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Case study
Demand Prediction in a bakery chain — fewer returns, higher turnover
by MABaker
Date 06:08:2026

Demand Prediction in a bakery chain — fewer returns, higher turnover

For a bakery chain running several dozen points of sale, working with MAWEB produced a 58% reduction in returns (at book prices) alongside a 10% rise in turnover. That combination matters, because in this industry the simplest way to cut returns is to produce and deliver less — which usually ends in empty shelves and falling sales. Here turnover did not merely hold; it grew, which shows the model was hitting real demand rather than just trimming deliveries.

Beyond the reduction in returns, the client points to a second benefit of the work: their sales data was put in order. Figures from individual points of sale, previously scattered and inconsistent, were unified and became a basis for managing the whole chain — not only for forecasting.

The client

A bakery chain operating across several dozen points of sale. Before the implementation, production and delivery planning rested on the experience of staff at each individual outlet, which at that scale produced inconsistency between locations and made centralised stock management difficult.

Service package

Demand Prediction — an AI model forecasting sales for individual outlets and products, supporting decisions on production and delivery volumes.

Objectives

  • Reduce returns and the losses caused by overproduction, without reducing product availability in the shops.
  • Hold or increase turnover — the reduction in returns could not come at the cost of sales.
  • Unify and organise sales data from every outlet as a basis for better management of the chain.

Method of measurement: a comparison of returns at book prices and of turnover, before and after the prediction model went live.

Delivery

MAWEB collected and organised the client’s historical sales data from each point of sale — sales, deliveries, returns and the external factors that move demand (calendar, days of the week, public holidays among them). On that basis we built a predictive model forecasting sales separately for every outlet and product, generating daily recommendations for production and delivery volumes.

A key element of the model was the safeguards limiting the risk that optimising returns would reduce product availability and turnover — the model does not cut deliveries mechanically, it matches them to the demand expected at that particular location.

Alongside the implementation, data from the individual outlets — previously kept differently depending on the location — was unified into a common format. Independently of the forecasts themselves, that gave the client a coherent picture of sales across the whole chain, which they also use for day-to-day management.

Results

  • Returns reduced by 58% (at book prices, against the period before implementation).
  • Turnover up 10% over the same period — the reduction in returns did not come at the cost of sales.
  • Sales data organised across the chain — named by the client as an additional benefit of the work, going beyond prediction itself and supporting better management of the business.