Defense Logistics Agency Disposition Services is embracing artificial intelligence to evaluate material handling equipment, and that is leading to potential broader adoption among the agency’s other major subordinate commands.
A commercially available AI data analytics platform, initially used at agency property disposal sites since early 2024, is now being tested as a scalable tool to support more accurate and timely logistics decisions within DLA.
Timothy Zweng heads DLA Disposition Services’ equipment management and training branch. He oversees more than 2,000 pieces of equipment worldwide, including 867 wheeled assets. He said that before using this platform, material handling equipment fleet management relied on printed spreadsheets generated from the Enterprise Logistics Management System, a process that was time-consuming and limited in scope.
“In the past, we were not making educated decisions,” Zweng said. “We basically went off paper reports from our fleet management system. Now, we can combine multiple data sources and automate the reports we need to make informed decisions.”
That shift has changed how decisions are made.
Zweng said the data allows his team to evaluate average time between failures, average time to repair, total maintenance costs, and overall availability. This helps identify underperforming assets and unnecessary replacements.
“Reports that used to take us about 45 days to build can now be done in two to three minutes, and that visibility allowed us to avoid about $10 million in equipment purchases we simply didn’t need to make,” Zweng said.
The application is helping identify data-entry errors, improve budgeting accuracy, and support a shift toward condition-based maintenance.
At property disposal field locations, the data allows equipment specialists to identify issues before they become failures, said Don Richards, the lead equipment specialist for DLA Disposition Services’ Northeast region.
Richards said this AI information helps regional teams quickly identify trends, compare equipment performance across sites and drill down to determine whether issues stem from training, maintenance practices or specific equipment types. That insight helps prevent unnecessary spending, reduces downtime and addresses potential safety concerns.
In one instance, Richards said the application helped identify abnormal downtime trends tied to a mast alignment issue affecting multiple pieces of equipment. By recognizing the trend early and sharing the information, regional sites were able to inspect and repair affected equipment before failures occurred.
In another case, Richards said, data revealed a recurring issue that led to changes in maintenance contract requirements, helping prevent future equipment damage and improving safety. He also praised it for allowing specialists to compare experiences and address problems collectively rather than in isolation.
“This gives leaders a common operational picture built on reliable data, something we were not capable of doing before,” Zweng said.
Zweng said he predicts adoption across the agency will be incremental due to differences in fleet size, mission and data maturity. But within the agency’s reverse logistics arm, the tool demonstrably improves readiness, reduces costs and strengthens decision making.
For DLA Disposition Services’ Data Team, the AI application has become more than a reporting tool. Lorenzo Lopez is the team’s lead property disposal specialist, and said the application now serves as the analytical engine behind daily, data-driven decision-making.
The team adopted the AI application during the transition from the Distribution Standard System to the Warehouse Management System, Lopez explained. As the new system produced larger and more complex data sets, the organization needed a way to consolidate, analyze and interpret operational and production metrics. Initially, the team used the application for basic reporting. As internal expertise grew, the team custom-built a dedicated stream of applications. Those applications allowed analysts to quantify performance, calculate complex operational figures and present results through detailed visualizations.
Lopez said the result is broader access to actionable data across the organization. Analysts can conduct deep dives into trends, while managers can quickly assess enterprisewide conditions. That shared visibility has fostered a more proactive culture, enabling leaders to respond quicker to challenges or reinforce strong performance.