The Effect of Implementing Different Maintenance Policies on the Production Rate of a Flexible Manufacturing Cell

Today, world-class competitiveness is a must for companies. Competitive world made the companies increase the productivity, quality of the product, lower price and better service and support with respect to safety and environment perspectives. Maintenance is one of the tools that can help the companies reach these objectives. Flexible manufacturing cells (FMCs) as a group of machines designed to produce a variety of similar products, often operate with increasing failure rate due to extensive utilization and wear-outs of equipment. While maintenance plans can eliminate wearout failures, random failures are still unavoidable. This research develops several simulation studies to compare the performance of a flexible manufacturing cell under six different maintenance polices namely: No maintenance policy, Corrective maintenance policy, Block-based policy, Age-based policy, Opportunity-triggered policy and conditional opportunity triggered policy. The simulation studies used in this thesis demonstrate production rate subject to maintenance policies under different mean time between failures. The software chosen for the simulation is Show Flow. The main focus of this work is to compare traditional corrective maintenance policies with different preventive maintenance policies that utilize real-time sensory information to assist in decisions regarding maintenance management and component replacement. It can be concluded that any FMC system under consideration must be analyzed with respect to several maintenance policies and the best policy should be selected before implementing a policy. The maintenance policy of FMC which is monitored in this research is block-based policy. Maintenance and production rate Information of this policy is used as a base to simulate for the other five policies. The analysis shows that different maintenances schedules have important effects on production rate of FMC. The best policy is Opportunity-triggered maintenance policy (OTP) with the best production rate near to fully reliable cell and the worst policy is the corrective maintenance policy (CMP) with the worst production rate in all of the MTBF to compare with other policies. From these results it could be said with a few changes in schedule time of preventive maintenance, a company can have better production rate. This research has been conducted using information obtained from Proton Company (car factory) in Malaysia but the results are not limited to this company or other car factories. It can be used for every factory that has flexible manufacturing cell.

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