How to speed up dispatch without expanding the warehouse

Dispatch is the result of several warehouse processes. If an order reaches the packing station late, the cause may be improperly stored goods, an unnecessarily long picking route, or waiting between individual zones. Simply adding more space may therefore fail to eliminate a problem caused by the organization of work.

It is therefore more useful to monitor warehouse throughput: how many orders the existing operation can reliably process within a given time. An advanced WMS can increase this capacity without automatically expanding the warehouse’s physical area.

A larger warehouse does not necessarily mean faster dispatch

Expanding a warehouse addresses a lack of space, but not necessarily a lack of performance. If warehouse workers travel excessively long routes, wait for their next task, or if the fastest-moving items are located far from dispatch, the same problems may simply move into a larger building. In extreme cases, the distances become even longer.

Before investing in additional floor space, it is therefore useful to examine:

• how much time picking itself takes,
• what proportion of a shift consists of movement without handling goods,
• where orders wait between operations,
• which warehouse zones are overloaded,
• how tasks are assigned,
• whether the most frequently picked items are stored in suitable locations,
• how many orders are waiting for packing or dispatch.

If the bottleneck is the process, more square metres merely provide more space in which to perform the same inefficient process.

Warehouse throughput is not the same as warehouse size

Two warehouses with the same floor area can achieve significantly different performance. The decisive factors are the number of handling operations, the organization of inventory placement, the order profile, the picking strategy, the replenishment method, and the coordination of people and equipment. Warehouse capacity is therefore a combination of space and how it is used.

In simplified terms, a distinction can be made between:

Problem	Typical response	Alternative
Growth in orders	Larger warehouse	Higher throughput
Long routes	More workers	Better slotting
Overloaded picking	More shifts	Different picking strategy
Queues at packing	More tables	Balanced workflow
Late dispatch	More overtime	Dynamic priorities
Insufficient inventory	More inventory at picking	Automated replenishment

 This difference is particularly important in rapidly growing e-commerce. A warehouse may first reach not the physical capacity limit of its racking, but the limit on the number of operations it can perform in a coordinated manner during a single shift.

Picking is often the first bottleneck

In a distribution warehouse, picking is one of the processes with a high proportion of manual work and movement. In a simple model, a warehouse worker receives one order, walks through the warehouse, hands it over for packing, and takes the next one. As volumes grow, however, this approach creates a large number of repeated routes.

If ten orders contain items from the same section of the warehouse, it may not be efficient to travel the same route ten times. A WMS can combine orders and apply a suitable picking strategy based on the operation’s profile.

Examples include:

• batch picking – picking items for multiple orders together,
• cluster picking – simultaneous picking into multiple destination units,
• zone picking – operators work within their own warehouse zones,
• wave picking – orders are processed in appropriately created waves,
• combinations of several methods based on the current order profile.

Dynamic and combined picking are among the strategies most frequently used to manage larger operations. The aim is not to force warehouse workers to move faster, but to reduce the amount of movement required per completed order.

Not every order requires the same picking method

A single picking strategy may not be optimal throughout the day. A single-item order, a wholesale order for a full pallet, and an e-commerce basket containing ten different SKUs create entirely different logistics tasks. If they are all processed in the same way, part of the warehouse’s capacity remains unused.

A WMS differentiates orders, for example, by:

• number of items,
• number of units,
• warehouse zones,
• shipping method,
• carrier cut-off time,
• customer priority,
• required dispatch time.

This type of decision-making is also important in managing e-commerce logistics, where a large number of smaller orders must be combined with the need for rapid processing. The fastest warehouse therefore does not necessarily use one best method; it knows how to select the right method for a specific group of orders.

Inventory placement itself is often the second bottleneck

If the best-selling goods are located at the opposite end of the warehouse, their position generates unnecessary travel for every order. A single trip may take only a few dozen seconds, but across hundreds or thousands of repetitions, the difference becomes hours of work. Poor inventory placement can therefore slow dispatch even when the warehouse is not physically full.

Slotting addresses the question of which item should be placed in which location. Decisions can take into account turnover, dimensions, weight, product compatibility, seasonality, or the co-occurrence of items in orders.

In practice, this may mean:

• fast-moving SKUs closer to packing,
• items frequently ordered together placed closer to one another,
• slow-moving products in more distant zones,
• seasonal bestsellers temporarily moved to more advantageous locations.

