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Case StudyFebruary 3, 2026 3 min read

How a Shopping Centre Cleaned 12,000 m² of Concourse During Trading Hours

A composite case study: running autonomous scrubbers safely through a busy mall in daylight, the zones that decided the fleet, and an 18-month payback.

By WhichBot Team

PUDU CC1 Pro autonomous scrubber-dryer

Illustrative scenario.This case study is a composite built from real industry benchmarks and our Fleet & ROI engine, not a specific named customer. Figures are representative, not a guarantee.

A shopping centre can't wait until midnight to clean — spills happen at 2pm, and a concourse that looks tired at lunchtime costs foot traffic. The challenge isn't overnight throughput; it's cleaning 12,000 m² of common area safely, in daylight, around thousands of shoppers. We built the plan around two PUDU CC1 Pro scrubbers running continuous passes.

12,000 m²
Common area maintained
during trading hours
3–4×
Daily passes on hot zones
food court, main mall
2 units
PUDU CC1 Pro fleet
rolling routes
18 mo
Payback
~A$70k upfront

The brief

Cleaning had to move from a nightly event to a continuous background service — without becoming a hazard or an eyesore during trading.

  • ~12,000 m² of polished concourse, food court and mall spine
  • Runs all day around the public — low-speed operation and reliable avoidance are non-negotiable
  • Hot zones (food court) need multiple passes; back corridors need one
  • Must dock and refill discreetly, off the shopper's sightline

Where the passes should land

Not every zone needs the same attention. Mapping foot-traffic against the floor showed where continuous passes actually pay off:

Daily pass coverage by zone
Food court99% coveredMain mall spine98% coveredCentre court98% coveredAnchor entries96% coveredSide malls94% coveredService corridors90% coveredDiscreet dock (BOH)

Routes weighted to foot traffic — the food court gets 3–4 passes a day, quiet corridors one. The robot freshens the busy zones without a human standing over a mop at noon.

The shortlist

Prices are indicative Australian retail, ex-GST. The daytime constraint put unobtrusive operation ahead of raw speed.

ModelCoverageNoiseIndicative price
PUDU CC1 Pro~650 m²/h~70 dB~A$32,800
PUDU CC1~490 m²/h~70 dB~A$30,100
Gausium Phantas~350–700 m²/h~60 dB (quieter)~A$44,600

Two CC1 Pro units covered the concourse with rolling passes and a mid-day charge. Where a centre wants the lowest possible noise in premium retail zones, the quieter Gausium Phantas is the trade-up.

The money

Two units plus mapping came to ~A$70,000 upfront, displacing most of a daytime cleaning shift for a net ~A$3,800/month:

Cumulative net savings vs. upfront cost
-$70k-$2k$67kbreak-even0mo12mo24mo36mo

Break-even at ~18.4 months on a $70,000 upfront outlay saving $3,800/month.

The payback captures labour. It misses the reason centre managers actually sign off: a concourse that is always presentable, not just clean at 6am and tired by lunch — which is the version shoppers see.

Would it work for your centre?

Frequently asked questions

Can cleaning robots run safely in a mall during trading hours?
Yes. Autonomous scrubbers like the PUDU CC1 Pro use obstacle avoidance and people-detection to slow and reroute around shoppers, so common areas can be cleaned continuously in daylight instead of only after close.
How many scrubbers does a shopping centre concourse need?
In this composite, two PUDU CC1 Pro units maintained ~12,000 m² of common area across trading hours, running rolling passes so the busiest zones were freshened several times a day.
What's the payback on cleaning robots for a shopping centre?
About 18 months here: ~A$70,000 upfront for two units plus mapping, against ~A$3,800 a month in displaced daytime cleaning labour — before counting the presentation value of a constantly-clean concourse.
Do daytime cleaning robots disrupt shoppers?
Handled well, no — they run at low speed, yield to foot traffic, and cover a zone in minutes. Centres report they read as part of the experience rather than an obstacle.
#retail#shopping-mall#scrubber#roi#case-study#pudu

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