AI for the Recycling Industry: Berga Recycling

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Price volatility in the recycling industry

The recycling industry operates in a volatile market where prices for recycled materials fluctuate significantly depending on economic cycles, industrial demand, and international policies. For brokers and processors, anticipating these fluctuations is key to making profitable buying and selling decisions. Berga Recycling, a Quebec-based player in the recycling and circular economy sector, turned to Explor.ai to support this decision-making process with artificial intelligence. The project focused on predictive price modeling and the systematic analysis of available market data.

One of Berga's core challenges was dealing with price fluctuations on international markets, which had a direct impact on profitability. To address this volatility, the company needed better forecasting to inform business decisions. Accurately predicting supply (from suppliers) and demand (from customers) became a key strategic priority.

" Very quick take up and understanding of our industry by ExplorAI by just looking at the data. I found the feedback with ExplorAI to be fast and concrete. Thanks to ExplorAI's work, Berga was sold for twice as much. "

- Richard Peladeau - Chief Technology Officer, Berga Recycling

Predictive modeling applied to recycled materials

To meet this challenge, machine learning models were integrated into Berga's existing technological infrastructure. These models draw on a dataset that includes international price trends, transaction records from public registers, and the company's own historical data. This approach has significantly enhanced Berga's forecasting capabilities. Custom dashboards were also developed to streamline data analysis and usage, enabling more informed decision-making.

Supporting purchasing and sales decisions in the circular economy

Within just 2 months, the tools implemented allowed Berga to reach a two-month forecasting horizon for global price fluctuations. The company is now able to anticipate up to 30% of demand variability. This predictive power has helped teams better target their actions with both customers and suppliers, leading to a more stable material flow. These gains in efficiency and competitiveness ultimately increased Berga's value - culminating in its acquisition.