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Improving demand forecasting accuracy using nonlinear programming software

Author

Listed:
  • J D Bermúdez

    (Universitat de València)

  • J V Segura

    (Universidad Miguel Hernández de Elche)

  • E Vercher

    (Universitat de València)

Abstract

We address the problem of forecasting real time series with a proportion of zero values and a great variability among the nonzero values. In order to calculate forecasts for a time series, the model coefficients must be estimated. The appropriate choice of values for the smoothing parameters in exponential smoothing methods relies on the minimization of the fitting errors of historical data. We adapt the generalized Holt–Winters formulation so that it can consider the starting values of the local components of level, trend and seasonality as decision variables of the nonlinear programming problem associated with this forecasting procedure. A spreadsheet model is used to solve the problems of optimization efficiently. We show that our approach produces accurate forecasts with little data per product.

Suggested Citation

  • J D Bermúdez & J V Segura & E Vercher, 2006. "Improving demand forecasting accuracy using nonlinear programming software," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(1), pages 94-100, January.
    • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:1:d:10.1057_palgrave.jors.2601941
    DOI: 10.1057/palgrave.jors.2601941
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Bermúdez, José D. & Corberán-Vallet, Ana & Vercher, Enriqueta, 2009. "Multivariate exponential smoothing: A Bayesian forecast approach based on simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1761-1769.
    2. Gould, Phillip G. & Koehler, Anne B. & Ord, J. Keith & Snyder, Ralph D. & Hyndman, Rob J. & Vahid-Araghi, Farshid, 2008. "Forecasting time series with multiple seasonal patterns," European Journal of Operational Research, Elsevier, vol. 191(1), pages 207-222, November.
    3. José V. Segura-Heras & José D. Bermúdez & Ana Corberán-Vallet & Enriqueta Vercher, 2022. "Analysis of Weighting Strategies for Improving the Accuracy of Combined Forecasts," Mathematics, MDPI, vol. 10(5), pages 1-12, February.
    4. Bacchetti, Andrea & Saccani, Nicola, 2012. "Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice," Omega, Elsevier, vol. 40(6), pages 722-737.
    5. Saeed, Khalid, 2008. "Trend forecasting for stability in supply chains," Journal of Business Research, Elsevier, vol. 61(11), pages 1113-1124, November.
    6. J. D. Bermudez & J. V. Segura & E. Vercher, 2007. "Holt-Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(9), pages 1075-1090.
    7. J D Bermúdez & J V Segura & E Vercher, 2010. "Bayesian forecasting with the Holt–Winters model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 164-171, January.

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