The Issues of Warehouse Order Picking and Assembly

The Fundamental Issues of Order Assembly

Order assembly, a pivotal task in warehouses and distribution centres, is the linchpin of the supply chain, ensuring the completion of customer orders. It underpins both retail and wholesale distribution, facilitating the movement of goods from storage to shipment. The role of order assembly in the supply chain is not just paramount; it is the heartbeat of the supply chain that keeps the entire process alive and functioning, ensuring that customers receive the correct products at the proper location, at the correct cost, and at the correct time.

The actual process of order assembly involves retrieving items from designated warehouse storage locations to meet specific order requirements. These items may be placed in containers, loaded onto carts, or stacked on pallets or trailers, depending on the warehouse setup. The nature of the facility and its logistics orientation dictate whether the process is referred to as order picking or order assembly. Order picking typically refers to the process of selecting items from storage to fulfil a specific order. In contrast, order assembly is the broader process of gathering all the necessary items and preparing them for shipment. The terminology varies based on whether reverse logistics or segmented logistics functions are involved in the operation.

Order assembly has grown into one of the most significant cost centres in logistics operations. In many warehouses, it represents between 25% and 55% of total operating expenses. It is deeply embedded in all layers of the supply chain, affecting distributors, retailers, freight forwarders, and carriers alike. As the expectations for fast and accurate delivery rise, the pressure on order assembly processes to be both cost-effective and efficient has intensified substantially.

Current Challenges in Order Fulfilment

The early 21st century ushered in a wave of changes that continue to reshape order fulfilment. The rise of third-party and fourth-party logistics providers, entities that manage and execute logistics operations on behalf of their clients, has led to an increase in outsourced operations. This trend has driven demand for scalable and responsive logistics services, as these providers often possess the necessary resources and expertise to manage complex supply chain operations. Concurrently, the push toward just-in-time inventory systems has shortened lead times, requiring faster turnaround in order preparation and increased agility in handling fluctuating stock levels.

Globalisation has introduced more complexity into demand planning. As businesses expanded their reach, variability in demand across regions increased, creating instability in supply chain flows. At the same time, consumer behaviour has undergone a dramatic shift. Customers now order a broader variety of products in smaller quantities and with higher frequency. This diversification makes demand less predictable, forcing warehouses to adapt and accommodate a broader range of stock-keeping units (SKUs) and shorter order cycles.

Adding to the complexity is the meteoric growth of e-commerce. Customers no longer rely solely on brick-and-mortar retail stores; they expect fast and seamless online ordering experiences. This transformation has intensified the pressure on logistics teams to manage larger volumes of smaller, time-sensitive shipments. The need to maintain high service levels while controlling costs has turned order fulfilment into one of the most challenging aspects of modern logistics strategy.

Importance of Efficiency in Warehousing

Warehouse efficiency is not just a factor, but the primary determinant of overall profitability in logistics. Labour, the most significant cost component in most warehouse operations, can significantly impact total sales. As companies shift shipping costs to consumers and compete in price-sensitive markets, the ability to minimise internal handling costs becomes crucial for sustaining a competitive advantage and improving margins.

Profitability in warehousing hinges on maximising output while minimising input, producing more value per unit of labour and space used. Efficiency metrics inform warehouse design, pricing strategies for logistics providers, and decisions around the reuse of warehouse layouts. These metrics are also fundamental in assessing whether operational performance aligns with planned objectives, guiding both strategic and tactical improvements across warehouse networks.

Beyond financial considerations, efficient warehousing also supports broader logistics goals. Warehouses serve as buffer zones that accommodate supply and demand mismatches, store seasonal or surplus products, and facilitate order accuracy. The picking process, transferring goods from storage to shipping, is often the most labour-intensive function and represents a significant portion of operating costs. Improving efficiency here directly reduces total warehouse expenditure while enhancing speed and service quality.

