Novel data-driven approach for optimizing workforce alignment in smart manufacturing systems

Workforce alignment is the process of assuring employees' skills, roles, and responsibilities in manufacturing companies to assess individual performance to meet the vision and mission of the organization effectively. This technique is useful to improve the engagement of employees in the corresponding organization. It has some limitations like an increasing resignation rate and reduction in satisfaction with job, which leads to a fall in productivity in more industries. It also creates more pressure on the managers who maintain the respective organizations. This research aims to improve operational effectiveness and production by creating a new data-driven strategy for workforce alignment optimization in smart manufacturing systems. This research focused on determining the most efficient use of worker resources by examining data at each worker's level. To guarantee proper workforce characterization, data collecting entails obtaining confidential data about employees, such as their talents, experience, and performance indicators. On-Line Analytical Processing (OLAP) methods are used for data pretreatment to enable multiple-dimensional analysis and effective data aggregation, which allows for a deeper knowledge of labor dynamics. The hybrid Red Deer Optimizer driven Efficient K-Nearest Neighbors (RDO-EKNN) model utilizes optimization approaches to increase the efficiency of the classification of KNN, guaranteeing effective labor synchronization in the manufacturing setting. According to the results, the RDO-EKNN approach improves workforce synchronization and optimization of resources in intelligent production systems exhibits a 90.5% F1 score and a 95.6% recall, the suggested model performs better than conventional techniques, which helps to improve labor management.