Cost‐effective importance measure : A new approach for resource prioritization in a production plant

Purpose – The purpose of this paper is to propose a new measure for criticality analysis of different components of a production plant, called cost‐effective importance measure (CEIM) that considers the component's performance, system structure and economic aspects. Design/methodology/approach – In this work, an explorative literature study covering the concept of importance measure and criticality analysis has been carried out on contemporary literature. The literature study shows that the commonly used importance measures consider the probability of failure of a component and systems structure, and ignore the effect or severity of failures, which is an important factor in engineering decision making. It is not clear how to use the concept of importance measure in combination with cost parameter. Hence, a cost‐effective importance measure (CEIM) is defined and a case study is presented, to demonstrate the application of the proposed importance measure. Findings – The paper indicates that CEIM useful for the analysis of production plants where reliability and cost of break down are of paramount importance. Research limitations/implications – The concept of CEIM is demonstrated using only a case study of a belt conveyor system in the underground mine of Singareni Coal Company Ltd. However, the concept of CEIM can be used in other area. Practical implications – The concept of CEIM can be a handy and effective tool for scheduling of inspection, maintenance and fault diagnosis and these activities can be carried out as per the rank of the components to maximize the benefits in skilled manpower crunch. It also indicates that the upgradation of the production plant's performance can also be done by improving performance of components with relatively large CEIM values. Originality/value – In this paper, the concept of importance measure is extended to include the effect of severity of failures and cost parameter in the criticality analysis.