A couple of days ago we were having a discussion about the extent to which the artillery branch of the military has changed its frames of reference and its operating concepts in the last couple of decades. In the course of discussing this, we started comparing the extent to which artillery has changed since, say, the late 1800s, as compared to how much air or naval power has changed.
The conversation ultimately led me to visualize a way to frame the real issue under discussion: challenging the assumption that the future of artillery is necessarily close to the center line forecast (i.e. all assumptions held constant into the future). While this particular conversation was about a military matter, I think the resulting framework is quite useful for a number of sectors.
The resulting framework was a variant of layered analysis, but specifically built for discussing change in the military.
The basic idea is that, looking back through history, we can examine the specific historical contexts in which the military has employed artillery, the change in technology across those contexts, and the resulting experiences for the military. In time (i.e. with a delay), the military translates those experiences into changes in doctrine, which ultimately manifest as changes in force structure. Using this kind of historical analysis, we can look to see if and when technological change was an important factor in changing artillery, what role historical contexts had, and what lessons the military took away from those experiences.
Note: I wouldn’t assume that Technology always drives Context, as is implied in the chart. Technology is the top layer right now simply because it was the first layer I thought of during the conversation. Your actual analysis would in part reveal what the historical relationships were between Technology and Context, if any.
Having done that historical analysis, we can now look forward and explore how both technology and contexts might change in the future and so put the military through new experiences. This futures exploration might then expose key load bearing assumptions in how we think about and train for using artillery. This is what figure 2 is illustrating.
Now, the day after this conversation my colleague brought up the DOTMLPF-P framework and wondered if there might not be a good connection between it and what I outlined above. A first response is certainly that a DOTMLPF-P analysis – or some variant of it – could be nested into the framework where the Doctrine and Force Structure layers sit. Essentially, it would represent a more detailed and comprehensive analysis where I originally envisioned a fairly simple discussion.
Going further, we can start to see how standard foresight methods, stakeholder analysis, and DOTMLPF-P all have their places in the framework, as illustrated in figure 3. When exploring alternative futures in order to develop foresight about how we might need to – and want to – change artillery, we would:
- use our normal futures research tool box for the first couple of layers, then,
- use tools like stakeholder analysis (and stakeholder forecasting) and implications analysis to explore what lessons the institution would learn and what lessons and preferences competing stakeholder groups would have, then,
- use a variant of DOTMLPF-P to both look for opportunities for innovation and change and to consider in more detail, using what we learned from a previous historical analysis, how we might expect the future lessons learned to cascade across the institution and drive (delayed) change in different directions.
Number 3 in the steps above might in fact provide insight into where we most expect resistance to new lessons learned to be greatest, and how such forces might act to deflect or deform the pressures for change.
And why would we conduct such a study? In no small part to help illustrate how artillery may be forced – by logical future circumstances – to challenge its own assumptions about how it is organized and how it is employed in future conflicts.
The framework will certainly get refinement, but it seems like a decent place to start.