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 - Deuse, Jochen

AU - Schmidt, Matthias

N1 - Funding Information: Funded by the Lower Saxony Ministry of Science and Culture under grant number ZN3489 within the Lower Saxony “Vorab” of the Volkswagen Foundation and supported by the Center for Digital Innovations (ZDIN) and by the German Federal Ministry of Economics and Technology (BMWi), through the Working Group of Industrial Research Associations (AIF), by the project “Predictive sales and demand planning in customer-oriented order manufacturing using ML methods” (PrABCast, 22180 N). Publisher Copyright: © 2024, The Author(s).

PY - 2024/1/12

Y1 - 2024/1/12

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

JO - Journal of Intelligent Manufacturing

JF - Journal of Intelligent Manufacturing

SN - 0956-5515

ER -