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概要

A Data Analytic Guide for Defence Management and Budgeting

Melese Francois

This two-part guide reviews several standard data analytic tools with a wide variety of military financial management applications. Consider defence fuel budgets. The better our budget estimates, greater the efficiency and effectiveness of our forces. Budgeting too little can impact future operations, training, equipment, or sacrifice other priorities reprogrammed to fill funding gaps. Budgeting too much also risks sacrificing military priorities, as extra funds may be discovered too late to be reprogrammed efficiently. To minimize costly adjustments and wasteful reprogramming requires better guesses. The challenge with fuel prices is that they vary a lot. We have three choices, to: Ignore variability, Capture variability, or explain variability. The first section reviews extrapolative Forecasting and Sample Means (Averages) that mostly ignore variability, and then proceed to Confidence Intervals and Critical Values that attempt to capture variability. The next section explores the power of Simulation and Regression Analysis (“parametric estimation”) that tries to explain variability. This two-part guide briefly summarizes each approach in the context of fuel budgets, together with hints of how to apply the techniques in Excel. Widespread application of these and other data analytic tools could improve military management and budgeting and increase the efficiency and effectiveness of our forces.