Case study on delivery time determination using a machine learning approach in small batch production companies

Alexander Rokoss (), Marius Syberg, Laura Tomidei, Christian Hülsing, Jochen Deuse and Matthias Schmidt
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Alexander Rokoss: Leuphana University of Lüneburg
Marius Syberg: Technical University Dortmund
Laura Tomidei: University of Technology Sydney
Christian Hülsing: Leuphana University of Lüneburg
Jochen Deuse: Technical University Dortmund
Matthias Schmidt: Leuphana University of Lüneburg

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 8, No 18, 3937-3958

Abstract: Abstract 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.

Keywords: Delivery time; Machine learning; Production planning and control; Artificial intelligence; Case study (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-023-02290-2

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