Cost-Optimization of Condition-Based Maintenance Policies for a Two-Component Machine System with General Repairs and Process Rejects

This study discusses how to optimize the cost for condition-based maintenance, specifically in the case of a two-component machine system. This is important because, in manufacturing systems that are highly dependent on machines, the total cost of maintaining stochastically degrading components can be high (i.e., as a result of inspections, general repair including replacement, and also the cost of lost production and process rejects due to machine breakdowns). This study hypothesizes that the minimum total cost of a condition-based maintenance policy for a two-component series system can be found by jointly solving for (a) the optimal inspection interval, (b) the general repair conditions for the two components, and (c) the cost estimation of process rejects and breakdown during every maintenance inspection and maintenance performed; doing all of these will give a realistic cost structure as encountered in real-life manufacturing systems. Given this hypothesis, this study models the two-component system by setting up the operating states and the breakdown states and defining the system variables. Then, this study establishes the decision variables, i.e., for the inspection state and the maintenance states, and thereafter performs the exhaustive Monte Carlo simulation approach to get the optimal variables. Finally, this study uses the optimum decision variables to come up with policies for condition-based maintenance. To prove the validity of this hypothesis, a case study is performed on a two-component process of a cutter-grinder assembly of a local fast-moving consumer goods company in the Philippines. By applying the approach proposed by this study that integrates stochastic degradation, frequency of inspections, process rejects and general repair scenarios, the total cost of annual maintenance is seen to be reduced by 44%.