Snam Rete Gas improves customer service through forecasting

Snam Rete Gas, Italy’s leading natural gas distributor, has chosen SAS, supplier in business analytics software and services, to forecast daily gas requirements and ensure efficient and safe transport to all network users.

Snam Rete Gas is the leading distributor of natural gas in Italy, with over 60 years experience planning, building and managing a network of pipelines of over 31,000 kilometers, 11 compressor stations, eight regional operating centers and 55 maintenance centers. The only Italian company to perform regasification of liquefied natural gas (GNL Italia), it moves gas for over 60 shippers.

Snam Rete Gas, listed on the Milan stock exchange since 2001, follows a system of governance in line with international best practices. Per its 2008-2011 plan, it is investing €4.3 billion (US$5.5 billion) to increase transport capacity.  

The Gas Demand Forecasting project, implemented by Accenture with SAS, delivers accurate and clear data, segmenting gas requirements by user type and geography. Its reliable and user-friendly forecast models integrate with existing applications and are easily accessible online. The system, which automatically generates new models for specific time periods, greatly improves the company's ability to deliver timely and accurate forecasts.

"The solution meets all technological standards and needs of Snam Rete Gas," explains Carmine Artone, Accenture Senior Executive. "The software's evolution is completely managed by Snam Rete Gas, eliminating technological obsolescence and reducing operating costs. Integrating the solution with other applications supports current business processes. The unique synergy of SAS' analytics capabilities and Accenture IT skills delivers real business value to Snam Rete Gas." 

"Increased precision was our priority, but we also needed better forecasting," says Natale Maiocchi, CEO of Snam Rete Gas. "Operating in a regulated market, we must comply with very strict service quality rules that range from forecast segmentation by geography and user type to predictive forecasting where the frequency of nominations increases. We also needed to update technologies that were implemented at different times based on older models that don't meet today's business needs."