It anticipates the needs of your customers, improves productivity and increases process sustainability.
This solution is used to manage in advance all the variables that drive the variation of your customers’ demand, to allow you to define an effective business strategy, based on your specific needs.
Thanks to demand forecasting you can:
Pre-empt the maintenance of your systems.
By exploiting the predictive logics of artificial intelligence, you can predict the risk of malfunctions or faults with your system, planning interventions in advance and saving huge resources. From historical data, we assume the factors that cause specific problems, and, by interpreting the algorithms, we help you to optimize the preventive maintenance process to maximize its effectiveness.
Leverage the predictive power of algorithms
to define winning Energy Trading strategies.
We offer models for the forecasting of energy demand, on an hourly basis, of the transits between the areas of relevance and the expected imbalance on the lines. These models are based on algorithms that use weather data and historical market trends to help you predict future behavior and define effective participation strategies.
Predicting user behavior and building personalized marketing campaigns help to anticipate subscription cancellations. Data on browsing, habits and characteristics of customers become useful information to create precise and effective forecasts.
Churn risk forecast seven months quicker
80-90% accuracy in predicting churn risk
200% total improvement over as-is methods
To ensure the continuous coverage of requirements with the aim of minimizing overstock, a source of waste, we develop predictive models to predict future demand on more than a thousand references, then determining, through optimization algorithms, the reorders to be made day by day.
Demand forecasting and reordering optimization of all references
+55% increase in effectiveness in forecasting demand
-20% reduction in the value of immobilized warehouse stock
Predicting possible network imbalances allows for the optimization of risk hedging strategies. Our predictive algorithms are updated daily to follow the trends of this highly variable market.
-5% reduction of false positives
+15% increase in accuracy of
predictive models
For technological products, whose characteristics evolve quickly, forecasting sales volumes is complex. Machine learning models, enriched with exogenous data, are used to predict demand and optimize warehouse reorders.
Demand forecasting and reordering optimization of all references
+50% increase in effectiveness in forecasting demand
We implement predictive logics on the maintenance of distributed networks to anticipate breakdowns and to direct controls to the highest risk areas. This system helps increase verification capacity, maximizing coverage and improving service.
+ 30% accuracy of Fault Prediction
– 30% losses reported by third parties
+ 20% of km monitored with the same teams
