A model for optimal maintenance interval incorporating the cost of rejections in manufacturing

Purpose – The purpose of this paper is to develop a model that incorporates the effect of rejection cost on optimal maintenance planning decisions. Such a model will help in further modelling the interrelationships between preventive maintenance and quality control policy. Design/methodology/approach – In this paper a model is developed for obtaining optimal preventive maintenance interval based on block replacement policy to incorporate the effect of rejection cost. An illustrative example is presented to compare economic performance of the proposed model (M2) and the conventional model (M1). Model M1 stands for optimal preventive maintenance interval without considering the rejection cost and model M2 stands for optimal preventive maintenance interval considering the rejection cost. The comparison is done for different production rates, costs of rejection and cost of lost production. The impact of control chart parameters on preventive maintenance decision is also studied. Findings – In this paper it is found that model M2 gives better results as compared to model M1. The improvements are more significant at higher production rate, lower cost of lost production and higher rejection cost. The impact of control chart parameters on preventive maintenance planning decision becomes significant as the cost of rejection increases. Research limitations/implications – Conventionally only the down time cost and the cost of repair/replacement are considered for optimal maintenance interval determination. However in the case of machine tools, failure may not always bring the system immediately under complete breakdown but may lead to the functioning of system with degraded performance like process shift from in‐control state to out‐of‐control state. It results into poor quality and thus may lead to higher rejection cost. The cost of rejections may be significantly high in some production systems and, if not incorporated properly during maintenance planning decision may adversely affect the effectiveness of the maintenance planning. Hence the approach presented in this paper gives a better way of maintenance planning. Though the work presented here is illustrated through a simple example considering a single component operating as a part of machine, the approach can be extended to multi‐component system. Originality/value – The outcome is of significant value as it opens up a new perspective into the development of integrated model for maintenance planning and quality control decisions for reducing the operating costs associated with the manufacturing processes.