Proper AI Application Fuels Food and Beverage Innovation

The collection of actionable data generates insights to optimize production, reduce downtime, and increase overall efficiency, and can be scaled throughout a company’s infrastructure.

Sridhar Sudarsan, chief technology officer at SparkCognition
Sridhar Sudarsan, chief technology officer at SparkCognition

The food and beverage industry has been shifting to adopt more artificial intelligence (AI) applications to improve and address issues of operational efficiency, food quality and safety, sustainability, and supply chain challenges. In a recent webinar, Demonstrating the Power of AI for Food and Beverage Innovation, Sridhar Sudarsan, chief technology officer at SparkCognition—a provider of AI systems for the industrial space, including manufacturing—discussed how AI and machine learning (ML) can be invaluable tools in addressing these issues.

Sridhar Sudarsan, chief technology officer at SparkCognitionSridhar Sudarsan, chief technology officer at SparkCognitionSudarsan stated that over the last year and half, companies have had to learn that demand is always evolving, often resulting in challenges faced on the manufacturing side with inventory, line optimization, maximizing production, minimizing unscheduled downtown, and increasing overall efficiency. Additionally, according to the 2021 State of Manufacturing Report, by Fictiv and Dimensional Research, quoted in the webinar, 31% of manufacturers are concerned about lack of visibility throughout the supply chain.

To explain how AI addresses these challenges and concerns, Sudarsan compared the supply chain to a universe with the manufacturing plant at its core. He included upstream and downstream supply chains to close the loop. Both the supply chain and the plant involve thousands of different types of systems, hundreds of processes, numerous assets, and hundreds of people that interface with the production of materials and products daily—and the essential tie between them is data.

Sudarsan added that “It isn’t enough to have the data being produced and used. It’s about making the data consumable and usable in the right way. It’s about making the data actionable.” Sudarsan broke down the process of making data actionable into four steps along with considerations companies would take within the steps, which are data availability, leveraging data, usability of insights, and scaling infrastructure.

1. Available data vs. actionable data

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