Space optimization does not mean fitting as much inventory as possible into the warehouse. It means storing inventory so that the warehouse requires as little work as possible to process an order.

Dynamic slotting responds to what is selling right now

Static ABC analysis can provide a good foundation, but e-commerce demand can change very quickly. A product that was an average item a month ago may become a bestseller after a marketing campaign or the start of a season. Its original warehouse location may then no longer correspond to the actual number of movements.

A WMS provides data for the continuous evaluation of turnover and the suitability of warehouse locations. In more advanced management, inventory can be reorganized according to actual consumption instead of a fixed layout created several years ago. Optimized placement and dynamic flow management are among the current directions in warehouse logistics. However, the point is not to move the entire warehouse constantly. A move has value only if the future savings in picking operations exceed the work required to reorganize the inventory.

The third problem is waiting between individual processes

A warehouse worker may pick an order quickly and yet dispatch may still be delayed. The order may then wait for consolidation, inspection, a free packing station, or carrier assignment. Dispatch performance is therefore determined not by the fastest part of the process, but by its narrowest bottleneck.

A typical flow may look like this:

picking → consolidation → inspection → packing → labelling → sorting by carrier → dispatch

If picking can prepare 500 orders per hour but the packing zone can handle only 300, increasing picking performance will not solve the problem. It will merely create a larger queue in front of the packing zone.

A WMS therefore needs to provide a view of the entire flow, not just individual tasks. Management can adjust the release of work to what the downstream zones can actually process.

Balancing work is more important than maximizing the performance of one zone

A warehouse may not need more workers if the existing workforce is allocated according to the situation from several hours earlier. Receiving may be the bottleneck in the morning, picking in the afternoon, and dispatch before carriers depart. Static workforce allocation may therefore fail to reflect the current order flow.

A WMS provides information about the status of work in progress and can help identify where a queue is forming. Priorities can then be changed or capacity shifted between processes according to the nature of the operation.

It is important to monitor, for example:

• the number of open picking tasks,
• the number of orders waiting for packing,
• utilization of individual zones,
• time remaining until dispatch,
• operator availability,
• the number of orders ready for a specific carrier.

The aim is not for every worker to appear fully utilized. The aim is for an order to pass through the warehouse without unnecessary waiting.

Replenishment of picking locations must not stop picking

A fast-moving item may be available in large quantities in the warehouse and still bring picking to a halt. It is enough for the inventory to remain in the reserve zone while the picking location becomes empty. The operator then waits for replenishment or has to look for the material elsewhere.

A reactive model begins replenishment only after a problem has occurred. A controlled model creates a task before the location is completely empty, based on defined minimum inventory levels or expected consumption. Automated real-time replenishment is one of the mechanisms used in more advanced warehouse management.

The setup should take into account:

• consumption rate,
• size of the picking location,
• inventory in the reserve zone,
• expected orders,
• time required for replenishment.

Well-configured replenishment is almost invisible to a warehouse. Its success is evident in the fact that picking does not have to wait for inventory.

Dispatch can also be slowed by the wrong order sequence

The principle that “the oldest order goes first” is simple, but it may not always be effective. Orders use different carriers, have different cut-off times, and may have different SLAs. If priorities are determined only by receipt time, some shipments may unnecessarily miss their scheduled collection.

When prioritizing, a WMS takes into account:

• the carrier’s dispatch cut-off time,
• express shipping,
• completion status,
• inventory availability,
• sales channel,
• customer SLAs,
• current zone utilization.

An order created later may therefore justifiably receive a higher priority if its carrier departs earlier. Dispatch speed is not only about working quickly, but about performing work in the correct order.

Multi-carrier dispatch adds another layer of complexity

A growing e-shop or retail network may work with several couriers, pallet carriers, or collection-point networks. Each carrier may have its own time windows, labels, data interfaces, and shipment-sorting method. Manually switching between systems therefore gradually creates another bottleneck.

An integrated dispatch process can link warehouse processing with carrier selection or carrier data. Once packed, an order then does not have to enter another separate manual workflow.

Its importance increases particularly with:

• a large number of shipments,
• international markets,
• the combination of multiple carriers,
• different types of transport,
• seasonal peaks.

In advanced logistics operations, multi-carrier dispatch can be a natural part of order-flow management rather than a separate administrative step.

