December 2019CIOAPPLICATIONS.COM9requirement for human intervention. At scale, technologies like these could be applied to produce huge volumes of configurable, and even customizable, products but with accuracy and cost-efficiency levels infinitely greater than what's been previously possible.In process manufacturing, new, advancing machine vision technologies such as hyperspectral imaging can be applied to food products and ingredients to for functions like measuring pH, color, tenderness, ripeness and more. Hyperspectral imaging combines computer vision with spectroscopy, a technique that that can analyze the chemical makeup of foods and other ingredients from the light of a single pixel. This means that companies can apply the technology to take images of food and other ingredients, and immediately understand chemical composition, moisture levels, nutritional content and more. These technologies can be applied to move food quality and safety beyond the realm of measuring small representative samples and then applying the results of these tests to the total available lot of a given ingredient or food product. Instead, companies would gain the ability to assess the entire lot for food and other ingredients--every side of beef, every strawberry in the carton, every head of lettuce, every ounce of dye.In doing so, companies can ensure that effectively all the ingredients and food products they are producing or processing are fresh and help to eliminate waste resulting from over or under-ripe food products. They can also help ensure the safety of all ingredients and food products by checking to confirm that lots are free from foreign objects like plastics, metals or unsafe chemical compounds. Finally, these technologies can help eliminate fraud from production processes by helping to confirm that food products that are marked `fresh' have never been frozen, or that the Atlantic cod that's going in to those fish sticks is, in fact, Atlantic cod--and not a different variety of fish packaged as cod.Much like the configurability and personalization that machine vision could help enable in discrete manufacturing, it's within the realm of possibility that similar capabilities might be enabled by machine vision and other advanced technologies for process manufacturing, too. These could, for example, be combined to regulate quantities of specific nutrients added to foods for specific consumer segments, or the amount of dye blended in to a cosmetic to produce a color specifically tailored to an individual consumer.Extending the implications even further, while machine vision is being deployed successfully in manufacturing and production processes, there are also examples of how it is being deployed in complementary processes to improve production planning and optimize output. For example, beverage and snack companies are placing computer vision cameras inside of coolers in stores and vending machines. These cameras monitor the stock inside the cooler to:· Ensure merchandising compliance by monitoring to ensure only authorized products are placed in the cooler and that the all products are placed properly, alerting the retailer and the consumer products company in the event of any exceptions.· Monitor consumer demand at the individual cooler level, taking periodic images of available stock to measure the velocity of consumer demand for the products in the cooler. Machine learning capabilities applied to this data help companies anticipate and mitigate compliance risk before issues occur, and fine-tune replenishment and the variety of products inside of a given cooler down to an individual level. In turn, this helps to maximize future sales based on actual consumer demand. Armed with this data, companies can update short-term forecasts and manufacturing production capacity plans in real-time, helping to ensure that capacity is balanced with demand and that production output is optimized based on predictions of future demand--thus maximizing consumer sell-through while simultaneously eliminating waste from over-production and spoilage.These are just a few of many examples of machine vision application, but each represents a bright future for the technology, and the manufacturing processes it's helping to transform. In process manufacturing, new, advancing machine vision technologies such as hyperspectral imaging can be applied to food products and ingredients for functions like measuring pH, color, tenderness, ripeness and more
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