FODA

A Modern Approach to Army Targeting Methodology
Part 3

By LTC Westly T. LaFitte, CW3 Jerrad W. Rader, CW2 Jon R. Delima

Article published on: April 1, 2026 in the Field Artillery 2026 E-Edition

Read Time: < 6 mins

U.S. Army paratroopers assigned to the 173rd Airborne Brigade fire a FIM-92 Stinger during an air defense live-fire exercise alongside soldiers with the Croatian Air Defense Regiment. This training is part of Exercise Shield 22 at Kamenjak near Medulin, Croatia.
(Photo by Staff Sgt. John Yountz)

This article is the third in a three-part series aimed at revolutionizing and proposing necessary changes to the Army’s current targeting methodology. Please make sure to read parts one and two prior to this article for a full overview.

Figure 1. Targeting effectiveness over time given consideration of all variables during the frame step.

Assessment, continued

Assessment relationship with the Frame step visualization

To maximize the effectiveness of friendly targeting efforts, organizations must consider as many known variables as possible. For instance, Figure 1 illustrates how the plan’s effectiveness improved by identifying the variables as previously discussed in the Frame step.

As time progresses and the targeting process continues, it is crucial to consistently re-frame this feedback loop to maintain the targeting efforts’ maximum level of effectiveness. This particular feedback loop seeks to question the underlying assumptions associated with variables previously identified. Figure 2 illustrates how Frame and Assess feedback loops, when applied regularly, can enhance targeting effectiveness.

Assessment relationship between the Orient and Decide step visualization

As the unit executes their plan over time, the enemy will inevitably react to friendly forces’ actions. This reaction will lead to two possible outcomes. Ideally, if the appropriate variables have been identified, the enemy’s reaction will enhance the effectiveness of the friendly plan. Alternatively, the enemy’s response could weaken the targeting efforts. If the plan becomes less effective, it is crucial for organizations to make timely adjustments. Waiting until the next planning horizon, when framing occurs, is insufficient to prevent the atrophy of the targeting efforts. As depicted in Figure 3, this particular feedback loop seeks to assess the relationship between the Orient and Decide steps, specifically focusing on the relationship between targeting objectives and the asset assigned to detect, deliver, and assess. These assessments will occur on the current operations floor, and only under extreme circumstances should the current operations floor ever execute the assess-frame feedback loop. This form of tactical patience in turn helps prevent the whack-a-mole targeting often observed with immature operations staffs.

Figure 2. Effective and ineffective Frame and Assess feedback loops.

Scientific Foundations for an Untested Methodology

While the proposed methodology has not yet been field-tested, this paper does not rely on intuition or heuristics. Instead, the approach is grounded in scientific concepts, drawing upon principles of nomological deduction to establish logical consistency within theoretical frameworks. Additionally, the Frame, Orient, Decide, and Assess (FODA) framework is formulaic in specifying and weighting variables to predict a unit’s ability to target. Without a field test, research still supports that even simple formulas of equal weight can be just as accurate in prediction as a logistical regression formula.¹ A version of logistical regression is displayed in the following paragraphs. This scientific and philosophical backing provides a robust foundation, moving beyond subjective assurances to offer credible, data-driven evidence for the methodology’s potential effectiveness.

Figure 3. Effective and ineffective Orient, Decide, and Assess feedback loops.

Nomological Deduction

The current framework that provides predictive analysis follows the philosophical method of nomological deduction. Nomological deduction was developed by Hempel and Oppenheim to derive predictions based on general laws or principles. Its underpinnings are shown in Figure 4. As a simple example, if one knows the general laws of gravity, an individual can predict how certain objects may fall. Similarly, if one knows the general tactics, techniques, and procedures of a threat group, those defined principles can be used to predict how the threat will operate in a specific Operational Environment (OE).

Figure 4. Deductive nomological model for scientific explanations.²

Through this traditional framework, Army staffs derive similar conditions and laws, resulting in a repository of products that comprise Mission Analysis (MA), which should drive the remainder of the Military Decision-Making Process (MDMP). However, staffs routinely stay too conceptual, failing to refine their initial estimates to drive effective targeting during execution. Throughout an operation, the OE will look significantly different from the basic sketch and provide unique conditions. FODA methodology emphasizes this nomological model to enhance targeting through the Frame and Orient steps, which precede the Decide step—the traditional initial step to targeting. Figure 5 shows an example of how the Army uses general laws, combined with observations of environmental conditions to form nomological deductions. To take it a step further, FODA is another form of nomological deduction.

Measuring Targeting Effectiveness

FODA improves the ability to measure targeting effectiveness by providing specific criteria that can be measured in a qualitative capacity. The traditional framework within Decide, Detect, Deliver, and Assess (D3A) often influences Targeting and Intelligence Officers to rely on intuition and conceptual understanding of the OE to initiate a scientific approach to targeting.

Figure 5. Nomological deduction applied in an Army context.

Real estate agencies can use data sets to predict a house’s selling price based on different attributes such as number of bedrooms, size of the living area, age of the home, etc. Suppose there was a similar dataset to predict a unit’s target effectiveness. The FODA framework could be represented in a logistical regression model as:

Targeting Effectiveness = β₀ + β₁X₁ + β₂X₂ + … + β_nX_n

The understanding of every variable (X) derived in the FODA framework (enemy/friendly vulnerabilities, Correlation of Forces and Means [COFMs], windows of opportunity, etc.) could be compared against a subjective evaluation of targeting effectiveness. The resulting coefficient (β) will inform the correlations between variables and help train the targeting cycle. Although there is no data available to test the significance of the model, historic observations of the traditional D3A format can inform what is most probable. Little to no understanding of these variables will result in low targeting effectiveness while a higher understanding will increase it.

Conclusion

“Those able to assume a meta-paradigmatic approach, where they consider a complex topic through a variety of models, will often gain advantages unrealized by those employing one social paradigm.³

Current Army Targeting Methodology is antiquated and requires an update to keep up with the changing requirements of the modern battlefield. When targeting is inefficient, leaders often revert to increasing repetition or time spent in traditional processes and procedures. Rarely do leaders take a step back to assess their approach within those processes, asking themselves if the means are achieving the right ends.

Targeting is an economic dilemma for commanders at any echelon. In its most basic abstraction, targeting cells must answer both how much of an effect needs to be made and when those effects need to be delivered. This same concept is observed in commercial applications, creating a deep reliance on prediction and forecasting. In these organizations, endless amounts of variables are considered, measured, and monitored to create informed assessments instead of reliance on intuition and experience.

This identification of variables is what is missing in the current D3A model. There are many more cognitive decisions and considerations that need to be made before simply selecting targets to affect. Following the FODA framework is the initial step to closing that gap and truly making informed, data-driven decisions to targeting.

The time has come for the Army to relook how we select, align resources toward, and assess targets. FODA provides a viable and simplified alternative to D3A. The battlefield has grown in complexity over the last three decades, yet D3A has not kept pace with the environment. FODA is the next step in targeting evolution.

Thank you for reading the conclusion to this three-part series.

Endnotes