When we recently surveyed architectural and engineering/construction firms (A&E/Cs) and asked what trends or forces they see driving automation projects, every firm mentioned “labor” or “workforce availability” in its initial response.
AI use in food processing is expected to have a compound annual growth rate of 45% between now and 2026, promising improved safety and food quality, according to HUB Risk Services. But AI in food processing is not without risk.
Good data—and lots of it—is key to making artificial intelligence/machine learning (AI/ML) production, inspection and packaging systems work without a hitch, plus well written algorithms to analyze the data and make decisions that will help people and machines function more intelligently.
Without sensor data, you can’t control a process—much less begin a digital transformation at your facility. KPIs (Key performance indicators) are a way to measure how your process, packaging or even your palletizing areas are performing, but to get these KPIs requires sensor data.
The processing of food at high volumes has traditionally posed many problems for robots and cobots, and has lagged behind other industries. Foods have a variety of shapes and sizes and can be delicate in nature, and these variables can be challenging when a robot tries to grasp an item.
Within its walls is one of the most technically advanced food processing plants in the country—a greenfield project built from the ground up with automation, labor savings, sustainability and smart design throughout.