Technological Innovations in Warehousing

Technology is not just revolutionising, but it is also reshaping warehouse operations, particularly in order picking and assembly. Innovations such as automated data capture systems, radio frequency identification (RFID), and computerised management systems are accelerating process flows and enhancing accuracy. The integration of machine vision systems, laser scanners, and infrared technologies into the modern warehouse toolkit is not just changing; it is revolutionising the way orders are processed, making it more efficient and error-free.

Despite the availability of new tools, order picking remains the costliest and error-prone activity in warehouses. Studies show that while many warehouse processes have undergone technological advances, order picking has remained essentially unchanged for decades. However, the rising demand for speed and accuracy has triggered new investments in automation, prompting a shift in how items are picked, packed, and shipped, particularly in high-volume e-commerce environments.

Advancements in packaging and storage are also affecting warehouse operations. Palletised, prepackaged items are becoming more common, making them easier to automate. At the same time, standard storage formats such as shelves, cartons, and containers have remained stable. While the infrastructure has not undergone drastic changes, the tools used within it have evolved rapidly, opening the door to greater throughput and lower handling costs across warehouse facilities.

Warehouse Automation and Robotics

Warehouses are transitioning from manual labour-heavy operations to sophisticated, automated environments. Robotics now play a central role in moving goods, with AI-driven systems capable of adapting to complex workflows. These robots can sort, transport, and even collaborate with human workers, making them increasingly viable for a wide range of logistical needs, from small-scale picking to large-scale freight movement.

Implementing automation depends on product types, volume, and operational scope. Light automation in picking and packing has become more common, particularly for repetitive tasks. Palletised and floor-stocked item automation, long present in industrial settings, has gained momentum in consumer-focused warehouses. The COVID-19 pandemic further accelerated automation adoption by highlighting vulnerabilities in labour-dependent systems and boosting demand for direct-to-home delivery models. The need for social distancing and the increased demand for e-commerce during the pandemic have underscored the importance of automation in maintaining efficient and safe warehouse operations.

AI is also expanding its footprint in warehouse decision-making. From warehouse management system (WMS) integration to real-time inventory tracking and port control systems, AI technologies now help streamline complex processes. For instance, AI can optimise inventory levels based on demand forecasts, thereby reducing the risk of stockouts or overstocking situations. Sensor tech, including cameras and laser range finders, enhances accuracy in sorting. Drones and uncrewed vehicles are also gaining popularity, while micro-fulfilment centres equipped with agile robotics are emerging as the future of retail logistics.

Warehouse Management Systems

Order picking and assembly require precise planning to keep logistics costs down while meeting service expectations. These functions have become more critical due to trends such as smaller order sizes, multichannel distribution, faster delivery demands, and the financial impact of fulfilment errors. As logistics networks become increasingly complex, structured approaches to managing warehouse operations are essential to staying competitive.

Warehouse Management Systems have become a key solution in managing modern warehouses. They allow for real-time oversight of inventory, space utilisation, and workflow processes. WMS platforms support a wide range of operations, from storage optimisation to order verification and shipment tracking. This visibility enhances control and efficiency while minimising errors and resource waste.

In the past, supply chains focused solely on cost minimisation. However, today's WMS tools enable businesses to maintain high service levels even in volatile environments. They adapt to changing market needs and help scale operations without compromising speed or accuracy. By connecting data flows across the supply chain, WMS systems serve as the backbone of efficient, responsive, and future-ready warehouse operations.

Data Analytics in Order Assembly Management

Modern warehouse operations rely heavily on data-driven decision-making. Analysing trends and patterns across large datasets enables more accurate and strategic choices within the order assembly (AS) domain. Key decisions, such as which products to stock, how to package them, where to locate them within the facility, and how to organise picking zones, are increasingly shaped by analytics. Forecasting plays a pivotal role, especially in managing demand for individual items, grouped SKUs, or customer-specific order behaviours.

