Monitoring the supply of products in a supply chain environment: A fuzzy neural approach

Abstract

Fuzzy logic principles and neural networks, both being computational intelligence technologies, can be combined to produce synergetic effects through the formation of a unified approach which takes advantage of the benefits and at the same time counterbalances the flaws of the two technologies. In this paper, a fuzzy neural approach, which is characterized by its ability to suggest the appropriate adjustment of product quantity from various suppliers with different quality standards in a supply chain network, is presented. This approach is particularly useful in situations where multiple supply chain partners are involved to achieve the common objective of producing products to the best satisfaction of customer demands at the lowest possible cost. To validate the feasibility of this approach, a test has been conducted based on the proposed fuzzy neural approach with the objective of suggesting the appropriate selection of suppliers and the optimal quantity allocated to them to meet the required quality standards. This paper describes the methodology for the deployment of this proposed hybrid approach to enhance the machine intelligence of a supply chain network with the description of a case study to exemplify its underlying principles.