Details
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) - IEEM 2022 |
| Publisher | IEEE Computer Society |
| Pages | 716-721 |
| Number of pages | 6 |
| ISBN (electronic) | 9781665486873 |
| ISBN (print) | 9781665486880 |
| Publication status | Published - 26 Dec 2022 |
| Event | 2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022 - Kuala Lumpur, Malaysia Duration: 7 Dec 2022 → 10 Dec 2022 |
Publication series
| Name | IEEE International Conference on Industrial Engineering and Engineering Management |
|---|---|
| Volume | 2022-December |
| ISSN (Print) | 2157-3611 |
| ISSN (electronic) | 2157-362X |
Abstract
Predicting throughput times is of particular interest to production planners to schedule the production flow or communicate reliable delivery times to customers. Most established prediction methods are based on general assumptions, expert knowledge or simple statistical techniques. With the increasing use of data mining in production management, it is possible to provide more sophisticated predictions of throughput time. However, current research often does not describe the application or locate the particular prediction approach within the time and task structure of Production Planning and Control (PPC). Therefore, this paper aims to develop a systematisation approach to classify prediction models within the PPC task structure. To this end, applications along the order fulfilment process are first defined and then elaborated. A systematic literature review is conducted to classify current throughput time prediction approaches within the previously described application domains. In a case study, the application possibilities of throughput time predictions based on the provided systematisation are demonstrated, and differences in data availability and prediction quality are highlighted.
Keywords
- Data Mining, Production Planning and Control, Throughput Time Prediction
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Business, Management and Accounting (miscellaneous)
- Engineering(all)
- Industrial and Manufacturing Engineering
- Engineering(all)
- Safety, Risk, Reliability and Quality
Cite this
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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) - IEEM 2022. IEEE Computer Society, 2022. p. 716-721 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2022-December).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Systemising Data-driven Methods for Predicting Throughput Time within Production Planning & Control
AU - Hiller, T.
AU - Deipenwisch, L.
AU - Nyhuis, P.
N1 - Funding Information: This project is funded by the German Federal Ministry of Education and Research, as part of the Aviation Research and Technology Program of the Lower Saxony Ministry of Economics, Labor, Transport and Digitalization (funding code ZW 1 - 80157862).
PY - 2022/12/26
Y1 - 2022/12/26
N2 - Predicting throughput times is of particular interest to production planners to schedule the production flow or communicate reliable delivery times to customers. Most established prediction methods are based on general assumptions, expert knowledge or simple statistical techniques. With the increasing use of data mining in production management, it is possible to provide more sophisticated predictions of throughput time. However, current research often does not describe the application or locate the particular prediction approach within the time and task structure of Production Planning and Control (PPC). Therefore, this paper aims to develop a systematisation approach to classify prediction models within the PPC task structure. To this end, applications along the order fulfilment process are first defined and then elaborated. A systematic literature review is conducted to classify current throughput time prediction approaches within the previously described application domains. In a case study, the application possibilities of throughput time predictions based on the provided systematisation are demonstrated, and differences in data availability and prediction quality are highlighted.
AB - Predicting throughput times is of particular interest to production planners to schedule the production flow or communicate reliable delivery times to customers. Most established prediction methods are based on general assumptions, expert knowledge or simple statistical techniques. With the increasing use of data mining in production management, it is possible to provide more sophisticated predictions of throughput time. However, current research often does not describe the application or locate the particular prediction approach within the time and task structure of Production Planning and Control (PPC). Therefore, this paper aims to develop a systematisation approach to classify prediction models within the PPC task structure. To this end, applications along the order fulfilment process are first defined and then elaborated. A systematic literature review is conducted to classify current throughput time prediction approaches within the previously described application domains. In a case study, the application possibilities of throughput time predictions based on the provided systematisation are demonstrated, and differences in data availability and prediction quality are highlighted.
KW - Data Mining
KW - Production Planning and Control
KW - Throughput Time Prediction
UR - http://www.scopus.com/inward/record.url?scp=85146329011&partnerID=8YFLogxK
U2 - 10.1109/IEEM55944.2022.9989885
DO - 10.1109/IEEM55944.2022.9989885
M3 - Conference contribution
AN - SCOPUS:85146329011
SN - 9781665486880
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 716
EP - 721
BT - IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) - IEEM 2022
PB - IEEE Computer Society
T2 - 2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
Y2 - 7 December 2022 through 10 December 2022
ER -