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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Plan peak-season warehouse automation around the constraint your operation actually faces—not the assumption that the busiest weeks justify buying the largest system. Use demand and warehouse data to identify where capacity, staffing, safety, or accuracy breaks down; compare solutions for both peak and non-peak use; then validate one defined use case in a pilot before scaling.
1. Build a picture of seasonal demand
Start with historical peak, average, and valley periods alongside the current forecast. Use WMS or ERP data to examine when surges begin and end, how many SKUs are involved, inventory positions, order profiles, and where work queues form. MHI’s July 2025 seasonal-planning guidance stresses starting with accurate numbers and using system performance data to understand peaks, valleys, and averages. That is industry guidance, not a universal forecasting formula. MHI seasonal order-picking guidance.
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Describe the workload in operational terms: which order lines or handling units dominate, which processes constrain fulfillment, and which service failures are most costly. If your business has several seasonal surges, analyze each rather than assuming one peak represents them all.
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2. Choose a bottleneck worth automating
Automation is most defensible when it addresses a measurable capacity, staffing, safety, or consistency problem. MHI’s 2025 implementation account describes a distribution center that struggled to maintain sortation staffing during peak periods and piloted automation; it also discusses physically demanding and undesirable tasks. These are examples to investigate, not evidence that sortation or any other process is always the right target. MHI implementation guidance.
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Before comparing equipment, record the current baseline and decide how a pilot will be judged. Select measures that match the constraint and the required operating window:
- Throughput during the hours and shifts that need added capacity.
- Accuracy, error, or reject rate.
- Labor hours and staffing reliability.
- Safety effects and changes to worker tasks.
- Whether the process can run alongside existing operations without disrupting service.
MHI’s field account notes that organizations use different success measures, including throughput, labor-cost effects, errors or rejects, and safety. Choose measures before deployment so that a busy peak alone is not mistaken for proof of success.
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3. Compare options for peak and non-peak conditions
Warehouse automation is a portfolio of approaches, not a single machine. MHI’s July 2025 seasonal-planning article discusses automated storage and retrieval systems (AS/RS), running more existing pick stations during the surge, temporary pick stations, leased automated equipment, phased adoption, and equipment that may also support returns. ASCM’s warehouse technology overview also covers autonomous mobile robots (AMRs) for transport, AS/RS for storage and retrieval, and Internet of Things (IoT) monitoring for visibility. MHI seasonal-planning guidance; ASCM warehouse technology overview.
| Approach | What to assess | Seasonal planning question |
|---|---|---|
| Expand use of existing pick stations | Available floor space, labor coverage, and the ability to staff added stations. | Can existing capacity be activated for the surge without creating a new constraint elsewhere? |
| Temporary pick stations | Space, layout changes, process flow, and setup effort. | Can the stations be added and removed when demand changes? |
| AS/RS | SKU and order profile, storage density, retrieval needs, and system integration. | Will the system suit both the peak workload and ordinary operating conditions? |
| AMRs | Travel patterns, task coordination, controls and software integration, and worker safety. | Can robot capacity be coordinated with real inventory and warehouse workflows? |
| Leased automated equipment | Availability, integration, deployment timing, and lease economics. | Does temporary capacity meet the need without committing to permanent peak-scale equipment? |
| IoT monitoring | Data quality, monitoring coverage, and how visibility will inform operational decisions. | Will better information help identify or manage the constraint, or is physical capacity the issue? |
For each candidate, compare peak throughput and non-peak flexibility alongside space, SKU and order fit, WMS/ERP and controls integration, deployment disruption, workforce and safety effects, and the economics of temporary or leased capacity. The cited sources provide no comparable costs or performance figures that establish a generally best technology or universal return on investment. Obtain facility-specific estimates and test assumptions in a pilot rather than relying on vendor claims alone.
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4. Prepare systems, workers, and operations before scaling
Check inventory-data quality and the interfaces among the warehouse management system (WMS), enterprise resource planning (ERP) system, equipment controls, and robotics software where applicable. ASCM cautions that integration takes careful planning and describes IoT data as a way to monitor movement and support resource allocation. MHI’s March 2026 robotics guidance says robotics integration requires an up-to-date WMS able to coordinate robot movement and inventory data. ASCM warehouse technology overview; MHI robotics integration guidance.
Give frontline workers a role in designing and evaluating the pilot. Explain what will change, provide role-specific training, and review safety implications before expanding deployment. MHI’s implementation account emphasizes communication and training as part of scaling robotics.
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MHI’s March 2026 article recommends starting with an integrator or original equipment manufacturer (OEM) and a pilot when exploring robotic packaging and solutions. Treat that as a route to evaluate the facility’s needs, not an endorsement of a particular provider. MHI robotics guidance.
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5. Review the peak and revise the plan
After the surge, compare actual demand and operating results with the forecast and the pilot’s assumptions. Use available records to capture throughput, missed service targets, overtime or labor usage, exceptions, equipment uptime, safety observations, and returns burden. MHI recommends learning from peak performance and using WMS or ERP data to compare peaks, valleys, and averages. Apply the results to the next forecast, capacity plan, and technology scope rather than carrying forward assumptions that the operation did not validate.
What the available figures do—and do not—show
ASCM’s December 2024 updated article says picking can account for up to 55% of a distribution center’s operating costs. This is a general claim in that article, not a facility-specific cost model or a forecast of savings from automation. The same article reports U.S. e-commerce sales of about $1.119 trillion in 2023, compared with $1.040 trillion in 2022; those historical figures are context, not a current demand forecast. ASCM warehouse technology overview.
Neither those figures nor the cited industry guidance supplies a universal capacity threshold, ROI, or ranking of automation technologies. The right decision depends on a facility’s throughput, order profile, SKU mix, layout, labor, software, safety requirements, and budget.
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