Machine Learning For Determining Planned Order Lead Times In Job Shop Production: A Systematic Review Of Input Factors And Applied Methods
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Proceedings of the Conference on Production Systems and Logistics: CPSL 2025. ed. / David Herberger; Marco Hübner. Vol. 7 Offenburg: publish-Ing., 2025. p. 374-384 32.
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Machine Learning For Determining Planned Order Lead Times In Job Shop Production: A Systematic Review Of Input Factors And Applied Methods
AU - Wolter, Ferenc
AU - Rokoss, Alexander
AU - Schmidt, Matthias
N1 - Conference code: 7
PY - 2025
Y1 - 2025
N2 - The accurate planning of order lead times enables companies to confirm feasible delivery times to their customers and facilitates more efficient planning of production capacities and procurement processes. In practice, the commonly used methods for determining planned order lead times are constrained by simplified assumptions and the limited consideration of input factors. As a result, they struggle to adapt to changing conditions, such as varying production workloads or short-term employee shortages. For manufacturers engaged in make-to-order production with complex structures, such as job shop production, consequently delayed operations result in an insufficient delivery performance. Conversely, early operations lead to the accumulation of unnecessary inventories. In this context, machine learning methods are expected to offer significant potential for dealing with changing circumstances due to their ability to utilize a wide range of input factors. A variety of machine learning approaches incorporating diverse data sets have been proposed in the literature. This paper presents the findings of a systematic literature review on the potential input factors for determining planned order lead times in job shop production. Moreover, the utilized machine learning methods to quantify these input factors are identified. For this purpose, the input data used in case studies, the machine learning methods applied for both feature selection and regression analysis, as well as the evaluation metrics and explainable artificial intelligence approaches, are analyzed and synthesized. This allows the identification of research gaps regarding input factors and their quantification for determining planned order lead times.
AB - The accurate planning of order lead times enables companies to confirm feasible delivery times to their customers and facilitates more efficient planning of production capacities and procurement processes. In practice, the commonly used methods for determining planned order lead times are constrained by simplified assumptions and the limited consideration of input factors. As a result, they struggle to adapt to changing conditions, such as varying production workloads or short-term employee shortages. For manufacturers engaged in make-to-order production with complex structures, such as job shop production, consequently delayed operations result in an insufficient delivery performance. Conversely, early operations lead to the accumulation of unnecessary inventories. In this context, machine learning methods are expected to offer significant potential for dealing with changing circumstances due to their ability to utilize a wide range of input factors. A variety of machine learning approaches incorporating diverse data sets have been proposed in the literature. This paper presents the findings of a systematic literature review on the potential input factors for determining planned order lead times in job shop production. Moreover, the utilized machine learning methods to quantify these input factors are identified. For this purpose, the input data used in case studies, the machine learning methods applied for both feature selection and regression analysis, as well as the evaluation metrics and explainable artificial intelligence approaches, are analyzed and synthesized. This allows the identification of research gaps regarding input factors and their quantification for determining planned order lead times.
KW - Ingenieurwissenschaften
KW - Planned Order Lead Time
KW - Job Shop Production
KW - Input Factors
KW - Production Planning And Control
KW - Informatik
KW - Machine Learning
KW - Prediction Methods
U2 - 10.15488/18882
DO - 10.15488/18882
M3 - Aufsätze in Konferenzbänden
VL - 7
SP - 374
EP - 384
BT - Proceedings of the Conference on Production Systems and Logistics: CPSL 2025
A2 - Herberger, David
A2 - Hübner, Marco
PB - publish-Ing.
CY - Offenburg
T2 - 7th Conference on Production Systems and Logistics - CPSL 2025
Y2 - 18 March 2025 through 21 March 2025
ER -