A Budget-Sensitive Approach to Scheduling Maintenance in a Total Productive Maintenance (TPM) Program

Unexpected failures can reduce throughput—especially if the failure affects the process bottleneck. Furthermore, when a failure occurs, it usually takes longer to correct than a scheduled maintenance activity would, resulting in significantly higher costs. It has been empirically shown that preventive maintenance can reduce the frequency of unexpected failures and, if done at appropriate time intervals, can reduce the overall costs (Askin and Goldberg, 2002). TPM is now viewed as an integral part of regular operations in production firms. With the advent of computers and the cheap availability of personal computers in the last couple of decades, computerized maintenance systems have become increasingly popular in industry (Westerkamp, 2006). Such systems make it easy to collect and maintain historical data of machine failures, their frequencies, and down-times due to repairs and maintenance. These databases can be used to determine parameters (such as distribution type, mean, etc.) of system failures, providing an excellent basis to model and improve the maintenance process. It is no exaggeration to state that production systems cannot remain healthy and productive without a good TPM program; however, developing effective operational strategies for TPM can be quite challenging because of numerous complicating factors, such as random failures of the different machines and pieces of equipment in a system, randomness in repair/maintenance times due to variability in the availability of spare parts and repairpersons, and the complex stochastic dynamics of production systems. The manager has to analyze the underlying stochastic processes, costs, and revenues, and a host of other factors in order to develop an effective TPM program.