The Defense Logistics Agency — an organization responsible for supplying everything from spare parts to food and fuel — is turning to artificial intelligence and machine learning to fix a long-standing problem of predicting what the military needs on its shelves.

While demand planning accuracy currently hovers around 60%, DLA officials aim to push that baseline figure to 85% with the help of AI and ML tools. Improved forecasting will ensure the services have access to the right items exactly when they need them. 

“We are about 60% accurate on what the services ask us to buy and what we actually have on the shelf.  Part of that, then, is we are either overbuying in some capacity or we are under buying. That doesn’t help the readiness of our systems,” Maj. Gen. David Sanford, DLA director of logistics operations, said during the AFCEA NOVA Army IT Day event on Jan. 15.

Rather than relying mostly on historical purchase data, the models ingest a wide range of data that DLA has not previously used in forecasting. That includes supply consumption and maintenance data, operational data gleaned from wargames and exercises, as well as data that impacts storage locations, such as weather.

The models are tied to each weapon system and DLA evaluates and adjusts the models on a continuing basis as they learn. 

“We are using AI and ML to ingest data that we have just never looked at before. That’s now feeding our planning models. We are building individual models, we are letting them learn, and then those will be our forecasting models as we go forward,” Sanford said.

Some early results already show measurable improvements. Forecasting accuracy for the Army’s Bradley Infantry Fighting Vehicle, for example, has improved by about 12% over the last four months, a senior DLA official told Federal News Network.

The agency has made the most progress working with the Army and the Air Force and is addressing “some final data-interoperability issues” with the Navy. Work with the Marine Corps is also underway. 

“The Army has done a really nice job of ingesting a lot of their sustainment data into a platform called Army 360. We feed into that platform live data now, and then we are able to receive that live data. We are ingesting data now into our demand planning models not just for the Army. We’re on the path for the Navy, and then the Air Force is next. We got a little more work to do with Marines. We’re not as accurate as where we need to be, and so this is our path with each service to drive to that accuracy,” Sanford said.

Demand forecasting, however, varies widely across the services — the DLA official cautioned against directly comparing forecasting performance.

“When we compare services from a demand planning perspective, it’s not an apples-to-apples comparison.  Each service has different products, policies and complexities that influence planning variables and outcomes. Broadly speaking, DLA is in partnership with each service to make improvements to readiness and forecasting,” the DLA official said.

The agency is also using AI and machine learning to improve how it measures true administrative and production lead times. By analyzing years of historical data, the tools can identify how industry has actually performed — rather than how long deliveries were expected to take — and factor that into DLA stock levels.  

“When we put out requests, we need information back to us quickly. And then you got to hold us accountable to get information back to you too quickly. And then on the production lead times, they’re not as accurate as what they are. There’s something that’s advertised, but then there’s the reality of what we’re getting and is not meeting the target that that was initially contracted for,” Sanford said.

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The Defense Logistics Agency is initially focusing its use of artificial intelligence across three main mission areas: operations, demand planning and forecasting, and audit and transparency.

At the same time, DLA isn’t waiting for everyone to be trained or for its data to be perfect.

Adarryl Roberts, the chief information officer at DLA, said by applying AI tools to their use cases, employees can actually clean up the data more quickly.

“You don’t have a human trying to analyze the data and come up with those conclusions. So leveraging AI to help with data curation and ensuring we have cleaner data, but then also not just focusing on ChatGPT and things of that nature,” Roberts said on Ask the CIO. “I know that’s the buzzword, but for an agency like DLA, ChatGPT does not solve our strategic issues that we’re trying to solve, and so that’s why there’s a heavier emphasis on AI. For us in those 56 use cases, there’s a lot of that was natural language processing, a lot around procurement, what I would consider more standardized data, what we’re moving towards with generative AI.”

A lot of this work is setting DLA up to use agentic AI in the short-to-medium term. Roberts said by applying agentic AI to its mission areas, DLA expects to achieve the scale, efficiency and effectiveness benefits that the tools promise to provide.

