Case study · Retail and Logistics

Turning consumer behavior into forecasts

A forecast that learns from real consumer behavior and keeps the shelf stocked with what actually sells. Sales, purchasing and production, aligned on the same data.

Shelves always stocked

Demand anticipated with AI

Weather, events and trends

Where it all started

Building on a solid base, the next leap

A consumer goods company with a wide product catalog and a presence across multiple channels. For years, planning relied on sales history, a reliable base.

The next step was clear: add the signals that drive demand today (weather, events and trends) to that history, to anticipate more accurately, product by product.

The challenge

Anticipate real demand for every product by factoring in current consumer behavior.

Keep shelves stocked with the products that sell fastest.

Match inventory to the real pace of sales and free up space and capital.

Work from a single forecast shared across sales, purchasing and production.

Give the planning team more time to decide.

What we proposed

A forecasting model that learns from real consumer behavior and adds the signals that drive demand (weather, events and trends) to sales history, built on governed data with a single forecast for the whole organization.

The shift

From a solid base to a forecast that gets ahead

From history to real behavior, product by product.

Before

Forecasting started from sales history

Assortment was planned with the best information available

Each area worked from its own view of demand

Consolidating data took up a large part of the team's time

Now

The model adds weather, events and trends to the history

The forecast is refined by product and channel, week by week

A single forecast aligns sales, purchasing and production

The team spends its time deciding and improving the plan

stockouts

- 1 %

forecast accuracy

+ 1 %

demand signals integrated

The value generated

What your planning team gains

Shelves stocked with what actually sells

The forecast prioritizes the products that sell fastest, so your customers always find what they're looking for.

Inventory at the pace of demand

You restock at the real pace of sales and free up space and capital for what actually moves.

Weather and events, working for you

The forecast reads weather, events and trends, and gets ahead of demand peaks.

One plan that aligns everyone

Sales, purchasing and production decide on the same forecast, in an S&OP process that flows.

A forecast that explains its reasoning

Every number is explainable and traceable: the team understands why it goes up or down, and decides with confidence.

Gets better every week

The model is retrained and monitored in production: the more your business sells, the more accurate it gets.

What the client says

The forecast has become the starting point for our decisions. We walk into sales, purchasing and production meetings with a number we trust, and it shows on the shelf and in the team’s day-to-day.

Demand Planning Manager

Consumer goods company

F&Q

Frequently asked questions

What is AI demand forecasting?

AI demand forecasting estimates how much of each product will sell by combining your sales history with external signals like weather, events and trends. It learns from real consumer behavior and updates continuously.

What does it offer over forecasting in Excel or a dashboard?

It adds anticipation: Excel and BI dashboards are a solid base for seeing what already happened, and AI adds the ability to predict what’s coming and explain why. It’s also built on governed data, so sales, purchasing and production share the same forecast.

Can the model be trusted?

Yes. The model learns from your data and from verifiable signals, and every forecast is explainable and traceable. We measure its accuracy continuously (with MAPE, for example) and retrain it as the market evolves.

How is data security and governance handled?

A forecast is only as reliable as the data behind it, so we start with data governance and quality. At BertIA, an AI consultancy based in Barcelona, we deploy models with access control, traceability and compliance with the EU AI Act.

What does it take to implement this in my company?

All you need is your sales history and access to the sources that drive your demand. We start with an assessment, integrate and govern the data, train the model, and put it into production while measuring results. It’s an approach that works for any consumer goods company with a wide catalog and multiple channels.