Predictive AI · Custom-built
Sales forecasting and inventory alerts for a food distributor
A food distributor with thousands of products could not predict sales, and nobody could watch every inventory level by hand. Self-learning statistical models now forecast each product every day and send alerts before stock runs out or piles up.
The problem
The company distributes thousands of food products, and sales were hard to predict. Seasonality, monthly and weekly cycles, long term social and economic trends, changes in prices and exchange rates, and random one-time spikes all pulled each product in different directions.
The sheer number of products made it virtually impossible for staff to check and analyse inventory levels every day, so a looming shortage or overstock could go unnoticed for weeks. Products ran out, losing sales. Others were overstocked and expired, wasting stock and cutting profit.
What we did
We developed self-learning computational statistical models that take each product’s sales history apart into its separate components (seasonality, weekly and monthly cycles, long term trends, price and exchange rate effects, one-time spikes) and predict how each will behave in the future.
The models recalculate every product’s sales forecast daily and use it to predict inventory levels. From those predictions the system sends alerts:
- When to reorder, and how many to order.
- When to reduce stock through sales or promotions, before it expires.
What changed
Unintended out-of-stock events fell to near zero, which raised top line sales. Spoilage dropped sharply, cutting waste and improving profit.