Analyst Insight: Artificial intelligence and machine learning have emerged as transformative technologies for the supply chain, including in the warehouse. AI-powered systems enhance warehouse operations and improve order fulfillment. AI tools can sit on top of a warehouse management system to dynamically orchestrate all activities within the warehouse, and make the WMS more responsive to demand changes.  

Every shipper running a supply chain is concerned with order fulfillment. Will they be able to get the right product to the correct location at the right time? Such a concern isn’t unfounded. Even with the best planning team in the world, the execution phase is where the rubber meets the road.

Most warehouses use a WMS to automate warehouse operations. Those systems are good at managing inventory, age, and space; managing a work queue; zoning a distribution center; and cataloging and visualizing all receipt, shipment and order information. A WMS is terrible at:

• Understanding labor, space, and task constraints to optimize entire site operations;
• Intelligently allocating inventory and managing cuts;
• Creating chained tasks that factor in proximity-to-task for all associates;
• Simultaneously managing receipts and shipments to ensure that the right inventory movements are optimized, and
• Transferring inventory across multi-building campuses.

Today, there are tools that sit on top of a WMS to provide intelligent warehouse orchestration, as well as a holistic view of inventory, receipts, labor, equipment and customer shipments. The objective is clear: streamline the inventory flow through the facility and ensure timely product dispatch. What sets these tools apart is their ability to employ mathematical optimization based on AI. They can process many constraints, from equipment capacities and shift schedules to space availability.

To implement AI in a warehouse, companies typically need to invest in AI-powered software and hardware, integrate them with the existing WMS, and ensure data collection and connectivity to support AI algorithms. Continuous monitoring, training and optimization of AI models are also essential for adapting to changing conditions and maximizing the benefits of AI in warehouse operations.

By analyzing and processing vast amounts of data in real time, these tools can forecast potential bottlenecks, allowing supply chain managers to proactively address them before they escalate. Furthermore, they provide a view of the entire supply chain, ensuring that every stakeholder, from the warehouse floor worker to the top-tier executive, has a clear understanding of how execution will unfold.

Shippers can use the data to understand the future state of the warehouse. They can draw on digital twin technology to engage in “what-if” scenario planning. A digital twin is a mathematical model of the warehouse that analyzes all future-facing activities to predict what’s likely to happen. An effective digital twin will account for labor, shipments, inventory availability, tasking, space and other resources.

Constraint-based mathematics can be combined with AI to build optimal plans, working backward from loading and work in parallel with other tasks. When paired with a digital twin, constraint-based optimization technology can prescribe a sequence of events, create a feasible schedule, and minimize touches and labor. The result is less direct labor requirements, higher customer fulfillment rates and greater operational efficiency. 

Outlook: Together, these tools that bolt on to traditional software (including ERP, TMS and WMS) are game-changers in order fulfillment. They offer a fresh perspective and innovative approach without necessitating a complete overhaul of existing systems. Supply chains can optimize existing infrastructure as they boost efficiency and ensure that customers receive their orders as promised. This is where innovation meets practicality to deliver unparalleled results. 

Resource Link:

www.autoscheduler.ai