Developing a prescriptive decision support system for shop floor control

Purpose The reported study aims at connecting the two crucial aspects of manufacturing of future, i.e. advanced analytics and digital simulation, with an objective to facilitate real-time control of manufacturing operations. The work puts forward a framework for designing prescriptive decision support system for a multi-machine manufacturing environment. Design/methodology/approach The schema of the decision support system design begins with the development of a simulation model for a manufacturing shop floor. The developed model facilitates prediction followed by prescription. As a connecting link between prediction and prescription mechanism, heuristics for intervention have been proposed. Sequential design and simulation-based demonstration of activities that span from development of a multi-machine shop floor model; a prediction mechanism and a scheme of intervention that ultimately leads to prescription generation are the highlights of the current work. Findings The study reveals that the effect of intervention on the observed predictors varies from one another. For a machine under observation, subject to same intervention scheme, while two of the predictive measures namely penalty and desirability stabilize after a certain point, a third measure, i.e. complexity, shows either an increase or decrease in percent change. The work objectively establishes that intervention plans have to be evaluated for every machine as well as for every environmental variable and emphasizes the need for dynamic evaluation and control mechanism. Originality/value The proposed prescriptive control mechanism has been demonstrated through a case of a high pressure die casting (HPDC) manufacturer.

Automated summary

The reported study aims to connect advanced analytics and digital simulation in order to facilitate real-time control of manufacturing operations. The study proposes a framework for designing a decision support system and demonstrates it using a case study of a high pressure die casting manufacturer. The findings highlight the importance of evaluating and controlling intervention plans for each machine and environmental variable dynamically.