Predictive Analytics for Workforce Planning in the Context of Digital Transformation of Manufacturing
Abstract
This paper examines the application of predictive analytics for workforce planning in manufacturing undergoing digital transformation. Through comparative and inductive analysis of existing literature and industry reports, we explore how predictive modeling and big data can address challenges in forecasting labor needs amidst rapid technological change. Key findings include the potential for machine learning algorithms to analyze labor market trends, integration of IoT data from production lines into HR analytics, and development of digital employee profiles for automated skills gap analysis. We propose strategies for flexible workforce management based on predictive models, including the concept of a “digital twin” for the workforce. While predictive analytics shows promise for improving efficiency, ethical considerations and the need for HR upskilling present challenges for implementation. Overall, this research highlights predictive analytics as a critical tool for strategic human resource management in digitally transforming industries.