Using Adaptive Neuro-Fuzzy Inference System, Artificial Neural Network and Response Surface Method to Optimize Overall Equipment Effectiveness for An Automotive Supplier Company

Artikel i vetenskaplig tidskrift, 2017

Total Productive Maintenance (TPM) is a successful technique that is important in identifying the success and overall effectiveness of the manufacturing process for long term economic viability of business. Overall equipment effectiveness (OEE) is commonly used and well-accepted metric for TPM implementation in many manufacturing industries. As OEE is an important performance measure for effectiveness of any equipment, careful analysis is required to know the effect of various components. An attempt has been done in this research to predict the OEE by using simulation software. The objective is to identify an optimal OEE level to maximize the time between failures and simultaneously minimize the mean repair time. The process of OEE is optimized by using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference system (ANFIS) to identify optimized zone for maximizing output. Finally it is determined the feasible values of inputs using Sequential Quadratic Programming (SQP) algorithm based on trained ANFIS predictive model. The result from this study can be used the inconvenient impact of the failures on the production process, it is strongly recommended to upgrade the operation management, i.e. TPM program, capacity analysis, parts replacement decisions, training programs for technicians/operators, spare parts requirement etc.