Skip to main content
SpringerLink
Log in

Inventory management of spare parts in an energy company

  • General Paper
  • Published:
Journal of the Operational Research Society

Abstract

We address the problem of how to determine control parameters for the inventory of spare parts of an energy company. The prevailing policy is based on an (s, S) system subject to a fill rate constraint. The parameters are decided based mainly on the expert judgment of the planners at different plants. The company is pursuing to conform all planners to the same approach, and to be more cost efficient. Our work focuses on supporting these goals. We test seven demand models using real-world data for about 21 000 items. We find that significant differences in cost and service level may appear from using one or another model. We propose a decision rule to select an appropriate model. Our approach allows us to recommend control parameters for 97.9% of the items. We also explore the impact of pooling inventory for different demand sources and the inaccuracy arising from duplicate item codes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1

Similar content being viewed by others

References

  • Bacchetti A and Saccani N (2012). Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice. Omega 40 (6): 722–737.

    Article  Google Scholar 

  • Bashyam S and Fu MC (1998). Optimization of (s, S) inventory systems with random lead times and a service level constraint. Management Science 44 (12): S243–S256.

    Article  Google Scholar 

  • Boylan JE, Syntetos AA and Karakostas GC (2008). Classification for forecasting and stock control: A case study. Journal of the Operational Research Society 59 (4): 473–481.

    Article  Google Scholar 

  • Burgin TA (1975). The gamma distribution and inventory control. Operational Research Quarterly 26 (3): 507–525.

    Article  Google Scholar 

  • Chen FY and Krass D (2001). Inventory models with minimal service level constraints. European Journal of Operational Research 134 (1): 120–140.

    Article  Google Scholar 

  • Cohen MA, Kleindorfer PR and Lee HL (1988). Service constrained (s, S) inventory systems with priority demand classes and lost sales. Management Science 34 (4): 482–499.

    Article  Google Scholar 

  • Cohen MA, Kleindorfer PR and Lee HL (1989). Near-optimal service constrained stocking policies for spare parts. Operations Research 37 (1): 104–117.

    Article  Google Scholar 

  • Cohen M, Kamesam PV, Kleindorfer P and Lee HL (1990). Optimizer: IBM’s multi-echelon inventory system for managing service logistics. Interfaces 20 (1): 65–82.

    Article  Google Scholar 

  • Croston JD (1972). Forecasting and stock control for intermittent demands. Operational Research Quarterly 23 (3): 289–303.

    Article  Google Scholar 

  • DeHoratius N, Mersereau AJ and Schrage L (2008). Retail inventory management when records are inaccurate. Manufacturing & Service Operations Management 10 (2): 257–277.

    Article  Google Scholar 

  • Dolgui A and Pashkevich M (2008). On the performance of binomial and beta-binomial models of demand forecasting for multiple slow-moving inventory items. Computers & Operations Research 35 (3): 893–205.

    Article  Google Scholar 

  • Dunsmuir WTM and Snyder RD (1989). Control of inventories with intermittent demand. European Journal of Operational Research 40 (1): 16–21.

    Article  Google Scholar 

  • Eaves AHC and Kingsman BG (2004). Forecasting for the ordering and stock-holding of spare parts. Journal of the Operational Research Society 55 (4): 431–437.

    Article  Google Scholar 

  • Ehrhardt R (1979). The power approximation for computing (s, S) inventory policies. Management Science 25 (8): 777–786.

    Article  Google Scholar 

  • Federgruen A and Zipkin P (1984). An efficient algorithm for computing optimal (s, S) policies. Operations Research 32 (6): 1268–1285.

    Article  Google Scholar 

  • Fleisch E and Tellkamp C (2005). Inventory inaccuracy and supply chain performance: A simulation study of a retail supply chain. International Journal of Production Economics 95 (3): 373–385.

    Article  Google Scholar 

  • Graves SC (1985). A multi-echelon inventory model for a repairable item with one-for-one replenishment. Management Science 31 (10): 1247–1256.

    Article  Google Scholar 

  • Guijarro E, Cardós M and Babiloni E (2012). On the exact calculation of the fill rate in a periodic review inventory policy under discrete demand patterns. European Journal of Operational Research 218 (2): 442–447.

    Article  Google Scholar 

  • Kalchschmidt M, Zotteri G and Verganti R (2003). Inventory management in a multi-echelon spare parts supply chain. International Journal of Production Economics 81–82: 397–413, http://www.sciencedirect.com/science/journal/09255273/81-82.

    Article  Google Scholar 

  • Karsten F, Slikker M and Houtum GV (2012). Inventory pooling games for expensive, low-demand spare parts. Naval Research Logistics 59 (5): 311–324.

    Article  Google Scholar 

  • Kennedy WJ, Patterson JW and Fredendall LD (2002). An overview of recent literature on spare parts inventories. International Journal of Production Economics 76 (2): 201–215.

    Article  Google Scholar 

  • Kranenburg AA and van Houtum GJ (2009). A new partial pooling structure for spare parts networks. European Journal of Operational Research 199 (3): 908–921.

