At PredictLand we have designed several solutions related to logistics and supply chain optimization that rely on Machine Learning and, therefore, on Artificial Intelligence.

We would like to highlight several projects carried out to forecast demand for various clients and a project carried out for another of our clients whose objective has been to minimize the total costs associated with their product distribution network.

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Demand Forecast

Demand forecasting applications based on Artificial Intelligence are seamlessly incorporated into the workflow of operational and commercial departments.

This is because, unlike classic demand forecasting algorithms that only allow working with sales data, AI algorithms allow incorporating more information into the models, such as data related to product taste, nutritional properties, promotional campaigns, etc.

Using these techniques we have developed various demand forecasting solutions for different clients, each of them completely adapted to their business reality.


Using an optimization model based on Linear Programming, and exploiting the demand history which in turn has been enriched with other data sources such as supply, storage, handling and transport costs, it has been possible to identify the number and geographical locations of warehouses needed to ensure that costs are minimized, while at the same time maintaining a high level of service to the end customer.

Additionally, the algorithm has been encapsulated within a “what-if” simulator, thus creating a flexible tool that allows testing and validating any economic effect caused by demand or price alterations in a quick and easy way, thus allowing to anticipate possible operational changes and measure their potential impact.

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