Thinklytics

Mid-Market E-Commerce Brand · Retail & E-Commerce · Austin, TX · 10 weeks

Demand forecasting AI reduced overstock by $2

A mid-market e-commerce company with 4,200 SKUs struggled to manage inventory using a 90-day rolling average, which ignored trends, promotions, and seasonality. We built a demand forecasting model that lowered overstock costs by $2.8 million per year and reduced stockouts from 312 to 81 each quarter.

Challenge

The brand held $11M in inventory on $38M annual sales. Their 90-day average replenishment ignored SKU trends, causing overstock on slow sellers and stockouts on fast movers during peaks. We replaced this with a demand signal that separated growth from decline, improving inventory allocation and availability.

Approach

We created a demand forecast using past sales data, promotional schedules, search and social trends, and seasonal patterns. The model produced weekly SKU-level forecasts with confidence ranges. These forecasts directly guided safety stock levels and automated purchase orders.

Outcome

We cut inventory carrying costs by $2.8 million a year. Stockouts fell sharply, from 312 to 81 each quarter. We lowered inventory from $11 million to $7.4 million without causing more stockouts. Forecast accuracy for an 8-week horizon jumped from 54 to 82 of every 100.

How we dug into the data to spot what’s coming next for the business

Over three months, we saw that early demand changes were slipping past us. So, we pulled in search volume and social engagement data to spot those shifts 4 to 6 weeks before orders showed up. That tweak boosted our 8-week forecast accuracy by 14 points.

How We Used Automation to Fix Inventory Shortages for Good

We plugged the demand forecasts straight into the ERP’s replenishment system. That way, anytime inventory dropped below the safety stock, it automatically triggered purchase orders. No more manual hassle. Inventory stayed tight and matched what we planned to sell.

Results

  • $2.8M Annual inventory carrying cost reduction
  • 312 to 81 Quarterly stockout incidents
  • $3.6M Inventory balance freed
  • 54 to 82 of 100 8-week forecast accuracy

We had $11 million stuck in inventory but still ran out of our best sellers during peak times. Thinklytics built a forecasting model that actually delivers. In the first quarter, we freed up $3.6 million in working capital and cut stockouts by 74%.

VP of Operations, Mid-Market E-Commerce Brand

In the client's terms

The question

Is the data in a state that will survive what we want to build on it?

The situation

Eleven million dollars of inventory against $38M of annual sales, replenished on a 90-day average that treated every SKU the same. Slow sellers accumulated and fast movers went out of stock 312 times a quarter, concentrated in peaks.

What we found

A single average across the catalogue hides the only thing that matters, which is which SKUs are growing and which are declining. The replenishment rule was structurally unable to tell those apart.

What it produced

Carrying cost fell $2.8M a year and quarterly stockouts from 312 to 81. Inventory came down from $11M to $7.4M without increasing stockouts. Eight-week forecast accuracy rose from 54 in 100 to 82.

The roadmap

A forecast built on sales history, promotional calendar, search and social trend signals and seasonality, producing weekly SKU-level output with confidence ranges. Those drove safety stock levels and automated purchase orders directly, so the forecast changes what is ordered rather than informing a conversation about what to order.

The work behind this result

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Frequently asked questions

Why do e-commerce stockouts and overstock happen at the same time?

Because a single replenishment rule treats every SKU alike. This brand replenished on a 90-day average across the catalogue, so slow sellers accumulated while fast movers went out of stock 312 times a quarter.

What early signals predict e-commerce demand?

Search volume and social engagement gave 4 to 6 weeks of warning here before the order data reflected a shift, and added 14 points to 8-week forecast accuracy, taking it from 54 correct in 100 to 82.

How much working capital does SKU-level forecasting release?

This brand took inventory from $11M to $7.4M against $38M of annual sales without increasing stockouts, cutting carrying cost by $2.8M a year and quarterly stockouts from 312 to 81.