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Praval Technologies

Case study

The shelves were empty. The warehouse was full.

A distributor asked for a better demand-forecasting model. A smarter forecast still died in a two-day manual gap. The issue wasn't prediction, it was that nobody owned the decision.

Fewer stockouts
40%Fewer stockoutsReal-time signals replaced rolling averages
Less working capital tied up
28%Less working capital tied up
Faster order fulfilment
30%Faster order fulfilment

What they asked for

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

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

The distributor faced a paradox: stockouts and overstock at once. Slow stock had frozen working capital while hero SKUs bled repeat sales.

Forecasting on four-week averages. Missed seasonal spikes, promo lifts and regional demand gaps.

Manual replenishment, two-day lag. In a fast-moving category, two days is expensive.

Slotting by arrival date, not turnover. Fast movers sat in the least accessible locations.

What we built

Predictive forecasting. Sales, promotions, weather and regional signals fused into daily replenishment calls, per SKU, per region.

A conversational interface. Buying managers got a plain-language way to ask, and to get ranked, confidence-scored answers. The AI surfaces; they decide.

AI-led slotting. The top 20% of SKUs by turnover moved into primary pick locations, and fulfilment speed rose immediately.

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

Recognise any of this in your own estate?

Start with the problem rather than the technology, and we will tell you honestly whether it is ours to solve.