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Journal of Artificial Intelligence and Soft Computing Research

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A Data Mining Approach To Improve Military Demand Forecasting1

Rajesh Thiagarajan
  • Defence Science and Technology Organization, Edinburgh SA 5111, Australia.
  • Email:
/ Mustafizur Rahman
  • Defence Science and Technology Organization, Edinburgh SA 5111, Australia.
/ Don Gossink
  • Defence Science and Technology Organization, Edinburgh SA 5111, Australia.
/ Greg Calbert
  • Defence Science and Technology Organization, Edinburgh SA 5111, Australia.
Published Online: 2015-03-01 | DOI: https://doi.org/10.1515/jaiscr-2015-0009


Accurately forecasting the demand of critical stocks is a vital step in the planning of a military operation. Demand prediction techniques, particularly autocorrelated models, have been adopted in the military planning process because a large number of stocks in the military inventory do not have consumption and usage rates per platform (e.g., ship). However, if an impending military operation is (significantly) different from prior campaigns then these prediction models may under or over estimate the demand of critical stocks leading to undesired operational impacts. To address this, we propose an approach to improve the accuracy of demand predictions by combining autocorrelated predictions with cross-correlated demands of items having known per-platform usage rates. We adopt a data mining approach using sequence rule mining to automatically determine cross-correlated demands by assessing frequently co-occurring usage patterns. Our experiments using a military operational planning system indicate a considerable reduction in the prediction errors across several categories of military supplies.


  • 1

    A preliminary version of this work appeared in ACIIDS 2014 [1]


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About the article

Published Online: 2015-03-01

Published in Print: 2014-07-01

A preliminary version of this work appeared in ACIIDS 2014 [1]

Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2015-0009. Export Citation

© Academy of Management (SWSPiZ), Lodz. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

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