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Food and Beverages | Tuesday, November 02, 2021
Predictive and preventative maintenance can help improve safety standards by maximizing automation and reducing risk.
FREMONT CA: The last 18 months have thrown a bright light on inefficiencies in the food processing industry and the larger supply chain. Many organizations failed to adapt to the constant disruption, unable to keep up with rapidly changing market pressures and industry challenges.
Manufacturers who are thinking ahead are seeking new solutions to future-proof their operations. More food firms are pursuing a strategy of focused investments in smart manufacturing initiatives, buoyed by the success of recent technological investments, many of which were accelerated by the epidemic.
In an increasingly digitized economy, savvy organizations are upgrading capacities to gain that all-important competitive edge. Today, one is witnessing the rise of the future factory, with intelligent technology assisting in developing more agile, flexible, and efficient firms and supply chains capable of responding swiftly and effectively to both difficulties and opportunities.
The Industrial Internet of Things (IIoT), autonomous cars in warehouses, and heavy lifting robotics are becoming more ubiquitous across food industrial processes. The factory of the future is a networked enterprise that seamlessly connects equipment, people, and supply chains, leveraging sensors, remote diagnostics, and artificial intelligence (AI) to boost overall efficiency.
Predictive and preventative maintenance can help improve safety standards by maximizing automation and reducing risk. A shift away from manual procedures, which are prone to human mistakes, results in less unexpected downtime, fewer compliance concerns and recalls, and less waste. AI and machine learning (ML) both play important roles in enabling more automation while also delivering new levels of interconnected insight across the plant and throughout the supply chain.
Taking the issue of dynamic best before and use by dates as an example, AI and ML skills assist manufacturers in taking into consideration the various variables present at all stages of the farm-to-fork supply chain to construct a dynamic shelf life for each product. This includes upstream and downstream condition monitoring of ingredients and finished products, pre-, during, and post-production storage and transportation times and conditions, as well as raw ingredient quality profiling and examining what will happen to the product once it reaches the retailer. IIoT devices are ideal for this strategy because they can measure critical variables and feed that data back into intelligent systems for analysis to determine the best use by or best before dates based on the specific quality features of a batch of products.