Intralogistics in E-Commerce: How Warehouses Handle Order Peaks, Returns, and Same-Day Delivery

E-commerce poses unique challenges for intralogistics. Order volumes can fluctuate significantly, the product range changes dynamically, and customers expect ever-shorter delivery times. At the same time, the demands for flexibility in order fulfillment are increasing. What runs smoothly in the warehouse today can quickly reach its limits if order volume doubles at short notice, during a major discount promotion, or when there is a high return rate.

It is no longer simply a matter of getting goods from the shelf into the shipping box as quickly as possible. What matters most is the interplay of all intralogistics processes: from goods receipt through putaway and order picking to consolidation, packaging, and preparation for shipment. Added to this are returns processing, the control of automated systems, and adherence to shipping cutoff times.

High-performance e-commerce intralogistics must therefore meet two seemingly conflicting requirements: It must operate cost-effectively in day-to-day business while also being able to respond to short-term changes. How well this is achieved depends not only on warehouse technology or the software used, but above all on how consistently the processes are planned, controlled, and coordinated.

In brick-and-mortar retail, the flow of goods can be planned relatively well. In e-commerce, the situation is significantly more dynamic. Orders arrive throughout the day, different shipping options create varying priorities, and marketing campaigns can lead to significant spikes in volume within just a few hours.

These fluctuations are particularly pronounced during seasonal sales peaks, such as the pre-Christmas shopping season, around Black Friday and Cyber Monday, or during limited-time discount promotions. Added to this are industry-specific characteristics: Fashion retailers, for example, must deal with high return rates and seasonal changes in product assortments, while in the electronics sector, certain product launches can generate high demand at short notice.

For warehouse organization, this means that capacities should not be planned solely on the basis of average daily volumes. Other critical factors include the distribution of orders throughout the day, the number of line items per order, the availability of individual items, and the required shipping methods.

A warehouse that processes an average of 5,000 orders per day does not necessarily have to be designed to handle 5,000 orders per day. If a large proportion of these orders is received within a short time window or must be processed shortly before the shipping cutoff, this creates different demands on staff, conveyor systems, order picking, and order preparation.

The real challenge lies in identifying these fluctuations as early as possible and allocating available resources accordingly. This includes proactive workforce planning, adjusted order release procedures, and a control system that addresses bottlenecks before they occur.

When order volume increases, the first instinct is often to provide additional capacity. More staff, additional picking stations, or extended operating hours can help absorb short-term spikes. However, they do not solve every problem.

In a complex warehouse, the individual process areas influence one another. For example, if too many orders are released at the same time, order picking may initially achieve high throughput. Subsequently, however, more containers or order lines may arrive at the consolidation area than can be processed there. Backlogs develop in the packing area, shipping stations become blocked, and orders that have been picked are left waiting for further processing.

The bottlenecks thus simply shift from one process step to the next.

Effective control must therefore take the entire process chain into account. This includes aligning order release with available capacity, sensibly limiting work queues, and prioritizing orders so that downstream areas are continuously supplied without being overloaded.

Especially during pronounced peaks, the ability to adapt to different operating conditions is crucial. During normal operations, different rules for order release may apply than during a high-volume phase. Similarly, additional priorities may be necessary as specific shipping cut-offs approach or when individual areas of the facility have limited availability.

However, the control system should not react exclusively to short-term productivity metrics. A high number of picking movements is of little help if it causes a backlog in front of the sorter. Similarly, maximizing the utilization of individual systems offers no advantage if it reduces the warehouse’s overall throughput.

The goal is not to maximize the utilization of every single process step, but to achieve the most stable overall throughput possible.

Picking is one of the key performance factors in e-commerce. Especially when dealing with many small customer orders, consolidating multiple orders can unlock significant efficiency potential. A common approach is batch picking.

In batch picking, multiple orders are consolidated into a single picking order. Instead of making a separate trip through the warehouse for each customer order, an employee or an automated system retrieves items for multiple orders in a single operation.

