IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v62y2011i3d10.1057_jors.2010.40.html
   My bibliography  Save this article

Incorporating demand uncertainty and forecast error in supply chain planning models

Author

Listed:
  • R Fildes

    (Lancaster University)

  • B Kingsman

    (Lancaster University)

Abstract

This paper develops a framework for examining the effect of demand uncertainty and forecast error on unit costs and customer service levels in the supply chain, including Material Requirements Planning (MRP) type manufacturing systems. The aim is to overcome the methodological limitations and confusion that has arisen in much earlier research. To illustrate the issues, the problem of estimating the value of improving forecasting accuracy for a manufacturer was simulated. The topic is of practical importance because manufacturers spend large sums of money in purchasing and staffing forecasting support systems to achieve more accurate forecasts. In order to estimate the value a two-level MRP system with lot sizing where the product is manufactured for stock was simulated. Final product demand was generated by two commonly occurring stochastic processes and with different variances. Different levels of forecasting error were then introduced to arrive at corresponding values for improving forecasting accuracy. The quantitative estimates of improved accuracy were found to depend on both the demand generating process and the forecasting method. Within this more complete framework, the substantive results confirm earlier research that the best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. However, size matters, both in the demand uncertainty and forecasting errors. The quantitative differences depend on service level and also the form of demand uncertainty. Unit costs for a given service level increase exponentially as the uncertainty in the demand data increases. The paper also estimates the effects of mis-specification of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high demand uncertainty and high forecast error, improved forecast accuracy should lead to substantial percentage improvements in unit costs. Methodologically, the results demonstrate the need to simulate demand uncertainty and the forecasting process separately.

