The concept of smart manufacturing has greatly developed following the advent of Industry 4.0, which has made it possible to incorporate the use of new technology into a conventional production system due to the added value of the Internet of Things (IoT), data analysis, and intelligent automation. However, operational inefficiency of many manufacturing organizations by segmented and secluded application of the Lean practices, Internet of Things-based monitoring, and maintenance approaches remains a significant problem in spite of all the improvements made. In this work, the researcher fills this gap by suggesting a data-centric optimisation paradigm that could combine the Lean manufacturing concepts, IoT analytics, and predictive maintenance into one system. This is mainly aimed at improving operational efficiency in the relationship by minimising waste through reducing the machine downtime, and making better use of resources. The study uses simulation models and secondary data as analytical methods to examine the performance of the system at varying operational conditions. The results show that the combined framework enhances productivity, minimises unexpected downtime, and decreases the cost of operations as compared to conventional isolated strategies. The presented model offers a well-organised means through which the manufacturing industries can shift towards data-driven and intelligent operations. The work is valuable to the current literature as it provides a thorough and scalable framework that addresses the divide between Lean approaches and digital transformation technologies in smart factory settings.
Parmar et al. (2026) studied this question.