Bloom Retail: Inventory forecasting automation
AI predicts demand per store and triggers reorders automatically, shelves stay stocked without the spreadsheet gymnastics.
31%
Less capital locked in overstock
22%
Fewer stockouts on bestsellers
89%
Forecast accuracy at SKU level
15 hrs
Manual planning work eliminated weekly
Overview
Bloom Retail operates 24 home-goods stores, and every one of them was ordering on gut feel. Bestsellers stocked out in busy locations while slow stores drowned in overstock, and the planning team spent two days a week wrestling spreadsheets to make it all work.
We built a demand-forecasting engine that predicts sales per store per SKU, then triggers reorders automatically. Planners went from spreadsheet operators to exception managers: the system handles the routine, humans handle the judgment calls.
The challenge
- 01
Overstock tied up cash in slow stores while busy locations lost sales to empty shelves, the same SKU, opposite problems.
- 02
Ordering ran on manager intuition and a 40-tab spreadsheet updated twice a week by hand.
- 03
Seasonal swings and local events (festivals, weather) made demand unpredictable with manual methods.
- 04
No one could answer the basic question: how accurate are our forecasts? Because there were none.
Approach
How we got there.
No magic, just a disciplined process, applied with care at every step.
Step 1
Unify the data
Connected POS, warehouse, and supplier feeds into one clean dataset, two years of sales history, promotions, and seasonality, deduplicated and normalized.
Step 2
Forecast per store
Built demand models at store × SKU granularity, factoring in seasonality, promotions, local events, and weather, refreshed nightly.
Step 3
Automate reorders
Reorder triggers fire automatically when forecasted demand crosses thresholds, with safety-stock buffers tuned per category. Planners approve exceptions, not every order.
Step 4
Make it visible
A simple dashboard shows forecast accuracy, stock health, and money tied up in overstock, the numbers the CFO actually cares about.
The outcome
31% less overstock
“We stopped guessing. The system orders better than our best manager did, and our planners finally spend their time on strategy instead of spreadsheets.”
- 22% fewer stockouts on bestsellers
- 89% forecast accuracy at SKU level
- 15 hrs manual planning work eliminated weekly
Have an idea? Let’s ship it.
One conversation is all it takes to turn “someday” into a launch date. Free consultation, zero pressure, fixed quote.