Suggested Citation

  • R Fildes & B Kingsman, 2011. "Incorporating demand uncertainty and forecast error in supply chain planning models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 483-500, March.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:3:d:10.1057_jors.2010.40
    DOI: 10.1057/jors.2010.40
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/jors.2010.40
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/jors.2010.40?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fildes, Robert & Hibon, Michele & Makridakis, Spyros & Meade, Nigel, 1998. "Generalising about univariate forecasting methods: further empirical evidence," International Journal of Forecasting, Elsevier, vol. 14(3), pages 339-358, September.
    2. Hau L. Lee & Kut C. So & Christopher S. Tang, 2000. "The Value of Information Sharing in a Two-Level Supply Chain," Management Science, INFORMS, vol. 46(5), pages 626-643, May.
    3. Everette S. Gardner, 1990. "Evaluating Forecast Performance in an Inventory Control System," Management Science, INFORMS, vol. 36(4), pages 490-499, April.
    4. Yossi Aviv, 2002. "Gaining Benefits from Joint Forecasting and Replenishment Processes: The Case of Auto-Correlated Demand," Manufacturing & Service Operations Management, INFORMS, vol. 4(1), pages 55-74, December.
    5. 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.
    6. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    7. Ziliak, Stephen T. & McCloskey, Deirdre N., 2004. "Size matters: the standard error of regressions in the American Economic Review," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 33(5), pages 527-546, November.
    8. Newbold, Paul & Bos, Ted, 1989. "On exponential smoothing and the assumption of deterministic trend plus white noise data-generating models," International Journal of Forecasting, Elsevier, vol. 5(4), pages 523-527.
    9. T. S. Lee & Everett E. Adam, Jr., 1986. "Forecasting Error Evaluation in Material Requirements Planning (MRP) Production-Inventory Systems," Management Science, INFORMS, vol. 32(9), pages 1186-1205, September.
    10. Stefan Voß & David L. Woodruff, 2006. "Introduction to Computational Optimization Models for Production Planning in a Supply Chain," Springer Books, Springer, edition 0, number 978-3-540-29879-3, June.
    11. Enns, S. T., 2002. "MRP performance effects due to forecast bias and demand uncertainty," European Journal of Operational Research, Elsevier, vol. 138(1), pages 87-102, April.
    12. Zhao, Xiande & Xie, Jinxing & Leung, Janny, 2002. "The impact of forecasting model selection on the value of information sharing in a supply chain," European Journal of Operational Research, Elsevier, vol. 142(2), pages 321-344, October.
    13. Xu, Kefeng & Dong, Yan & Evers, Philip T., 2001. "Towards better coordination of the supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(1), pages 35-54, March.
    14. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    15. Jeunet, Jully, 2006. "Demand forecast accuracy and performance of inventory policies under multi-level rolling schedule environments," International Journal of Production Economics, Elsevier, vol. 103(1), pages 401-419, September.
    16. 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.
    17. repec:dau:papers:123456789/2078 is not listed on IDEAS
    18. Yossi Aviv, 2001. "The Effect of Collaborative Forecasting on Supply Chain Performance," Management Science, INFORMS, vol. 47(10), pages 1326-1343, October.
    19. Lee, TS & Cooper, FW & Adam, EE, 1993. "The effects of forecasting errors on the total cost of operations," Omega, Elsevier, vol. 21(5), pages 541-550, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Syntetos, A.A. & Teunter, R.H., 2014. "On the calculation of safety stocks," Research Report 14003-OPERA, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    2. Kourentzes, Nikolaos & Trapero, Juan R. & Barrow, Devon K., 2020. "Optimising forecasting models for inventory planning," International Journal of Production Economics, Elsevier, vol. 225(C).
    3. Barrow, Devon K. & Kourentzes, Nikolaos, 2016. "Distributions of forecasting errors of forecast combinations: Implications for inventory management," International Journal of Production Economics, Elsevier, vol. 177(C), pages 24-33.
    4. Athanasopoulos, George & Kourentzes, Nikolaos, 2023. "On the evaluation of hierarchical forecasts," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1502-1511.
    5. Giovanni Battista Gardino & Rosa Meo & Giuseppe Craparotta, 0. "Multi-view Latent Learning Applied to Fashion Industry," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    6. Gardner, Everette Shaw & Acar, Yavuz, 2016. "The forecastability quotient reconsidered," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1208-1211.
    7. Sagaert, Yves R. & Kourentzes, Nikolaos & De Vuyst, Stijn & Aghezzaf, El-Houssaine & Desmet, Bram, 2019. "Incorporating macroeconomic leading indicators in tactical capacity planning," International Journal of Production Economics, Elsevier, vol. 209(C), pages 12-19.
    8. Bruzda, Joanna, 2019. "Quantile smoothing in supply chain and logistics forecasting," International Journal of Production Economics, Elsevier, vol. 208(C), pages 122-139.
    9. George Athanasopoulos & Nikolaos Kourentzes, 2021. "On the Evaluation of Hierarchical Forecasts," Monash Econometrics and Business Statistics Working Papers 10/21, Monash University, Department of Econometrics and Business Statistics.
    10. repec:dgr:rugsom:14003-opera is not listed on IDEAS
    11. Toyin Clottey, 2016. "Development and evaluation of a rolling horizon purchasing policy for cores," International Journal of Production Research, Taylor & Francis Journals, vol. 54(9), pages 2780-2790, May.
    12. Giovanni Battista Gardino & Rosa Meo & Giuseppe Craparotta, 2021. "Multi-view Latent Learning Applied to Fashion Industry," Information Systems Frontiers, Springer, vol. 23(1), pages 53-69, February.
    13. Niu, Baozhuang & Dai, Zhipeng & Zhuo, Xiaopo, 2019. "Co-opetition effect of promised-delivery-time sensitive demand on air cargo carriers’ big data investment and demand signal sharing decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 29-44.
    14. E. Vercher & A. Corberán-Vallet & J. Segura & J. Bermúdez, 2012. "Initial conditions estimation for improving forecast accuracy in exponential smoothing," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 517-533, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    2. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael, 2014. "Collaborative forecasting in the food supply chain: A conceptual framework," International Journal of Production Economics, Elsevier, vol. 158(C), pages 120-135.
    3. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    4. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
    5. Danese, Pamela & Kalchschmidt, Matteo, 2011. "The role of the forecasting process in improving forecast accuracy and operational performance," International Journal of Production Economics, Elsevier, vol. 131(1), pages 204-214, May.
    6. Zhao, Xiande & Xie, Jinxing & Wei, Jerry C., 2007. "The value of early order commitment in a two-level supply chain," European Journal of Operational Research, Elsevier, vol. 180(1), pages 194-214, July.
    7. Tang, Christopher S., 2006. "Perspectives in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 103(2), pages 451-488, October.
    8. Sari, Kazim, 2008. "On the benefits of CPFR and VMI: A comparative simulation study," International Journal of Production Economics, Elsevier, vol. 113(2), pages 575-586, June.
    9. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    10. Li Chen & Hau L. Lee, 2012. "Bullwhip Effect Measurement and Its Implications," Operations Research, INFORMS, vol. 60(4), pages 771-784, August.
    11. Mirko Kremer & Enno Siemsen & Douglas J. Thomas, 2016. "The Sum and Its Parts: Judgmental Hierarchical Forecasting," Management Science, INFORMS, vol. 62(9), pages 2745-2764, September.
    12. Babai, M.Z. & Ali, M.M. & Boylan, J.E. & Syntetos, A.A., 2013. "Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis," International Journal of Production Economics, Elsevier, vol. 143(2), pages 463-471.
    13. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 2004. "Comments on "Information Distortion in a Supply Chain: The Bullwhip Effect"," Management Science, INFORMS, vol. 50(12_supple), pages 1887-1893, December.
    14. Kenneth Gilbert, 2005. "An ARIMA Supply Chain Model," Management Science, INFORMS, vol. 51(2), pages 305-310, February.
    15. Xiangwen Lu & Jing-Sheng Song & Amelia Regan, 2006. "Inventory Planning with Forecast Updates: Approximate Solutions and Cost Error Bounds," Operations Research, INFORMS, vol. 54(6), pages 1079-1097, December.
    16. Ketzenberg, Michael E. & Rosenzweig, Eve D. & Marucheck, Ann E. & Metters, Richard D., 2007. "A framework for the value of information in inventory replenishment," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1230-1250, November.
    17. Xie, Jinxing & Zhou, Deming & Wei, Jerry C. & Zhao, Xiande, 2010. "Price discount based on early order commitment in a single manufacturer-multiple retailer supply chain," European Journal of Operational Research, Elsevier, vol. 200(2), pages 368-376, January.
    18. Tratar, Liljana Ferbar, 2010. "Joint optimisation of demand forecasting and stock control parameters," International Journal of Production Economics, Elsevier, vol. 127(1), pages 173-179, September.
    19. Kefeng Xu & Yang Dong & Yu Xia, 2014. "‘Too Little’ or ‘Too Late’: The Timing of Supply Chain Demand Collaboration," Working Papers 0203mss, College of Business, University of Texas at San Antonio.
    20. Lu, Jizhou & Feng, Gengzhong & Shum, Stephen & Lai, Kin Keung, 2021. "On the value of information sharing in the presence of information errors," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1139-1152.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorsoc:v:62:y:2011:i:3:d:10.1057_jors.2010.40. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.