Order picking is a big part of the fulfillment process, yet it's often riddled with inefficiencies and errors that cost logistics providers both time and money. These problems can be difficult to manage because effective warehouse operations depend on collaboration between the warehouse staff and the Warehouse Management System (WMS). If either underperforms, it can disrupt the fulfillment process, leading to inefficiencies and reduced productivity.
An inefficient or inaccurate picking process often indicates deeper underlying issues, such as outdated picking logic or method, poor inventory management, or inefficient use of labor. Addressing these challenges not only improves daily operations but also provides a competitive advantage, helping your warehouse stand out in an increasingly demanding industry.
Warehouse workers select items from storage without the aid of automation or optimization tools. Inefficient especially when handling large order volumes or multi-item orders scattered across the warehouse.
Impact: When pickers manually select their routes, decisions based on intuition often result in 30% longer distances than necessary. This inefficiency varies based on the number of items a picker needs to retrieve. The gap between manual and optimized routes widens as the order size grows, leading to higher labor costs and delayed shipments.
Expertise-based picking relies on workers’ experience and memory to locate items in the warehouse. While this method may work for long-term employees familiar with the layout, it becomes problematic when training new or temporary workers. This approach lacks structure, consistency, and scalability.
Impact: longer onboarding times for new or temporary staff and a higher rate of errors during order fulfillment. When seasoned workers are unavailable, temporary staff may struggle, causing delays, mispicks, and incorrect orders, which lowers productivity and customer satisfaction.
Adjusting the picking method or logic can be necessary based on the specific product types and order profiles being processed. For instance, certain products may benefit from zone picking, where pickers are responsible for designated areas of the warehouse. In contrast, cluster picking can be more efficient for orders involving multiple items, allowing pickers to retrieve items for several orders in one trip. Learn about different order picking methods.
Impact: Slows down the entire fulfillment process. A one-size-fits-all approach often results in increased travel time, higher labor costs, and slower fulfillment rates. This is especially true for e-commerce businesses, which frequently deal with a wide variety of order profiles, ranging from single-item orders to bulk shipments.
Misplaced, inaccurately tracked, or untracked items, making it difficult for pickers to locate products. Items may be stored in the wrong locations or not recorded properly, creating confusion during the picking process. This can also lead to partially picked orders, where items in an order cannot be fulfilled due to stock shortages or disorganization. Pickers may only be able to fulfill part of an order while waiting for the rest of the stock to be located or replenished.
Impact: Delays in order fulfillment and incorrect shipments. Pickers waste time searching for misplaced items, increasing labor costs and lengthening lead times. This causes orders to be shipped partially, resulting in higher shipping costs as multiple shipments may be needed to complete a single order. Partial fulfillment also leads to customer dissatisfaction, as customers receive their items in multiple deliveries or experience longer wait times, harming the company’s reputation and leading to potential loss of repeat business.
All orders are treated equally, regardless of urgency, deadlines, or customer requirements. This means high-priority or time-sensitive orders are processed alongside regular orders.
Impact: urgent orders may be delayed as they compete with less critical ones. This can lead to missed shipping deadlines, resulting in customer dissatisfaction, canceled orders, and loss of business. It can be particularly damaging during peak seasons or when fulfilling B2B orders that require strict adherence to timelines.
Each product or SKU is permanently assigned to a specific spot, regardless of changes in demand or warehouse layout needs. This method may help keep the warehouse organized initially, but it becomes inefficient over time as inventory levels and demand fluctuate. High-demand items may be stored far from packing stations, while low-demand items take up prime space, limiting the warehouse's overall efficiency.
Impact: Fixed locations hinder flexibility and adaptability in the warehouse. Over time, as product demand shifts or new items are added, the layout remains static, resulting in longer travel times for pickers who have to move across the warehouse to pick frequently ordered items. Leads to increased labor costs, reduces picker productivity, and an inefficient use of valuable warehouse space. Moreover, the inability to adapt the storage locations to actual usage patterns leads to underutilization of key areas and overcrowding in others.
The root cause of many warehouse picking challenges lies in the warehouse management system (WMS) that oversees the picking operations. Systems like SAP EWM, Blue Yonder (formerly JDA), NetSuite, and others are the central element in warehouse & supply chain operation management designed to cover a broad range of functionalities and features to manage all aspects of warehouse operations.
However, they try to address so many different needs that they often fall short in providing specialized, intelligent, and optimized solutions for specific tasks within those operations. In picking operations basic business rules often govern the process. These rules typically focus on fundamental criteria and do not account for nuanced factors that could boost efficiency and minimize the walking distance covered by pickers.
As a result, there is often untapped potential for optimizing picking processes within even the top WMS providers. This gap can limit warehouse performance and lead to higher operational costs, prompting companies to seek external solutions beyond their current WMS capabilities.
Effectively addressing the challenges of modern warehouse picking begins with selecting the right technology. This could as simple as adopting a new order picking method, implementing a warehouse management system (WMS) that aligns with your operational needs, introducing automation like robotics to fully take over the picking process, or enhancing existing systems with additional software functionality.
In recent years, both optimization (software-driven solutions) and automation (robotics) have become increasingly important topics among logistics providers. These two approaches are playing an ever-larger role in the future of warehousing. However, the decision to implement one, the other, or a combination of both depends on the unique demands of the operation.
Each approach has its strengths and limitations. The right solution often involves balancing both, leveraging automation where speed and consistency are needed, while using optimization to streamline processes and reduce inefficiencies.
From a logistics provider’s perspective, building expertise in both automation and optimization is critical to delivering tailored, effective solutions. Providers need to answer key questions, such as: When should automation or optimization be implemented? and Which processes benefit most from each? By mastering these areas, logistics providers can develop customized strategies that apply the right mix of technology and process improvements.