Swisslog AutoStore can be a strong option when a warehouse needs compact storage for suitable items and a goods-to-person picking process. It is not automatically the right answer to a space shortage: the system must also meet order peaks, fit the building, connect with warehouse software and justify its full operating cost. AutoStore uses robots to retrieve bins from a stacked grid and bring them to workstations; Swisslog’s role is to design and integrate a solution around that technology. Before comparing proposals, establish which products belong in the system, what throughput the operation needs and what constraints the site imposes.
AutoStore is a cube-based automated storage and retrieval system. Bins are stored in a closely packed grid, with robots moving across the grid to retrieve bins and deliver them to workstations, often called ports. This reduces the need for workers to walk through storage aisles to collect each item. The exact system design, supporting equipment and software depend on the project.
Swisslog is an integrator, not the name of a different storage technology. A Swisslog AutoStore project therefore involves the AutoStore system within a broader warehouse solution. The proposal may include system design, workstations, software connections and other material-handling elements, but the precise scope should be set out in the supplier’s documentation. Confirm who is responsible for each interface, from order release through picking and onward to packing or dispatch.
The grid’s density is useful when a significant share of inventory consists of items that can be stored and picked from the system’s bins. It does not replace every warehouse function. Pallets, oversized products, goods with unsuitable handling needs and reserve stock may still require separate storage and handling processes.
Swisslog AutoStore is most worth investigating when storage space is constrained, a large proportion of the active range is suitable for bin storage, and orders involve frequent picks from that range. It can also make sense where reducing walking and bringing goods to staffed workstations would improve the picking process. Those benefits depend on the complete operation, not just the grid.
It is less compelling if storage density is not a genuine constraint, if most products do not fit the intended bin-based process, or if the main bottleneck lies elsewhere. For example, automating storage will not resolve delays caused by slow replenishment, packing capacity, order release rules or dispatch handling. A site with highly variable demand should also assess whether its peak requirement can be met without building an unnecessarily large system for quieter periods.
Start with an item-level review rather than a headline storage-capacity estimate. For each SKU, examine its dimensions, weight, packaging, handling requirements and stock profile. A product that appears small in a catalogue may arrive in packaging that changes how it can be stored. The system’s permitted bin sizes and load limits depend on the configuration, so have the supplier validate item eligibility against the actual product data.
Next, separate storage volume from picking demand. A slow-moving item may occupy space but contribute little to daily workstation activity; a compact, frequently ordered item may drive a large share of picks. Review order lines by SKU, order type and time period. Include seasonal peaks, promotions and anticipated range changes rather than relying only on an annual average.
Decide which inventory belongs in AutoStore and which should remain in other locations. A mixed warehouse can use the grid for eligible, active items while retaining pallet racking, shelving or other methods for reserve, bulky or non-compatible stock. The design should account for how those storage zones work together: split orders may need consolidation, and replenishment must keep the automated area supplied without creating congestion.
Throughput is the rate at which the whole fulfillment process can complete work, not simply the rate at which robots move bins. The practical output depends on the system configuration and on downstream tasks such as confirming picks, replenishing inventory, consolidating orders, packing and moving completed cartons. Ask Swisslog to show how its proposed design supports your actual order profile and identify the assumptions behind the estimate.
Port count, staffing and system configuration are design choices, not interchangeable guarantees of output. More workstations alone may not improve performance if orders are released unevenly, replenishment cannot keep pace or packing becomes the bottleneck. A credible proposal should explain where its throughput assumptions come from and what operational conditions must hold for them to be achieved.
Assess the site before treating a warehouse plan as settled. The grid’s footprint and height must work with the building’s structure, columns, ceiling services, access routes and other fixed constraints. Have qualified professionals review the floor and structural requirements for the proposed installation. Also plan for maintenance access, safe working areas, fire protection, emergency egress and any required building changes. These issues are easier to resolve during concept design than after equipment specifications have been fixed.
Map the material flow around the grid as well as within it. Workers need a practical route for receiving and replenishing stock, while picked goods must move efficiently to consolidation, packing and dispatch. Check how empty bins, exceptions, returns and damaged stock will be handled. A dense storage arrangement can still create poor flow if the workstations or transfer points are placed in the wrong part of the building.
