Bel Group leans on AI to accelerate innovation, tighten value chain efficiencies

By Teodora Lyubomirova

- Last updated on GMT

The company behind brands Babybel, Boursin and The Laughing Cow is hoping to improve operational efficiencies and also bolster R&D to deliver unique product recipes. Image: Getty/grafvision
The company behind brands Babybel, Boursin and The Laughing Cow is hoping to improve operational efficiencies and also bolster R&D to deliver unique product recipes. Image: Getty/grafvision
The French cheese company will leverage artificial intelligence to come up with unique recipes while achieving end-to-end efficiency gains across its global value chain.

Bel Group has entered into a long-term partnership with French software company Dassault Systémes to optimize its entire value chain, from product development to manufacturing and marketing.

Dassault Systémes is well-established in industries such as aviation and transport with its 3D modelling and simulation solutions. It also works with CPG firms to provide integrated supply chain solutions that reduce delivery times and logistics mileage while saving operational costs.

For the Bel Group, the partnership has a scope to decrease product development times, strengthen manufacturing efficiencies, and provide transparency across its operations, starting with 11 global locations. The food manufacturer will leverage the French software company's Perfect Production suite, which provides a unified source of intelligence across manufacturing, product development, inventory availability, and more, all in real time. Dassault Systémes claims its product can accelerate new product launches by 20% while decreasing cost of goods sold by up to 27%.

Bel says using Perfect Production will enable it to become more responsive to market demands, helping it to optimize inventory levels and better control raw materials use. This, in turn, will improve sustainability and help the firm achieve consistent quality in any location. In addition, the food manufacturer will leverage AI to analyze data and use machine learning to optimize formulations, time-to-market, product performance, and more.

Product lifecycle management will also be bolstered, as the system is expected to improve collaboration across the entire product development process.

How can AI bolster global supply chain optimization strategies?

“There is a saying that no two factories are alike,” a spokesperson for Dassault Systémes told us. “Machinery, equipment, layout, production capacity, workforce, skills and experience may be variable from plant to plant. In addition to physical variables, the ability to capture, read and communicate data to make decisions across the entire network of facilities is challenging without the right systems and processes in place.

“With digital continuity across plants, manufacturers can improve visibility, synchronization and control of all production processes; this leads to continuous improvements in efficiency and best practices.”

But improving supply chain efficiency is just one part of the equation the French software firm is trying to solve; the other relies on artificial intelligence (AI) and machine learning (ML) techniques to make predictions that aid product development. 

“The CPG industry is all about speed,” the spokesperson told us. “Consumer tastes and preferences are changing faster than ever. Consumers want more flavor options, lower fat, reduced salt, more natural, more localized ingredients, and so on. The pace of change is placing significant strain on R&D teams to do more, faster, cheaper and ‘greener’, and constant regulatory changes on materials and ingredients add to the workload. But the real challenge is making ingredient and formulation changes without losing the taste, flavor, mouthfeel, and quality of the product. This is where artificial intelligence and machine learning play an important role.”

Specifically, AI and machine learning use algorithms to analyze historical data to make predictions and help companies with decision-makings, such as coming up with unique product recipes. Dassault Systémes told us its semantic and sentiment analysis software can analyze large amounts of customer conversations and feedback – collected from emails, web reviews, surveys, social networks, brand forums, and more beyond – in order to produce insights about brand attributes and drivers of satisfaction.

AI can also contribute to bolstering sustainability, the spokesperson told us. “Using AI improves new material discovery, eliminates physical testing by learning from past to predict the future, and uses in silico worlds to iterate and simulate virtually before producing in the physical world. This saves time and labor resources and produces less waste while providing more sustainable materials.”

The technology can also be used to reduce downtime in the factory. “In production, its role is all about mathematically optimizing variables to enhance predictive maintenance, production efficiency, and overall supply chain optimization. AI is crucial in predicting when machinery or equipment might need maintenance. This is done to minimize downtime and costs.”

In the end, it’s about synchronizing events across the entire supply chain in order to improve efficiency significantly. “Say, a plant uses machine learning to increase efficiency by 10%,” the Dassault Systémes spokesperson said. “While this substantial improvement is noteworthy, it doesn't automatically translate into seamless integration with the broader supply chain. Without synchronization, this boost in plant efficiency may result in inventory surpluses and logistical challenges across the supply chain.”

Bel’s foray into emerging technologies continues

Bel Group CEO Cécile Béliot said the partnership with Dassault Systémes would accelerate the food manufacturer’s transition to ‘augmented R&D’. “The joint capabilities of our two groups, sharing the same vision, will empower our teams to shift towards ‘augmented R&D’ through AI, and to re-shape our manufacturing and product management processes for the future of food,” she explained.

Bel Group has been actively exploring emerging technologies as it bids to improve sustainability and grow its non-dairy portfolio. The company is aiming to achieve a 50-50 split in dairy and plant-based/fruit products by 2030 in order to reduce its climate impact.

Last year, the maker of Babybel partnered with AI company Climax Foods​ to accelerate Bel’s plant-based cheese product development and ideation strategies. Climax Foods leverages machine learning and AI to predict which plant varieties would make superior plant-based cheese, in both functional and sensory terms. That would allow Bel to come up with improved plant-based formulations and are naturally rich in protein. The first products are expected to launch in the US in Q4 2024.

In another foray into emerging technology, the company is exploring precision fermentation dairy through a partnership with Paris-based start-up Standing Ovation.

Bel is also using Bovaer, the methane-suppressing feed additive developed by dsm-firmenich, across its dairy-producing farms in Slovakia to reduce the carbon footprint of milk used to produce Babybel products shipped to the UK and Central Europe. Implementing the feed ingredient, which was also recently approved for use in the US, is expected to reduce enteric methane emissions for each farm by approximately 1/4 and would represent an overall yearly 400-ton methane reduction.

Related topics R&D

Related news