AI in Warehousing: How AutoScheduler Drives Supply Chain Efficiency

Keith Moore, CEO of AutoScheduler AI, discusses how AI-powered warehouse orchestration is transforming supply chain operations. AutoScheduler helps businesses tackle complex warehousing challenges, optimize decision-making, and significantly improve efficiency, throughput, and service while cutting costs. At the heart of AutoScheduler's approach is the concept of an "operational twin," a digital representation of a facility that maps every inventory flow, models trade-offs between service levels and truck utilization, and continuously replans when conditions change.

Moore argues that the lack of true warehouse efficiency lies in the absence of an orchestration layer, not a lack of robots. According to a survey cited during an interview with FreightWaves, only 4% of supply chain operations have deployed robotics beyond a single point. Moore suggests that the gap between isolated automation and full warehouse efficiency is due to missing orchestration.

AutoScheduler, co-founded by Moore and his father in 2020, is an AI-powered warehouse orchestration platform. It builds an operational twin of a facility, maps inventory flows, models trade-offs, and replans continuously in response to changes like truck no-shows, automation failures, or workers calling in sick. Moore demonstrates that the platform can increase pick density by 25%, leading to a decrease in labor requirements and an increase in automation utilization.

Moore believes that the slow adoption of AI in warehousing is due to a structural talent and attention problem. Skilled software engineers and machine learning researchers often prefer working for tech giants like Google or Microsoft rather than in warehousing or transportation. Meanwhile, executives controlling IT budgets typically prioritize marketing, sales, or finance tools they are more familiar with, leaving supply chain operations underfunded.

Over the past six years, however, supply chain visibility has reached higher-level executives, but cultural change across the warehouse workforce remains slow.

Moore's background includes scaling SparkCognition, an Austin-based AI company that reached unicorn status, and working in machine learning since 2012. The idea for AutoScheduler emerged from conversations with his father about digitizing the decision-making layer that separates high-performing warehouse sites from underperformers. At Procter & Gamble, Moore observed that facilities with identical technology, processes, and hiring practices still had divergent outputs due to unaligned floor-level managers.

For warehouses with some automation but no orchestration layer, Moore advises documenting decision-making processes before implementing AI tooling. He emphasizes that most companies talk about data with AI but rarely document the human decision-making processes applied to that data. Once processes are mapped, operators can identify low-hanging fruit decisions to automate incrementally. Moore predicts significant mindset shifts across the industry in the next 12 months.