In the natural gas distribution sector, effective emergency call management is critical. BitBang supported one of Italy’s leading energy operators in developing an advanced predictive model capable of forecasting operational load and enabling more efficient resource planning.

The client is one of Italy’s leading natural gas distribution operators, responsible for managing emergency interventions and service calls across an extensive territorial infrastructure.

The client needed to improve the management and allocation of resources dedicated to handling emergency calls.

The challenge was compounded by several factors:

  • Highly heterogeneous data coming from multiple sources, with varying levels of quality and completeness
  • Data not originally collected for analytical purposes
  • A large number of variables, not all of which had been fully validated
  • The need for explainable forecasts to support reliable operational decision-making

The solution is based on advanced data science and predictive modeling techniques, integrating:

  • Internal operational data
  • Historical and real-time data
  • Contextual information
  • Third-party data, such as weather conditions

The model was designed to ensure a high level of transparency and explainability in the forecasts it generates.

BitBang developed a custom predictive algorithm capable of estimating, for a given day or period, both the volume and type of emergency calls.

An initial analysis of internal processes and data correlations made it possible to define key constraints required to achieve reliable forecasts, including appropriate and manageable time horizons.

Thanks to the implemented solution, the client is now able to:

  • Plan and allocate resources in a more balanced and effective way
  • Improve operational efficiency in emergency management
  • Support decision-making with reliable and explainable forecasts
  • Reduce waste while improving overall service quality