Case Study — Goodwill of Kentucky

Forecasting Donations Across 68 Stores

Goodwill of Kentucky used Safari AI to count vehicles at the drive-thru donation lanes of its 68 stores, turning every drop-off into forecasting data.

68 stores
Donation Lanes
Hourly
Drop-Off Counts
Daily
Donation Forecasts

Measured from existing drive-thru cameras

Cars lined up at a thrift store donation drive-thru as an attendant unloads bags

A long line of cars at a donation drive-thru with one attendant
The Challenge

Donation volume was the hardest number to plan around.

Every store depended on donations it could not schedule. Counting cars by hand took staff away from the work, surges caught teams short-handed, and inventory piled up in some stores while others ran thin.

  • Donation volume was counted by hand, if at all
  • Drive-thru surges caught stores short-staffed
  • Inventory was balanced across 68 stores on estimates
See How Safari AI Solves This
What We Deployed

Every drop-off counted, in every store.

STEP 01

Connected to drive-thru cameras via an on-premise server

Existing cameras over each donation drive-thru fed a lightweight on-premise server Safari AI deployed, so video was processed on site.

STEP 02

Counted every donor vehicle

Computer vision counted vehicles entering each donation lane and how long they waited to unload, store by store and hour by hour.

STEP 03

Turned counts into forecasts

Daily and hourly drop-off counts fed donation forecasts, surge predictions and staffing plans across all 68 stores.

Donation Drive-Thrus
All 68 stores · Today · Updated 17:05
LIVE
Donor Vehicles
3,412
All stores today
Vs. Forecast
96%
Within forecast band
Unload Wait
4.2min
↓ 0.8 min vs target
Surge Stores
7
Need extra cover
Donor vehicles per hour · Store #12
NormalSurge
STAFF ALERT 40
08:0009:0010:0011:0012:0013:0014:0015:0016:0017:00
StoreVehiclesForecastWaitStatus
Store #123182905.1mSurge
Store #41881952.8mOn forecast
Store #31961401.9mUnder plan
Store #572412363.4mOn forecast

Note: Illustrative data

The Result

Better forecasts, better-staffed drop-offs.

Staff sorting donated clothing and housewares into bins and carts
68 stores
Donation Lanes

Every donor vehicle was counted at every store's drive-thru, giving the whole network one consistent baseline.

Two attendants quickly unloading donations from a car
Daily
Donation Forecasts

Forecasts built on counted drop-offs showed which stores would be flooded and which would run short, so inventory moves before it piles up.

A well-stocked thrift store sales floor
Hourly
Surge Planning

Hour-by-hour patterns showed when surges hit, so stores schedule help for the rush instead of reacting to it.

Action Center

Action Center: From Insight to Action.

Measurements like these feed Safari AI's Action Center, which flags a store heading into a donation surge while there is still time to add help.

Measure
Every lane, every store
→
Alert
Surge above forecast
→
Recommend
Add cover, per SOP
→
Dispatch
Store manager notified
→
Outcome
Faster drop-offs
  • Real-time recommendations to improve KPIs via email, SMS, robocall, mobile app, Power BI, or API.
  • Recommendations grounded in your own written SOPs, not generic best practices.
What it means for donors Surge flagged → ↑ Staff at the lane → ↓ Unload wait → ↑ Donations processed
Get In Touch

See this across your own locations.

Goodwill of Kentucky forecast donations from cameras already watching its drive-thru lanes. We'll walk through which stores and lanes are the best fit for a pilot on your cameras.

Vehicle Counts Speed of Service

Get In Touch

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