This reduces travel distances—especially for frequently requested items—and can significantly lower the number of picking movements required. The extent of this effect depends, among other things, on the order structure, warehouse organization, item distribution, and the technology used.

However, bundling places additional demands on downstream processes. The retrieved items must still be clearly assigned to the individual customer orders. To achieve this, sorting bins, order containers, or downstream sorting processes are used, for example.

Batch size is a key factor here. Batches that are too small do not fully exploit the potential of route bundling. Batches that are too large, on the other hand, can overfill the picking bins, complicate sorting, or extend processing times in downstream areas.

Order priority must also be taken into account. If time-sensitive orders are combined with less urgent orders in a single batch, this can make it difficult to meet shipping deadlines. Effective batching therefore considers not only the physical proximity of the items but also order characteristics, shipping methods, and the remaining processing time.

The Warehouse Management System (WMS) plays a central role in this process. It can group suitable orders, generate picking tasks, and ensure that the picked items are assigned to the respective customer orders. A prerequisite for this is that the strategies employed align with the order structure and the physical warehouse organization.

Batch picking is therefore not an isolated optimization approach. Its benefits depend largely on how well picking, sorting, and consolidation work together.

In larger e-commerce warehouses, conveyor systems and sorters handle a significant portion of internal transport. Containers, boxes, or individual items are automatically moved between different process areas and distributed according to their destination.

Sorters can, for example, distribute picked items to order bins, route shipments to specific packing stations, or sort fully packed boxes by shipping carrier and destination region. The specific tasks a system performs depend on the warehouse layout.

However, as the level of automation increases, so does the importance of end-to-end material flow control. This is because a conveyor system does not operate independently of the processes that feed material to it or receive material from it.

For example, a sorter can only operate continuously if sufficient material is available and the designated destination points have capacity. If individual discharge points are blocked or packing stations are at full capacity, the backlog can extend to upstream conveyor lines. Conversely, an insufficient supply results in expensive system capacities remaining unused.

It is therefore crucial to align logical process decisions with the actual movements of the material.

The WMS typically manages inventory, orders, and overarching warehouse processes. For example, it determines which orders are released, which items are to be picked, and which processing priorities apply. Material flow control (MFC), on the other hand, coordinates the operational workflow within the technical plant infrastructure. It controls, for example, transport routes, switches, transfer points, and the routing of load carriers.

The exact division of tasks depends on the system architecture. It is important that both levels collaborate via clearly defined interfaces and exchange relevant status information in a timely manner.

Such integration makes it possible to view orders not only from the perspective of inventory and order management but also to take the current situation in the material flow into account. If, for example, a section of the facility is unavailable, transport orders may need to be rerouted or subsequent processes adjusted.

The quality of this coordination becomes particularly evident during disruptions and peak loads. As long as all facilities are available and order volumes remain within the expected range, even poorly coordinated processes can appear to function without issues. Under high load, however, weaknesses in order release, backlog control, or prioritization quickly become apparent.

Cooperation between the WMS and MFC is one of the key prerequisites for stable warehouse operations.
Both systems address different aspects of the same process chain and must nevertheless provide a unified view of the current operational status.

The WMS maps the logical level of warehouse processes. This typically includes inventory management, order management, putaway and picking strategies, as well as the management of storage locations and load carriers. It ensures that the correct items are made available for the correct orders and that the process steps are documented in a traceable manner.

The MFS handles the operational control of material transport. It takes the technical layout of the facility into account and ensures that containers or cartons reach their intended destinations. Depending on its configuration, it can also detect bottlenecks, utilize alternative routes, and coordinate the release or rerouting of shipments.

The interface between the two levels must do more than simply exchange basic transport orders. Feedback regarding completed movements, the condition of load carriers, malfunctions, and the availability of transfer points is also crucial. Only when this information is processed consistently can discrepancies between the logical order status and the actual material flow be avoided.

An example illustrates the importance: A customer order must be completed before a specific shipping cutoff time. The WMS prioritizes the order and releases the necessary picking tasks. The MFS must then ensure that the corresponding containers reach the sorting or packaging areas in a timely manner via the available conveyor routes. If a route is blocked, the original prioritization alone is not sufficient. The affected transport orders must be handled while taking facility conditions into account.

