Data & Innovation | Financial Services
Intelligent Forecasting for demand planning
An insurance company performs demand forecasting in its various business units and reduces forecasting errors.
About the client
The insurance company provides 24/7 assistance solutions, offering users assistance in case of unexpected events in their homes, vehicles, bicycles, or accompanying their pets.
Founded in Mexico in 1988, it already has a presence in Argentina, Brazil, and Colombia, and is in the process of expanding to other countries in the region.
Needs
The client is looking to implement a solution that allows them to automate the demand planning process for the various business units of the company.
Solution
The proposed solution is the implementation of the Intelligent Forecasting solution, a tool created with Machine Learning, utilizing both supervised and unsupervised learning.
This solution learns from data and helps companies become efficient in their operations, achieve accurate forecasts, and minimize forecasting errors.
Results
The model selects from 10 algorithms and chooses the correct one for each SKU, product family, and market/region to minimize forecast error. 160 SKUs representing the highest service volume were selected, and a demand forecast was obtained with an error ranging from 7.45% to 20%. Additionally, complete independence from the Business Intelligence (BI) area was achieved by the end users, as they currently have total freedom to consume information in the various formats they require.
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