Details
| Original language | English |
|---|---|
| Pages (from-to) | 332-337 |
| Number of pages | 6 |
| Journal | Procedia CIRP |
| Volume | 107 |
| Early online date | 26 May 2022 |
| Publication status | Published - 2022 |
| Event | 55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022 - Lugano, Switzerland Duration: 29 Jun 2022 → 1 Jul 2022 |
Abstract
The order processing strategy directly influences the economic and logistic objectives of manufacturing companies. In industrial practice, the order processing strategy is usually chosen based on a few primarily qualitative decision criteria or employees' experience. The increasingly volatile markets require a differentiated decision of the order processing strategy for each product and a continuous review of the decisions made. The high number of factors influencing the decision of order processing strategy and a wide range of products offered by manufacturing companies make it impossible to manually make a fast and at the same time holistic decision. The digitalization and the increasing availability of data and its quality provide the basis for developing a decision support system. This paper presents an approach for a data based analysis of order processing strategies. As logistic models reflect the interdependencies of conflicting economic and logistic objectives, they are applied to derive the costs of the strategies based on desired adherence to the delivery time and the desired service level of the finished goods store. In addition to the description of the procedure for comparing different order processing strategies, the transfer of the theoretical model into a software demonstrator is outlined. A case study is presented to show the practicality of the proposed approach.
Keywords
- Data analysis, Decision-making, Interdependencies, Order processing strategies, Software demonstrator
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 107, 2022, p. 332-337.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Data based analysis of order processing strategies to support the positioning between conflicting economic and logistic objectives
AU - Maier, Janine Tatjana
AU - Heuer, Tammo
AU - Stoffersen, Henrik
AU - Nyhuis, Peter
AU - Schmidt, Matthias
N1 - Funding Information: The research project was carried out in the framework of the industrial collective research programme (IGF no. 20906 N). It was supported by the Federal Ministry for Economic Affairs and Climate Action (BMWK) through the AiF (German Federation of Industrial Research Associations eV) and the BVL (Bundesvereinigung Logistik eV) based on a decision taken by the German Bundestag.
PY - 2022
Y1 - 2022
N2 - The order processing strategy directly influences the economic and logistic objectives of manufacturing companies. In industrial practice, the order processing strategy is usually chosen based on a few primarily qualitative decision criteria or employees' experience. The increasingly volatile markets require a differentiated decision of the order processing strategy for each product and a continuous review of the decisions made. The high number of factors influencing the decision of order processing strategy and a wide range of products offered by manufacturing companies make it impossible to manually make a fast and at the same time holistic decision. The digitalization and the increasing availability of data and its quality provide the basis for developing a decision support system. This paper presents an approach for a data based analysis of order processing strategies. As logistic models reflect the interdependencies of conflicting economic and logistic objectives, they are applied to derive the costs of the strategies based on desired adherence to the delivery time and the desired service level of the finished goods store. In addition to the description of the procedure for comparing different order processing strategies, the transfer of the theoretical model into a software demonstrator is outlined. A case study is presented to show the practicality of the proposed approach.
AB - The order processing strategy directly influences the economic and logistic objectives of manufacturing companies. In industrial practice, the order processing strategy is usually chosen based on a few primarily qualitative decision criteria or employees' experience. The increasingly volatile markets require a differentiated decision of the order processing strategy for each product and a continuous review of the decisions made. The high number of factors influencing the decision of order processing strategy and a wide range of products offered by manufacturing companies make it impossible to manually make a fast and at the same time holistic decision. The digitalization and the increasing availability of data and its quality provide the basis for developing a decision support system. This paper presents an approach for a data based analysis of order processing strategies. As logistic models reflect the interdependencies of conflicting economic and logistic objectives, they are applied to derive the costs of the strategies based on desired adherence to the delivery time and the desired service level of the finished goods store. In addition to the description of the procedure for comparing different order processing strategies, the transfer of the theoretical model into a software demonstrator is outlined. A case study is presented to show the practicality of the proposed approach.
KW - Data analysis
KW - Decision-making
KW - Interdependencies
KW - Order processing strategies
KW - Software demonstrator
UR - http://www.scopus.com/inward/record.url?scp=85132282129&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2022.04.054
DO - 10.1016/j.procir.2022.04.054
M3 - Conference article
AN - SCOPUS:85132282129
VL - 107
SP - 332
EP - 337
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022
Y2 - 29 June 2022 through 1 July 2022
ER -