Redefining Military Readiness
- CANA Advisors

- Apr 16, 2021
- 2 min read

Two recent articles have been published on the subject of military readiness, and specifically, how it ought to be quantitatively assessed. Both pieces referenced Richard Betts’ 1995 book, Military Readiness: Concepts, Choices, Consequences, and engage on Betts’ three critical readiness questions: what should we be ready for, when is it that we need to be ready, and what needs to be ready?

In Redefine Readiness or Lose, published on the website, War On The Rocks, Commandant of the Marine Corps, General David H. Berger, and Chief of Staff of the Air Force, General Charles Q. Brown, Jr., address these questions and demand an analytical framework capable of answering them. An idea getting their attention is the use of data analytics, artificial intelligence, and probability to predict the changes in future military capabilities from current readiness decisions. In other words, is it possible to predict now how the resources requested, utilized, and allocated now will affect military readiness far into the future?

By coincidence, the Military Operations Research (MOR) Journal, a peer-reviewed academic journal, concurrently published Military Readiness Modeling: Changing the Question from 'Ready or Not?' to 'How Ready for What’? The article, written by CANA Advisors’ Connor McLemore, and co-authors Shaun Doheney, Sam Savage, and Philip Fahringer, paraphrase Betts’ trio of questions as: how ready for what? The authors describe how a better data framework can lead to better military readiness metrics, which could then lead to better readiness and cost tradeoff discussions. Their proposed data framework, if implemented, would go a long way towards helping the military readiness system to answer the two service chiefs’ questions. In their words,
[a] data framework that uses stochastic scenario libraries would allow the military to characterize its probability of being ready for foreseeable missions across all its organizational levels while allowing for mathematically sound aggregation of the readiness of its units. This comprehensive approach would benefit from a stochastic representation of readiness that allows lower-level readiness reports to be rolled up into higher-level reports across unit types and military branches. Such an approach allows planners, commanders, and decision makers to speak the same language to communicate, ‘How ready are units for what?’ (McLemore et al., 2021, p. 23)
Their analytic approach does not remove subjectivity from the readiness calculations of “boots-on-the-ground” commanders. However, the authors argue that this subjectivity based on a structured, auditable approach towards explicitly acknowledged uncertainty is likely to outrun subjectivity alone. They provide a tool that could be extremely valuable to military commanders and planners, greatly expanding their available knowledge of present and future military capability. The authors’ approach also shows potential to conserve enormous amounts of valuable military resources including time, money, manpower, and material.
CANA believes this is exciting content, and we know it will continue to generate meaningful discussion. We are pleased to provide a link to both timely articles here Redefine Readiness or Lose and here Military Readiness Modeling: Changing the Question from 'Ready or Not?' to 'How Ready for What’? and encourage your feedback and continued conversation. Can the future be foretold?
We also have a PDF available here for you to view the Military Readiness Modeling: Changing the Question from 'Ready or Not?' to 'How Ready for What’? article.
Article by CANA Resource Lead, Cherish Joostberns , and Principal Operations Research Analyst, Connor McLemore.





I found the discussion on using AI and stochastic scenario libraries for readiness really eye‑opening, and if you need a quick AI writing boost, check out free ai writing tools. The article’s point about predicting future capability shifts with data analytics mirrors what we see in other fields, so having a solid writing assistant can help translate those complex ideas. Has anyone tried plugging those models into a simple report generator yet?
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