Production planning and operational control using real-time data

Abstract

Many production planning and control systems in use today were designed under a paradigm of a static, deterministic world with hierarchical control systems and regular periodic updates. These systems were conceived in a time of lower competition, more stable markets, and limited information and communication capability. The modern manufacturing world is characterized by much different conditions. We present a conceptual overview of a production planning system that maintains the hierarchical structure but is networked across interdependent components and integrates real-time status and sensor information to guide planning and control in a dynamic fashion. Information indicating deviations from expected status initiates the replanning activity. Beginning with linear programming for aggregate planning, duality theory is used to measure the validity of existing plans. Distributed models are integrated to continuously monitor and update model parameters. Several examples are provided to illustrate how the dynamic data-driven planning and control framework can significantly improve manufacturing performance.