Managing energy consumption in manufacturing environments is increasingly complex. By applying AI-based forecasting and optimization on a daily basis, this manufacturing company transformed how energy decisions are made, improving efficiency and delivering substantial cost savings.

The client is an Italian manufacturing brand operating an energy intensive production plant. The plant can produce energy internally, purchase energy from the market, and sell excess energy back to the public grid.

Optimizing energy usage in the plant was a complex task due to multiple available energy sources and fluctuating production requirements.

Key challenges included:

  • Choosing the optimal mix between self produced, purchased, and resold energy
  • Energy decisions highly dependent on daily production needs
  • A legacy system that provided only monthly level predictions, limiting operational impact
  • Lack of actionable, short term recommendations for plant managers

As a result, energy usage was not consistently optimized at the operational level.

The solution is based on AI-driven forecasting and optimization models that analyze fresh operational data on a daily basis.

By combining time series forecasting, optimization logic, and real time data pipelines, the system generates per hour recommendations on how to configure energy sources for the following working day.

BitBang designed and implemented a new energy optimization system capable of:

  • Accessing updated production requests on a daily basis
  • Forecasting energy needs with hourly granularity
  • Recommending the optimal energy configuration for each working hour of the next day
  • Supporting plant managers with data-driven decision making

A dedicated dashboard provides visibility into actual consumption versus forecasts, while automated alerts flag anomalies.

The project delivered concrete and measurable outcomes:

  • €450,000 saved per month during fiscal year 2022–2023
  • Significantly more efficient energy usage
  • Improved operational awareness through real time monitoring
  • Faster reaction to anomalies thanks to automated alerts

Energy decisions are now optimized continuously, not just planned at a high level.