Analysis of the relevance of models, influencing factors and the point in time of the forecast on the prediction quality in order-related delivery time determination using machine learning
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Proceedings of the CPSL 2024. ed. / David Herberger; Marco Hübner. Hannover: publish-Ing., 2024. p. 415-431 (Proceedings of the Conference on Production Systems and Logistics; Vol. 2024).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Analysis of the relevance of models, influencing factors and the point in time of the forecast on the prediction quality in order-related delivery time determination using machine learning
AU - Rokoss, Alexander
AU - Popkes, Lennart
AU - Schmidt, Matthias
N1 - Conference code: 6
PY - 2024
Y1 - 2024
N2 - One of the main objectives of manufacturing companies that structure their manufacturing system according to the workshop principle is to meet the delivery dates communicated to the customer. One approach to avoid large delivery time buffers to stabilize liability of communicated delivery dates is to improve the forecasting quality of the initially determined planned delivery dates. In this context, machine learning methods are a promising approach for the dynamic, order-related forecasting of delivery times. In the development process of machine learning based applications for delivery time forecasting companies are challenged by the following questions: which influencing factors must be considered? Which machine learning models generate the best forecast quality? At what point in the production process does the application of machine learning methods for delivery time forecasting make sense from an economic perspective? Existing approaches do not adequately address these questions. In most cases, only few process steps are considered and only throughput times are forecasted instead of delivery times. The information available at the point in time when the delivery time is forecasted is not discussed. The considered input factors influencing the delivery time are reduced to the company's internal supply chain and therefore do not allow for a satisfactory forecast quality of the delivery time. External influencing factors are often not included. Therefore, this paper describes the influence of different machine learning models, different points in time for the forecasting itself and included influencing factors on the achievable forecast quality. The influence is determined by applying machine learning methods on delivery time forecasting to five real-world use cases.
AB - One of the main objectives of manufacturing companies that structure their manufacturing system according to the workshop principle is to meet the delivery dates communicated to the customer. One approach to avoid large delivery time buffers to stabilize liability of communicated delivery dates is to improve the forecasting quality of the initially determined planned delivery dates. In this context, machine learning methods are a promising approach for the dynamic, order-related forecasting of delivery times. In the development process of machine learning based applications for delivery time forecasting companies are challenged by the following questions: which influencing factors must be considered? Which machine learning models generate the best forecast quality? At what point in the production process does the application of machine learning methods for delivery time forecasting make sense from an economic perspective? Existing approaches do not adequately address these questions. In most cases, only few process steps are considered and only throughput times are forecasted instead of delivery times. The information available at the point in time when the delivery time is forecasted is not discussed. The considered input factors influencing the delivery time are reduced to the company's internal supply chain and therefore do not allow for a satisfactory forecast quality of the delivery time. External influencing factors are often not included. Therefore, this paper describes the influence of different machine learning models, different points in time for the forecasting itself and included influencing factors on the achievable forecast quality. The influence is determined by applying machine learning methods on delivery time forecasting to five real-world use cases.
KW - artificial intelligence
KW - data mining
KW - delivery time
KW - machine learning
KW - workshop manufacturing
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85205969165&partnerID=8YFLogxK
U2 - 10.15488/17732
DO - 10.15488/17732
M3 - Article in conference proceedings
AN - SCOPUS:85205969165
T3 - Proceedings of the Conference on Production Systems and Logistics
SP - 415
EP - 431
BT - Proceedings of the CPSL 2024
A2 - Herberger, David
A2 - Hübner, Marco
PB - publish-Ing.
CY - Hannover
T2 - 6th Conference on Production Systems and Logistics - CPSL 2024
Y2 - 9 July 2024 through 12 July 2024
ER -