Effective integration thus enables a coordinated response to changing conditions. However, it does not replace careful process design. Unclear responsibilities, inappropriate prioritization rules, or erroneous master data cannot be fully compensated for even with a technically powerful interface.

Short delivery times have long been a key competitive factor in e-commerce. Services such as same-day delivery increase the pressure on intralogistics processes because there is only a limited time window between order receipt and shipment preparation.

It is important to distinguish between same-day delivery and same-day shipping. Same-day delivery typically refers to delivery on the day the order is placed. Same-day shipping, on the other hand, means that the goods are shipped on the same day. Both promises place different demands on the warehouse and the transportation network.

For the warehouse, cutoff times are particularly crucial. They determine the latest time by which an order must be received or reach a specific processing status in order to meet the planned shipping option. These deadlines depend, among other things, on shipping carriers, pickup times, packaging processes, and any necessary quality controls.

Prioritizing orders solely based on the time they are received is not sufficient in this context. Two orders can be received at the same time yet still require different processing priorities. An order with an imminent shipping cutoff may be more urgent than an order whose shipping window is still open for several hours.

At the same time, it is important to consider how much processing time is likely to be required. An order that passes through many different warehouse areas may require more time than an order with a single, easily accessible item.

A sensible control approach therefore takes several factors into account:

• Time remaining until the cutoff: How much time is still available for picking, sorting, packing, and preparing for shipment?
• Expected process effort: How many items and process steps does the order involve?
• Current capacity: Which picking areas, sorting lines, and packing stations are available?
• Shipping requirements: Which service providers, shipping methods, or special processing steps are planned?
• Current order status: Which process steps have already been completed, and which are still pending?

The priorities derived from this must be consistently taken into account in warehouse operations. It is of little use to mark an order as particularly urgent in the WMS if it subsequently ends up in an unsuitable batch or has to wait in front of an overloaded area of the facility.

At the same time, consistent management of rush orders must not result in other orders being permanently put on hold. If new urgent orders are constantly being inserted, regular orders may be left unprocessed, causing additional disruption to the material flow. Effective management therefore requires transparent priority rules and must take into account the impact of short-term interventions on overall throughput.

In e-commerce, returns are an essential part of the flow of goods. Unlike in the traditional shipping process, items do not move exclusively from the receiving area toward the customer; rather, after delivery, they often find their way back to the warehouse.

The challenge lies in processing these returns as quickly and reliably as possible without disrupting regular outbound shipments. This is because returned merchandise is not automatically resalable. They must first be identified, inspected, and, depending on their condition, assigned to an appropriate follow-up process.

Depending on the item and business model, this could involve, for example, restocking, refurbishment, forwarding to a repair department, or the removal of damaged goods. Incomplete shipments or items without a clear assignment can also result in additional inspection efforts.

A key factor is processing time. The longer a returned product remains uninspected in the warehouse, the later it may become available for sale again. This can have economic consequences, particularly for seasonal items or rapidly changing product lines.

At the same time, returns processing is often less predictable than regular goods receipt. Returns arrive at staggered intervals, their composition is only partially known in advance, and the actual inspection effort depends on the condition of the items.

This raises the question for warehouse management of how to integrate returns into existing processes. One option is to set up dedicated processing areas with clearly defined capacities and priorities. In other cases, partial sharing of resources may be advisable, provided that the different process requirements are compatible.

The WMS plays a key role here as well. It must provide a traceable overview of the status of returned items and ensure that inventory is not made available for sale again until the necessary inspections have been completed. Assigning appropriate storage locations and integrating with other systems—such as those for quality control or repairs—may present additional challenges.

The control system should not only consider the number of incoming returns but also the processing backlog. A high number of unprocessed returns may indicate that available capacity does not match the current workload or that individual process steps have become bottlenecks.

Not every challenge can be solved through better software control. The physical layout of the warehouse also significantly influences the performance of e-commerce intralogistics.

