Skip to main content
Log in

Breadth of range and depth of stock: forecasting and inventory management at Euro Car Parts Ltd.

  • Case-Oriented Papers
  • Published:
Journal of the Operational Research Society

Abstract

This paper investigates inventory management issues in a distribution network. The study is motivated by examining the operation of a wholesaling car parts company. Customer service requirements are of paramount importance in this market sector. The nature of the demand facing the company is characterised. The breadth of range of stock keeping units (SKUs) held at a stocking location and the quantity of each SKU held are normally treated in isolation but in this case, the rule developed to select the range of SKU was extended to determine the level of stock to hold. It is intuitively obvious that these two factors should be linked, yet the authors have not found any other literature developing the connection in a practical context. Forecasting issues are explored as the rule on stock range depends on a forecast of the number of orders received for each SKU at each stocking unit. Some implementation issues and extensions are indicated.

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

Access this article

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

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

References

  • Burgin TA and Wild AR (1967). Stock control—Experience and usable theory. Opl Res Q 18: 35–52.

    Article  Google Scholar 

  • Chen YF, Drezner Z, Ryan JK and Simchi-Levi D (2000). Quantifying the bullwhip effect in a simple supply chain. Mngt Sci 46: 436–443.

    Article  Google Scholar 

  • Cohen MA, Zheng Y and Agrawal V (1997). Service parts logistics: A benchmark analysis. IIE Trans 29: 627–639.

    Google Scholar 

  • Croston JD (1972). Forecasting and stock control for intermittent demands. Opl Res Q 23: 289–304.

    Article  Google Scholar 

  • Dominey MJG and Hill RM (2004). Performance of approximations for compound Poisson distributed demand in the newsboy problem. Int J Prod Econ 92: 145–155.

    Article  Google Scholar 

  • Forester JW (1958). Industrial dynamics—A major breakthrough for decision makers. Harvard Bus Rev 36: 37–66.

    Google Scholar 

  • Johnston FR, Boylan JE and Shale EA (2003). An examination of the size of orders from customers, their characterisation and the implications for the inventory control of slow moving items. J Opl Res Soc 54: 833–837.

    Article  Google Scholar 

  • Kennedy WJ, Patterson JW and Fredenhall LD (2002). An overview of recent literature on spare parts inventories. Int J Prod Econ 76: 201–215.

    Article  Google Scholar 

  • Korevaar P, Schimpel U and Boedi R (2007). Inventory budget optimization: Meeting system-wide service levels in practice. IBM J Res Dev 51: 447–465.

    Article  Google Scholar 

  • Kutanoglu E and Mahajan M (2009). An inventory sharing and allocation method for a multi-location service parts logistics network with time-based service levels. Eur J Opl Res 194: 728–742.

    Article  Google Scholar 

  • Matheus P and Gelders L (2000). The (R, Q) inventory policy subject to a compound Poisson demand pattern. Int J Prod Econ 68: 307–317.

    Article  Google Scholar 

  • Mitchell GH (1962). Problems of controlling slow-moving engineering spares. J Opl Res Soc 13: 28–39.

    Article  Google Scholar 

  • Murali KM, Levy M, Kahn BE, Fox EJ, Gaidarev P, Dankworth B and Shah D. (2009). Why is assortment planning so difficult for retailers? A framework and research agenda. J Retailing 85: 71–83.

    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. Eur J Opl Res 184: 101–113.

    Article  Google Scholar 

  • Shale EA, Boylan JE and Johnston FR (2006). Forecasting for intermittent demand: The estimation of an unbiased average. J Opl Res Soc 57: 588–592.

    Article  Google Scholar 

  • Shale EA, Boylan JE and Johnston FR (2008). Characterizing the frequency of orders received by a stockist. IMA J Mngt Math 19: 137–143.

    Article  Google Scholar 

  • Syntetos AA and Boylan JE (2001). On the bias of intermittent demand estimates. Int J Prod Econ 71: 457–466.

    Article  Google Scholar 

  • Syntetos AA and Boylan JE (2005). The accuracy of intermittent demand estimates. Int J Forecasting 21: 303–314.

    Article  Google Scholar 

  • Teunter RH, Babai MZ and Syntetos AA (2010). ABC classification: Service levels and inventory costs. Prod Opns Mngt. 19: 343–352.

    Google Scholar 

  • Zhang RQ, Hopp WJ and Supatgiat C (2001). Spreadsheet implementable inventory control for a distribution center. J Heuristics 7: 185–201.

    Article  Google Scholar 

  • Zipkin P (1991). Evaluation of base-stock policies in multiechelon inventory systems with compound-Poisson demands. Nav Res Log 38: 397–412.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Sukhpal Singh for permission to publish this study and to thank all colleagues at ECP for their help and support during this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E A Shale.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Johnston, F., Shale, E., Kapoor, S. et al. Breadth of range and depth of stock: forecasting and inventory management at Euro Car Parts Ltd.. J Oper Res Soc 62, 433–441 (2011). https://doi.org/10.1057/jors.2010.189

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2010.189

Keywords

Navigation