The aspects of real-time responsiveness and operational efficiency in the intelligent manufacturing systems gain importance in terms of sustainability in the changing environment of Industry 4.0. In this paper, a new model of real-time predictive maintenance and workforce optimization is proposed which integrates the management analytics to increase the productivity and reduce the downtime and workforce optimization. The model then combines predictive maintenance algorithm based on machine learning driven by sensors and smart workforce allocation algorithms driven by demand forecasting and constraint-based optimization approaches. The system has the capacity to improve the timing of actions and tasks allocation within a dynamically changing shop-floor since the state of machines and human resources is regularly tracked. It also includes a unified layer of management analytics that ushers in actionable information enabling production managers to make informed choices based on their data. A simulated manufacturing environment has served to verify the proposed model and to achieve considerable results of reduction in unforeseen failures of the equipment and enlargement of labor utilization rates. The extent of experimental findings demonstrates a maximum of 91 percent accuracy in predicting failures and a 25 percent improvement in the efficiency of the workforce due to a conventional reactive system. Adding real-time data flows, predictive analysis, and intelligent scheduling to operations does not only enhance operational continuity, but also supports the smart factory objective. Moreover, the framework is scalable and customizable to accommodate multiple industrial possibilities, which means that it can be transferred to different manufacturing sectors. The contribution of this research is the synthesis of an analytics-oriented perspective on maintenance forecasting, which can be considered transformational in the domain of intelligent manufacturing because it integrates forecasting and planning of maintenance workforce. The paper ends with proposing its further development, such as incorporating the digital twin concept and an adaptive learning process to achieve even more autonomy and accuracy in industrial management.
Volume 15 | Issue 1
Pages: 18-25