Tracing the Interrelationship between Key Performance Indicators and Production Cost using Bayesian Networks

摘要

Key performance indicators (KPIs) are used to monitor and improve manufacturing performance. 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 complex interactions between manufacturing decisions, variables, and desired targets. A Bayesian network is developed to characterize the interrelationships between manufacturing decisions, variables, and selected KPIs. For an additive manufacturing case, it is shown that the approach enables appropriate value estimation for decisions and variables for achieving desired KPI values and production cost targets in a manufacturing enterprise.