AI and Data-Driven Warehouse Decision Making

Warehouses generate vast amounts of data every day, from fulfillment rates and inventory levels to labor efficiency and stock movement, but that raw data alone isn’t enough. The real value lies in how businesses use artificial intelligence (AI) and that data to optimize operations, reduce costs, and stay competitive.

Derek Morse, Senior Director of Operational Excellence at Barrett Distribution Centers, understands this transformation firsthand. With years of experience in supply chain logistics, Morse has seen warehouse operations shift from manual tracking to data-powered decision-making. He recently joined me on the “Warehouse Automation Matters” podcast to discuss the importance of data analytics in warehousing.

The Evolution of Analytics in Warehousing

Warehouses have long relied on key performance indicators (KPIs) to measure efficiency. Metrics such as dock-to-stock time, order fulfillment rates, and inventory accuracy provide critical insights into operational performance. But Morse emphasized that today’s analytics go much deeper, providing real-time insights that not only highlight problems but also prescribe solutions.

For Barrett Distribution Centers, one of the most impactful data-driven strategies involves tracking “skip picks.” This KPI, which measures instances where a picker bypasses a designated pick location, serves as an early warning system for inefficiencies. A high number of skip picks might indicate issues with inventory placement, equipment availability, or replenishment timing. By analyzing this data, Barrett has been able to fine-tune operations, reduce lag time, and ultimately cut costs.

Optimize Inventory Placement with Data

Slotting strategies, which is the science of placing inventory in the most efficient locations within a warehouse, play a critical role in productivity. Morse explains that analytics help warehouses identify optimal product placement by considering order frequency, demand trends, and even the physical layout of the facility. The goal? Ensuring that high-velocity items are stored in “golden zones,” where they can be accessed quickly with minimal movement.

When executed well, a data-driven slotting strategy reduces labor costs, enhances picking speed, and maximizes space utilization. And in an era where speed-to-fulfillment is a competitive advantage, having the right product in the right place at the right time is non-negotiable.

Overcome Data-Driven Warehousing Challenges

Despite the clear benefits, adopting a data-driven approach isn’t without its challenges. Morse identifies three major hurdles: data quality issues, resistance to change, and cost constraints. Poor data quality can lead to inaccurate insights, while resistance from employees can slow down adoption. Additionally, implementing advanced analytics tools requires investment in both technology and training.

For warehouse leaders looking to integrate analytics into their operations, Morse advises starting with high-impact areas. Outbound processes, for example, are often the most labor-intensive and can yield significant gains when optimized. Ensuring data accuracy, investing in scalable analytics tools, and fostering a data-driven culture within the organization are key steps toward long-term success.

The Future of Analytics in Warehousing

Looking ahead, Morse sees artificial intelligence (AI) and machine learning playing an even larger role in warehouse analytics. While still in the early stages of adoption, these technologies are making it possible to predict demand fluctuations, optimize workflows in real time, and enhance decision-making. However, Morse believes that the future of warehousing won’t be fully automated. Instead, it will be a collaboration between human expertise and AI-driven insights.

Automation and analytics will handle repetitive tasks and provide predictive recommendations, but human oversight will remain essential for strategic planning, ethical considerations, and creative problem-solving. The warehouses that strike the right balance will be the ones that thrive in an increasingly competitive landscape.

Make Data Work for Your Warehouse

For warehouse leaders ready to take a data-driven approach, Morse’s advice is simple: define the KPIs that matter most, ensure your data is clean and reliable, and adopt tools that can scale with your business. The path to a smarter, more efficient warehouse starts with understanding what you need to measure and having the right systems in place to act on those insights.

Want to hear more about how Barrett Distribution Centers is using analytics to drive efficiency? Listen to my full conversation with Derek Morse on the latest episode of Warehouse Automation Matters.