AI in Warehousing: How AutoScheduler Drives Supply Chain Efficiency

Only 4% of supply chain operations have deployed robotics beyond a single point, according to a survey cited during a FreightWaves interview with Keith Moore, CEO of AutoScheduler AI — and Moore argues that the gap between isolated automation and true warehouse efficiency comes down to a missing orchestration layer, not missing robots.

AutoScheduler, which Moore co-founded with his father in 2020 after spinning out a project originally built for Procter & Gamble, offers what he calls an AI-powered warehouse orchestration platform. The software builds an “operational twin” of a facility, maps every inventory flow, models trade-offs between service levels and truck utilization, and continuously replans when conditions shift — a truck no-shows, automation goes down, or workers call in sick.

“I can show you that your pick density has gone up by 25%, which means that your labor requirements has gone down by this much, your automation utilization has gone up by this much,” Moore said, arguing that the platform gives supply chain leaders the hard numbers they need to justify AI investment — a critical gap given that Gartner found 55% of supply chain leaders are unclear on their AI returns.

“Supply chain has historically been viewed as a cost center, not a value driver. Big companies like Walmart and Amazon realized that wasn’t the case. Look how they’re doing.” — Keith Moore, CEO, AutoScheduler AI

Moore traces slow AI adoption in warehousing to a structural talent and attention problem. Skilled software engineers and machine learning researchers have historically gravitated toward companies like Google or Microsoft rather than warehousing or transportation. At the same time, executives controlling IT budgets often prioritize marketing, sales, or finance tools they understand intimately, leaving supply chain — which Moore notes drives 10% of U.S. GDP — underfunded. That dynamic, he said, has shifted materially over the past six years as supply chain visibility reached the CEO, CIO, and CSCO level, but cultural change across the broader warehouse workforce takes more time.

Moore’s own background spans both worlds. Before launching AutoScheduler, he helped scale SparkCognition — an Austin, Texas-based AI company that reached unicorn status — running product through Series A to Series D. He has worked in machine learning since 2012. The idea for AutoScheduler grew from conversations with his father, a supply chain consultant, about digitizing the decision-making layer that separates high-performing warehouse sites from underperformers. At P&G, Moore said, facilities with identical technology, process, and hiring practices still diverged sharply in output because floor-level managers — typically five to 10 decision-makers per shift — were not aligned on sequencing trucks and releasing inventory.

For warehouse operators who already have some automation but no orchestration layer, Moore’s advice is straightforward: start by documenting decision-making processes before touching AI tooling. “Everybody talks about data with AI. Very few companies actually document the decision-making processes that then are applied to that data to actually run the supply chain,” he said. Once processes are mapped, operators can identify low-hanging fruit decisions to automate first and build up incrementally. Moore said the technology and market conditions needed to deliver that kind of tailored, deterministic AI solution credibly did not exist even six months ago, but predicted significant mindset shifts across the industry over the next 12 months.

Only 4% of supply chain operations have deployed robotics beyond a single point, highlighting a large gap between isolated automation and full orchestration.

AutoScheduler’s platform can demonstrate a 25% improvement in pick density, giving supply chain leaders concrete ROI data against a backdrop where 55% of leaders are unclear on AI returns.

Moore advises warehouses to document human decision-making processes first before layering in AI orchestration tools.

This Summary is generated thanks to a transcription of the interview, for the full interview please enjoy the video above.

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