Accurate forecasting is complicated by several variables, including seasonal fluctuations, promotional campaigns not captured in historical data, and varying forecast horizons. Special orders, product returns, and backorders introduce further unpredictability. While historical data remains essential, it often tells only what has happened, not what is likely to occur. With the rise of the Internet of Things (IoT), real-time data streams are becoming more accessible, offering new possibilities for predictive modelling and real-time decision-making in asset-intensive environments.

Machine learning and artificial intelligence are transforming this space by identifying complex relationships within fragmented datasets. These advanced tools surpass traditional time-series forecasting by providing adaptive, non-linear models that continually improve over time. Significantly, the AS decision-making framework is evolving to reflect broader priorities, striking a balance between cost efficiency, environmental sustainability, and social responsibility. The rise of omnichannel retail has further heightened the need for adaptable, analytics-driven solutions in item placement and process optimisation.

Future Trends in Warehouse Operations

Both rapid short-term shifts and slow-burning long-term transformations increasingly influence warehouse operations. The continued growth of e-commerce, especially the surge in directed demand, has led to an explosion in the volume and frequency of goods processed through distribution centres. Businesses are now moving from centralised mega-warehouses to decentralised, regionally dispersed hubs closer to end consumers. This shift brings new pressure on order picking, inventory management, and warehouse space planning.

The operational impact is substantial: warehouses now require more floor space, greater picking speed, and tighter integration with retail store networks. Inventory decentralisation is becoming more common, resulting in higher local stock levels and rising inventory-related costs. In response, researchers and practitioners are exploring new strategies in inventory and warehouse management to curb these costs while maintaining service quality. Tools such as automated retrieval systems and enhanced data integration are part of this evolving toolkit.

Despite the promises of automation, these technologies are not without downsides. Some systems lead to overly centralised inventory, creating risks in flexibility and responsiveness. Moreover, labour shortages and workforce-related issues remain critical challenges, particularly when transitioning to more tech-heavy operations. Warehouse research is becoming increasingly interdisciplinary, drawing on fields such as operations research, industrial engineering, and systems management to solve complex and evolving logistical problems.

Integration of Artificial Intelligence

Artificial Intelligence (AI) has become a cornerstone technology in modern logistics. It mimics human decision-making using advanced techniques such as neural networks, statistical learning, and optimisation algorithms. AI applications range from pattern recognition in demand forecasting to automated decision-making in warehouse routing and stock replenishment. Machine Learning, a subset of AI, enables systems to learn from their environment and refine actions over time, making it invaluable in dynamic warehouse settings.

AI is merging with robotics to create AI-driven robotic systems designed specifically for warehouse tasks. These systems now power automated guided vehicles (AGVs), robotic pickers, and innovative conveyors capable of executing complex actions with high precision. In warehousing, these technologies handle repetitive, labour-intensive tasks such as picking, sorting, packing, and palletising. Research and commercialisation in this area have accelerated rapidly, especially as companies strive to improve efficiency without sacrificing accuracy.

The synergy between AI, robotics, and warehouse software is revolutionising logistics. These systems not only reduce human error and operational costs but also enhance adaptability and scalability. AI-driven robotics allows for flexible warehouse layouts and more efficient use of space. As e-commerce and omnichannel fulfilment grow more demanding, AI technologies are playing an increasingly strategic role in enabling warehouses to respond faster, with fewer resources and greater reliability.

Global Best Practices in Warehousing

Global and regional companies recognise the strategic role of warehouses in their supply chains and constantly seek ways to improve efficiency and reduce costs. A major strategic decision involves whether to maintain in-house warehousing or outsource operations to third-party logistics (3PL) providers. Best practices in order picking and assembly operations aim to ensure accurate execution of all activities, from receiving to dispatch, supporting seamless distribution and final product assembly.

The adoption of automated guided vehicles (AGVs) represents one such best practice, significantly enhancing operational performance. However, their implementation can also introduce complexity, especially in facilities handling vast SKU ranges. Automation investments must be justified by throughput volumes and managed carefully to avoid operational bottlenecks. Companies often adopt a phased approach to automation, integrating technologies that align with existing infrastructure and product profiles.

