Machine Learning For Determining Planned Order Lead Times In Job Shop Production: A Systematic Review Of Input Factors And Applied Methods

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

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.
OriginalspracheDeutsch
TitelProceedings of the Conference on Production Systems and Logistics: CPSL 2025
HerausgeberDavid Herberger, Marco Hübner
Anzahl der Seiten11
Band7
ErscheinungsortOffenburg
Verlagpublish-Ing.
Erscheinungsdatum2025
Seiten374-384
Aufsatznummer32
DOIs
PublikationsstatusErschienen - 2025
Veranstaltung7th Conference on Production Systems and Logistics - CPSL 2025 - University of San Ignacio de Loyola, Lima, Peru
Dauer: 18.03.202521.03.2025
Konferenznummer: 7

    Fachgebiete

  • Ingenieurwissenschaften - Planned Order Lead Time, Job Shop Production, Input Factors, Production Planning And Control
  • Informatik - Machine Learning, Prediction Methods

DOI

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