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OR Spectrum

, 31:121 | Cite as

Setting safety stocks in multi-stage inventory systems under rolling horizon mathematical programming models

  • Youssef BoulaksilEmail author
  • Jan C. Fransoo
  • Ernico N. G. van Halm
Open Access
Regular Article

Abstract

This paper considers the problem of determining safety stocks in multi-item multi-stage inventory systems that face demand uncertainties. Safety stocks are necessary to make the supply chain, which is driven by forecasts of customer orders, responsive to (demand) uncertainties and to achieve predefined target service levels. Although there exists a large body of literature on determining safety stock levels, this literature does not provide an effective methodology that can address complex multi-constrained supply chains. In this paper, the problem of determining safety stocks is addressed by a simulation based approach, where the simulation studies are based on solving the supply chain planning problem (formulated as a mathematical programming model) in a rolling horizon setting. To demonstrate the utility of the proposed approach, an application of the approach at Organon, a worldwide operating biopharmaceutical company, will be discussed.

Keywords

Safety stocks Advanced planning and scheduling Simulation Supply chain planning Organon 

References

  1. Billington PJ, McClain JO, Thomas LJ (1983) Mathematical programming approaches to capacity- constrained MRP systems: review, formulation and problem reduction. Manage Sci 29:1126–1141Google Scholar
  2. Callarman TE, Mabert VA (1978) Using material requirements planning with demand uncertainty. In: Proceedings of the 9th annual midwest AIDS conference. pp 151–155Google Scholar
  3. Callarman TE, Hamrin RS (1984) A comparison of dynamic lot sizing rules for use in a single stage MRP system with demand uncertainty. Int J Oper Prod Manage 4(2):39–49CrossRefGoogle Scholar
  4. Clark AJ, and Scarf H (1960) Optimal policies for a multi-echelon inventory problem. Manage Sci 6: 475–490Google Scholar
  5. De Bodt MA, Van Wassenhove LN (1983) Lot sizes and safety stocks in MRP: a case study. Prod Inventory Manage 24(1):1–16Google Scholar
  6. De Kok AG, Fransoo JC (2003) Planning supply chain operations: definition and comparison of planning concepts. In: De Kok AG, Graves SC (eds) Design and analysis of supply chains: design, coordination and operation (Handbooks in Operations Research and Management Science, Volume 11). North Holland, Amsterdam, pp 597–675Google Scholar
  7. Diks EB, De Kok AG, Lagodimos AG (1996) Multi-echelon systems: a service measure perspective. Euro J Oper Res 95:241–263CrossRefGoogle Scholar
  8. Eilon S, Elmaleh J (1968) An evaluation of alternative inventory control policies. Int J Prod Res 7(1):3–14CrossRefGoogle Scholar
  9. Graves SC, Willems SP (2000) Optimizing strategic safety stock placement in supply chains. Manu Serv Oper Manage 2(1):68–83CrossRefGoogle Scholar
  10. Heath DC, Jackson PL (1994) Modeling the evolution of demand forecasts with application to safety stock analysis in production/distribution systems. IIE Trans 26(3):17–30CrossRefGoogle Scholar
  11. Inderfurth K (1991) Safety stock optimization in multi-stage inventory systems. Int J Prod Econ 24:103–113CrossRefGoogle Scholar
  12. Inderfurth K, Minner S (1998) Safety stocks in multi-stage inventory systems under different service measures. Eur J Oper Res 106:57–73CrossRefGoogle Scholar
  13. Kleijnen JPC, Wan J (2006) Optimization of simulated inventory systems: OpQuest and alternatives. Tilburg University: CentER Discussion Paper, no. 2006-75Google Scholar
  14. Kohler-Gudum CK, De Kok AG (2002) A safety stock adjustment procedure to enable target service levels in simulation of generic inventory systems. Technische Universiteit Eindhoven: BETA Working Paper 71Google Scholar
  15. Law AM, Kelton WD (2000) Simulation modelling and analysis, 3rd edn. McGraw-Hill, New YorkGoogle Scholar
  16. Minner S (1997) Dynamic programming algorithms for multi-stage safety stock optimization. OR Spektrum 19:261–271Google Scholar
  17. Silver EA, Pyke DF, Peterson R (1998) Inventory management and production scheduling. Wiley, New YorkGoogle Scholar
  18. Spitter JM, Hurkens CAJ, De Kok AG, Negenman EG, Lenstra JK (2005) Linear programming models with planned lead times. Eur J Oper Res 163:706–720CrossRefGoogle Scholar
  19. Stadtler H (2003) Multi-level lot sizing with setup times and multiple constrained resources: internally rolling schedules with lot-sizing windows. Oper Res 51(3):487–502CrossRefGoogle Scholar
  20. Stadtler H, Kilger C (2005) Supply chain management and advanced planning, 3rd edn. Springer, BerlinGoogle Scholar
  21. Tempelmeier H, Derstroff M (1996) A Lagrangean-based heuristic for dynamic multi-level multi-item constrained lotsizing with setup times. Manage Sci 42(5):738–757CrossRefGoogle Scholar
  22. Van Houtum GJ, Inderfurth K, Zijm WHJ (1996) Materials coordination in stochastic multi-echelon system. Eur J Oper Res 95:1–23CrossRefGoogle Scholar
  23. Wemmerlöv U, Whybark DC (1984) Lot-sizing under uncertainty in a rolling schedule environment. Int J~Prod Res 22(3):467–484CrossRefGoogle Scholar
  24. Whybark DC, Williams JG (1976) Material Requirements Planning under uncertainty. Decis Sci 8(4)Google Scholar
  25. Wijngaard J, Wortmann JC (1985) MRP and inventories. Eur J Oper Res 20:281–293CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Youssef Boulaksil
    • 1
    Email author
  • Jan C. Fransoo
    • 1
  • Ernico N. G. van Halm
    • 2
  1. 1.Department of Technology ManagementTechnische Universiteit EindhovenEindhovenThe Netherlands
  2. 2.Supply Chain Management DepartmentOrganon N.V.OssThe Netherlands

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