“At DLA, that’s when we’re able to have digital employees work just like humans, to make us work at scale so that we’re not having to redo work. That’s where you get the loss in efficiency from a logistics perspective, when you have to reorder or re-ship, that’s more cost to the taxpayer, and that also delays readiness to the warfighter,” Roberts said at the recent DLA Industry Collider day. “From a research and development perspective, it’s really looking at the tools we have. We have native tools in the cloud. We have SAP, ServiceNow and others, so based upon our major investments from technology, what are those gaps from a technology perspective that we’re not able to answer from a mission perspective across the supply chain? Then we focus on those very specific use cases to help accelerate AI in that area. The other part of that is architecting it so that it seamlessly plugs back into the ecosystem.”

He added that this ensures the technology doesn’t end up becoming a data stovepipe and can integrate into the larger set of applications to be effective and not break missions.

A good example of this approach leading to success is DLA’s use of robotics process automation (RPA) tools. Roberts said the agency currently has about 185 unattended bots that are working 24/7 to help DLA meet mission goals.

“Through our digital citizen program, government people actually are building bots. As the CIO, I don’t want to be a roadblock as a lot of the technology has advanced to where if you watch a YouTube video, you can pretty much do some rudimentary level coding and things of that nature. You have high school kids building bots today. So I want to put the technology in the hands of the experts, the folks who know the business process the best, so it’s a shorter flash to bang in order to get that support out to the warfighter,” Roberts said.

The success of the bots initiative helped DLA determine that the approach of adopting commercial platforms to implement AI tools was the right one. Roberts said all of these platforms reside under its DLA Connect enterprisewide portal.

“That’s really looking at the technology, the people, our processes and our data, and how do we integrate that and track that schematically so that we don’t incur the technical debt we incurred about 25 years ago? That’s going to result in us having architecture laying out our business processes, our supply chain strategies, how that is integrated within those business processes, overlaying that with our IT and those processes within the IT space,” he said. “The business processes, supply chain, strategies and all of that are overlapping. You can see that integration and that interoperability moving forward. So we are creating a single portal where, if you’re a customer, an industry partner, an actual partner or internal DLA, for you to communicate and also see what’s happening across DLA.”

Training every employee on AI

He said that includes questions about contracts and upcoming requests for proposals as well as order status updates and other data driven questions.

Of course, no matter how good the tools are, if the workforce isn’t trained on how to use the AI capabilities or knows where to find the data, then the benefits will be limited.

Roberts said DLA has been investing in training from online and in person courses to creating a specific “innovation navigators course” that is focused on both the IT and how to help the businesses across the agency look at innovation as a concept.

“Everyone doesn’t need the same level of training for data acumen and AI analytics, depending on where you sit in the organization. So working with our human resources office, we are working with the other executives in the mission areas to understand what skill sets they need to support their day-to-day mission. What are their strategic objectives? What’s that population of the workforce and how do we train them, not just online, but in person?” Roberts said. “We’re not trying to reinvent how you learn AI and data, but how do we do that and incorporate what’s important to DLA moving forward? We have a really robust plan for continuous education, not just take a course, and you’re trained, which, I think, is where the government has failed in the past. We train people as soon as they come on board, and then you don’t get additional training for the next 10-15 years, and then the technology passes you by. So we’re going to stay up with technology, and it’s going to be continuous education moving forward, and that will evolve as our technology evolves.”

Roberts said the training is for everyone, from the director of DLA to senior leaders in the mission areas to the logistics and supply chain experts. The goal is to help them answer and understand how to use the digital products, how to prompt AI tools the best way and how to deploy AI to impact their missions.

“You don’t want to deploy AI for the sake of deploying AI, but we need to educate the workforce in terms of how it will assist them in their day to day jobs, and then strategically, from a leadership perspective, how are we structuring that so that we can achieve our objectives,” he said. “Across DLA, we’ve trained over 25,000 employees. All our employees have been exposed, at least, to an introductory level of data acumen. Then we have some targeted courses that we’re having for senior leaders to actually understand how you manage and lead when you have a digital-first concept. We’re actually going to walk through some use cases, see those to completion for some of the priorities that we have strategically, that way we can better lead the workforce and their understanding of how to employ it at echelon within our organization, enhancing IT governance and operational success.”

The courses and training has helped DLA “lay the foundation in terms of what we need to be a digital organization, to think digital first. Now we’re at the point of execution and implementation, putting those tools to use,” Roberts said.

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