Use of Rough Set Theory and Neural Networks Methods in Supply Chain Management

Introduction The decision to align a specific order with a supplier depends on a no of criteria. Generally the buyer’s decision depends on his assessment of the supplier’s ability to meet the criteria of quality, volume, terms of delivery, price and service. But to evaluate these criteria, the company needs to manage information from different sources through whole supply chain. One way to control may comprise artificial intelligence methods. The main purposes of this article are to identify the AI subsectors that are most suitable for SCM programs, and characterize other subsectors in terms of their usefulness for improving SC performance. Synthesize the existing research on the appliance of rough set theory and neural networks methods touching SCM, on their practical implications and technical merits. Summarize research trends in rough set theory and neural networks methods and identify potential utilization of SCM that haven’t yet been studied in Ukrainian science field. Justify future prospects for expanding existing AI literature and unused AI research in Ukrainian science field topics related to SCM. Results The article identifies the sub-sectors of artificial intelligence that are most suitable for supply chain management programs, and describes other sub-sectors in terms of their usefulness for improving the efficiency of supply chain management. Synthesize the existing literature on the appliance of rough set theory and neural networks methods in supply chains, on their practical implications and technical merits. The tendencies of researches of rough set theory and neural networks methods are generalized and potential spheres of their appliance in management of supply chains which haven’t been investigated yet are defined. Conclusions. Despite the long history of AI, the potential of AI as a means of solving complex issues and finding info in the field of SC hasn’t been fully used in the past especially in the Ukrainian scientific literature. In particular, some groups of AI technologies, such as expert systems and GAs, are increasingly used to solve management issues, including inventory management, procurement, location planning, shipment coordination between contractors, and routing / planning issues. Further study of the issue requires consideration of the use of other AI methods in supply chain management, such as fuzzy logic and agent modeling and recognition of their practical aspects.