The distribution of items across available warehouse areas is particularly important. Frequently requested products should be stored in such a way that they can be accessed with as little effort as possible. At the same time, storage locations must remain flexible enough to respond to changing sales patterns and seasonal shifts in the product mix. Dynamic storage allocation can help make better use of available space. However, placement should not be based solely on available capacity. Item frequency, common ordering patterns, replenishment requirements, and suitability for specific picking methods also play a role.

For example, if items that are frequently ordered together are poorly distributed across different warehouse areas, this can result in additional walking distances and longer order turnaround times. Conversely, well-planned item placement can support the picking process and reduce the workload on individual areas.

Replenishment control is also closely linked to picking performance. An empty picking station can result in orders being delayed or require additional stock transfers. Conversely, if replenishment movements are triggered without considering current utilization, they can unnecessarily burden the material flow.

The challenge lies in coordinating putaway, replenishment, and picking. To achieve this, the underlying rules must align with the actual order structure and be reviewed regularly. A warehouse layout that works well for a specific product range may not necessarily remain optimal if item quantities change or the distribution of orders shifts.

To assess the performance of an e-commerce warehouse, it is not enough to measure only the number of orders processed or the performance of individual employees. What matters most is how reliably and cost-effectively the entire process chain operates.

Relevant key performance indicators include, among others, order throughput, lead time from order receipt to shipment readiness, adherence to cutoff times, and the error rate in order picking. Additionally, the utilization of sorting systems, the number of waiting containers, the accumulation of backlogs in individual areas, and the processing time for returns can be considered.

It is important to interpret these metrics collectively. High equipment utilization may indicate efficient use of technology, but it can also signal a lack of capacity reserves. Similarly, short picking times are meaningful only if downstream processes can handle the picked items without excessive wait times.

It is particularly helpful to examine the entire process chain. For example, if the number of fully picked orders increases but shipment preparation slows down at the same time, the bottleneck may not lie in picking, but rather in consolidation or packaging.

A solid data foundation makes it possible to identify such correlations and implement targeted measures where they will have the greatest impact on the overall process. To achieve this, information from the WMS, material flow control, and, if necessary, other systems must be effectively consolidated.

Analyzing past order peaks can also provide valuable insights. Which areas were under particularly heavy load? Which order types led to backlogs? How many time-sensitive orders were completed within the designated time windows? These insights can be used to derive both operational adjustments and longer-term measures for capacity planning and automation.

E-commerce warehouses must cope with fluctuating order volumes, high shipping demands, and complex goods flows. Order peaks, batch picking, automated sorting, same-day delivery, and returns processing are not independent issues. They influence one another and must be considered in context.

The central task is to maintain a stable flow of goods across all process areas. Additional capacity and high-performance warehouse technology can help achieve this, but they are not sufficient on their own. Equally important are appropriate picking strategies, proactive order release, and consistent coordination between available resources and actual requirements.

The interaction between the warehouse management system (WMS) and material flow control is of particular importance here. The WMS maps out the logical warehouse processes and organizes orders and inventory. Material flow control coordinates movements within the technical equipment landscape. Only when both levels work together reliably can priorities, equipment status, and operational processes be taken into account across the board.

High-performance intralogistics is therefore not demonstrated solely by high throughput rates under ideal conditions. What matters most is how well a warehouse responds to deviations: unexpected order spikes, last-minute equipment malfunctions, changed shipping deadlines, or an increase in returns.

For e-commerce companies, the ability to manage such changes has become a key success factor. It is not the maximum speed of a single process step that determines a warehouse’s performance, but rather the interaction of all involved processes, systems, and resources.

More interesting articles, case studies, project reports, and explanations on the topics of intralogistics and e-commerce:

Retrofit par excellence: TUP modernizes material flow control at Hermes shipping center

Four perspectives on the ultra-modern multi-channel warehouse WDZ Sachsenheim

Returns Management in the Warehouse

Production, shipping and intralogistics: the rush order

Material flow computer from TUP in operation at Zalando distribution center in Poland

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