Cross-docking can eliminate storage entirely for some inventory

Not all received goods need to spend time in a warehouse location. If incoming inventory is already known to be needed for specific orders or a subsequent logistics flow, some operations may benefit from directing it from receiving closer to dispatch. This cross-docking or flow-through model eliminates one handling phase.

Instead of:

receiving → put-away → storage → retrieval → dispatch

part of the flow can follow:

receiving → sorting or consolidation → dispatch

This model is not suitable for every product or operation. However, when conditions permit its use, the fastest warehouse operation is sometimes the one the warehouse does not have to perform at all.

Packing can be just as much of a bottleneck as picking

Warehouse optimization focuses heavily on picking, but once its performance is increased, the bottleneck often moves further downstream. The packing station must inspect the items, select suitable packaging, prepare documentation, label the shipment, and direct it to the correct carrier. With a high number of orders, each of these operations can cause delays.

Standardization can help, with the system providing the worker with the information needed for a specific order. This reduces the number of manual decisions and the need to search for information in other applications.

When optimizing, it is useful to examine:

• how long an order waits before packing,
• which types of orders take the longest,
• how much time administration takes,
• whether packaging materials are missing,
• whether an inspection repeats an activity already performed.

Faster picking has no value if the picked orders merely create a larger queue in front of the packing table.

Inspection should not mean checking everything twice

When error rates are high, an additional manual inspection may be introduced. One worker picks the order and another checks every item in full. This model may reduce errors, but it also consumes additional capacity.

A better objective is to move inspection as close as possible to the point at which an error can occur. Scanning the correct location, item, or handling unit can prevent an error during picking. The final inspection then does not have to compensate for an inadequately controlled process.

This is particularly important as volumes grow. Dispatch cannot be accelerated over the long term by having a company add another person to every operation to check the previous worker.

Automation makes sense only after the process has been optimized

Conveyors, sorters, AMRs, automated storage and retrieval systems, or robotic systems can significantly increase warehouse performance. Technology itself, however, will not resolve poor inventory placement or an incorrect task sequence. In an unoptimized process, it may simply move the problem faster from one zone to another.

Before automating, it is therefore useful to know:

• which operations generate the greatest amount of work,
• where waiting times arise,
• what the order profile is,
• which routes are repeated most often,
• how the workload changes during the day and across seasons.

A WMS creates the data and process foundation on which physical automation can build. Larger operations can then coordinate people as well as automated warehouse and transport equipment within a common flow.

More orders do not automatically have to mean more people

If a warehouse responds to growth only by adding workers, productivity may stop increasing beyond a certain point. More people in the same aisles create collisions, waiting for equipment, and more complicated coordination. The warehouse floor area remains unchanged.

Before increasing the number of workers, it is therefore useful to determine:

• how much time consists of productive handling,
• how much time consists of walking or driving,
• how much time the operator spends waiting,
• how many tasks are assigned manually,
• whether several workers regularly meet in the same zones.

A seasonal peak reveals the warehouse’s true throughput

An ordinary working day may not reveal process inefficiency. The problem becomes apparent during Black Friday, Christmas, or a marketing campaign, when order numbers rise sharply. At that point, the extra minutes on each order create hours of accumulated delay.

Before a peak, it is therefore useful to model:

• the maximum number of orders per hour,
• picking performance,
• packing capacity,
• carrier cut-off times,
• available inventory in picking zones,
• capacity of consolidation areas.

If one part of the process can handle twice as much as another, the warehouse’s overall capacity is still limited by the weaker link. The aim of peak preparation is therefore not to maximize the performance of each zone separately, but to balance the entire flow from order to loading.

Five questions to ask before deciding whether to expand a warehouse

Expanding an operation may be the right step if a company is genuinely reaching its physical capacity. Before investing, however, it makes sense to separate a space problem from a process problem.

1. Is the warehouse truly full, or merely being used inefficiently?

Low storage density, unsuitable locations, or reserved but unused positions can create the impression of insufficient space. More accurate location visibility may show that some capacity still exists.

2. What is actually limiting dispatch today?

If orders are waiting for picking, the problem is different from a situation in which they are waiting for packing. Without measuring the individual phases, it is impossible to make the right investment decision.

3. How many movements add no value to the order?

This group includes transfers between temporary locations, searching for goods, empty travel, or repeated inspections. Eliminating them can increase capacity without construction work.

4. Is inventory stored according to current demand?

A layout created according to historical turnover may not correspond to today’s product range. Slotting can reveal a significant reserve of performance within the existing floor area.

