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FLEX. Logistics
We provide logistics services to online retailers in Europe: Amazon FBA prep, processing FBA removal orders, forwarding to Fulfillment Centers - both FBA and Vendor shipments.
Introduction
Order picking, the process of retrieving products from storage to satisfy a customer order, stands as the most labour-intensive and costly activity within the modern warehouse, often consuming over half of a facility's total operational budget. In the relentless pursuit of speed, accuracy, and profitability demanded by the e-commerce era, the identification and systematic elimination of inefficiencies in this core function are paramount. Logistics managers must transition from managing volume to optimising workflow, leveraging process design and technology to mitigate the friction points inherent in traditional picking methodologies. This comprehensive analysis identifies nine of the most pervasive picking inefficiencies that undermine profitability and details practical, data-driven strategies for their resolution.
1. Excessive Travel Time Due to Suboptimal Layout and Routing
The most significant contributor to picking inefficiency in nearly every conventional warehouse is excessive travel time, an inefficiency directly tied to suboptimal facility layout and rudimentary routing methodologies. In many traditional setups, workers traverse the floor using basic, straight-line, or serpentine routes determined by proximity alone, failing to account for the geometric complexities of the storage architecture or the statistical frequency of item demand. This results in pickers spending a disproportionate amount of their working hours simply walking between locations rather than performing value-added activities.
The solution requires a two-pronged approach: strategic layout redesign and the implementation of dynamic, intelligent routing. Layout optimisation should adhere to the principle of "golden zoning," wherein the fastest-moving Stock Keeping Units (SKUs)—those with the highest demand and pick frequency—are strategically positioned closest to the shipping or packing stations. This reduces the distance travelled for the majority of the work. For example, in a large distribution centre, high-velocity items should occupy the lowest rack levels near the conveyor belts, while slow-moving, or "dead-stock" items are relegated to upper levels or the periphery of the facility. Once the layout is optimized, intelligent routing systems, often integrated within modern Warehouse Management Systems (WMS) or dedicated routing software, become essential. These systems use sophisticated algorithms, such as the S-shape or largest gap heuristics, to calculate the absolute shortest path for a multi-item picklist. These systems do not simply connect points in sequence; they dynamically generate a route that ensures the picker passes an aisle only once and minimises turns and retracing steps, achieving path reductions often exceeding 20 percent compared to simple routing methods.
2. Manual Paper-Based Processes and Data Entry Errors
Reliance on manual, paper-based picking processes represents a critical inefficiency characterised by slow execution, high susceptibility to human error, and a complete lack of real-time visibility. Workers must constantly pause to locate, read, and mark pick list items, diverting cognitive resources from the primary task of retrieval. Furthermore, the necessary post-picking data entry—transferring paper records into the WMS—introduces lag and a high probability of transcription mistakes.
The fix involves a complete transition to digital, hands-free picking technologies. The most advanced and efficient solution is the implementation of Pick-by-Vision systems, utilising Augmented Reality (AR) smart glasses. These devices display pick instructions, item locations, and quantities directly in the worker’s field of vision, eliminating the need for paper lists or handheld scanners. The system communicates directly with the WMS, providing instant confirmation upon picking and logging the action immediately. Alternatively, Pick-to-Voice (voice-directed picking) systems offer similar hands-free benefits, guiding the picker through the warehouse using auditory commands and requiring verbal confirmation of the task completion. By eliminating the time spent reading, writing, and manually entering data, these technologies dramatically increase picking speed and accuracy. For instance, a pharmaceutical distributor adopting a Pick-to-Voice system can ensure that compliance and lot tracking data are captured verbally and validated instantly, preventing manual transcription errors that could be costly and dangerous.
3. Inefficient Item Search and Verification Due to Poor Slotting
A significant amount of non-value-added time is wasted on inefficient item search and verification. This inefficiency is often a direct result of poor or static slotting—the decision of where to store a particular SKU—or a lack of clear visual identification at the bin level. When multiple SKUs with similar appearances are stored in close proximity, or when the WMS location data is imprecise, the picker must spend valuable seconds or even minutes locating and triple-checking the correct product.
