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 -