Industry 4.0 Hyper-Factory Planning, Scheduling, and Execution with Real-Time Data

Scholars and practitioners continue to be challenged by manufacturing complexity and uncertainty, particularly in nowadays rapidly evolving markets characterized by diverse customer demands and expanding product lines. The advent of Industry 4.0 technologies significantly mitigates these challenges by enhancing transparency and traceability through real-time data integration across physical and digital realms. However, the decision-making frameworks inherited from the previous industrial revolution for Planning, Scheduling, and Execution (PSE) are ill-equipped to leverage Industry 4.0 real-time data for overcoming the challenges of complexity and uncertainty. This study proposes a novel Industry 4.0 PSE methodology, Hyper-Factory Planning, Scheduling, and Execution (HF-PSE), designed to enable adaptive and robust decision-making amidst diverse uncertainties in contemporary manufacturing settings. HF-PSE integrates the fundamental spirit and adaptable principles of university graduation ceremonies into the production management process, including clustering-based planning with a rolling horizon, real-time scheduling based on workstation-job ticket suitability, and Out-of-Order ticketing for production execution. A case study demonstrates that HF-PSE surpasses others on average and exhibits minimal statistical variations in cost-efficiency, punctuality, and simultaneity metrics, highlighting its superior effectiveness, stability, and resilience in stochastic settings.