Addressing this requires a commitment to dynamic slotting and enhanced location identification. Slotting should not be a one-time exercise; it must be a continuous process driven by analytics. The system should identify items frequently picked together—a process known as affinity slotting—and place them in adjacent locations to minimise travel time. Crucially, the system must also track dimensional data to ensure that items are stored in the smallest feasible bin location to prevent product mix-ups. Furthermore, the physical environment must support verification. Clear visual aids, such as large, high-contrast bin labels and photographs of the product at the location, are essential. Advanced systems use Pick-to-Light technology, where lights on the shelving illuminate the exact bin from which an item must be picked, instantly eliminating search time and providing an immediate visual confirmation of the location, slashing verification errors.
4. High Congestion and Queuing at Picking Zones or Choke Points
When multiple workers, or multiple pieces of automation equipment, attempt to access the same high-velocity storage locations or pass through narrow transfer aisles simultaneously, high congestion and queuing occur. This inefficiency is particularly prevalent during peak demand periods or when using simplistic zone-based picking strategies where one zone holds an overwhelming number of fast-moving items. The time spent waiting is pure waste, translating directly into reduced throughput and delayed order completion.
The strategic solution involves load balancing through flexible zoning and technology implementation. Firstly, a shift from fixed, rigid zones to flexible, wave-based, or dynamic zoning is necessary, using real-time demand data to adjust the boundaries and assigned pickers to specific areas, ensuring no single zone becomes an impenetrable bottleneck. Secondly, the facility design should incorporate wider main thoroughfares and staging areas to accommodate peak traffic flow. Thirdly, high-density storage zones experiencing extreme congestion should be converted to Goods-to-Person (GTP) automation, which removes the picker from the travel process entirely. For example, implementing automated cube storage or autonomous mobile robots (AMRs) to bring the most frequently needed items directly to a stationary worker eliminates congestion in the aisle. By isolating the human element from the high-traffic storage grid, queuing is effectively eliminated, allowing workers to focus exclusively on picking.
5. Inaccurate Inventory Levels Leading to "No-Picks" and Rework
A breakdown in inventory accuracy, where the WMS indicates an item is available at a location when it is, in fact, not (a "phantom inventory" or "no-pick" scenario), is a hidden yet profoundly disruptive inefficiency. The picker wastes time travelling to the location, searching for the non-existent item, manually reporting the discrepancy, and the operation incurs rework time for a supervisor to conduct a count verification and subsequent rescheduling of the order.
The definitive solution lies in implementing cycle counting driven by real-time data and integrated error reporting. Moving away from periodic, disruptive physical inventory counts, continuous cycle counting targets specific locations based on transactional history and known risk factors. For example, a WMS should automatically generate a cycle count task for a bin whenever a "no-pick" is reported, or if a discrepancy is detected between the expected inventory and the pick-system confirmation (e.g., the Pick-to-Light system registers fewer confirmed picks than the inventory record indicates). Furthermore, integrating the inventory validation directly into the picking process, such as by using handheld scanners or AR vision systems to scan the bin location before and after a pick, ensures that any inventory anomalies are captured and flagged before the picker walks away. This proactive error capture significantly reduces the number of failed pick attempts and guarantees the reliability of the system's stock availability information.
6. Poor Utilisation of Picking Capacity Through Suboptimal Batching
In environments where single-order picking is performed, workers may travel the entire length of the warehouse for just one or two items. Conversely, if batching is too large, the carts become unwieldy, or the complexity increases, slowing the worker down. Suboptimal batching, the strategy of grouping multiple customer orders into a single pick run, represents a core inefficiency when not executed based on advanced analytics.