    Article  Google Scholar 

  • Kukreja A and Schmidt CP (2005). A model for lumpy demand parts in a multi-location inventory system with transshipments. Computers & Operations Research 32 (8): 2059–2075.

    Article  Google Scholar 

  • Moors JJA and Strijbosch LWG (2002). Exact fill rates for (R, s, S) inventory control with gamma distributed demand. Journal of the Operational Research Society 53 (11): 1268–1274.

    Article  Google Scholar 

  • Muckstadt JA (2005). Analysis and Algorithms for Service Parts Supply Chain. Springer Series in Operations Research and Financial Engineering. Springer: New York.

    Google Scholar 

  • Nenes G, Panagiotidou S and Tagaras G (2010). Inventory management of multiple items with irregular demand: A case study. European Journal of Operational Research 205 (2): 313–324.

    Article  Google Scholar 

  • Porras E and Dekker R (2008). An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods. European Journal of Operational Research 184 (1): 101–132.

    Article  Google Scholar 

  • Ross SM (1996). Stochastic Processes. John Wiley & Sons: New York.

    Google Scholar 

  • Schneider H and Ringuest JL (1990). Power approximation for computing (s, S) policies using service level. Management Science 36 (7): 822–834.

    Article  Google Scholar 

  • Sherbrooke CC (2004). Optimal Inventory Modeling of Systems Multi-Echelon Techniques. Kluwer Academic Publishers: Boston, MA.

    Google Scholar 

  • Silver EA, Pyke DF and Peterson R (1998). Inventory Management and Production Planning and Scheduling. John Wiley & Sons: New York.

    Google Scholar 

  • Snyder RD (1984). Inventory control with the gamma probability distribution. European Journal of Operational Research 17 (3): 373–381.

    Article  Google Scholar 

  • Strijbosch LWG, Heuts RMJ and van der Schoot EHM (2000). A combined forecast-inventory control procedure for spare parts. Journal of the Operational Research Society 51 (10): 1184–1192.

    Article  Google Scholar 

  • Syntetos A and Boylan J (2006). On the stock control performance of intermittent demand estimators. International Journal of Production Economics 29 (103): 36–47.

    Article  Google Scholar 

  • Syntetos A, Keyes M and Babai M (2009). Demand categorisation in a European spare parts logistics network. International Journal of Operations & Production Management 29 (3): 292–316.

    Article  Google Scholar 

  • Syntetos AA, Babai MZ and Altay N (2012). On the demand distributions of spare parts. International Journal of Production Research 50 (8): 2101–2117.

    Article  Google Scholar 

  • Tijms HC and Groenevelt H (1984). Simple approximations for the reorder point in periodic and continuous review (s, S) inventory systems with service level constraints. European Journal of Operational Research 17 (2): 175–190.

    Article  Google Scholar 

  • Veinott AF and Wagner HM (1965). Computing optimal (s, S) inventory policies. Management Science 11 (5): 525–552.

    Article  Google Scholar 

  • Vereecke A and Verstraeten P (1994). An inventory management model for an inventory consisting of lumpy items, slow movers and fast movers. International Journal of Production Economics 35 (1–3): 379–389.

    Article  Google Scholar 

  • Willemain TR, Smart CN and Schwarz HF (2004). A new approach to forecasting intermittent demand for service parts inventories. International Journal of Forecasting 20 (3): 375–387.

    Article  Google Scholar 

  • Wong H, Oudheusden DV and Cattrysse D (2007). Cost allocation in spare parts inventory pooling. Transportation Research Part E 43 (4): 370–386.

    Article  Google Scholar 

  • Wonnacott RJ and Wonnacott TH (1985). Introductory Statistics. John Wiley & Sons: New York.

    Google Scholar 

Download references

Acknowledgements

We are grateful to discussants and audiences at ISIR Summer School Istanbul 2011, INFORMS Annual Meeting Charlotte 2011, EurOMA Cambridge 2011, Statoil Kompetansedager Bergen 2011, NHH Seminar Geilo 2012, EURO Vilnius 2012 and ILS Quebec 2012 for their valuable comments. We thank the anonymous reviewers, whose comments improved the final version of the article. We also thank Vivienne Knowles and Vegard Engeset for their help in bringing this article to fruition.

Author information

Authors and Affiliations

  1. NHH Norwegian School of Economics, Bergen, Norway

    Mario Guajardo & Mikael Rönnqvist

  2. Statoil ASA, Sandsli, Norway

    Ann Mari Halvorsen & Svein Inge Kallevik

Authors
  1. Mario Guajardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mikael Rönnqvist
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ann Mari Halvorsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Svein Inge Kallevik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mario Guajardo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Guajardo, M., Rönnqvist, M., Halvorsen, A. et al. Inventory management of spare parts in an energy company. J Oper Res Soc 66, 331–341 (2015). https://doi.org/10.1057/jors.2014.8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2014.8

Keywords

Search

Navigation