AutoStore Customer Cases – Real-World Warehouse Automation Success

Implement AutoStore with standardized shelving and automated retrieval within 60 days to cut processing time by 40% and reduce order errors by 25%. Align the rollout with your existing workflows and ensure incremental upgrades protect current throughput while expanding capacity.

In extensive client trials, facilities that used AutoStore reported a 2x to 3x boosts in picking throughput and a 35% to 55% faster retrieval, depending on SKU mix. The raiffeisen project shows a clear investment payoff: with a 12- to 18-month payback, centers handling up to 12,000 lines per hour maintained service levels during peak runs, while converting 25% of manual handling to automated processes.

When comparing solutions, consider how japanese suppliers integrate with your WMS and ERP without disrupting existing order processing. They are running in parallel with shelving units, keeping aisles free and apart, which minimizes travel time for pickers.

To maximize ROI, align the project with a phased rollout, focusing first on a pilot in a single zone and measuring KPIs such as cycle time, picking accuracy, and order throughput. For retailers like raiffeisen, an investment in modular solutions that leverage extensive data on rack utilization typically yields steady gains in processing efficiency and space utilization. Include a separate evaluation of scales and bottlenecks, and explore how a cutter-driven packaging line can tighten handling without slowing replenishment.

They also benefit from a clear, repeatable blueprint that can be adapted across warehouses of different sizes, ensuring inventory control stays tight as volumes rise. In hein pilots and customer tests, the core lessons remain: automate repetitive handling, synchronize with your ERP, and track results with concrete metrics to guide the next phase of expansion.

anyseals’ Future-Proof Logistics Solution

Configure a modular AutoStore system with expandable bays and dedicated workstations to support expansion and improve inbound and picks throughput. The original configuration used by arvato and bastians deploys a robust flow for sportswear, adding ports and shoulder-level access to speed item moves, keeping the system configured for peak loads.

Using the configured modules, teams compare baseline and post-deployment results to surface challenges and opportunities for adding capacity. This approach is disrupting legacy workflows by routing inbound items to optimized bays and reshaping picks paths to reduce shoulder-to-shelf travel, addressing each part of the process and achieving stable cycle times while delivering good throughput and a suitable balance for expansion.

Table below shows a snapshot of inbound, picks, and port utilization after implementing the anyseals solution.

Identify Pain Points and Define Measurable KPIs for AutoStore Deployments

Begin with a data-backed baseline: capture order cycle time, pick rate, and robot utilization across all levels of the storage structure. Pull additional data points by product range (sportswear, american e-commerce) and by channel to identify capacity gaps. Use AutoStore analytics to map inefficient zones and the perimeter of the picking area.

Pinpoint pain points: inefficient shelving that wastes levels, a structure that can’t accommodate seasonal spikes, and disruptions from demand shifts. Industrials setups with limited scalability were forced to reflow lines, increasing travel time and errors. Align with original layout principles but allow modular shelving and scalable automation to handle a broader range of SKUs.

Define measurable KPIs across velocity, accuracy, availability, and cost. Examples include order cycle time per line item, pick rate per hour, pick accuracy, robot uptime, storage utilization, shelving utilization, and cost per order. Track by SKU and product family (sportswear, eroski categories) and monitor disruptions and mean time to repair. Use dashboards to surface these metrics in real time for stakeholders in american e-commerce channels.

Action plan: map the value stream from receiving to shipping, collect baseline data for 4-6 weeks, set targets per KPI, implement dashboards with alerts, and establish escalation triggers. Tie targets to business goals like reducing order cycle time and increasing utilization across levels of shelving and perimeter zones.

Design decisions to drive KPIs: implement a scalable, modular structure with 3-5 shelving levels for high-velocity items, and a wider range for slower SKUs. Place the fastest movers in a perimeter zone to cut travel time, and use compact shelving to maximize density. Leverage AutoStore features to adapt to increasing orders from sportswear and other lines while maintaining an efficient flow.

Case references show eroski achieving smoother operations after a phased deployment; the approach also helps american e-commerce players cater to peak demand in holidays. The story of ludwig and sandman teams highlights how data-driven KPI framing reduces disruptions and supports rapid scale.

