A Systematic Literature Review Of Machine Learning Approaches For The Prediction Of Delivery Dates
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 2022, hosted at the University of British Columbia in Vancouver, Canada, 17th May 2022 – 20th May 2022, Proceedings. ed. / David Herberger; Marco Hübner. Hannover: publish-Ing., 2022. p. 121-130 (Proceedings of the Conference on Production Systems and Logistics; Vol. 3).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - A Systematic Literature Review Of Machine Learning Approaches For The Prediction Of Delivery Dates
AU - Maier, Janine Tatjana
AU - Rokoss, Alexander
AU - Green, Thorben
AU - Brkovic, Nikola
AU - Schmidt, Matthias
N1 - Funding Information: The study was carried out in the framework of the research project Ä.,-Werkstatt Künstliche - Intelligenz in 3URGXNWLRQVXQWHUQHKPHQ .,:H ³. It was supported by the European Union and the State of Lower Saxony within the EFRE program. Publisher Copyright: © 2022, Publish-Ing in cooperation with TIB - Leibniz Information Centre for Science and Technology University Library. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Manufacturing companies tend to use standardized delivery times. The actual delivery times requested by the customers and the current capacity utilization of the production are often not taken into account. Therefore, such a simplification likely results in a reduction of the efficiency of the production. For example, it can lead to an obligation to use rush orders, an unrealistic calculation of inventories or an unnecessary exclusion of a Make-to-Order production. In the worst case, this results not only in an economically inadequate production, but also in a low achievement of logistic objectives and therefore in customer complaints. To avoid this, the delivery dates proposed to the customer must be realistic. Given the large number of customer orders, a wide range of products, varying order quantities and times, as well as various delivery times requested by customers, it is not economical to determine individual delivery dates manually. The ongoing digitalization and technological innovations offer new opportunities to support this task. In the literature, various approaches using machine learning methods for specific production planning and control tasks exist. As these methods are in general applicable for different tasks involving predictions, they can also assist during the determination of delivery dates. Therefore, this paper provides a comprehensive review of the state of the art regarding the use of machine learning approaches for the prediction of delivery dates. To identify research gaps the analyzed publications were differentiated according to several criteria, such as the overall objective and the applied methods. The majority of scientific publications addresses delivery dates only as a subordinate aspect while focusing on production planning and control tasks. Therefore, the interrelationships with several production planning and control tasks were considered during the analysis.
AB - Manufacturing companies tend to use standardized delivery times. The actual delivery times requested by the customers and the current capacity utilization of the production are often not taken into account. Therefore, such a simplification likely results in a reduction of the efficiency of the production. For example, it can lead to an obligation to use rush orders, an unrealistic calculation of inventories or an unnecessary exclusion of a Make-to-Order production. In the worst case, this results not only in an economically inadequate production, but also in a low achievement of logistic objectives and therefore in customer complaints. To avoid this, the delivery dates proposed to the customer must be realistic. Given the large number of customer orders, a wide range of products, varying order quantities and times, as well as various delivery times requested by customers, it is not economical to determine individual delivery dates manually. The ongoing digitalization and technological innovations offer new opportunities to support this task. In the literature, various approaches using machine learning methods for specific production planning and control tasks exist. As these methods are in general applicable for different tasks involving predictions, they can also assist during the determination of delivery dates. Therefore, this paper provides a comprehensive review of the state of the art regarding the use of machine learning approaches for the prediction of delivery dates. To identify research gaps the analyzed publications were differentiated according to several criteria, such as the overall objective and the applied methods. The majority of scientific publications addresses delivery dates only as a subordinate aspect while focusing on production planning and control tasks. Therefore, the interrelationships with several production planning and control tasks were considered during the analysis.
KW - Engineering
KW - Delivery Date
KW - production planning and control
KW - prediction
KW - machine learning
KW - literature review
UR - https://doi.org/10.15488/12314
UR - https://cpsl-conference.com/wp-content/uploads/2022/06/Proceedings-of-the-Conference-on-Production-Systems-and-Logistics-CPSL-2022.pdf
UR - http://www.scopus.com/inward/record.url?scp=85164438820&partnerID=8YFLogxK
U2 - 10.15488/12157
DO - 10.15488/12157
M3 - Article in conference proceedings
T3 - Proceedings of the Conference on Production Systems and Logistics
SP - 121
EP - 130
BT - Conference on Production Systems and Logistics
A2 - Herberger, David
A2 - Hübner, Marco
PB - publish-Ing.
CY - Hannover
T2 - 3rd Conference on Production Systems and Logistics - CPSL 2022
Y2 - 17 May 2022 through 20 May 2022
ER -