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 -