Skip to main content
Log in

An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis

  • Supply Chain Forecasting
  • Published:
Journal of the Operational Research Society

Abstract

Intermittent demand patterns are characterised by infrequent demand arrivals coupled with variable demand sizes. Such patterns prevail in many industrial applications, including IT, automotive, aerospace and military. An intuitively appealing strategy to deal with such patterns from a forecasting perspective is to aggregate demand in lower-frequency ‘time buckets’ thereby reducing the presence of zero observations. However, such aggregation may result in losing useful information, as the frequency of observations is reduced. In this paper, we explore the effects of aggregation by investigating 5000 stock keeping units from the Royal Air Force (UK). We are also concerned with the empirical determination of an optimum aggregation level as well as the effects of aggregating demand in time buckets that equal the lead-time length (plus review period). This part of the analysis is of direct relevance to a (periodic) inventory management setting where such cumulative lead-time demand estimates are required. Our study allows insights to be gained into the value of aggregation in an intermittent demand context. The paper concludes with an agenda for further research.

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.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

Notes

  1. For presentation purposes, and for the remainder of the paper, the ADIDA process will be denoted by: ADIDA (aggregation level, extrapolation method, disaggregation method).

  2. 95% confidence intervals were constructed at aggregation level=1 through the calculation of the sample standard errors for both methods. Subsequently, all average errors at the various aggregation levels (>1) indicated in Figure 4 were evaluated as to whether they constitute statistically significant improvements/reductions.

References

  • Assimakopoulos V and Nikolopoulos K (2000). The theta model: A decomposition approach to forecasting . Int J Forecast 16: 521–530.

    Article  Google Scholar 

  • Boylan JE, Babai MZ, Syntetos AA, and Smith L (2008). Bootstrapping for intermittent demands: Theory and practice. Presented at the 15th International Symposium on Inventories. Budapest, Hungary.

  • Brännäs K, Hellström J and Nordström J (2002). A new approach to modelling and forecasting monthly guest nights in hotels . Int J Forecast 18: 19–30.

    Article  Google Scholar 

  • Croston JD (1972). Forecasting and stock control for intermittent demands . Opl Res Quart 23: 289–303.

    Article  Google Scholar 

  • Hyndman RJ and Koehler AB (2006). Another look at measures of forecast accuracy . Int J Forecast 22: 679–688.

    Article  Google Scholar 

  • Johnston FR and Harrison PJ (1986). The variance of lead-time demand . J Opl Res Soc 37: 303–308.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Porras EM and Dekker R (2008). An inventory control system for spare parts at a refinery: An empirical comparison of different reorder point methods . Eur J Opl Res 184: 101–132.

    Article  Google Scholar 

  • Silvestrini A, and Veredas D (2008). Temporal aggregation of univariate and multivariate time series models: A survey. Temi di discussione (Working papers) 685 (August 2008), Banca d'Italia.

  • Snyder R (2002). Forecasting sales of slow and fast inventories . Eur J Opl Res 140: 684–699.

    Article  Google Scholar 

  • Snyder RD, Koehler AB and Ord K (1999). Lead time demand for simple exponential smoothing: An adjustment factor for the standard deviation . J Opl Res Soc 50: 1079–1082.

    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 Forecast 21: 303–314.

    Article  Google Scholar 

  • Syntetos AA and Boylan JE (2006). On the stock-control performance of intermittent demand estimators . Int J Prod Econ 103: 36–47.

    Article  Google Scholar 

  • Syntetos AA, Boylan JE and Croston JD (2005). On the categorization of demand patterns . J Opl Res Soc 56: 495–503.

    Article  Google Scholar 

  • Syntetos AA, Boylan JE and Croston JD (2006). Reply to Kostenko and Hyndman . J Opl Res Soc 57: 1257–1258.

    Article  Google Scholar 

  • Syntetos AA, Babai MZ, Dallery Y and Teunter R (2009). Periodic control of intermittent demand items: Theory and empirical analysis . J Opl Res Soc 60: 611–618.

    Article  Google Scholar 

  • Syntetos AA, Nikolopoulos K and Boylan JE (2010). Judging the judges through accuracy-implication metrics: The case of inventory forecasting . Int J Forecast 26: 134–143.

    Article  Google Scholar 

  • Teunter R and Sani B (2009). On the bias of Croston's forecasting method . Eur J Opl Res 194: 177–183.

    Article  Google Scholar 

  • Tsai H and Chan KS (2005). Temporal aggregation of stationary and non-stationary discrete-time processes . J Time Ser Anal 26: 613–624.

    Article  Google Scholar 

  • Willemain TR, Smart CN, Shockor JH and DeSautels PA (1994). Forecasting intermittent demand in manufacturing: A comparative evaluation of Croston's method . Int J Forecast 10: 529–538.

    Article  Google Scholar 

  • Willemain TR, Smart CN and Schwarz HF (2004). A new approach to forecasting intermittent demand for service parts inventories . Int J Forecast 20: 375–387.

    Article  Google Scholar 

Download references

Acknowledgements

The work conducted by Aris Syntetos and John Boylan has been funded by the Engineering and Physical Sciences Research Council (EPSRC, UK) grant no. EP/F012632/1 (a project entitled: Forecasting and inventory management: bridging the gap). More information on this project may be obtained at: http://www.mams.salford.ac.uk/CORAS/Projects/Bridging_the_Gap/.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A A Syntetos.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nikolopoulos, K., Syntetos, A., Boylan, J. et al. An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis. J Oper Res Soc 62, 544–554 (2011). https://doi.org/10.1057/jors.2010.32

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2010.32

Keywords

Navigation