Use case
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Accurately match supply and demand

Use historical operational data and contextual information to accurately forecast demand, and efficiently assign resources from work schedules to production capacity.

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Apply modern Machine Learning techniques to get the most accurate forecasts.
Automate the process using modern data engineering tools.
Limit excess supply and minimize costs.
Apply modern Machine Learning techniques to get the most accurate forecasts.
Automate the process using modern data engineering tools.
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Forecasting and supply optimization for B2B tech startup.

Challenge

A B2B startup in the food delivery industry needed to find a way to efficiently assign schedules to the delivery persons, and reduce costs.

Solution

We built a robust Machine Learning system that uses historical operational data and other data sources (like weather data) to accurately forecast real-time demand. This allowed the client to minimize the number of people available in each area while maintaining their Service Level Agreements (SLA).
Results
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Results
We achieved 40% better predictions and a 70% reduction of excess capacity.

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