Automatic translation of plant data into management performance metrics: a case for real-time and predictive production control. Issue 17 (2nd September 2017)

• Record Type: Journal Article
• Authors: Mousavi, A.; Siervo, H.R.A.
• Is Part Of: International journal of production research. Volume 55:Issue 17(2017)
• Page Start: 4862
• Page End: 4877
• Publication Date: 2017-09-02
• Subjects: control -- real-time -- predictive -- overall equipment/line effectiveness -- simulation -- brewery; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2016.1265682
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : 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 real-time 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.