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Intelligent Techniques for Safety Stock Optimization in Networked Manufacturing Systems

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Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management

Part of the book series: Springer Series in Advanced Manufacturing ((SSAM))

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Abstract

This chapter discusses some intelligent techniques for the solution of the safety stock optimization problem in networked manufacturing systems. These solutions techniques are based on normal approximation models for the involved critical safety stock parameters. The first and second sections introduce the issue of multi-echelon inventory control and review the related literature. The next three sections discuss safety stock optimization in distribution systems, assembly systems and then in generic networked manufacturing systems. The proposed approximation models are tested on small example systems and are benchmarked with results obtained from discrete-event simulation. As the various simulations show, these proposed approximations prove to be rather conservative and provide good upper bounds on the required system safety stocks.

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Author information

Authors and Affiliations

  1. MÖBIUS Business Redesign, Kortrijksesteenweg 152, 9830, Sint-Martens-Latem, Belgium

    B. Desmet & H. Vanmaele

  2. Department of Industrial Management, Faculty of Engineering, Ghent University, Technologiepark Zwijnaarde 903, 9052, Zwijnaarde, Belgium

    E.-H. Aghezzaf

Authors
  1. B. Desmet
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  2. E.-H. Aghezzaf
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  3. H. Vanmaele
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Corresponding author

Correspondence to E.-H. Aghezzaf .

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Editors and Affiliations

  1. COSTEAM/LGIPM ISGMP Bat. A, INRIA Nancy-Grand Est, Ile du Saulcy, 57000, Metz, France

    Lyes Benyoucef

  2. LGP/ENIT, 47, Avenue d’Azereix, 65016, Tarbes CX, France

    Bernard Grabot

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© 2010 Springer-Verlag London Limited

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Desmet, B., Aghezzaf, EH., Vanmaele, H. (2010). Intelligent Techniques for Safety Stock Optimization in Networked Manufacturing Systems. In: Benyoucef, L., Grabot, B. (eds) Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management. Springer Series in Advanced Manufacturing. Springer, London. https://doi.org/10.1007/978-1-84996-119-6_13

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  • DOI: https://doi.org/10.1007/978-1-84996-119-6_13

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-118-9

  • Online ISBN: 978-1-84996-119-6

  • eBook Packages: EngineeringEngineering (R0)

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