Troubleshooting

Why Your AMR and AGV Numbers Are Wrong: Troubleshooting Intralogistics Automation

The costly, repeatable mistakes that wreck AMR and AGV justifications, and how to catch each one before you sign a purchase order.

Symptom: your AMR ROI Calculator shows a 14 month payback but the fleet actually lands at 26 months. Root cause is almost always a phantom utilization assumption. People plug in 90 percent utilization when the AMR Utilization Calculator, fed real telemetry, shows 55 to 65 percent because robots idle at charge stations, wait behind traffic, and sit through shift changeovers. Fix: derate planned throughput by the measured idle fraction. If a robot does 18 moves per hour at 60 percent utilization, size for 10.8 effective moves per hour, not 18. That single correction usually adds 8 to 12 months to a payback estimate.

Symptom: the AGV Fleet Size Capacity Calculator says 6 vehicles cover demand, then you hit backlogs at 4 pm daily. Root cause is averaging demand over a 24 hour day instead of sizing to the peak hour. A plant moving 480 loads per shift does not move 60 per hour evenly. The 3 pm to 5 pm window often carries 35 to 40 percent of volume. Fix: pull the busiest 60 minute bucket from your WMS and size to it. If peak is 95 loads per hour and each vehicle clears 16, you need 6 vehicles for peak, not the 4 an average would suggest.

Symptom: cycle time predictions are 30 percent optimistic across every route. Root cause is ignoring congestion at intersections and pick faces. The Pickup and Dropoff Cycle Time Calculator gives clean travel-plus-handling numbers, but real corridors add queueing. Run the Route Congestion Score Calculator: a score above 0.7 on a shared aisle typically inflates cycle time by 20 to 45 percent. Fix: apply a congestion multiplier. A 90 second theoretical cycle on a heavily shared aisle realistically runs 115 to 130 seconds. Budget the extra 25 to 40 seconds per cycle or reroute to a less contested lane.

Symptom: battery math looks fine on paper but robots die mid-shift. Root cause is confusing nameplate capacity with usable capacity and forgetting depth-of-discharge limits. A 40 amp-hour lithium pack is not 40 usable amp-hours. Most fleets cap discharge at 20 to 80 percent state of charge to protect cycle life, leaving roughly 60 percent, or 24 amp-hours, actually available. Fix: run the AMR Battery Charge Capacity Calculator on usable energy and include opportunity charging windows. A robot drawing 8 amps continuous on 24 usable amp-hours runs 3 hours between charges, not the 5 hours a nameplate calc implies.

Symptom: the tugger train justification collapses because trip counts are too high. Root cause is loading tuggers to axle limit instead of dock-to-line takt. The Tugger Route Capacity Calculator only helps if you feed it the real cart count the line consumes per loop, not the maximum the train can pull. A train rated for 6 carts that only needs 4 per delivery loop wastes 33 percent of capacity and forces extra staging. Fix: match cart count to line consumption rate. If the line burns 4 carts every 45 minutes, size the loop to deliver 4 carts on a 40 minute cycle with a 5 minute buffer.

Symptom: labor savings evaporate after go-live and finance claws back the business case. Root cause is counting displaced labor hours that never leave the payroll. The Internal Logistics Labor Savings Calculator shows 3.5 operators freed, but if you cannot redeploy or attrition them, you saved motion, not money. Fix: separate hard savings from soft. Only count a headcount as removed if there is a real backfill plan. A realistic first-year capture is 60 to 75 percent of theoretical labor savings; a plant claiming 100 percent on day one is almost always overstating the case by 8,000 to 15,000 dollars per displaced operator.

Symptom: two calculators disagree on cost per move by 40 percent. Root cause is inconsistent denominators and mixed units. One model divides annual cost by scheduled moves, another by completed moves, and completed moves run 10 to 15 percent below scheduled once you subtract failed pickups and aborted missions. Fix: standardize on completed moves in the Material Move Cost Calculator and log the failure rate separately. If a fleet schedules 100,000 moves but completes 88,000, a cost model on the wrong denominator understates cost per move by roughly 12 percent, quietly hiding a real reliability problem.

Symptom: the autonomous forklift payback keeps slipping quarter after quarter. Root cause is treating integration, safety scanners, floor marking, WiFi coverage, and change management as free. The Autonomous Forklift Payback Calculator is only honest if the denominator includes these. Hardware is often 55 to 65 percent of true project cost; integration and infrastructure add another 35 to 45 percent. Fix: add a 1.4 to 1.6 multiplier on sticker price before computing payback. A 120,000 dollar forklift with a 20 month hardware-only payback realistically pays back in 28 to 32 months once the fully loaded 180,000 dollar cost is used.

Published 2026-07-01.