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Judging the judges through accuracy-implication metrics: The case of inventory forecasting

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  • Syntetos, Aris A.
  • Nikolopoulos, Konstantinos
  • Boylan, John E.

Abstract

A number of research projects have demonstrated that the efficiency of inventory systems does not relate directly to demand forecasting performance, as measured by standard forecasting accuracy measures. When a forecasting method is used as an input to an inventory system, it should therefore always be evaluated with respect to its consequences for stock control through accuracy implications metrics, in addition to its performance on the standard accuracy measures. In this paper we address the issue of judgementally adjusting statistical forecasts for 'fast' demand items, and the implications of such interventions in terms of both forecast accuracy and stock control, with the latter being measured through inventory volumes and service levels achieved. We do so using an empirical dataset from the pharmaceutical industry. Our study allows insights to be gained into the combined forecasting and inventory performance of judgemental estimates. It also aims to advance the practice of forecasting competitions by arguing for the consideration of additional (stock control) metrics when such exercises take place in an inventory context.

Suggested Citation

  • Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E., 2010. "Judging the judges through accuracy-implication metrics: The case of inventory forecasting," International Journal of Forecasting, Elsevier, vol. 26(1), pages 134-143, January.
  • Handle: RePEc:eee:intfor:v:26:y::i:1:p:134-143
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    References listed on IDEAS

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    1. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, pages 451-476.
    2. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, pages 15-27.
    3. John Boylan & Aris Syntetos, 2006. "Accuracy and Accuracy Implication Metrics for Intermittent Demand," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, pages 39-42.
    4. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E. & Fildes, Robert & Goodwin, Paul, 2009. "The effects of integrating management judgement into intermittent demand forecasts," International Journal of Production Economics, Elsevier, vol. 118(1), pages 72-81, March.
    5. Everette S. Gardner, 1990. "Evaluating Forecast Performance in an Inventory Control System," Management Science, INFORMS, pages 490-499.
    6. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, pages 405-408.
    7. 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, pages 3-23.
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    Citations

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    Cited by:

    1. Chiang, Chung-Yean & Lin, Winston T. & Suresh, Nallan C., 2016. "An empirically-simulated investigation of the impact of demand forecasting on the bullwhip effect: Evidence from U.S. auto industry," International Journal of Production Economics, Elsevier, vol. 177(C), pages 53-65.
    2. repec:eee:proeco:v:191:y:2017:i:c:p:85-96 is not listed on IDEAS
    3. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, pages 635-660.
    4. Rekik, Yacine & Syntetos, Aris & Jemai, Zied, 2015. "An e-retailing supply chain subject to inventory inaccuracies," International Journal of Production Economics, Elsevier, vol. 167(C), pages 139-155.
    5. Syntetos, Aris A. & Kholidasari, Inna & Naim, Mohamed M., 2016. "The effects of integrating management judgement into OUT levels: In or out of context?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 853-863.
    6. Ali, Mohammad M. & Boylan, John E. & Syntetos, Aris A., 2012. "Forecast errors and inventory performance under forecast information sharing," International Journal of Forecasting, Elsevier, pages 830-841.
    7. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, pages 485-496.
    8. repec:lrk:eeaart:35_2_5 is not listed on IDEAS
    9. Syntetos, Aris A. & Zied Babai, M. & Gardner, Everette S., 2015. "Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping," Journal of Business Research, Elsevier, vol. 68(8), pages 1746-1752.
    10. 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.
    11. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    12. Yavuz Acar, 2014. "Forecasting Method Selection Based on Operational Performance," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 28(1), pages 95-114.
    13. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    14. Wan, Xiang & Sanders, Nadia R., 2017. "The negative impact of product variety: Forecast bias, inventory levels, and the role of vertical integration," International Journal of Production Economics, Elsevier, vol. 186(C), pages 123-131.
    15. Gardner, Everette Shaw & Acar, Yavuz, 2016. "The forecastability quotient reconsidered," International Journal of Forecasting, Elsevier, pages 1208-1211.
    16. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
    17. Petropoulos, Fotios & Fildes, Robert & Goodwin, Paul, 2016. "Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 842-852.
    18. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, pages 635-660.
    19. Saedi, Samira & Kundakcioglu, O. Erhun & Henry, Andrea C., 2016. "Mitigating the impact of drug shortages for a healthcare facility: An inventory management approach," European Journal of Operational Research, Elsevier, vol. 251(1), pages 107-123.
    20. Jussim, Maxim, 2014. "Entwicklung eines Simulationstools zur Analyse von Prognose- und Dispositionsentscheidungen im Krankenhausbereich," Bayreuth Reports on Information Systems Management 57, University of Bayreuth, Chair of Information Systems Management.
    21. Halkos, George & Kevork, Ilias, 2013. "Forecasting the optimal order quantity in the newsvendor model under a correlated demand," MPRA Paper 44189, University Library of Munich, Germany.
    22. 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.
    23. Moon, Seongmin & Hicks, Christian & Simpson, Andrew, 2012. "The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy—A case study," International Journal of Production Economics, Elsevier, vol. 140(2), pages 794-802.
    24. Halkos, George & Kevork, Ilias, 2012. "Unbiased estimation of maximum expected profits in the Newsvendor Model: a case study analysis," MPRA Paper 40724, University Library of Munich, Germany.
    25. Acar, Yavuz & Gardner, Everette S., 2012. "Forecasting method selection in a global supply chain," International Journal of Forecasting, Elsevier, pages 842-848.

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