The role of AI and machine learning in food technology

man shopping at a supermarket

December 17, 2020By Mikael Bengtsson

The food and beverage industry is ripe with complexities, and the journey from idea to commercialization is long and arduous for food technologists. New product ideas or packaging developments have to endure rigorous data collection, testing, and certifications to make it to consumer shelves—processes carried out by food technologists working with manual tools, creating a wide margin for potential error.

Excel and pen-and-paper methods are common ways to record data and make calculations regarding research and development, quality standards, raw supplies orders, and dozens of other variables associated with the manufacturing process. Luckily, the industry has seen an uptick in the adoption of digital transformation methodologies. Accessible, commodity-based technologies are making it easier to accurately record data and make mission-critical calculations.

Food and beverage companies are taking a tiered approach to their digital transformation, first by investing in technology that supports their data needs. This frontline technology supports accurate data transfer while automating repetitive tasks like ingredients declarations, statements on nutrition labels, and allergen warnings to ensure label compliance, prevent recalls, and defend against brand erosion with minimal manual input.

Transformation on the front end provides a strong foundation for companies to turn data into action. The introduction of artificial intelligence and machine learning can take rudimentary data and turn it into a competitive advantage with added visibility and utilization. AI brings with it the opportunity to optimize four core areas of operations:

  1. Food Science: AI can perform in-depth market analysis, automate recipe building, predictive yields on raw materials, and ensure safety measures.
  2. Distribution & Supply Chain Management: Predictive analytics contribute to cost savings and waste minimization, visual pattern recognition, and accurate, agile forecasting.
  3. Customer Experience: Monitor and learn from insights on customer traffic and engagement while facilitating the use of self-service point-of-sale systems.
  4. Manufacturing: Reduce the risk of downtime with asset health and predictive maintenance, create a more connected enterprise with the Internet of Things (IoT).

Digital transformation through the use of AI and machine learning will continue in the next 5-10 years as organizations continue to see the remarkable value of investing in intelligent and supportive technology. AI will spearhead the trend toward innovation, enabling better predictions in the consumer landscape, and creating a more agile supply chain.

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