Forecasting

The strategic power of forecasting.

Predicting the occurrence of a future event,
such as a fault, a breakage, or the progress of
a process, such as the evolution of the market,
is a decisive element that can be used to take
preventive measures and actions, minimizing
risks and maximizing profits.

Thanks to the customized development
of artificial intelligence predictive models,
we automate forecasting operations, allowing
the precise prediction of processes behavior.

How it works

From the available data, internal and
external to the system, the models
composetime series on the variables
that are representative of the phenomenon.

This data base becomes raw material for
the training of machine learning and
deep learning models, which learn to
recognize the fundamental components
of the phenomenon and predict the
evolution of the process.

Demand
forecasting

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:

  • better understand the influencing factors of the market
  • increase the productivity of the planner
  • improve inventory management
  • reduce the risk of an ineffective responseon the market
  • reduce the risks of understock and overstock

Demand
forecasting

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:

  • better understand the influencing factors
    of the market
  • increase the productivity of the planner
  • improve inventory management
  • reduce the risk of an ineffective response
    on the market
  • reduce the risks of understock and overstock

Churn risk
prediction

Prevent the risk of subscribers leaving.

Pre-quantify the risk of unsubscribing from your services or products to take prompt corrective actions. Our model develops the forecast basing on the historical data of your customers: the patterns of use of the products, the engagement with your contents and the overall loyalty to your company.

Fault
prediction

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.

Fault
prediction

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.

Anomalies
and consumption
forecast

Monitor the status of your machinery
and prevent possible faults or
performance deteriorations.

This monitoring system is based on consumption forecasting algorithms which, for each system and at each moment, calculate a specific expected consumption value and assign it to the recorded value, verifying that the two match. This enables the timely identification of any anomalous peaks or trends in performance deterioration, based on the deviation of values.

Energy Trading Forecasting

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.

Energy Trading Forecasting

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.

contact
an expert

Case studies

Churn Risk Forecast
in Publishing

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

Demand Forecasting
in the world of food

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

Forecasting for
participation in the
electricity markets

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

Market demand forecast
for mobile telephony

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

Predictive maintenance
of gas networks

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