Why Food Processors Are Expanding Their Toolbox
How to address food-chain transparency a growing issue both for processors and consumers.
As the previous blog on this subject explained, processed food does not automatically equal junk food. While admittedly there’s a world of difference in a bag of prewashed salad compared to a carton of Twinkies, both are the product of a modern food-processing supply chain dedicated to keeping the public safe and free to choose what they eat.
Thomas Insights reports that food processors are responding to increasing consumer demands for food processing transparency. “Transparency is the currency of trust,” said Deborah Arcoleo, director of product transparency at The Hershey Company, speaking at a 2018 Future Food-Tech conference. “Telling the full story and engaging the consumer with as much information as you can possibly share is going to be very beneficial.”
Research supports her point of view. A 2018 survey of 2,022 U.S. grocery shoppers found 75% of consumers will switch to brands providing more in-depth information, up from 39% who said the same in 2016. The title of the 2018 Food Marketing Institute (FMI) study speaks volumes as well: The Transparency Imperative.
Eighty-six percent of the FMI report’s respondents agreed that providing complete and understandable definitions of food ingredients would further boost trust with 54% indicating they were willing to pay more for products providing such in-depth information.
Better Data, Better Communication
Not surprisingly, food processors are responding to demands for better communication with improved data-collection strategies that not only supply better information, but also improve plant efficiencies and reduce downtime.
Enterprise software supplier OSIsoft says, “Food and beverage companies say they are struggling to turn data into insights because there is so much of it, but the ‘bigness’ of the data isn’t the problem. What’s needed is an open data infrastructure with the ability to collect, analyze, visualize and share large amounts of high-fidelity, time-series data with people and systems across all operations. For F&B companies, these capabilities are important to protecting brand integrity.”
The company showcases four critical food processing areas where this is so:
Reducing recalls. Recalls can damage a brand beyond repair. Identifying and collecting the right data is a big step to becoming proactive. Establishing critical parameters mitigates recall risk. Should a recall occur, the right batch processing and storage data will make the process much easier for regulatory agencies and crisis communications efforts.
Improving food safety. Analytics are the foundation of a top-shelf food safety program. Measuring data and variances helps establish a corporate food safety culture for processors and suppliers alike.
Addressing information transparency. As reported previously, consumers are paying closer attention to ingredient lists and are willing to switch brands or pay more for better information. Collecting operational data means you can create a farm-to-fork report including source of origin and other issues that can build a real competitive advantage.
Building sustainability. Supplying the environmental impact of your operation entails measuring energy use, identifying energy sources and verifying claims to sustainable operations. All of these depend on verifiable and accurate data collection and analysis.
The Convergence of IT and OT
Data-collection and analysis programs for food processors do not occupy their own operational silos but instead exist alongside food processing plant operations and packaging. Mike Chen, director of the Omron Automation Center Americas, concurs that interest is building in an artificial intelligence (AI) machine-learning approach to food processing but specifies it has to take place in the real world. “Whether it’s people or the technology making the decisions, it has to be based on real time in the real world—collecting, analyzing and utilizing the data, with all its intellectual, ethical and often safety-related implications.”
Such an approach makes AI an effective bridge between IT (information technology) and OT (operational technology). This leverages the intelligence of human assets (manufacturing engineers, operators, quality and maintenance personnel) with smart devices including sensors and controllers. “The entire process becomes more robust the more we decide what to do with both the data and the decisions we can trust technology to make,” Chen says.
For example, controller devices such as Omron’s Sysmac AI controller can identify abnormal machine behavior without being explicitly programmed to do so. Since there could be many different factors and measurements that indicate an issue when observed together, automating the feature-extraction process saves a significant amount of time and resources. Leveraging machine learning results during production is key to backing process efficiencies with real data, not guesswork.
Keeping that data safe from bad actors is also a factor. Omron’s Sysmac AI operates with its own CPU and function blocks, requiring no internet connectivity or cloud computing. Data collection and analysis is performed within the same hardware as the controls program, which also improves data-processing speed and accuracy.
Respecting the Consumer
“The new shopper mindset requires brand owners to think about their products well beyond the traditional label and respect a more digitally-minded consumer,” Food Marketing Institute Vice President, Industry Relations, Doug Baker notes. Whether it is satisfying the informational needs of online shoppers or health-conscious individuals (allergen information, etc.), intelligent data collection increasingly being supplied by AI-enabled operations is proving that measurement is knowledge.
Some opinions expressed in this article may be those of a contributing author and not necessarily Gray Construction.