty. The combined fuzzy SAW-MOORA methodology offers many benefits such as simplicity, usability, ability to overcome complex situations, flexibility, and simultaneously optimizing two or more conflicting attributes. The criteria, consisting of AI success factors, are weighted with the fuzzy SAW method. Then, AI technologies such as machine learning, autonomous systems, natural language processing, multi-agent systems, etc. are evaluated with the fuzzy MOORA method. To illustrate the effectiveness of the research methodology, an application is also provided.