14 research outputs found
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 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