Journal
52ND CIRP CONFERENCE ON MANUFACTURING SYSTEMS (CMS)
Volume 81, Issue -, Pages 500-505Publisher
ELSEVIER
DOI: 10.1016/j.procir.2019.03.136
Keywords
Manufacturing performance indicators; Cost modeling; Bayesian networks; Additive manufacturing
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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. (C) 2019 The Authors. Published by Elsevier Ltd.
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