Sustainable Futures

The full performance and involvement of employees in the manufacturing industry can increase the productivity and profitability of that particular industry. Simultaneously the probability of human error presenting the entire production system of specific operations such as Human Error Rate (HER) directly affects the efficiency of the production plant. Because it leads to maximizing the maintenance cost, downtime of the machine, and the service activities in the industry. In the Small and Medium-sized Enterprises (SMEs) significance of service and maintenance management systems has been increased due to the product demand, and customer necessity in the recent world. The optimal Decision Support System (DSS) for maintenance management acts as a vital role in maintaining better product quality, safety requirements, maximum availability, and the effectiveness of the machines in the industry. This research mainly focuses on proposing a consistent, optimal decision support model for the best maintenance management systems by prioritizing the human error factors in the electronic switch and sensors manufacturing industry. The objective of this research is to identify the most critical factors and alternatives influencing the human error rate of SMEs. Through the application of hybrid Multi-Criteria Decision Analysis (MCDA) methods such as Fuzzy Analytic Hierarchy Process (AHP), and Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. A real-time case study of SMEs demonstrates the effectiveness of this proposed decision-making framework. The outcome of this research unveiled that the alternative job process and working mode (organizational and environmental factors) was a maximum influence on the human error factors that need the utmost attention among all others. To organize the optimal maintenance strategy and maximum productivity in the sensor and electronic switch manufacturing plant of SMEs in the southern region of Tamil Nadu, India.

Hybrid fuzzy AHP-TOPSIS framework on human error factor analysis: Implications to developing optimal maintenance management system in the SMEs