Tracing the interrelationship between key performance indicators and production cost using bayesian networks

Abstract

Key performance indicators (KPIs) are used to monitor and improve production cost, quality, and time. A plethora of manufacturing KPIs are currently in use, with others continually being developed to meet organizational needs. However, obtaining the optimum KPI values at different organizational levels is challenging due to the complex interactions between manufacturing decisions, variables, and the desired targets. A Bayesian network is developed to characterize the interrelationships between manufacturing decisions and variables, selected KPI, and total production cost. For an additive manufacturing case, the approach enables appropriate KPI value estimation for achieving desired production cost targets in a manufacturing enterprise.