Automatic translation of plant data into management performance metrics: a case for real-time and predictive production control

Abstract

© 2016 The Author(s). A scalable and repeatable solution for linking shop-floor control system to a discrete event simulation (DES) model is presented. The key objective is to automatically translate the real-time data from the control system (e.g. supervisory control and data acquisition, SCADA) into KPI transfer functions of the production process. Such a seamless translation allows for the integration of engineering data emitted at plant level to higher level information system for decision-making. The solution provides a platform for researchers and practitioners to utilise the capabilities of realtime DAQ and control with that of discrete event simulation to accurately measure the key manufacturing systems performance metrics. In addition to the real-time capabilities, the predictive capabilities of the solution provide the managers to look ahead and to conduct What-if scenarios. Such capability enables line management to optimise performance and predict destabilising factors in the system ahead of time. A fully operational version of the designed solution has been deployed in a brewery’s live production system for the first time. The brewhouse production line model measures the utilisation of resources, Overall Equipment Effectiveness, and Overall Line Effectiveness in real-time and fast-forward mode simulation. The results of the predictive models (What-if-Scenarios) have been validated and verified by statistical means and direct observations. The accuracy of the estimated parameters is highly satisfactory.