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Decision Trees for Forecasting Trended Demand

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Service Parts Management

Abstract

In this chapter, we review method selection protocols for three of the commonly used exponential smoothing methods. In addition to protocols which have been previously established, we introduce a new protocol, based on serial variation curves, and a modification of a protocol suggested by Gardner and McKenzie. We also introduce two new decision trees, based on the new protocols, to provide simple ways of choosing between exponential smoothing methods for no trend, damped trend and linear trend. Operational rules are determined for the new rules, determined by detailed experimentation on simulated data. We test the new protocols on real data, and compare the results with established protocols and universal application of smoothing methods. The results show the new approaches to be promising, yielding some improvements in forecasting accuracy. In those cases where no improvement was observed, neither was there any deterioration in forecasting accuracy. This confirms that the new rules introduced in this chapter are robust and worthy of consideration for practical service parts applications.

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Notes

  1. 1.

    A consequence of this choice is that a direct comparison with M3-competition results will not be possible.

  2. 2.

    A drawback of the DW statistic has been taken into account. The Durbin-Watson statistic has a gap between the significant positive autocorrelation, representing the DTM model, and not significant autocorrelation, representing the LGM model. Therefore, if the result belongs to that gap it follows that the DW test is inconclusive. Therefore, an operational rule had to be adopted in order to overcome the problem. There were two possibilities: either to allocate the inconclusive time series to the LGM model or to the DTM model. Having analysed the above issue in both cases during the simulation experiment, it was concluded that the penalty in terms of forecast accuracy is lower, if the inconclusive time series are allocated to the DTM model. Since the LGM model is a special case of the DTM model (for ϕ = 1), it follows that the LGM could be detected, but not vice versa.

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Author information

Authors and Affiliations

  1. Belgrade University, Belgrade, Serbia

    Natasha N. Atanackov

  2. Buckinghamshire New University, High Wycombe, UK

    John E. Boylan

Authors
  1. Natasha N. Atanackov
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  2. John E. Boylan
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Correspondence to Natasha N. Atanackov .

Editor information

Editors and Affiliations

  1. , Department of Management, DePaul University, E. Jackson Blvd. 1, Chicago, 60604, Illinois, USA

    Nezih Altay

  2. , Robins School of Business, University of Richmond, Westhampton Way 28, Richmond, 23173, Virginia, USA

    Lewis A. Litteral

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Atanackov, N.N., Boylan, J.E. (2011). Decision Trees for Forecasting Trended Demand. In: Altay, N., Litteral, L. (eds) Service Parts Management. Springer, London. https://doi.org/10.1007/978-0-85729-039-7_3

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  • DOI: https://doi.org/10.1007/978-0-85729-039-7_3

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