Next-Gen Fulfillment: Harnessing AI for Smarter Warehousing

AI acts as the brains behind complex operations, optimizing the flow of goods through the warehouse.

By Keith Moore

Every product that arrives at your front door after an online order has undergone a meticulous process within a warehouse. This process, known as order picking, is the heartbeat of fulfillment operations. Order picking involves selecting the items from storage that make up a customer’s order and is often very complex, given the sheer number of products housed inside a fulfillment center. Because of that complexity, order picking can be accomplished through various methods, such as cart picking—where workers traverse the warehouse with carts to collect items—or bulk picking, where large quantities of items are picked together and later sorted into individual orders at a location called a “put wall,” or something similar. These processes ensure that each item is precisely where it needs to be and ready for shipment.

The Multi-Stage Dance of Fulfillment Warehouses

Fulfillment warehouses function like a well-choreographed dance, with each process carefully planned to ensure efficiency and speed. To understand how they operate, let’s break down the key tasks that people perform in these warehouses:

1. Replenishment: This is the process of restocking shelves and storage areas with products. Imagine workers or robots moving items from storage areas to the shelves where pickers can easily access them.
2. Picking: This involves selecting the items needed to fulfill a customer’s order. Workers or robots move around the warehouse to collect these items, often using carts or specialized picking equipment. High-speed picking zones handle items that must be picked quickly, while bulkier items are managed in areas with equipment designed for heavier loads.
3. Packaging: Once the items are picked, they need to be packaged for shipping. This can involve putting items into boxes, adding packing materials, and sealing the packages. Packaging areas are equipped with everything needed to prepare items for safe delivery.
4. Loading: The final step is loading the packaged items onto trucks for delivery. This involves organizing the packages to maximize space utilization and needs to happen before the truck’s “cut-off time” when it leaves the facility (or else the order will ship late!).

Each of these stages generally occurs in different areas of the warehouse and is often executed by human or robotic labor with specialized tools. For example, companies like Locus Robotics and Dematic provide robotic solutions that make these operations smoother in tandem with the people working inside the facility. These robots handle everything from retrieving items from shelves to sorting them for packaging.

In complex operations, human workers and robots work together, each performing the tasks they’re best suited for. This creates a seamless flow from storing items to shipping them out, maximizing throughput for minimal cost. That said, orchestrating all of these activities is a huge challenge.

AI: The Mastermind of Warehouse Flow

One area in which AI has been revolutionary for these sites is by acting as the brains behind the operation, optimizing the flow of goods through the warehouse. Given all of the “flow constraints” that exist in operation (i.e., people can only pick so fast in a given pick zone, and multiple pick areas feed into the same packaging stations), AI anticipates moves and plans several steps ahead, balancing orders and labor to ensure maximum efficiency. AI analyzes vast amounts of data, such as order volumes, inventory levels, and worker availability, to balance orders and labor through a facility that keeps the warehouse running smoothly. By predicting demand and identifying potential bottlenecks, AI ensures that every resource is used effectively, whether a human worker or a robotic assistant.

This AI in place is not fictional – it’s running in innovative operations today. Advanced manufacturers and retailers rely on AI to balance workloads across the facility every few minutes, ensuring that no area is overburdened while others remain underutilized. This optimization maximizes throughput and enhances the overall efficiency of human & robotic labor in the warehouse, reducing delays and improving the speed at which orders are processed.

Smarter Batching for Optimal Efficiency

A second way in which AI has made its way into fulfillment is by optimizing order batching and transforming what was once a labor-intensive task into an exercise in precision and efficiency. The best way to think of order batching is as an incredibly organized shopping list, grouping items most logically and efficiently possible to reduce overall travel while still getting out the door in time. AI batches orders to carts or pick systems based on capacity & proximity, ensuring that travel time within the warehouse is minimized while meeting service levels. This means pickers aren’t wandering aimlessly or via serpentines; instead, they follow a well-planned route that saves time and reduces unnecessary steps. This is highly complex due to the large volume of orders and the risk that batching purely by proximity will reduce service levels.

In practice, if several orders contain items from the same warehouse area, AI will group these orders together so a picker can collect all needed items in one trip. This reduces the time spent walking back and forth across the warehouse, significantly speeding up the order fulfillment process. The result is a more efficient warehouse operation where orders are fulfilled faster and workers are less fatigued.

Dynamic Routing for Minimal Travel

A third way AI transforms fulfillment operations is its ability to dynamically route pickers, minimizing their travel paths and enhancing overall efficiency. This technology, rooted in operations research, has been used for years to optimize complex systems. Still, its integration with modern AI solutions like the aforementioned batch optimization & flow management has made it even more effective. By continuously analyzing and adjusting routes, AI ensures that pickers always take the most efficient path to collect items, reducing travel time and increasing productivity. Most robotic solutions offer this as a part of their solution today, but it can also be delivered for manually intensive pick sites.

Imagine a picker with a smart pick-to-light or voice pick device that guides them through the warehouse, providing real-time directions to their next pick location. This dynamic routing adjusts based on current warehouse conditions, such as traffic in aisles or changes in order priority. The technology helps reduce picker fatigue and increase the number of orders that can be processed within a given timeframe.

The Balanced Approach to AI in Fulfillment

AI presents incredible opportunities for enhancing warehouse operations, from optimizing order flow and batching to implementing dynamic routing. However, it’s crucial for fulfillment operations to thoroughly understand their unique processes, constraints, and goals before integrating AI solutions. The key is to balance using advanced technology with a clear understanding of how the warehouse operates. Blindly implementing AI without this understanding can often lead to more problems than solutions.

AI can be a transformative force in the world of warehouse fulfillment. By carefully planning and understanding the specific needs and constraints of your operation, you can harness the power of AI to streamline processes, reduce costs, and improve customer satisfaction. The future of warehouse efficiency is here, and with AI, it’s smarter, faster, and more effective than ever before.

Keith Moore is CEO of AutoScheduler.AI, a predictive warehouse optimization platform that integrates with an existing WMS. While flagship AutoPilot dynamically orchestrates optimal activity at a site level, the AutoPilot Central dashboard now enables unprecedented aggregated multi-site visibility across all sites. https://autoscheduler.ai/