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The cost impact of using simple forecasting techniques in a supply chain

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  • Heung‐Kyu Kim
  • Jennifer K. Ryan

Abstract

In this paper we consider an inventory model in which the retailer does not know the exact distribution of demand and thus must use some observed demand data to forecast demand. We present an extension of the basic newsvendor model that allows us to quantify the value of the observed demand data and the impact of suboptimal forecasting on the expected costs at the retailer. We demonstrate the approach through an example in which the retailer employs a commonly used forecasting technique, exponential smoothing. The model is also used to quantify the value of information and information sharing for a decoupled supply chain in which both the retailer and the manufacturer must forecast demand. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 388–411, 2003

Suggested Citation

  • Heung‐Kyu Kim & Jennifer K. Ryan, 2003. "The cost impact of using simple forecasting techniques in a supply chain," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(5), pages 388-411, August.
  • Handle: RePEc:wly:navres:v:50:y:2003:i:5:p:388-411
    DOI: 10.1002/nav.10065
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    References listed on IDEAS

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    1. Katy S. Azoury, 1985. "Bayes Solution to Dynamic Inventory Models Under Unknown Demand Distribution," Management Science, INFORMS, vol. 31(9), pages 1150-1160, September.
    2. Frank Chen & Jennifer K. Ryan & David Simchi‐Levi, 2000. "The impact of exponential smoothing forecasts on the bullwhip effect," Naval Research Logistics (NRL), John Wiley & Sons, vol. 47(4), pages 269-286, June.
    3. Philip Kaminsky & Jayashankar M. Swaminathan, 2001. "Utilizing Forecast Band Refinement for Capacitated Production Planning," Manufacturing & Service Operations Management, INFORMS, vol. 3(1), pages 68-81, August.
    4. Warren H. Hausman, 1969. "Sequential Decision Problems: A Model to Exploit Existing Forecasters," Management Science, INFORMS, vol. 16(2), pages 93-111, October.
    5. Stephen C. Graves, 1999. "A Single-Item Inventory Model for a Nonstationary Demand Process," Manufacturing & Service Operations Management, INFORMS, vol. 1(1), pages 50-61.
    6. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    7. Guillermo Gallego & Özalp Özer, 2001. "Integrating Replenishment Decisions with Advance Demand Information," Management Science, INFORMS, vol. 47(10), pages 1344-1360, October.
    8. Frank Chen & Zvi Drezner & Jennifer K. Ryan & David Simchi-Levi, 2000. "Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information," Management Science, INFORMS, vol. 46(3), pages 436-443, March.
    9. Yossi Aviv, 2001. "The Effect of Collaborative Forecasting on Supply Chain Performance," Management Science, INFORMS, vol. 47(10), pages 1326-1343, October.
    10. Stephen C. Graves, 1999. "Addendum to "A Single-Item Inventory Model for a Nonstationary Demand Process"," Manufacturing & Service Operations Management, INFORMS, vol. 1(2), pages 174-174.
    11. George R. Murray, Jr. & Edward A. Silver, 1966. "A Bayesian Analysis of the Style Goods Inventory Problem," Management Science, INFORMS, vol. 12(11), pages 785-797, July.
    12. Donald L. Iglehart, 1964. "The Dynamic Inventory Problem with Unknown Demand Distribution," Management Science, INFORMS, vol. 10(3), pages 429-440, April.
    13. Ananth. V. Iyer & Mark E. Bergen, 1997. "Quick Response in Manufacturer-Retailer Channels," Management Science, INFORMS, vol. 43(4), pages 559-570, April.
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    Cited by:

    1. Rupesh Kumar Pati, 2014. "Modelling Bullwhip Effect in a Closed Loop Supply Chain with ARMA Demand," IIM Kozhikode Society & Management Review, , vol. 3(2), pages 149-164, July.
    2. Saoud, Patrick & Kourentzes, Nikolaos & Boylan, John E., 2022. "Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation," Omega, Elsevier, vol. 110(C).
    3. Michna, Zbigniew & Disney, Stephen M. & Nielsen, Peter, 2020. "The impact of stochastic lead times on the bullwhip effect under correlated demand and moving average forecasts," Omega, Elsevier, vol. 93(C).

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