Optimisation requires implementing intelligent wave and batching strategies that leverage advanced WMS algorithms. Instead of simple batching by volume, the system should consider factors like customer priority, required shipping time, item commonality across orders, and the spatial clustering of items. Wave picking groups orders for simultaneous release to the floor, often to meet specific carrier cut-off times. Batch picking involves retrieving all identical items for a group of orders in a single pass before sorting them at a dedicated station. The most sophisticated technique is Cluster Picking, where a single picker uses a multi-tote cart to pick items for several small orders simultaneously, placing the correct quantity of each item directly into the corresponding customer tote. This method dramatically compresses travel time by turning multiple discrete pick trips into a single, comprehensive circuit, often multiplying the lines picked per hour without increasing the walking distance.
7. Excessive Product Handling and Manual Repackaging at the End of the Line
Inefficiencies do not stop once the item is retrieved from the shelf; they often manifest as excessive product handling and manual repackaging at the packing station. If the picking process results in a heterogeneous mix of items in a single large tote, the packer must spend time sorting, verifying, and then finding the correct shipping container and dunnage. This secondary handling is often a source of bottleneck.
The correction lies in integrating the picking process with the packaging requirements. This is achieved through the use of Tote-Specific Picking and Dimensioning Technology. For high-volume e-commerce operations, the WMS should instruct the picker not only what to pick but into which specific, pre-sized shipping container the item should be placed. By using dimensioning systems integrated with the WMS, the system can calculate the optimal box size for the order before the pick starts. The picker can then use a multi-tote cart loaded with the required box sizes (or specific customer totes). This strategy, known as Pick-and-Pack, eliminates the need for a separate verification and repacking step, as the picker effectively performs the packing function during the retrieval process. The carton arrives at the shipping dock ready for final sealing and labelling, compressing the end-of-line cycle time and significantly reducing the labour intensity of the packaging stage.
8. Lack of Performance Transparency and Individual Accountability
In many warehouses, performance tracking is based on aggregate metrics (e.g., total orders shipped) or general time standards, leading to a lack of performance transparency and individual accountability. Workers may not receive timely feedback on their pick rates, accuracy scores, or the inefficiencies they are personally contributing to, hindering continuous improvement and preventing the identification of workers who may require targeted retraining or process support.
The resolution requires the implementation of a granular, real-time performance management system directly linked to the digital picking technology. Systems like Pick-to-Voice or Pick-by-Vision track every worker action: time spent traveling, time spent picking, time between picks, and error rates. This data should be visualised on a real-time dashboard accessible to supervisors and, judiciously, to the workers themselves. The focus should be on actionable metrics, such as "lines per hour" and "accuracy rate," which are compared against engineered standards. For example, if the system detects that a worker's time spent "searching" has increased dramatically in a particular zone, a supervisor can immediately intervene, identify the cause (e.g., new, unfamiliar product), and provide targeted coaching. This data-driven feedback loop fosters a culture of accountability and continuous improvement, allowing managers to diagnose and correct human-level inefficiencies before they significantly impact overall throughput.
9. Inadequate Management of Seasonal or Promotional Volume Peaks
Warehouse picking is subject to massive fluctuations in demand, driven by seasonal peaks (e.g., holidays) or major promotional events. Inadequate management of these volume peaks often results in severe inefficiency, characterised by expensive temporary labour that is inefficiently trained, overwhelming congestion, and a collapse of service level agreement (SLA) compliance. The failure lies in treating peaks as temporary crises rather than predictable, manageable events.
The fix involves implementing scalable, cross-training, and flexible automation strategies. Firstly, the core workforce should be cross-trained in multiple picking methodologies and zones, allowing managers to dynamically surge labour into the most pressured areas as needed. Secondly, instead of relying solely on complex, permanent infrastructure, companies should invest in flexible, scalable automation, such as fleets of Autonomous Mobile Robots (AMRs). An AMR fleet can be rapidly scaled up by simply leasing or deploying more units during a peak period, providing immediate, highly efficient support for Goods-to-Person tasks without requiring new human staff to be trained on complex, fixed infrastructure. Furthermore, the WMS must be configured with a peak demand mode, prioritising the release of smaller, higher-priority orders (which are faster to pick) during critical windows to maintain a steady, manageable flow and avoid overwhelming the packing stations, ensuring that capacity is maximally utilised when it matters most.
