CASE STUDY

The shelves were empty.

The warehouse was full.

They asked us to build a better demand-forecasting model. The real problem: a smarter forecast still died in a two-day manual gap. Nobody owned the decision — and the system had quietly outgrown its design.

Real Problem

"Build us a better demand-forecasting model. Ours keeps getting it wrong."

Scope

A better forecast still died in a two day manual gap. The issue wasn't prediction — it was that nobody owned the decision.

The Challenge

A distributor moving 2,400+ SKUs daily across 340 retail accounts in Gauteng and the Western Cape faced a paradox: stockouts and overstock at once — slow stock had frozen [R_m] in working capital while hero SKUs bled repeat sales. A diagnostic found three compounding causes.

  1. Forecasting on four-week averages. Missed seasonal spikes, promo lifts, and Gauteng-vs-Cape demand gaps.
  2. Manual replenishment, two-day lag. In a fast-moving category, two days is expensive.
  3. Slotting by arrival date, not turnover. Fast movers sat in the least accessible locations.

The Solution

  • Predictive forecasting: We fused sales, promotions, weather, and regional signals into daily replenishment calls — per SKU, per region.
  • A conversational interface: We gave buying managers a plain-language way to ask, and get ranked, confidence-scored answers. The AI surfaces; they decide.
  • AI-led slotting: We moved the top 20% of SKUs by turnover into primary pick locations — fulfillment speed up immediately.

The Impact

40%

Fewer stockouts

Real-time signals replaced rolling averages.

28%

Less working capital tied up

Slow-moving overstock freed from warehouse.

30%

Faster order fulfilment

Tighter replenishment cycles plus AI-led slotting.

The win wasn't a better forecast. It was a decision that finally had an owner.