Logistics Research

, Volume 3, Issue 4, pp 221–232 | Cite as

Quantifying the impact of demand substitution on the bullwhip effect in a supply chain

Original Paper


In a supply chain, the distorted demand information when it goes upstream is commonly known as the bullwhip effect. In this paper, the impact of demand substitution on the bullwhip effect in a two-stage supply chain is investigated. In our model, a single retailer observes inventory levels for two products, among which product 1 can be used to substitute product 2. The retailer places orders to a single manufacturer following an order-up-to inventory policy and uses a simple moving average forecasting method to estimate the lead-time demand. The customers’ demands are modeled by an autoregressive process. By analyzing the bullwhip effect in such settings, quantitative relations between the bullwhip effect and the forecasting method, lead time, demand process, and the product substitution are obtained. Numerical results show that demand substitution can reduce the bullwhip effect in most cases.


Bullwhip effect Demand substitution Supply chain management 


  1. 1.
    Agrawal N, Smith SA (2003) Optimal retail assortments for substitutable items purchased in sets. Naval Res Logist 50(7):793–822MATHMathSciNetCrossRefGoogle Scholar
  2. 2.
    Bassok Y, Anupindi R, Akella R (1999) Single-period multiproduct inventory models with substitution. Oper Res 47(4):632–642MATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    Bitran G, Dasu S (1992) Ordering policies in an environment of stochastic yields and substitutable demands. Oper Res 40(5):177–185CrossRefGoogle Scholar
  4. 4.
    Chen F, Drezner Z, Ryan JK, Simchi-Levi D (2000) Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information. Manage Sci 46(3):436–443MATHCrossRefGoogle Scholar
  5. 5.
    Chen F, Ryan JK, Simchi-Levi D (2000) The impact of exponential smoothing forecasts on the bullwhip effect. Naval Res Logist 47(4):269–286MATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Drezner Z, Gurnani H (2000) Deterministic hierarchical substitution inventory models. J Oper Res Soc 51(1):129–133MATHCrossRefGoogle Scholar
  7. 7.
    Drezner Z, Gurnani H, Pasternack BA (1995) An eoq model with substitutions between products. J Oper Res Soc 46(7):887–891MATHCrossRefGoogle Scholar
  8. 8.
    Forrester J (1958) Industrial dynamics, a major breakthrough for decision makers. Harvard Bus Rev 36:37–66Google Scholar
  9. 9.
    Fuller JB, O’Connor J, Rawlinson R (1993) Tailored logistics: the next advantage. Harvard Bus Rev 71(3):87–98Google Scholar
  10. 10.
    Gilbert K (2005) An ARIMA supply chain model. Manag Sci 51(2):305–310MATHCrossRefGoogle Scholar
  11. 11.
    Karaesmen I, van Ryzin G (2004) Overbooking with substitutable inventory classes. Oper Res 52(1):83–104MATHMathSciNetCrossRefGoogle Scholar
  12. 12.
    Lee HL, Padmanabhan V, Whang S (1997) The bullwhip effect in supply chains. MIT Sloan Manage Rev 38(38):93–102Google Scholar
  13. 13.
    Lee HL, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manage Sci 43(4):546–558MATHCrossRefGoogle Scholar
  14. 14.
    Liu H, Wang P (2007) Bullwhip effect analysis in supply chain for demand forecasting technology. Syst Eng Theory Practice 27(7):26–33CrossRefGoogle Scholar
  15. 15.
    McGillivray AR, Silver EA (1978) Some concepts for inventory control under substitutable demand. Inf Syst Oper Res 16(1):47–63MATHMathSciNetGoogle Scholar
  16. 16.
    Metters R (1997) Quantifying the bullwhip effect in supply chains. J Oper Manage 15(2):89–100CrossRefGoogle Scholar
  17. 17.
    Netessine S, Rudi N (2003) Centralized and competitive inventory models with demand substitution. Oper Res 51(2):329–335MATHMathSciNetCrossRefGoogle Scholar
  18. 18.
    Parlar M, Goyal S (1984) Optimal ordering decisions for two substitutable products with stochastic demands. OPSEARCH 21:1–15MATHGoogle Scholar
  19. 19.
    Rajaram K, Tang CS (2001) The impact of product substitution on retail merchandising. Eur J Oper Res 135(3):582–601MATHCrossRefGoogle Scholar
  20. 20.
    Ryan JK (1997) Analysis of inventory models with limited demand information. PhD thesis, Northwestern University, Evanston, ILGoogle Scholar
  21. 21.
    Zhang X (2004) The impact of forecasting methods on the bullwhip effect. Int J Prod Econ 88(1):15–27Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Department of Industrial and Information EngineeringUniversity of TennesseeKnoxvilleUSA

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