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Service Level Driven Stock Allocation: A Model Based Enterprise Information System

Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Research › peer-review

Authors

The conflict between the objectives for logistic efficiency and storage costs largely influences inventory management. The positioning in this tension field poses a challenge to decision makers. The Logistic Storage Analysis (LSA) is an industry proven control model used to dimension stock within this field of tension. To this end, the LSA offers a variety of tools, derived from Logistic Operating Curves, for aggregating and visualizing data and is thus an ideal foundation and starting point for the Enterprise Information System (EIS) currently being developed.
Original languageEnglish
Title of host publicationResearch and Practical Issues of Enterprise Information Systems II - IFIP TC 8 WG 8.9 International Conference on Research and Practical lssues of Enterprise Information Systems, CONFENIS 2007
EditorsLi D. Xu, A. Min Tjoa, Sohail S. Chaudhry
Number of pages9
Place of PublicationBoston, MA
PublisherSpringer US
Publication date2008
Pages229-237
ISBN (print)978-1-4757-0563-8
ISBN (electronic)978-0-387-75902-9
DOIs
Publication statusPublished - 2008
Externally publishedYes
EventIFIP TC 8 WG 8.9 International Conference on Research and Practical Issues of Enterprise Information Systems 2007 - Beijing, China
Duration: 14.10.2007 → 16.10.2007
https://www.researchgate.net/publication/321619742_Research_and_Practical_Issues_of_Enterprise_Information_Systems_II_Volume_2_IFIP_TC_8_WG_89_International_Conference_on_Research_and_Practical_Issues_of_Enterprise_Information_Systems_CONFENIS_2007_Oc

Bibliographical note

IFIP TC 8 WG 8.9 International Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS 2007) October 14–16, 2007, Beijing, China

    Research areas

  • Engineering - EIS for manufacturing sector, Logistic operations, Logistic operation curves , Stock allocation, Supply chain planning and execution , Supply chain management

Other publications by the same author(s)

Implementierung von Produktrückführungsprozessen in bestehende Produktionsabläufe: Herausforderungen und Anforderungen

Sandner, S., Nübel, M., Schulz, J., Metternich, J. & Schmidt, M., 20.06.2025, In: ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb. 120, 6, p. 397-403 7 p.

Research output: Journal contributions › Journal articles › Research › peer-review

Machine Learning For Determining Planned Order Lead Times In Job Shop Production: A Systematic Review Of Input Factors And Applied Methods

Wolter, F., Rokoss, A. & Schmidt, M., 2025, Conference on Production Systems and Logistics: University of San Ignacio de Loyola Lima, Peru, 18th – 21th March 2025, Proceedings. Herberger, D. & Hübner, M. (eds.). Offenburg: publish-Ing., p. 374-384 11 p. 32. (Proceedings of the ... Conference on Production Systems and Logistics; no. 7).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Understanding Resulting Interdependencies Within Production Planning And Control In Hybrid Manufacturing/Remanufacturing Systems

Schulz, J., Hofmann, H., Rokoss, A. & Schmidt, M., 05.2025, Proceedings of the Conference on Production Systems and Logistics: CPSL 2025: 18th – 21th March 2025, University of San Ignacio de Loyola Lima, Peru. Herberger, D. & Hübner, M. (eds.). Offenburg: publish-Ing., p. 114 - 126 13 p. (Proceedings of the ... Conference on Production Systems and Logistics; vol. 7).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

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

Rokoss, A., Popkes, L. & Schmidt, M., 2024, Proceedings of the CPSL 2024. Herberger, D. & Hübner, M. (eds.). Hannover: publish-Ing., p. 415-431 17 p. (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

Case study on delivery time determination using a machine learning approach in small batch production companies

Rokoss, A., Syberg, M., Tomidei, L., Hülsing, C., Deuse, J. & Schmidt, M., 12.2024, In: Journal of Intelligent Manufacturing. 35, 8, p. 3937-3958 22 p.

Research output: Journal contributions › Journal articles › Research › peer-review

DOI

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