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 -