<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

A Data Mining Approach To Improve Military Demand Forecasting<xref ref-type="fn" rid="fn1_w2aab2b8b7b1b7b1ab1aaAa"><sup>1</sup></xref>

Defence Science and Technology Organization, Edinburgh SA 5111, Australia.

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Data Mining Approach To Improve Military Demand Forecasting

Accurately forecasting the demand of critical stocks is a vital step in the planning of a military operation. Demand prediction techniques, particularly...

A Data Mining Approach To Improve Military Demand Forecasting¹

Online veröffentlicht: 01. März 2015

Seitenbereich: 205 - 214

DOI: https://doi.org/10.1515/jaiscr-2015-0009

© Academy of Management (SWSPiZ), Lodz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Data Mining Approach To Improve Military Demand Forecasting1

Online veröffentlicht: 01. März 2015

Seitenbereich: 205 - 214

DOI: https://doi.org/10.1515/jaiscr-2015-0009

© Academy of Management (SWSPiZ), Lodz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Data Mining Approach To Improve Military Demand Forecasting¹