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

Suraj Panicker, Hari Nagarajan, Hossein Mokhtarian, Azarakhsh Hamedi, Ananda Chakraborti, Eric Coatanea, Karl Haapala, Kari Koskinen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)
103 Downloads (Pure)


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.
Original languageEnglish
Title of host publication52nd CIRP Conference on Manufacturing Systems (CMS)
Subtitle of host publicationLjubljana, Slovenia, June 12-14, 2019
EditorsPeter Butala, Edvard Govekar, Rok Vrabic
Number of pages6
Publication statusPublished - 2019
Publication typeA4 Article in conference proceedings
EventCIRP Conference on Manufacturing Systems -
Duration: 1 Jan 2000 → …

Publication series

NameProcedia CIRP
ISSN (Electronic)2212-8271


ConferenceCIRP Conference on Manufacturing Systems
Period1/01/00 → …

Publication forum classification

  • Publication forum level 1


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