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**The Role of Artificial Intelligence in Warehouse Management Systems**

Warehouse management systems (WMS) are crucial for ensuring efficient fulfillment in warehouses and supply chains. These systems rely on various technologies to ensure that inventory is located in the right place. However, artificial intelligence (AI) has its limitations in solving the location problem for inventory. This article explores the role of different technologies in warehouse management and the challenges that AI faces in optimizing supply chains.

**The Warehouse Management System: A Core Solution**

The core system used in managing a warehouse is the warehouse management system (WMS). A WMS guides workers by providing instructions on what to do. For example, a picker may receive instructions such as “go to location XB312 and pick three cases!” The picker verifies the location by scanning the barcode and confirms the selected items by scanning each case. Automatic identification solutions, such as scan guns or Voice Systems, enable the WMS to ensure that the right product is selected from the right location.

A true WMS is a real-time system that can achieve high inventory accuracy of 99.9% or better. In contrast, paper-based systems typically have lower accuracy in the low 90s. Real-time AutoID allows the WMS to constantly monitor the fill levels of pick slots and identify slots that require replenishment. Additionally, WMS employs cycle counting to maintain accuracy. Workers may be prompted to count items in specific locations and update the system if there is any discrepancy.

Moreover, a WMS incorporates advanced functionality for item placement. Slotting logic determines the ideal placement of inventory in a warehouse. For unautomated warehouses, dynamic slotting minimizes travel time between pick slots, leading to increased productivity. High-demand goods are strategically located near shipping docks to expedite fulfillment. In some cases, it may be more effective to receive inventory on a loading dock and move it directly to a shipping dock for immediate shipment. Real-time transportation visibility solutions aid in coordinating such flow-through processes.

**The Role of Upstream Applications**

While a WMS is crucial for warehouse management, other applications also play a significant role in ensuring inventory is in the right place. Network design solutions determine the optimal placement of warehouses based on long-term demand projections, aiming to achieve the desired service levels at the lowest cost. Inventory optimization solutions utilize advanced mathematical algorithms to determine the appropriate quantity of inventory to be stored in different locations, maximizing service levels while minimizing inventory levels.

In an omnichannel world, companies may have various flow paths to deliver products to customers. A distributed order management system determines the originating location, whether a warehouse or a store, for each order. These applications work synergistically with the WMS to optimize inventory placement and order fulfillment.

**AI’s Limitations and the Need for Holistic Optimization**

Despite the hype surrounding AI, its potential to optimize supply chains is limited. The location problem in warehouse management requires multiple systems and technologies working together. Without AutoID technologies like scan guns, even the most advanced AI algorithms cannot effectively solve this problem. In other words, AI needs the right building blocks in place to deliver value.

Moreover, warehouse management is just one aspect of the broader supply chain. Supply chain planners are responsible for ensuring the right product is in the right place at the right time across the entire supply chain. Achieving this goal requires the integration of several other applications, such as demand planning, transportation management, and supplier collaboration.

AI may improve specific functions within the supply chain, but it does not holistically optimize the system. Each function can be individually efficient, but the overall efficiency of the system depends on the interplay between these functions. To achieve true optimization, a more holistic approach to supply chain software is needed. This approach should reduce the number of models used in planning and consider the interconnectedness of various supply chain functions.

However, achieving this level of optimization would require significant investment, innovation, experimentation, and time. While AI has the potential to transform supply chains, it is essential to recognize its limitations and the need for a comprehensive and integrated approach to fully harness its benefits.

In conclusion, warehouse management systems rely on various technologies to ensure efficient fulfillment. While AI has the potential to optimize supply chains, it faces limitations in solving the location problem for inventory. Achieving true optimization requires a holistic approach that integrates various supply chain functions. Until such an approach is developed, the full potential of AI in supply chain management will not be realized.

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