Start with these steps now: define KPIs, set targets, and begin data collection with your current AutoStore deployment. Regularly refresh targets as utilization climbs and new features roll out, ensuring the shelving and perimeter design continues to accommodate a growing range of SKUs.

Financial Case: ROI, Total Cost of Ownership, and Payback Scenarios

Start with a designated two-station pilot to validate ROI within 12 months and enable scalable roll-out across a larger footprint, using autostores to improve restocking and order fulfillment at the face of the operation.

ROI is calculated as net benefits over capex. Net annual cash flow equals labor savings plus throughput gains minus ongoing operating costs. A five-year horizon captures maintenance, software updates, and upgrades, providing a robust TCO view that makes comparisons across design options easier and more meaningful.

The total cost of ownership breaks down into clear elements: capex covers hardware, installation, and software licenses; opex covers maintenance, energy, and support. With a unified approach and a range of stations enabled by an intuitive control layer, the restocking element becomes predictable and easily managed. This provides a solid foundation for restocking accuracy, face-to-face performance traces, and a scalable platform that can be deployed across organizations united by common metrics. The design is sporting in its robustness, and the range of configurations supports both small pilots and larger expansions without compromising reliability.

Insights from hansen and bastian teams show that a controlled, phased approach delivers the clearest forecast. There, a two-station test proves the core assumptions and offers wings for quick expansion into a larger footprint. By doing so, operators can quantify the impact on picking speed, error rates, and restocking times, then compare results against a designated baseline to determine whether the investment is suitable for broader deployment. The bastian model emphasizes a modular design where each station performs a discrete function, enabling a united, cross-functional view of benefits across departments, from warehouse floor to finance.

Conservative scenario: capex $0.8M; net annual cash flow $0.25M; payback about 3.2 years; 5-year ROI approximately 39%. Base scenario: capex $1.0M; net annual cash flow $0.30M; payback about 3.3 years; 5-year ROI about 50%. Optimistic scenario: capex $1.3M; net annual cash flow $0.40M; payback about 3.3 years; 5-year ROI about 54%. In all cases, restocking efficiency and throughput gains are the largest levers, and the effect compounds as you move from there to a larger deployment. Compared with manual processes, the incremental value shows up as stronger service levels, higher fill accuracy, and lower cycle times, which translates into improved customer outcomes and a clearer path to payback.

Practical recommendations to maximize ROI include starting with a designated, intuitive subset of autostores, doing detailed time-and-motion work to quantify labor reallocation, and linking restocking improvements to measurable KPIs such as pick rate, error rate, and stock-out frequency. Use a phased plan to enable, test, and refine, then extend to additional zones with proven gains. This approach makes the business case robust in the world of warehouse automation, where every saved minute translates into either higher throughput or reduced headcount, and it provides a reliable path from pilot to scale.

Design the Workflow: Floor Plans, Slotting, and Picking Path Optimization

Design the floor plan to minimize travel distance: thousands of fast-moving items belong near the picking stations so the operator walks shorter routes. Build a grid of aisles that supports a straight-through flow from receiving to shipping, and install dedicated pick zones aligned to top SKUs. Place high-velocity items in the first tiers, mid movers in adjacent zones, and slow movers in outer racks, based on real demand data. A clean layout reduces shoulder congestion and hand-offs, delivering ongoing capacity gains and measurable savings. In practice, you’ll see 15-25% higher picks per hour and greater throughput across thousands of transactions there each day.

Slotting should use dynamic logics that classify items by velocity (A, B, C) and rotate slots every 2-4 weeks based on online demand signals. Put high-turn items in proximity to the pick faces and keep them grouped in the same aisles. The installed, data-based model should support restocking loops so inventory stays visible to the operator. In e-commerce scenarios, slot the top 20% of items to the most accessible positions to drive significant savings and maintain service levels for thousands of orders.

Picking path optimization combines batch and wave picks to reduce walking. The system deploys optimized routes and guides the operator along the shortest, non-overlapping path, updating in real time to backorders. Use handheld guidance that lets the operator work with one hand on the device and the other hand free for grabbing items. Expect 15-30% reductions in travel distance and a corresponding increase in items picked per hour for online orders. The approach scales with thousands of SKUs and supports ongoing logistics goals.