For an existing warehouse, document operating constraints that a drawing may not show: shifts, aisle closures, construction access, live order processing and the space needed to maintain service during installation. Ask for a phased implementation plan if the facility must remain operational. Confirm which site changes, approvals and commissioning activities fall within the project scope.
The right comparison depends on the products and process. Conventional racking and shelving can be simpler for accessible storage or mixed product sizes; automated goods-to-person systems can reduce walking but differ in how they store and retrieve goods. The table is a screening aid, not a substitute for a design comparison using the same inventory and workload data.
| Approach | Typical strength | Best fit | Main point to check |
|---|---|---|---|
| Swisslog AutoStore | Dense bin storage with robot-driven delivery of bins to workstations | Operations with many eligible items and a need to reduce storage footprint or picking travel | Item compatibility, peak throughput, building fit, software integration and full lifecycle cost |
| Manual shelving or carton-flow storage | Direct access and straightforward handling for suitable small-item picking | Operations where labor access, flexibility or modest automation needs outweigh space pressure | Walking distance, labor requirements, aisle use and capacity limits as the range grows |
| Pallet racking | Storage and access for palletised goods and reserve inventory | Bulk stock, pallet movements and products that do not suit bin-based picking | Travel and handling for piece picking, space use and replenishment into pick locations |
| Other automated goods-to-person systems | Automated presentation of stored goods, with configurations varying by technology | Sites whose item mix, building or flow suits a different storage and retrieval design | Compare capacity, handling limits, interfaces, resilience and costs on equivalent assumptions |
These approaches can complement one another. A Swisslog AutoStore installation may serve a defined fast-pick or small-item zone while racking holds pallets and reserve stock. Compare alternatives on the same basis: eligible SKUs, order profile, peak demand, required service level, building modifications and operating costs. Otherwise, a dense-system capacity figure may look attractive without showing whether it solves the operation’s actual constraint.
Compare the complete project and operating cost, not equipment purchase price alone. A business case should identify the proposed system’s design and integration costs, software and controls, building work, commissioning, training and any connected equipment. It should also account for ongoing maintenance, support, energy, spare parts, staffing and the cost of keeping the operation running if part of the system is unavailable. The project proposal should make clear which items are included and which remain the warehouse operator’s responsibility.
Model the benefits against a realistic baseline. Potential benefits may include more storage within a constrained footprint, less walking for suitable picks or a different allocation of labor. Quantify them using the operation’s own data rather than assuming that automation automatically reduces headcount or increases output. Include the cost of any remaining manual processes and the equipment needed to serve products that do not fit the grid.
Test the business case under more than one demand scenario. A design that only pays back when the warehouse operates at forecast peak volume may be exposed if order patterns change. Conversely, a design sized for exceptional peaks may leave capacity underused for much of the year. Ask what happens if the SKU mix shifts, volumes grow more slowly than planned or the facility’s needs change.
Resolve these points with the project team before committing to a design. Clear answers help distinguish a solution that fits the warehouse from one that mainly looks attractive on a storage-density diagram.
No. AutoStore is the cube-based storage and retrieval technology; Swisslog can integrate it into a wider warehouse solution. The exact equipment, software and services included depend on the project proposal, so check the scope and responsibilities in writing.
No. Suitability depends on the product’s dimensions, weight, packaging and handling needs, along with the system configuration. Have the proposed item range checked against the applicable bin and load requirements rather than assuming that all small products qualify.
No. Storage density and throughput are different measures. Output also depends on the order profile, system design, staffing, replenishment and downstream capacity such as packing and dispatch.
Not necessarily. A bin-based system is intended for inventory that suits its storage and picking process, while pallet racking serves different stock and handling needs. Many operations should assess a combined layout rather than treating the two as direct substitutes.
Prepare SKU dimensions and weights, packaging details, inventory levels, order lines by time period, peak-demand patterns and current process times. Include building plans and known structural or operational constraints so the design discussion can address facility fit as well as storage capacity.
Swisslog AutoStore is worth evaluating when bin-compatible inventory, space pressure and picking demand align. Base the decision on a validated item profile, a peak-throughput model, a facility review and a full operating-cost case. If those checks show that automation addresses the warehouse’s actual bottleneck, the proposal can be assessed on its fit rather than density alone.
