Incorporating Human Factors in Delay Time Modeling of Inspection Maintenance Using Fuzzy Logic

Abstract

Human factors (HF) affecting maintenance performance are common in practice, but have never been considered in delay-time modelling (DTM). The objective of this study is to integrate HF affecting maintenance performance in DTM to obtain an accurate and realistic optimal inspection interval. First, a list of HF that affects maintenance performance is identified through a literature review. Then, a conceptual framework is proposed to illustrate the integration of HF in the maintenance system. Three significant HF are selected, based on experts' opinion to be incorporated into the DTM, namely operator experience level, operator fatigue level, and task seriousness. Fuzzy modeling is used to estimate a time allowance for human factors, which can be added to the inspection duration of the DTM. Two inspection models are developed based on the modified DTM with the objectives of minimizing expected downtime and total cost per unit of time. Both models are validated against a realistic case study. Sensitivity analysis is performed to study the effect of HF on determining the optimal inspection interval. The results show that failing to account for HF results in increasing the frequency of (unnecessary) inspections; hence interruptions of production by up to 45.5% based on expected downtime and 49% based on expected total cost. This also results in the allocation of 25% to 92% less inspection time, which may significantly affect operator performance and work quality. The developed models provide a mechanism for decision makers to set an accurate inspection duration that accounts for HF and hence determines a realistic optimal inspection interval. In addition, the proposed conceptual framework can help manufacturing firms in designing maintenance systems with superior longterm performance.