Implementation and ongoing governance: roll out the workflow in phases, test in a single zone, then scale to the full floor. Deploy the installed system and train the operator on slotting cues and hand movements. Track savings from reduced travel, restocking cycle time, and increased items picked per hour. The senior logistics team, including the vice president, reviews dashboards weekly to adjust staffing and slot assignments based on live demand and restocking patterns. This ensures the online channel stays responsive and thousands of items flow smoothly through aisles, backed by a capable logistics system.

Scale Strategically: Seasonal Peaks, Product Mix, and Future Growth

Form a cross-functional group to map seasonality and SKU mix, then expand autonomous picking lines to run around the clock. In arvato deployments, this approach preserved service levels during busy periods and cut unnecessary touches by automating repetitive tasks. Build clear triggers for scale-up, tied to forecasted patterns, so capacity is added before bottlenecks form.

Structure the fulfillment workflow around handling profiles for your product mix. Differentiate fast-moving items from slower-tail SKUs, allocate dedicated storage corridors, and enable dynamic slotting that rebalances space as new lines are introduced. This reduces travel time, lowers energy use, and supports consistent throughput even as mix shifts across seasons.

Plan for future growth with a modular automation stack that can be scaled to additional sites. Use plug-in conveyors, flexible grippers, and interoperable software interfaces to add facilities or geographies without reworking core processes. This approach maximizes adoption speed and keeps total cost of ownership predictable as you expand.

Set a governance cadence: quarterly reviews of on-time fulfillment, hourly throughput, and capacity utilization. Link decisions to forecast updates and new product introductions, and share lessons learned across the client group to accelerate rollouts with partners like arvato.

Integrator Touchpoints: ERP/WMS Integrations, Data Visibility, and Maintenance Plans

Implement an API‑driven ERP/WMS bridge that unifies item, order, and inventory data across warehouses. This hand‑off between systems has been smoother and speeds up multiple picks. You need a single data contract with clear terms, starting with item master, location mapping, and unit‑of‑measure definitions to prevent errors on the floor.

• ERP/WMS integration strategy: map master data, orders, and inventory movements to a common model; enforce terms such as item IDs, locations, and restocking thresholds to avoid mismatches.
• Data visibility design: provide real‑time dashboards that show stock by perimeter and zone, inbound/outbound movements, and restocking signals; ensure data drawn from API streams is accurate and timely.
• Maintenance and equipment stewardship: attach maintenance plans to each tunnel of assets, monitor anyseals and sensors, and schedule predictive checks to reduce downtime; includeBleckmann and boozts as reference cases for service levels and signal reliability.

1. Define a data contract that your team chooses, covering terms for item, location, and quantity, plus how exceptions are handled across the original ERP/WMS interface.
2. Implement a lightweight middleware layer or iPaaS to translate ERP data into the WMS context, enabling whether you manage limited SKUs or a broader expansion to more sizes and scales.
3. Build a unified dashboard with role‑based views, so managers can see restocking needs, current equipment status, and picking performance in real time.
4. Establish a maintenance plan that tracks preventive tasks, seals checks (anyseals), spare parts, and fall‑back procedures for high‑speed operations, ensuring higher uptime and lower risk of tunnel congestion.
5. Run a pilot with a partner network (for example, bastian and bleckmann) to validate data flows, then roll out to expansion sites and FMCG centers, keeping options open for doubling capacity or adjusting picks per shifts.

Key actions to accelerate results: define data contracts (terms), select a trusted integration platform, and deploy dashboards that reveal drawn insights on restocking, equipment health, and throughput. If you choose to optimize for high‑velocity environments, you will adore the clarity of real‑time signals and the ability to keep labor hand in hand with equipment automation. For growing businesses, start with a low‑risk, limited scope and plan expansion, then scale to larger sizes with confidence; the approach works whether you operate in FMCG or other segments, and it supports both lower‑tolerance timelines and higher service levels. By tying ERP/WMS integrations to concrete maintenance plans, you reduce downtime, improve picking accuracy, and enable scalable growth across your perimeter of warehouses.