Comprehensive operation plans (OPLANs) can help integrate the U.S. and its allies and partners across the Indo-Pacific—but to stay ahead of fast-moving changes in the region, it is increasingly important that the plans be frequently and rapidly updated. The challenge is that OPLANs tend to be static documents that often must be updated manually, a process that can be cumbersome, time-consuming, and incomplete.
However, by bringing their OPLANs into an interactive digital planning environment, the joint forces can use what’s known as “rapid modeling and simulation,” aided by AI, to test and refine their OPLANs—often as fast as conditions change. And they can use that same modeling and simulation to help put the plans into action in a confrontation.
A digital planning environment can be particularly valuable in integrating the coalition in the Indo-Pacific as a combined force of forces. The digital environment brings together vast amounts of data from across the coalition, making it possible to run tens of thousands of simulations to help planners determine how the U.S. and its allies and partners can work together in optimal ways.
And because the digital environment is interactive, planners can experiment hands-on with scenarios of their own—moving red or blue force assets in a particular area of the South China Sea, for example, and then watching as the AI-aided modeling and simulation predicts how a confrontation is likely to play out.
Planners can collaborate at the same time from multiple locations across the Indo-Pacific, including from allied and partner nations.
Nothing about this approach takes away decision making from planners or commanders. Rather, it gives them more hard data to work with, often in near-real time. They still need to use their experience, knowledge, and judgment to evaluate the data and update the OPLANs as they see fit.
BUILDING THE DIGITAL OPLAN ENVIRONMENT
Advances in data science are now making it possible to bring together and integrate an almost unlimited amount of OPLAN data from any number of sources. This includes all of the relevant time-phased force- deployment data now in spreadsheets, PowerPoint presentations, and other formats, which can be digitized through natural language processing and other techniques. Current OPLAN data can be combined with a wide range of unstructured data, from sources such as real-time intelligence reports, satellite imagery, acoustic signatures, and infrared thermography.
In addition, defense organizations can bring in large amounts of information about our potential adversaries, including detailed historical data—for example, how they have responded to certain activities by the joint forces in the past.
With this approach, all of the available data is ingested into a common, cloud-based repository, such as a data lake, and tagged with metadata. This breaks down stove-piped databases and makes it possible to analyze the entire repository of information— and all at once.
Although the data is consolidated, it is actually more secure than it would be in scattered, traditional databases. By tagging the data on a cellular level, defense organizations can tightly control who has access to each piece of data and under what circumstances.
TESTING AND REFINING OPLANS WITH RAPID MODELING AND SIMULATION
Once defense organizations have created a digital planning environment, they can test and refine their OPLANs with modeling and simulation, taking advantage of the combined information in the data lake to factor in tens of thousands of variables. With the help of AI, new rapid modeling and simulation tools can play out OPLANs’ courses of action, along with the branches and sequels, to determine the probability of coalition success every step of the way.
Planners might find, for example, that some bases would be at risk of running out of fuel or munitions during a conflict, or that certain U.S. aircraft would likely be more successful than others in particular missions. The AI might recommend courses of action, or specific branches and sequels, that planners may not have considered.
At the same time, advanced visualization tools, including interactive maps showing coalition and adversary forces, would allow planners to test out possible new scenarios. They might plug in different types of aircraft, for example, to see which are likely to be most effective, or pair manned and unmanned systems. Interactive visualization tools can also allow them to pose critical questions, such as whether a particular action would have a higher likelihood of success than others, but would cost more lives.
A digital environment also enables planners to take advantage of an emerging form of AI, known as reinforcement learning, to help predict adversaries’ first moves and subsequent actions. By analyzing vast amounts of data about a country— including its military capabilities, its doctrine, and its past actions— reinforcement learning can create an “AI agent” to represent that country in modeling and simulation. A unique feature of reinforcement learning is that allows the AI agent to pursue its own best interest, so that in modeling and simulation it would behave much like that country would.
RAPIDLY UPDATING OPLANS
Just as important, a digital environment makes it possible for planners to update OPLANs almost as fast as conditions change. New information— such as changes in coalition or adversary logistics and capabilities—is constantly fed into the digital environment. Ongoing AI-aided modeling and simulation quickly recalculates how current OPLANs are likely will play out and makes new recommendations.
Planners can see, often in near-real time, how they might need to modify their OPLANs. If they do decide to make changes, they can run their updated OPLANs through another round of modeling and simulation and see the new predicted outcomes. They can then continue to refine the plans as needed.
The same approach can help the joint forces make a seamless transition from operation plans to execution plans. As conditions rapidly cascade in a crisis or conflict, for example, decision-makers can quickly see the actions they might take that have the highest probability of success. Because the AI has already worked out tens of thousands of scenarios with the OPLANs, it can take advantage of what it has already learned to stitch together—in near-real time— new recommended courses of action.
The joint forces have a wealth of data available for operation planning. An interactive digital planning environment, along with AI-aided modeling and simulation, would allow them to take full advantage of that data to keep OPLANs updated and help integrate the allies and partners into a joint force of forces.
Maj. Gen. David E . Clary ([email protected]) is principal at Booz Allen, where he leads the firm’s support to coalition warfighters in the Republic of Korea.
Kevin Contreras ([email protected]) leads Booz Allen’s delivery of digital solutions for the rapid modeling, simulation, and experimentation of multi-domain concepts for DoD and global defense clients.
Doug Hamrick ([email protected]) leads Booz Allen’s development of AI-enabled predictive maintenance and supply-chain capabilities for clients throughout the DoD and other federal agencies.