Over the past ten years, manufacturers and consumers have become increasingly interested in the applications of smart, sustainable, and autonomous systems in the industry and in everyday life. Due to the recent industrial revolution (Industry 4.0), most of the existing Small and Medium-sized Enterprises (SMEs) also want to adapt their work environment into the smart system by the applications of these technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI) techniques, Machine Learning Algorithm (MLA), Internet Communication Technology (ICT), and Cyber-Physical System (CPS). Because they are very much interested in maximizing productivity, machine availability, reliability, and customer satisfaction in this competitive industrial world. This research study particularly focuses on the Predictive Maintenance (PdM) activity of critical machines and their components in the SME based on the maintenance history dataset through the application of the supervised machine learning algorithm such as Logistics Regression (LR) and K-Means (K-Nearest Neighbor) approaches. In accordance, the real-time case study is presented in SMEs in the southern region of Tamil Nadu, India with two-phase activities. Initially, the optimal failure rate of the machines is predicted by the utilization of LR trained models. Then trigger the man-machine communication and suitable decision-making process of service and maintenance activity through the application of the K-Mean approaches. The main objective of this research study is to organize the smart PdM activity of the smart factory systems in SMEs with the application of MLA based on the real-time maintenance history dataset.

In the recent trends, production plants in the automobile industries all over the world are facing a lot of challenges to achieve better productivity and customer satisfaction due to increasing the passenger’s necessity and demand for transportation. In this direction, the belt, tyre, and tube manufacturing plants act as vital roles in the day-to-day life of the automobile industries. Tyre production plant comprises five major units, namely, raw material selection, preparation, tyre components, finishing, and inspection. The main purpose of this research is to implement the new method to predict the most critical subsystems in the tyre manufacturing system of the rubber industry. As mathematically, any one maintenance parameter among reliability, availability, maintainability, and dependability (RAMD) parameters is evaluated to identify the critical subsystems and their effect on the effectiveness of the tyre production system. In this research, the effect of variation in maintenance indices, RAMD, is measured to identify the critical subsystem of the tyre production system based on the mathematical modeling Markov birth-death approach (MBDA), and the equations of the subsystems are derived by using the Chapman–Kolmogorov method. Besides, it also calculates the performance of certain maintenance parameters concerning time such as mean time between failures (MTBF), mean time to repair (MTTR), and dependability ratio for each subsystem of the tyre production system. Finally, RAMD analysis of the tyre production systems has been executed for predicting the most critical subsystem by changing the rates of failure and repair of individual subsystems with the utilization of MATLAB software. RAMD analysis reveals that the subsystem bias cutting is most critical with the minimum availability of 0.8387, dependability 5.19, dependability ratio 0.8701, and maximum MTTR 38.46 hours of the subsystem. In this implementation of the proposed method, a real-time case study of the industrial repairable system of tyre manufacturing system has been taken for evaluating RAMD indices of the production plant of rubber industry cited in the southern region of Tamil Nadu, India.

Purpose: Through the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.Design/methodology/approach: To obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.Findings: The major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.Originality/value: The proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.

Small and medium-sized enterprises (SMEs) in today’s world have numerous issues in ensuring the availability and safety of critical machines and their components in the working environment. As a result, SMEs considered planning and scheduling maintenance tasks to be a significant threat. The goal of this research is to identify the critical subsystem of the automobile spare parts production plant in the southern region of Tamil Nadu, India, and then to prioritize the maintenance activity and set up an architecture for autonomous preventive maintenance (PM) in SMEs that includes an optimal decision support system. The transition state diagram of the individual and simultaneous production system has been developed with the application of the Markov decision model approach. It was used to analyze the present variables of the production systems and forecast the optimal maintenance parameters such as the failure rate and the repair rate, through the production system’s availability analysis. This availability analysis reveals that system B (Piercing) is classified as the most critical because of the abrupt availability variation compared to all other production systems, concerning the corresponding maintenance parameters such as a failure rate of 0.0371, a repair rate of 0.7094, and the availability of the piercing system of 0.5056. Finally, the use of an autonomous PM management system and the most effective maintenance workforce has enhanced productivity and customer satisfaction in SMEs. The predictive maintenance management system has been further investigated to determine the real-time remaining useful life (RUL) of critical systems in the automobile spare parts manufacturing plant in the southern region of Tamil Nadu, India.