5. Can the system respond dynamically during a shift?

If priorities, people, and tasks are changed manually, the warehouse uses its capacity according to the quality of operational decision-making. With a larger number of operations, management itself may be the main limitation.

Which KPIs show whether dispatch is actually becoming faster

Dispatch speed cannot be assessed solely by the number of orders shipped per day. Daily volume may grow because of overtime or additional workers even though the process itself has not become more efficient. It is more appropriate to combine performance and quality indicators.

KPI	What it shows
Orders per hour	Throughput
Order → dispatch time	Overall flow speed
Picking time	Picking performance
Waiting time before packing	Bottleneck
Operations per worker	Productivity
Picking error rate	Process quality
Shipments dispatched on time	Meeting deadlines
Distance per order	Layout efficiency

 

The combination of metrics is particularly important. Reducing picking time by 20% has little value if the time from order to dispatch does not change because the bottleneck has merely moved.

A WMS should speed up decision-making, not just record-keeping

A warehouse system does not deliver higher throughput simply by digitizing a paper list of orders. Value is created when it uses current data to decide what should happen next.

Within a single flow, a WMS can connect:

• inventory availability,
• warehouse locations,
• orders,
• picking strategies,
• replenishment,
• workers,
• priorities,
• dispatch deadlines.

The difference between a record-keeping system and a management system lies precisely in whether the warehouse merely records a completed operation or determines the next most appropriate operation based on the current situation.

This is also why the selection of a WMS is not primarily about the number of features on a list. What matters is whether the system can support the warehouse’s specific profile, its orders, and its future level of automation.

When optimization is no longer enough and the warehouse really must expand

Not every problem can be solved with software. If a warehouse is operating at the physical limit of its storage locations over the long term, lacks space for the necessary dispatch zones, or its structural layout objectively restricts material flow, expansion may be unavoidable. A WMS cannot create physical capacity that does not exist within the building.

A well-managed warehouse, however, provides substantially better data for such a decision. A company can distinguish problems caused by insufficient floor space from those caused by processes and determine more precisely how much additional capacity it actually needs.

Optimization is therefore not an alternative to expansion at all costs. It is a way to ensure that a company does not invest in additional square metres before using the capacity of its existing space.

Nine signs that a warehouse has performance capacity to spare

A warehouse may not need another building if the problem lies primarily in how the flow is organized. The potential to increase throughput is indicated especially by situations in which:

1. warehouse workers travel long, repeated routes,
2. the same picking strategy is used for all orders,
3. fast-moving SKUs are stored far from dispatch,
4. picking locations regularly remain empty,
5. orders wait between picking and packing,
6. dispatch priorities are changed by phone or in Excel,
7. workers wait for their next task or for handling equipment,
8. during peak periods, overtime grows faster than the number of orders,
9. it is not known which phase of the process actually limits throughput.

These symptoms do not automatically mean that a WMS is needed. They do show, however, that process capacity should be measured and optimized before physical capacity is expanded.

More orders in the same space is a flow-management problem

Dispatch becomes faster when an order spends less time moving, waiting, and being handled repeatedly. Optimization therefore does not begin at the loading dock, but with the decision about where inventory will be stored and how it will later be picked. Every preceding step can speed up or slow down future dispatch.

A warehouse analysis should therefore monitor the entire flow:

1. inventory placement,
2. replenishment,
3. order release,
4. picking,
5. consolidation,
6. inspection,
7. packing,
8. sorting by shipping method,
9. dispatch.

Only this view shows whether the warehouse needs more space, workers, automation, or, above all, better management. The greatest capacity reserve is often found not in another building, but between two existing warehouse operations.

Increase warehouse throughput before expanding it

Growth in orders is a positive problem, but the solution does not automatically have to be a larger logistics operation. If the existing space contains inefficient routes, static priorities, and waiting times, a new building will not eliminate these shortcomings. It may merely move them into a larger space.

EMANS WMS supports the management of inventory, warehouse zones, picking strategies, and operator work, and enables integration with automated warehouse and transport technologies. In larger distribution operations, these principles can also be extended through more advanced workload balancing, dynamic replenishment, and the management of multiple picking methods.

The purpose of a WMS, however, is not to fit as much work as possible into a warehouse. It is to ensure that the existing space, people, and technologies can process more orders with fewer unnecessary movements and less waiting.

Frequently Asked Questions