Case study on delivery time determination using a machine learning approach in small batch production companies
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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in: Journal of Intelligent Manufacturing, Jahrgang 35, Nr. 8, 12.2024, S. 3937-3958.
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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TY - JOUR
T1 - Case study on delivery time determination using a machine learning approach in small batch production companies
AU - Rokoss, Alexander
AU - Syberg, Marius
AU - Tomidei, Laura
AU - Hülsing, Christian
AU - Deuse, Jochen
AU - Schmidt, Matthias
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - Delivery times represent a key factor influencing the competitive advantage, as manufacturing companies strive for timely and reliable deliveries. As companies face multiple challenges involved with meeting established delivery dates, research on the accurate estimation of delivery dates has been source of interest for decades. In recent years, the use of machine learning techniques in the field of production planning and control has unlocked new opportunities, in both academia and industry practice. In fact, with the increased availability of data across various levels of manufacturing companies, machine learning techniques offer the opportunity to gain valuable and accurate insights about production processes. However, machine learning-based approaches for the prediction of delivery dates have not received sufficient attention. Thus, this study aims to investigate the ability of machine learning to predict delivery dates early in the ordering process, and what type of information is required to obtain accurate predictions. Based on the data provided by two separate manufacturing companies, this paper presents a machine learning-based approach for predicting delivery times as soon as a request for an offer is received considering the desired customer delivery date as a feature.
AB - Delivery times represent a key factor influencing the competitive advantage, as manufacturing companies strive for timely and reliable deliveries. As companies face multiple challenges involved with meeting established delivery dates, research on the accurate estimation of delivery dates has been source of interest for decades. In recent years, the use of machine learning techniques in the field of production planning and control has unlocked new opportunities, in both academia and industry practice. In fact, with the increased availability of data across various levels of manufacturing companies, machine learning techniques offer the opportunity to gain valuable and accurate insights about production processes. However, machine learning-based approaches for the prediction of delivery dates have not received sufficient attention. Thus, this study aims to investigate the ability of machine learning to predict delivery dates early in the ordering process, and what type of information is required to obtain accurate predictions. Based on the data provided by two separate manufacturing companies, this paper presents a machine learning-based approach for predicting delivery times as soon as a request for an offer is received considering the desired customer delivery date as a feature.
KW - Engineering
KW - Delivery time
KW - Machine learning
KW - Production planning and control
KW - Artificial intelligence
KW - Case study
UR - http://www.scopus.com/inward/record.url?scp=85182215834&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/d429d820-0779-3171-adcf-7ef244189f41/
U2 - 10.1007/s10845-023-02290-2
DO - 10.1007/s10845-023-02290-2
M3 - Journal articles
VL - 35
SP - 3937
EP - 3958
JO - Journal of Intelligent Manufacturing
JF - Journal of Intelligent Manufacturing
SN - 0956-5515
IS - 8
ER -