A data mining approach to improve military demand forecasting

• Autorzy: Thiagarajan R. , Rahman M. , Gossink N. , Calbert G.

Identyfikatory

DOI

10.1515/jaiscr-2015-0009

• Języki publikacji: EN

Abstrakty

EN

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 crosscorrelated 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.

Słowa kluczowe

EN

critical stocks; demand; forecasting; military operation; military planning; military supplies; autocorrelated; cross-correlated; data mining

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2014
• Tom: Vol. 4, No. 3
• Strony: 205--214
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Thiagarajan R.

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

autor

Rahman M.

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

autor

Gossink N.

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

autor

Calbert G.

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

Bibliografia

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