Details
| Original language | English |
|---|---|
| Pages (from-to) | 208-214 |
| Number of pages | 7 |
| Journal | Procedia Manufacturing |
| Volume | 43 |
| Early online date | 30 Apr 2020 |
| Publication status | Published - 2020 |
| Event | 17th Global Conference on Sustainable Manufacturing 2019 - Shanghai, China Duration: 9 Oct 2019 → 11 Oct 2019 |
Abstract
In the regeneration process of complex capital goods, the definite workload is uncertain until the goods are disassembled and inspected. Due to the uncertainty and long repair lead times, regeneration service providers have difficulties in achieving low regeneration times and meeting delivery dates. Delays in delivery are associated with contractual penalties and keeping a high stock level of spare parts coincides with a high capital tie-up. Therefore, this paper deals with the use of prognostic data mining for long-term material disposition and scheduling to accomplish a high delivery date reliability and low stock levels.
Keywords
- complex capital good, material disposition, regeneration, scheduling
ASJC Scopus subject areas
- Engineering(all)
- Industrial and Manufacturing Engineering
- Computer Science(all)
- Artificial Intelligence
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In: Procedia Manufacturing, Vol. 43, 2020, p. 208-214.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Material Disposition and Scheduling in Regeneration Processes using Prognostic Data Mining
AU - Heuer, Tammo
AU - Lucht, Torben
AU - Nyhuis, Peter
N1 - Funding Information: The authors kindly thank the German Research Foundation (DFG) for the financial support to accomplish the research project D1 “Modelling Regeneration Supply Chains” within the Collaborative Research Centre (CRC) 871 – Regeneration of Complex Capital Goods. Publisher Copyright: © 2020 The Authors. Published by Elsevier B.V. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - In the regeneration process of complex capital goods, the definite workload is uncertain until the goods are disassembled and inspected. Due to the uncertainty and long repair lead times, regeneration service providers have difficulties in achieving low regeneration times and meeting delivery dates. Delays in delivery are associated with contractual penalties and keeping a high stock level of spare parts coincides with a high capital tie-up. Therefore, this paper deals with the use of prognostic data mining for long-term material disposition and scheduling to accomplish a high delivery date reliability and low stock levels.
AB - In the regeneration process of complex capital goods, the definite workload is uncertain until the goods are disassembled and inspected. Due to the uncertainty and long repair lead times, regeneration service providers have difficulties in achieving low regeneration times and meeting delivery dates. Delays in delivery are associated with contractual penalties and keeping a high stock level of spare parts coincides with a high capital tie-up. Therefore, this paper deals with the use of prognostic data mining for long-term material disposition and scheduling to accomplish a high delivery date reliability and low stock levels.
KW - complex capital good
KW - material disposition
KW - regeneration
KW - scheduling
UR - http://www.scopus.com/inward/record.url?scp=85088513387&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2020.02.138
DO - 10.1016/j.promfg.2020.02.138
M3 - Conference article
AN - SCOPUS:85088513387
VL - 43
SP - 208
EP - 214
JO - Procedia Manufacturing
JF - Procedia Manufacturing
SN - 2351-9789
T2 - 17th Global Conference on Sustainable Manufacturing 2019
Y2 - 9 October 2019 through 11 October 2019
ER -