Optimizing Maintenance Management System: Identification of Critical Risk and Safety Factors in Human Error Based Corrective Maintenance in the SMEs using Fuzzy DEMATEL Approach

A better and optimal maintenance management system is a key source of a sustainable manufacturing environment in the competitive world. In response to improving drastic and competitive manufacturing working environments, many Small and Medium-sized Enterprises (SMEs) wish for an optimal maintenance management system for business success and customer satisfaction. For the purpose of organizing optimal Corrective Maintenance Management (CMM) effectively, some literature and direct industrial surveys have suggested several critical factors of Human Error-Based (HEB) CMM in the working environment of SMEs. HEB accidents and machine breakdowns have always been a major concern in this type of maintenance management system in SMEs, especially in the 3D printing technology applied automotive industries. Such undesirable problems are more prevalent in SMEs of developing countries, especially in the SMEs of southern Tamil Nadu, India. Moreover, ranking the most significant critical factors in HEB maintenance inevitably involves the vagueness of human judgment in the traditional way of approach. This research objective is to predict, analyze, and evaluate the HEB maintenance factors that trigger undesirable machine accidents and corrective maintenance activities of Sensors and switches manufacturing operations in SMEs. Hence, this real-time case study research presents a favorable method for combining fuzzy set theory and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to rank the critical factors for optimal CMM implementation in the working environment of SMEs. According to this analysis identified the most critical factors (Meshing around - 0.205, Poorly written procedures and manuals, and work instructions – 0.163) were identified under two major sub-categories, such as environmental and organizational factors generally influenced in the SMEs. Also, an empirical case study is illustrated by presenting the proposed hybrid MCDM technique and demonstrating the application of implications.