Prediction of Production Capability for Subcontractors in Automotive Rubber Part Supply Chain Using Neuro-Fuzzy System

Abstract

The paper proposes prediction production capability for automotive rubber part supply chain subcontractors in order to remain competitive in the global market. Rubber parts are used widely in automotive, motorcycles, trucks and other types of vehicles which are mostly small sizes but huge quantities to support original equipment manufacturer (OEM) brands with specific parts. The rubber part manufacturing process is complex and uncertain with compression molding and rubber curing conditions. Therefore, good conditions can predict to obtain high production capability for customer commissioning and delivery on schedule. The Neuro-fuzzy system is adopted and developed to deal with the uncertain capability under multi-criteria decision making. The methodology development can be used in the actual situation of the rubber part manufacturing supply chain environment and can predict uncertain problems that might occur in the subcontractor factories. The prediction of the production capability of the rubber part supply chain might be more effective on the real-time monitoring control system and can be tracked location part progressive for further planning both successful or has to be rescheduled. The platform was applied to audit and test in the actual industrial supply chain in Thailand. The methodology development was originally created for the particular supply chain in rubber automotive parts that can replace the existing manual approach to obtain a more efficient process of monthly performance evaluation.