In the modern battlefield, rapid decision making capabilities are more critical than ever before, and in conflict scenarios, every second counts. Warfighters and military leaders rely not only on huge troves of data coming in from sensor networks and multiple sources, but more importantly on the analysis of that data to inform their most important decisions.
For the Army, data analysis is being harnessed to revolutionize predictive logistics. Although predictive logistics is not a new field for the service, its power is now really making an impact, according to Steven Morani, principal deputy assistant secretary of defense for sustainment within the Department of Defense.
“We had policy, and we had ways of sensoring equipment, we had ways of offboarding all that information, storing it and giving it to people to do… absolutely nothing with,” Morani explained during the How to Ensure Mission Success in the Modern Battlefield panel discussion at the Potomac Officers Club’s 8th Annual Army Summit. “That’s what was happening.”
“But now, the tools that we have available and the environments that we have available and the sharing of information, that’s the real power. What was missing was the data analytics tools in the environments,” Morani added.
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Morani also noted that the increase in data sharing both between services and across the broader DOD landscape is “helping us see ourselves.”
Rodney Davis, acting program executive officer for aviation within the Army, said plugging into larger systems and data sets is helping the service to cut its use of old and potentially outdated data in fleet maintenance efforts.
“History was always our best guide — we go in and pull the… data and we can look at what we did per hour, per system and things like that and try and understand what the future looks like. But over time, I think we’ve got some better tools where we’re not as reliant on past data,” Davis said.
He noted that hooking into the Army’s Vantage data analytics platform has helped the service to significantly reduce decision times and perform fleet management and maintenance more quickly than ever.
“There is a ton of data the Army already collects and we’re able to harness that in order to do fleet management in a lot more efficient way,” shared Davis. “I think we were able to do analysis on different aircraft that would’ve taken in the past maybe two weeks to do the analysis — we got there in about five minutes.”
While modeling and analytics are proving crucial to a myriad of defense operations and systems, Jay Meil, chief data scientist and managing director of artificial intelligence at SAIC, warned that decisions are only as good as the data upon which they are made. Meil said he focuses on two major factors concerning data.
“One is how do we make sure that the data… is conditioned, it’s harmonized, it’s labeled appropriately?” he posed.
Meil emphasized that it’s important to “understand intent around the data and what it was for — if it’s actually right, or if someone’s just filling in and going to check a box. Because that is going to inform all of the downstream analytics and algorithms going forward because we have to train the computers on that data.”
The second component Meil considers is the consumption level of the data and how warfighters will interact with data.
“It is not scalable to have a model where we’re going to have a bunch of data scientists or a bunch of warfighters trained as data scientists. We need to empower people to use these technologies on their own. So there is a level of user experience and system design to abstract some of the hard stuff away and start giving the end users, the warfighter, the ability to interact with data, run their own analytics, run models and get decisions in an environment that doesn’t require coding,” Meil explained.
Leigh Anne Alexander, director of the Integrated Logistics Support Center within the Army Medical Logistics Command, highlighted the importance of data in the defense supply chain and shared how she’s looking at the data issues that impact her.
“We have that critical relationship with commercial industry, which has typically a just-in-time inventory model. So for us, how do we connect the data coming off the sensor on a soldier all the way back to the industry with the depth and breadth of the supply chain?” Alexander asked.
“So how do we think about the models on the battlefield to proactively and predictively push supply packages to providers so it takes off that cognitive load for them? So what they need is gonna be there when you need it,” she explored.