Do We Really Know The Benefit Of Machine Learning In Production Planning And Control? A Systematic Review Of Industry Case Studies
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Conference on Production Systems and Logistics: International Conference, CPSL 2021, Digital Event hosted via publish-Ing; August 10-11, 2021; Proceedings. ed. / D. Herberger; M. Hübner. Offenburg: publish-Ing., 2021. p. 223-233 (Proceedings of the ... Conference on Production Systems and Logistics; Vol. 2).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Do We Really Know The Benefit Of Machine Learning In Production Planning And Control? A Systematic Review Of Industry Case Studies
AU - Kramer, Kathrin
AU - Rokoss, Alexander
AU - Schmidt, Matthias
N1 - Conference code: 2
PY - 2021
Y1 - 2021
N2 - The field of machine learning (ML) is of specific interest for production companies as it displays a perspective to handle the increased complexity within their production planning and control (PPC) processes in an economic and ecologic effective as well as efficient way. Several studies investigate applications of ML to different use cases. However, the research field lacks in research on industry case studies. A broad understanding from a practical perspective and in this context, an evaluation from a data mining and business standpoint is key for gaining trust in ML solutions. Therefore, this paper gives a comprehensive overview of evaluation dimensions and outlines the current state of research in ML-PPC by conducting a systematic research overview. First, the present work provides key dimensions of business and data mining objectives as evaluation metric. Business objectives are clustered into economic, ecological and social objectives and data mining objectives are grouped into prediction accuracy, model’s explainability, model’s runtime, and model’s energy use. Secondly, the systematic literature review identifies 45 industry case studies in MLPPC from 2010-2020. The work shows that the scientific publications only rarely reflect in detail on a wide range of evaluation metrics. Instead, researchers mainly focus on prediction accuracy and seldom investigate the effect of their results to a business context. Positively, some papers reflect on further aspects and can inspire future research. This resulting transparency supports decision makers of companies in their prioritization process when setting up a future ML-roadmap. In addition, the research gaps identified herein invite researchers to join the research field.
AB - The field of machine learning (ML) is of specific interest for production companies as it displays a perspective to handle the increased complexity within their production planning and control (PPC) processes in an economic and ecologic effective as well as efficient way. Several studies investigate applications of ML to different use cases. However, the research field lacks in research on industry case studies. A broad understanding from a practical perspective and in this context, an evaluation from a data mining and business standpoint is key for gaining trust in ML solutions. Therefore, this paper gives a comprehensive overview of evaluation dimensions and outlines the current state of research in ML-PPC by conducting a systematic research overview. First, the present work provides key dimensions of business and data mining objectives as evaluation metric. Business objectives are clustered into economic, ecological and social objectives and data mining objectives are grouped into prediction accuracy, model’s explainability, model’s runtime, and model’s energy use. Secondly, the systematic literature review identifies 45 industry case studies in MLPPC from 2010-2020. The work shows that the scientific publications only rarely reflect in detail on a wide range of evaluation metrics. Instead, researchers mainly focus on prediction accuracy and seldom investigate the effect of their results to a business context. Positively, some papers reflect on further aspects and can inspire future research. This resulting transparency supports decision makers of companies in their prioritization process when setting up a future ML-roadmap. In addition, the research gaps identified herein invite researchers to join the research field.
KW - Engineering
KW - Machine learning
KW - Production planning
KW - Production control
KW - process planning
KW - economic effect
KW - ecological effect
KW - practical reference
UR - https://www.repo.uni-hannover.de/handle/123456789/11315
UR - https://doi.org/10.15488/11229
UR - http://www.scopus.com/inward/record.url?scp=85144558654&partnerID=8YFLogxK
U2 - 10.15488/11296
DO - 10.15488/11296
M3 - Article in conference proceedings
T3 - Proceedings of the ... Conference on Production Systems and Logistics
SP - 223
EP - 233
BT - Conference on Production Systems and Logistics
A2 - Herberger, D.
A2 - Hübner, M.
PB - publish-Ing.
CY - Offenburg
T2 - 2nd Conference on Production Systems and Logistics - CPSL 2021
Y2 - 10 August 2021 through 11 August 2021
ER -