Modern best practices also involve strategic postponement of final assembly, intelligent inventory placement, and dynamic control systems that reduce overall picking costs. By implementing distributed inventory models and leveraging real-time data analytics, organisations can improve service levels while controlling logistics expenditures. With ongoing investment in technology and process innovation, leading companies treat warehousing not just as a cost centre, but as a competitive asset driving customer satisfaction and business growth.

Impact of E-commerce on Warehousing

E-commerce has profoundly reshaped warehouse operations. Once mere storage spaces, warehouses have become dynamic fulfilment hubs that support fast, flexible, and highly customised service. Today’s consumer expects rapid, often same-day delivery, pressuring warehouses to operate as high-speed, inventory-accurate retail enablers. The traditional supply-and-wait model has given way to proactive, real-time inventory movement shaped by digital purchasing behaviours.

In traditional retail, storage strategies were governed by seasonal demand cycles and space constraints. Retailers are ordered to cover short periods between replenishment, minimising stock levels. E-commerce has upended this logic. Warehouses now function as virtual shelves, with demand pulled directly from online transactions. This requires agile inventory turnover, faster picking processes, and precise coordination between warehousing and production.

Customer data tools such as segmentation and predictive analytics enable businesses to tailor inventory positioning to the most profitable consumer segments. Coordination with production scheduling is crucial to prevent overstocking, which increases storage costs, or stockouts, which erode customer trust. As warehousing becomes increasingly central to the retail model, its strategic importance and the need for operational precision will continue to grow.

Changing Consumer Expectations

Consumer expectations have evolved dramatically. They now demand speed, convenience, and customisation in every transaction. Same-day and next-day delivery are no longer differentiators; they are baseline requirements. These shifts have compelled distribution centres to reassess their operations, particularly in terms of order picking, storage configuration, and delivery logistics, to manage the increased frequency and complexity of retail shipments.

For items traditionally purchased in-store, the emphasis has moved to rapid delivery and zero-cost shipping. Online retailers and brick-and-mortar stores alike are leveraging hybrid distribution networks to meet these demands. Physical stores now function as mini-distribution hubs, supporting both in-store shopping and e-commerce fulfilment. This integration is reshaping the logistics behind product availability and consumer satisfaction.

Routing and delivery systems are continually refined. Fleet management software, real-time tracking, and intelligent routing algorithms are being deployed to handle the intense pressure of last-mile logistics. Meanwhile, picking strategies are evolving, with increased automation and software-assisted decision-making transforming how orders are prioritised and packed. Retailers that can adapt quickly to shifting consumer demands will maintain a competitive edge in an increasingly logistics-driven market.

Last-Mile Delivery Challenges

Last-mile delivery is the most complex and costly segment of the logistics chain. It accounts for 15–25% of the total product cost and is heavily influenced by the rise of e-commerce and online food services. With online transactions making up a significant portion of total retail and restaurant sales, the efficiency of last-mile logistics has become a crucial determinant of customer satisfaction and profitability.

Despite advancements, significant challenges persist. Demand fluctuates unpredictably by time, day, or season. Industries like grocery and food delivery face particularly volatile last-mile pressures. Fulfilment responsibilities are increasingly shared among delivery companies, gig-economy drivers, and even customers themselves. Alternative delivery methods, including crowd-shipping and drones, are gaining popularity, although their effectiveness and regulatory acceptance vary significantly.

Speed remains the most critical service factor; however, urban congestion, infrastructure limitations, and environmental policies often slow deliveries. Regulatory measures, such as restrictions on the use of delivery drones or mandates for electric vehicles, add layers of complexity to the system. As last-mile delivery becomes more regulated and consumer expectations continue to rise, companies must innovate to ensure sustainable, efficient, and timely service.

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