For food and beverage manufacturers, AI readiness begins with gaining visibility into the plant that exists today, identifying the risks and constraints that matter most, and establishing a practical roadmap for modernization.
Food and beverage manufacturers encounter significant challenges beyond simply delivering products, including tightening regulations, rising costs, and labor shortages. Despite these obstacles, they continue to prioritize investments in food safety, traceability, automation, and sustainability to enhance operational efficiency.
Submitted as a white paper to the Office of Management and Budget, the definition was drafted with “feedback from thousands of stakeholders, including industry, consumer organizations, researchers and members of the public,” the HHS says.
The U.S. HHS and FDA have submitted a proposed definition of "ultra-processed foods" to the OMB for review. Developed with feedback from various stakeholders, the definition aims to create federal uniformity, but it has not yet been published in the Federal Register.
In addition to artificial intelligence (AI), big data and sustainability concerns, food industry professionals taking part in PMMI's research emphasized health-driven product shifts, ongoing workforce constraints and heightened sanitation scrutiny.
You have several data islands of automation, but by giving each data context — and with help from AI — you can keep the aging equipment and gain knowledge of your process.
Plants often have aging equipment that works fine, and it may have sensors, but in many cases that data never connects with data from other siloed processes. Why not connect all the data together with intelligent networks and use AI to get a better understanding of the whole process?
New research from FlexXray explores how food manufacturers detect and prevent contamination from foreign materials, as well as their future investments in inspection technologies.
As automation has become increasingly sophisticated, reusable plastic packaging has evolved alongside it. Rather than viewing packaging simply as something that transports products from point A to point B, manufacturers can view it as an integral part of the automated system.
The recent Cyclospora outbreak highlights how VIS-NIR, Raman/SERS, chemometrics and machine learning are being investigated for faster food-safety screening.
A spectrometer watching a conveyor or checking a stainless steel surface between shifts may sound futuristic, but research suggests we may not be as far from that factory as we think.
For Pearl Crop, a processor of nuts and seeds based in California, seasonal labor pressures prompted a search for a more flexible automation strategy, ultimately leading the company to explore a robot rental model rather than a traditional capital equipment purchase.