Tracing the interrelationship between key performance indicators and production cost using bayesian networks
| Autor: | Suraj Panicker, Kari Koskinen, Eric Coatanéa, Hossein Mokhtarian, Karl R. Haapala, Hari P.N. Nagarajan, Ananda Chakraborti, Azarakhsh Hamedi |
|---|---|
| Přispěvatelé: | Butala, Peter, Govekar, Edvard, Vrabic, Rok, Tampere University, Automation Technology and Mechanical Engineering, Research area: Manufacturing and Automation, Research area: Design, Development and LCM |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Estimation
0209 industrial biotechnology Computer science Production cost Bayesian network 02 engineering and technology 222 Other engineering and technologies 010501 environmental sciences Tracing 01 natural sciences Industrial engineering 020901 industrial engineering & automation Value (economics) General Earth and Planetary Sciences Performance indicator 0105 earth and related environmental sciences General Environmental Science |
| Popis: | 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. publishedVersion |
| Databáze: | OpenAIRE |
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