Warehouse Management Systems: The Hidden Software Driving e-Commerce

      For numerous industries, executing a series of decisions in succession can be one of the most intricate yet crucial elements in ensuring that daily operations run smoothly. The e-commerce sector is no exception, as retailers are required to make dozens, if not hundreds, of decisions between the moment a sale is made and when the product is delivered. These decisions may encompass (but are not limited to): selecting which warehouse will fulfill the order, determining which shelf within that warehouse to retrieve the order from, choosing who will pick the order, deciding on the delivery sequence of that item with others in the delivery vehicle, and similar considerations.

      In essence, effectively managing an e-commerce business is fundamentally rooted in decision-making, a task that medium-to-large retailers cannot effectively manage at scale without the appropriate tools due to the vast number of choices involved. Fortunately, many of these retailers now possess the necessary tools, specifically warehouse management systems (WMS), which are software solutions that simplify decision-making processes, enabling businesses to accomplish more work in less time and with fewer errors.

      Over time, however, warehouse management systems have struggled to keep pace with increasing demands, necessitating new perspectives on the purpose and functionality of WMS. Today, this shift in perspective has led to the creation of a WMS designed to operate more intelligently and efficiently. Although this development is still evolving, its emergence signifies a noteworthy shift towards a potentially more beneficial direction for the software.

      **The Challenge of Intelligence**

      WMS was originally developed to solve a logistical issue: how workers would know how, when, and where to perform their tasks. In practice, this meant devising methods to inform workers of their next steps—a task that was previously quite difficult within expansive warehouses filled with inventory that needed to be moved quickly.

      WMS proved capable of addressing these and other operational questions effectively, allowing modern warehouses to achieve higher accuracy rates as workers transitioned more smoothly from one task to another. However, the ongoing expansion of online commerce has put pressure on warehouse management systems, which can only function so quickly.

      This growth has exposed a crucial flaw in WMS: while the system can implement plans efficiently, it cannot assess whether those plans have issues. In other words, WMS has become faster but has not necessarily advanced in intelligence, which may diminish its effectiveness during a time when the e-commerce sector is at its peak.

      Some of the emerging solutions addressing this intelligence gap have served primarily as temporary fixes rather than comprehensive solutions, masking the core problem without remedying it.

      For instance, many of today's WMS platforms leverage machine learning for forecasting and anomaly detection. Although this data may assist businesses in preventing some future issues, it is often less effective for immediate concerns. A dashboard may indicate a 12% drop in pick rates last week, but if it cannot explain the cause or offer guidance on rectifying the issue for the future, the information could arrive too late.

      **From Predictive to Prescriptive**

      The early difficulties in integrating AI tools like machine learning into WMS stemmed from those tools often being positioned outside the system. This arrangement meant that analysis was essentially lagging behind actual data, leading to outdated insights. Recent advancements in AI may have partially resolved this issue by embedding the analytics infrastructure within the WMS itself, enabling it to operate on live operational data.

      One company that is harnessing this technology is Deposco, an AI-driven supply chain platform designed to infuse intelligence throughout various stages of a WMS. Its supply chain intelligence suite tackles queries at the executive, inventory, labor, and shipping levels, all carried out within Deposco’s warehouse management software to enhance data connectivity.

      While this approach is not without its imperfections, it has the potential to transition AI from a solely predictive function into a more prescriptive capacity that offers informed recommendations based on real-time data. At Deposco, this AI is embodied by Felix, a team of AI agents that customers can activate within the platform.

      This system allows operators to continuously query these agents about current operations, keeping them informed with relevant data. According to Deposco, Felix can provide recommendations by leveraging transaction data that comparable operators have already been using. The agent can then adjust the system to potentially achieve a similar result, reducing the requirement for unnecessary operator involvement.

      In measurable terms, such automation could assist businesses in scaling operations by performing more in less time, effectively transforming otherwise squandered minutes into productive intervals where warehouse operators can better prepare for upcoming changes.

      **Leveraging Patterns**

      The strength of contemporary AI agents lies in their ability to make reasonably precise predictions based on extensive data sets from which they can discern clear patterns.

      Provided those patterns remain consistent, the agents analyzing them might offer suggestions that human analysts would take longer to identify and implement. However, it's important to recognize that AI can still misinterpret data or draw faulty assumptions. Thus, it remains crucial for even sophisticated WMS to implement human oversight to verify that automated suggestions are logical and applicable.

      Nonetheless, advancements in WMS AI should be viewed as a positive evolution within the