In the present scenario, the industry and manufacturers find hardship to run the business in a profitable manner by enhancing the production and efficiency of the system, Multiple factors influences the industry to meet out the customer’s requirements and to sustain in this competitive environment. The purpose of this research is to develop an Intelligent VSM Model which is used here to integrate with the Industry 4.0 and lean tools to monitor the real time manufacturing setup with recent trends in information technology. Our research is focused to develop an Intelligent VSM (IVSM) system enabled with IoT for the industry which caters the need to enhance the performance of any manufacturing setup. A study has been conducted in an electronics component industry through the Current state IVSM and the Intelligent VSM model for the future state was designed and implemented. The IoT enabled Future Intelligent VSM system facilitates the need of the today’s dynamic manufacturing environment scenario, which continuously monitors through Integrated Efficiency Monitoring System (IEMS). The improved performance of the system is visualized in the dashboard at the specified regular intervals. Further to improve the effectiveness of the Intelligent VSM system, Kaizen activities is been tasked in to eliminate the shortfalls and increase the efficiency in the future DVSM system. The proposed system integrates all the process in the industry to predict the uncertainty. This research work can further extend to all other manufacturing and service support system to predict the critical condition in advance.

The manufacturing industry is undergoing a transition from conventional manufacturing processes to process automation, with particular emphasis on factors contributing to its sustainability. Understanding the complexities of Industry 4.0 is fundamental for devising strategies that effectively address challenges and capitalize on the opportunities present in a competitive business environment. This study aims to identify and analyze the challenges influencing the implementation of Industry 4.0 in SMEs. A total of 10 key challenges were identified through extensive literature review and expert inputs. The challenges identified during implementation of Industry 4.0 in SMEs are: Device compatibility issues, Data security issues, Process-specific technology selection challenges, Data management challenges, Dynamic necessities in production, Unstable connectivity, Random practical issues, Data integration with product life cycle management, Scalability challenges, and Lack of interoperability with existing systems. The identified key challenges in Indian SMEs are analyzed to determine the significance of Industry 4.0 implementation and to explore the relationships among them. This knowledge will be instrumental in assisting practitioners in navigating the complexities of Industry 4.0 implementation in SMEs using the Total Interpretive Structural Modeling (TISM) and Matrice d’Impacts Croisées Multiplication Appliquée à un Classement (MICMAC) methods. The study results revealed that two challenges — namely, device compatibility and interoperability with existing systems — are highly impactful in Indian SMEs. This model was further validated through structural equation modeling, satisfying the criteria of goodness-of-fit indices. This study also offers insights into the significant benefits of Industry 4.0 in addressing contemporary challenges in SMEs and to provide a comprehensive analysis of the challenges that drive its successful implementation within the manufacturing sector, particularly with an emphasis on sustainability including technological, organizational, regulatory, economic, environmental, and social dimensions.