Distribution center automation is a capital-intensive, multi-year commitment β€” and the difference between a strong business case and a failed one usually comes down to how the inputs were set. This estimator is built for engineers and operations leaders who need a credible, defensible starting point before engaging vendors or presenting to a capital committee. Adjust the parameters below to reflect your facility, labor structure, and financial assumptions, and the model will instantly return fleet size, total CapEx, annual savings, payback period, NPV, and IRR across your evaluation horizon.

How to use This Tool

Work through the five input sections from top to bottom. Start with your Operations inputs β€” these drive fleet sizing. Move to Labor to set the savings baseline. Use Equipment & Hardware to reflect current vendor pricing or your integrator’s quote. Project Cost Factors captures the soft costs that most first-pass models miss. Finally, set your Financial Assumptions to match your organization’s hurdle rate and evaluation horizon. Every output updates in real time as you move the sliders.

How to Use This Tool

Operations

Forklift operators / shift β€” Count only the operators whose primary role is horizontal pallet transport, not those assigned to putaway, staging, or loading. Including non-automatable roles will overstate your labor savings. A typical mid-size distribution center running 500–1,000 pallets per day operates with 4–8 transport operators per shift.

Shifts / day β€” AGV economics improve significantly in multi-shift operations. A 2-shift configuration is the common break-even threshold; single-shift deployments rarely achieve a payback under 5 years unless labor rates are exceptionally high.

Working days / year β€” 254 days (the default) reflects a standard Monday–Friday operation with holidays. Extend to 280–310 for 6-day operations, or 350+ for near-continuous facilities. Higher utilization directly compresses payback.

Pallets / day β€” Use your average daily throughput, not your peak. Peak throughput is used for system design, but average throughput drives the savings calculation. If your operation has significant seasonal swings, consider running the model at both average and peak to understand the range.

Labor

Avg fully-burdened rate β€” This should include base wages, benefits, payroll taxes, workers' compensation, and an allocation for turnover and training costs. A defensible fully-burdened rate for a warehouse operator in most North American markets currently falls between $65,000–$95,000 USD per year. Using base wage alone will understate your savings by 25–40%.

Labor reduction % β€” This is not headcount elimination β€” it is the percentage of transport labor hours that the AGV system replaces. A well-designed system in a standard pallet-in / pallet-out environment typically achieves 55–75% labor reduction. Reductions above 80% should be validated carefully; they require highly standardized load types, minimal exception handling, and a mature WMS integration.

Residual FTE override β€” Every AGV deployment retains some human operators for exception handling, manual zones, and system oversight. A fleet of 8–15 AGVs typically requires 2–4 residual FTEs. This input prevents the model from projecting a zero-labor state, which is never realistic.

Wage inflation / yr β€” 3.0% is a reasonable long-run assumption for North American warehouse labor. In tight labor markets or union environments, 3.5–4.5% may be more appropriate. This input compounds over your evaluation period and meaningfully affects total savings in years 3–5.

Equipment & Hardware

AGV unit cost β€” List pricing for a standard autonomous counterbalanced pallet truck or unit-load AGV currently ranges from $150,000–$280,000 USD depending on payload, navigation technology, and vendor. AMR solutions for lighter payloads can come in lower. Use your integrator's budgetary quote if you have one; use the midpoint of the range ($200,000–$240,000) for early-stage feasibility.

AGV-to-fork ratio β€” This is how many AGVs you need per forklift operator replaced, accounting for the fact that AGVs are slower, require charging time, and cannot handle every task. A ratio of 1.2–1.5Γ— is realistic for well-optimized, high-utilization routes. Ratios above 2.0Γ— typically indicate route complexity, long travel distances, or significant idle time β€” and should prompt a layout review before proceeding.

Charger ratio (AGVs : charger) β€” In a continuous multi-shift operation, plan for one charger per 2–3 AGVs. Opportunity charging architectures may allow a higher ratio, but this needs to be confirmed with the OEM based on your duty cycle and shift structure.

Annual AGV maintenance % β€” Vendor-contracted maintenance programs typically run 7–10% of unit cost per year. Budget toward the higher end for older fleets or high-cycle applications. This cost is non-discretionary; excluding it from the model is a common mistake in first-pass analyses.

Project Cost Factors

WES software % β€” Warehouse Execution System software, which manages task dispatching and traffic, typically runs 12–20% of hardware cost for a standalone AGV deployment. If you are integrating with an existing WMS, budget for additional interface development on top of this.

Commissioning % β€” Commissioning β€” system testing, operator training, and go-live support β€” typically runs 15–22% of hardware cost. Larger fleets and more complex traffic environments trend toward the upper end. Underestimating commissioning is one of the most common causes of budget overruns in automation projects.

Integration % β€” ERP, WMS, and infrastructure integration costs typically add 8–15% on top of hardware. If your WMS is legacy or heavily customized, treat this as a risk factor and add contingency accordingly.

Contingency % β€” A 15–20% contingency is appropriate for early feasibility-stage estimates. If you are working from a detailed scope and vendor quotes, 10–12% is defensible. Never present an automation business case to a capital committee without contingency β€” it signals engineering maturity, not padding.

Floor prep β€” AGVs require flat, clean, well-marked concrete. Laser-guided and natural-feature navigation systems are more tolerant than wire-guided predecessors, but floor flatness (FF/FL values) and aisle marking still matter. Budget $3–$10/sq ft for the affected floor area; a 15% footprint assumption is reasonable for a standard selective racking environment.

Financial Assumptions

Discount rate (WACC) β€” Use your organization's weighted average cost of capital, or a hurdle rate between 7–12% for most industrial capital projects. A higher discount rate compresses NPV and extends effective payback β€” if your organization uses a 12%+ hurdle rate, AGV projects will need a correspondingly stronger labor savings case to clear the bar.

Evaluation period β€” 5 years is the standard evaluation horizon for warehouse automation in most capital planning frameworks. Extending to 7–10 years improves NPV and IRR optics but introduces more uncertainty in labor rate and throughput assumptions. Use 5 years for the base case; run 7–10 years as an upside scenario.

Disclaimer

This tool is designed for conceptual and feasibility-stage estimation only. It is not a substitute for a full engineering study, detailed vendor quotation, or formal capital appropriation analysis. Outputs are highly sensitive to input assumptions β€” particularly labor rates, labor reduction percentages, and project cost factors and should be stress-tested using the built-in sensitivity analysis before being presented to a capital committee or used to inform vendor selection. Fleet sizing produced by this model does not account for facility layout, traffic path design, load variability, or system availability requirements, all of which must be validated by a qualified systems integrator. Payback periods and NPV figures are estimates based on the parameters you provide and do not constitute a financial guarantee or professional investment recommendation. The Art of Intralogistics assumes no liability for decisions made based on outputs generated by this tool.

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