Impact of Demand Nature on the Bullwhip Effect. Bridging the Gap between Theoretical and Empirical Research

  • Juan R. Trapero
  • Fausto P. Garc′ıa
  • N. Kourentzes
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 242)

Abstract

The bullwhip effect (BE) consists of the demand variability amplification that exists in a supply chain when moving upwards. This undesirable effect produces excess inventory and poor customer service. Recently, several research papers from either a theoretical or empirical point of view have indicated the nature of the de- mand process as a key aspect to defining the BE. Nonetheless, they reached different conclusions. On the one hand, theoretical research quantified the BE depending on the lead time and ARIMA parameters, where ARIMA functions were employed to model the demand generator process. In turn, empirical research related nonlinearly the demand variability extent with the BE size. Although, it seems that both results are contradictory, this paper explores how those conclusions complement each other. Essentially, it is shown that the theoretical developments are precise to determine the presence of the BE based on its ARIMA parameter estimates. Nonetheless, to quan- tify the size of the BE, the demand coefficient of variation should be incorporated. The analysis explores a two-staged serially linked supply chain, where weekly data at SKU level from a manufacturer specialized in household products and a major UK grocery retailer have been collected.

Keywords

Bullwhip effect Demand forecasting Supply chain management 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis: Forecasting and Control. 3rd edn, Upper Saddle River, Prentice Hall, New JerseyGoogle Scholar
  2. 2.
    Byrne PJ, Heavey C (2006) The impact of information sharing and forecasting in ca- pacited industrial supply chains: A case study. International Journal of Production Economics 103(1):420–437Google Scholar
  3. 3.
    Cachon G, Randall T, Schmidt G (2007) In search of the bullwhip effect, M&som- Manufacturing & Service. Operations Management 9:457–479Google Scholar
  4. 4.
    Chen F, Drezner Z, Ryan JK et al (2000) The impact of exponential smoothing forecasts on the bullwhip effect. Naval Research Logistics 47(4):269–286Google Scholar
  5. 5.
    Dejonckheere J, Disney SM, Lambrecht MR et al (2003) Measuring and avoiding the bullwhip effect: A control theoretic approach. European Journal of Operational Research 147(3):567–590Google Scholar
  6. 6.
    Duc TTH, Luong HT, Kim YD (2008) A measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process. European Journal of Operational Research 187(1):243–256Google Scholar
  7. 7.
    Fildes R, Goodwin P (2007) Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces 37(6):70–576Google Scholar
  8. 8.
    Fildes R, Goodwin P, Lawrence M et al (2009) Effective forecasting and jugdmental ad- justments: An empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting 25(1):3–23Google Scholar
  9. 9.
    Fransoo JC, Wouters MJF (2000) Measuring the bullwhip effect in the supply chain. Supply Chain Management: An International Journal 5(2):78–89Google Scholar
  10. 10.
    Geary S, Disney SM, Towill DR (2006) On bullwhip in supply chains-historical review, present practice and expected future impact. International Journal of Production Economics 101(1):2–18Google Scholar
  11. 11.
    Gilliland M (2010) Defining ‘demand’ for demand forecasting. The International Journal of Applied Forecasting (18):4–8Google Scholar
  12. 12.
    Lee HL, Padmanabhan V, Whang S (1997) The bullwhip effect in supply chains. Sloan Man- agement Review 38(3):93–102Google Scholar
  13. 13.
    Li G, Wang S, Yan H et al (2005) Information transformation in a supply chain: A simulation study. Computers & Operations Research 32(3):707–725Google Scholar
  14. 14.
    Luong HT (2007) Measure of bullwhip effect in supply chains with autoregressive demand process. European Journal of Operational Research 180(3):1086–1097Google Scholar
  15. 15.
    Schwartz G (1978) Estimating the dimension of a model, Annals of Statistics 6(2):461–464Google Scholar
  16. 16.
    Trapero JR, Fildes R, Davydenko A (2011) Nonlinear identification of judgmental forecasts effects at SKU level. Journal of Forecasting 30(5):490–508Google Scholar
  17. 17.
    Zotteri G (2013) An empirical investigation on causes and effects of the bullwhip-effect: Evidence from the personal care sector. International Journal of Production Economics 143(2):489–498Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Juan R. Trapero
    • 1
  • Fausto P. Garc′ıa
    • 1
  • N. Kourentzes
    • 2
  1. 1.Department of Business AdministrationUniversidad de Castilla-La ManchaCiudad RealSpain
  2. 2.Department of Management ScienceLancaster UniversityLancasterUK

Personalised recommendations