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

doi:10.1016/j.procir.2019.03.136

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

Automation Technology and Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

4 Citations (Scopus)

103 Downloads (Pure)

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.

Original language	English
Title of host publication	52nd CIRP Conference on Manufacturing Systems (CMS)
Subtitle of host publication	Ljubljana, Slovenia, June 12-14, 2019
Editors	Peter Butala, Edvard Govekar, Rok Vrabic
Publisher	Elsevier
Pages	500-505
Number of pages	6
Volume	81
DOIs	https://doi.org/10.1016/j.procir.2019.03.136
Publication status	Published - 2019
Publication type	A4 Article in conference proceedings
Event	CIRP Conference on Manufacturing Systems - Duration: 1 Jan 2000 → …

Publication series

Name	Procedia CIRP
Volume	81
ISSN (Electronic)	2212-8271

Conference

Conference	CIRP Conference on Manufacturing Systems
Period	1/01/00 → …

Publication forum classification

Publication forum level 1

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10.1016/j.procir.2019.03.136Licence: CC BY-NC-ND

2019- CIRP CMS Conference-PanickerFinal published version, 909 KBLicence: CC BY-NC-ND

http://urn.fi/URN:NBN:fi:tty-201908272030Licence: CC BY-NC-ND

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Panicker, S., Nagarajan, H., Mokhtarian, H., Hamedi, A., Chakraborti, A., Coatanea, E., Haapala, K., & Koskinen, K. (2019). Tracing the interrelationship between key performance indicators and production cost using bayesian networks. In P. Butala, E. Govekar, & R. Vrabic (Eds.), 52nd CIRP Conference on Manufacturing Systems (CMS): Ljubljana, Slovenia, June 12-14, 2019 (Vol. 81, pp. 500-505). (Procedia CIRP; Vol. 81). Elsevier. https://doi.org/10.1016/j.procir.2019.03.136

Panicker, Suraj ; Nagarajan, Hari ; Mokhtarian, Hossein et al. / Tracing the interrelationship between key performance indicators and production cost using bayesian networks. 52nd CIRP Conference on Manufacturing Systems (CMS): Ljubljana, Slovenia, June 12-14, 2019. editor / Peter Butala ; Edvard Govekar ; Rok Vrabic. Vol. 81 Elsevier, 2019. pp. 500-505 (Procedia CIRP).

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title = "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. ",

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

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language = "English",

volume = "81",

series = "Procedia CIRP",

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Panicker, S, Nagarajan, H, Mokhtarian, H, Hamedi, A, Chakraborti, A , Coatanea, E, Haapala, K & Koskinen, K 2019, Tracing the interrelationship between key performance indicators and production cost using bayesian networks. in P Butala, E Govekar & R Vrabic (eds), 52nd CIRP Conference on Manufacturing Systems (CMS): Ljubljana, Slovenia, June 12-14, 2019. vol. 81, Procedia CIRP, vol. 81, Elsevier, pp. 500-505, CIRP Conference on Manufacturing Systems, 1/01/00. https://doi.org/10.1016/j.procir.2019.03.136

Tracing the interrelationship between key performance indicators and production cost using bayesian networks. / Panicker, Suraj; Nagarajan, Hari; Mokhtarian, Hossein et al.
52nd CIRP Conference on Manufacturing Systems (CMS): Ljubljana, Slovenia, June 12-14, 2019. ed. / Peter Butala; Edvard Govekar; Rok Vrabic. Vol. 81 Elsevier, 2019. p. 500-505 (Procedia CIRP; Vol. 81).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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AU - Hamedi, Azarakhsh

AU - Chakraborti, Ananda

AU - Coatanea, Eric

AU - Haapala, Karl

AU - Koskinen, Kari

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AB - 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.

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DO - 10.1016/j.procir.2019.03.136

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Panicker S, Nagarajan H, Mokhtarian H, Hamedi A, Chakraborti A , Coatanea E et al. Tracing the interrelationship between key performance indicators and production cost using bayesian networks. In Butala P, Govekar E, Vrabic R, editors, 52nd CIRP Conference on Manufacturing Systems (CMS): Ljubljana, Slovenia, June 12-14, 2019. Vol. 81. Elsevier. 2019. p. 500-505. (Procedia CIRP). doi: 10.1016/j.procir.2019.03.136

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

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