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

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Tracing the interrelationship between key performance indicators and production cost using bayesian networks. / Panicker, Suraj; Nagarajan, Hari; Mokhtarian, Hossein; Hamedi, Azarakhsh; Chakraborti, Ananda; Coatanea, Eric; Haapala, Karl; Koskinen, Kari.

52nd CIRP Conference on Manufacturing Systems (CMS): Ljubljana, Slovenia, June 12-14, 2019. ed. / Peter Butala; Edvard Govekar; Rok Vrabic. Vol. 81 Elsevier, 2018. p. 500-505 PROCIR-D-18-00-532 (Procedia CIRP).

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

Harvard

Panicker, S, Nagarajan, H, Mokhtarian, H, Hamedi, A, Chakraborti, A, Coatanea, E, Haapala, K & Koskinen, K 2018, 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, PROCIR-D-18-00-532, Procedia CIRP, Elsevier, pp. 500-505, CIRP Conference on Manufacturing Systems, 1/01/00. https://doi.org/10.1016/j.procir.2019.03.136

APA

Panicker, S., Nagarajan, H., Mokhtarian, H., Hamedi, A., Chakraborti, A., Coatanea, E., ... Koskinen, K. (2018). Tracing the interrelationship between key performance indicators and production cost using bayesian networks. Manuscript submitted for publication. 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). [PROCIR-D-18-00-532] (Procedia CIRP). Elsevier. https://doi.org/10.1016/j.procir.2019.03.136

Vancouver

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. 2018. p. 500-505. PROCIR-D-18-00-532. (Procedia CIRP). https://doi.org/10.1016/j.procir.2019.03.136

Author

Panicker, Suraj ; Nagarajan, Hari ; Mokhtarian, Hossein ; Hamedi, Azarakhsh ; Chakraborti, Ananda ; Coatanea, Eric ; Haapala, Karl ; Koskinen, Kari. / 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, 2018. pp. 500-505 (Procedia CIRP).

Bibtex - Download

@inproceedings{85c28ea73bab4b659cdac694da32c21c,
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",
note = "EXT={"}Haapala, Karl{"}",
year = "2018",
month = "12",
day = "31",
doi = "10.1016/j.procir.2019.03.136",
language = "English",
volume = "81",
series = "Procedia CIRP",
publisher = "Elsevier",
pages = "500--505",
editor = "Peter Butala and Edvard Govekar and Rok Vrabic",
booktitle = "52nd CIRP Conference on Manufacturing Systems (CMS)",
address = "Netherlands",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Tracing the interrelationship between key performance indicators and production cost using bayesian networks

AU - Panicker, Suraj

AU - Nagarajan, Hari

AU - Mokhtarian, Hossein

AU - Hamedi, Azarakhsh

AU - Chakraborti, Ananda

AU - Coatanea, Eric

AU - Haapala, Karl

AU - Koskinen, Kari

N1 - EXT="Haapala, Karl"

PY - 2018/12/31

Y1 - 2018/12/31

N2 - 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.

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.

U2 - 10.1016/j.procir.2019.03.136

DO - 10.1016/j.procir.2019.03.136

M3 - Conference contribution

VL - 81

T3 - Procedia CIRP

SP - 500

EP - 505

BT - 52nd CIRP Conference on Manufacturing Systems (CMS)

A2 - Butala, Peter

A2 - Govekar, Edvard

A2 - Vrabic, Rok

PB - Elsevier

ER -