Food giant Nestlé is harnessing Artificial Intelligence (AI) and data science as it gets to grips with a food system in transformation due to rising population levels, climate change, affordability – and even food ideologies.

The global food company’s products – which range from confectionery to pet foods – are bought by billions of consumers around the world. “With food systems under pressure, we must find ways to manage trade-offs between taste, nutrition, sustainability, and affordability,” says the company.

AI and data science complement Nestlé’s existing research and development (R&D) capabilities by helping it to handle the increasing complexities of the food business by improving efficiencies through innovation processes. For example, tools such as chatbots can help with productivity by creating personalised content, and identifying solutions to complex problems.

The company says: “We identify digital opportunities across our global R&D organisation to leverage AI, machine learning, data science, and predictive analytics. This helps us connect the dots quicker, gain valuable insights, and continue to deliver novel discoveries today, and for generations to come.”

Data science can support regenerative food systems

With around 30% of global greenhouse gas emissions linked to food and agriculture, Nestlé’s believes that data-based precision farming and artificial intelligence can play a key role in making reductions. Real-time monitoring of weather conditions as well as water and nutrient needs, for example, can reduce fertiliser use and optimise crop production. Further development of satellite-based data systems, drones and field data can also be used to collect and better monitor regenerative practices on farms.

Nestlé Nescafe Dalgona
Nescafé Dalgona coffee mixes were created with the help of AI.

The recently created Nestlé Institute of Agricultural Sciences is exploring the use of AI and data science to accelerate the translation of agricultural science into solutions that enable farmers to improve the environmental footprint of ingredients the company sources.

One key area is the application of AI to classical plant breeding to, for example, help select and identify high-yielding, drought- and disease-resistant coffee plant varieties. By embracing AI and data science-driven innovation we will continue to deliver relevant and highly differentiated innovations, with agility, and faster than ever,” says Nestlé.

Accelerating trend-based innovation

Another area of focus is accelerating trend-based innovation. Consumer needs and preferences are evolving rapidly and this has led to novel trends and food ideas that are amplified by social media. By coupling this with the use of connected devices and e-commerce, an abundance of insights can be leveraged for innovation purposes. “Today, the first generation of AI tools that gather social media insights based on advanced algorithms already exist to identify novel food concepts,” says the multinational.

The company continues: “We use AI to help us analyse information on trends, ingredients, flavours, and health benefits from social media, online publications, and other web sources.” The captured insights are ‘clustered’ to discover new ideas or trends quickly and then translated into compelling product innovations. For example, using these tools, we launched Nescafé Dalgona coffee mixes (a creamy coffee) and Nesvita plant probiotic supplements for adults in China.

Nestlé is also piloting tools to create virtual product prototypes to quickly test them using virtual reality, including in the metaverse and Web3 spaces. Although creating and testing physical prototypes remains vital, virtual capabilities are helping the company’s new product developers to see if they’ve hit the mark with a new product. This is saving time, effort, and materials, says Nestlé.

Beyond NPD, AI is also being applied in other areas such as making better predictions about the sensory properties of coffee innovations. “Think of it as an ‘electronic tongue’ for coffee tasting,” says the company.

Getting personal

AI and virtual reality are becoming a part of consumer-facing experiences and services. Soon vending and beverage machines will have holographic operators that interact with people in multiple languages while creating a personalised experience based on an individual’s taste preferences.

“We already deliver personalised products and solutions for people and pets,” says Nestlé. “Using home test kits, consumers can share physiological data to receive recommendations for personalised nutritional supplements targeted to specific health conditions.”

Machine learning was used to create gut-friendly recipes.

A year ago, together with the Crohn’s & Colitis Foundation, the company developed a free web-based digital tool to make it easier to prepare tasty and gut-friendly recipes. Researchers leveraged Nestlé’s expertise in digital health and nutritional science, plus a machine-learning algorithm and nutrition data platform to generate personalised meal plans.

In pet care, the company developed a holistic product ecosystem to improve cat health by tracking changes in unique health and behavioural data patterns, while providing tailored recommendations to pet owners. The ecosystem includes a Petivity Smart Litterbox where pet owners can track changes in urination, defecation, and weight patterns for indoor cats.

Using this data together with proprietary algorithms, the ecosystem can identify an increased risk of developing conditions such as kidney disease, urinary tract infections, and obesity. It is not hard to see how such data could also be extended to humans.

Leveraging historic trial data

In the past 20 years, Nestlé has conducted over 300 clinical trials to support the health benefits of its products leading to a wealth of clinical data. Today, through advanced data analytics, the company is further maximising the return on investment from the data across many different types of clinical studies, as well as external ones.

By combining and comparing the data sets, Nestlé says it can discover new relationships between different physiological, behavioural and health indicators, and translate this information into novel nutritional solutions for specific populations.

One example is Nestlé’s research into breast milk. Its scientists consolidated more than two million data points from 25 studies and, using advanced data mining, it made important discoveries that led to the development of an infant formula with human milk oligosaccharides (HMOs) tailored to the specific needs of C-section babies.

[The future of food will be revealed at the SIAL show this coming October during SIAL Talks, the platform for top-level international experts to discuss innovation in the food business.]

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