The world population is forecast to grow to 9.3 billion by 2050. To support this, food production must increase by 60%, according to the Food and Agricultural Organization of the United Nations. At the same time, a third of our food is wasted globally and that amount only continues to grow. If we could reduce food waste and loss by 25%, we would have additional food for about 500 million people.
A rapidly growing number of food and beverage manufacturers are committed to the Sustainable Development Goals of the UN and try to reduce food waste, but much of it is lost to inefficiencies. For example, a farm may eliminate an oddly shaped fruit, spoilage may occur during overseas transportation due to a cooling malfunction, and the supermarket may end up throwing it away because of unbalanced supply and demand. Finally, a consumer often throws perfectly good food away because it’s after the printed “best before” date.
Eliminating waste to better feed the planet is a complex challenge for both premium and commodity food production. The keys to solving this massive problem are supply chain transparency and data sharing from farm to fork. For premium foods the consumer wants to see and values data on the food’s pedigree, for commodities you need the data from the food supply chain to drive down waste and become more cost efficient.
When there is transparency on the quality or grade of the ingredients then, for example, the straight green bananas or bent cucumbers can be routed for food processing rather than wasted. When the outbound cold chain has the ideal temperature readings for the perfect delivery, a longer “best before” date can be displayed on the supermarkets shelf.
The efficient way to feed the planet is to use a digital platform that collects information from the food supply chain and applies artificial intelligence (AI) to drive automated and smarter decisions. One example of this in action is capturing quality attributes from the farm’s produce and during processing, so that supply can be matched to demand in an optimal way, and waste is reduced.
Another example is dynamically determining a “best before” date and adjusting prices with a machine learning algorithm. Grocery stores that implemented smart shelves with dynamic prices based on the remaining shelf life of the product report one third less waste, and that savings is expected to grow as the machine learning algorithm becomes smarter over time. This all adds up to less waste, lower costs, more revenue and happy, well-fed consumers.
The technology is here. It’s now up to the food and beverage processors to put the solutions to work to help feed the planet while improving the health of their business all at the same time.
To learn more about creating a sustainable food future from farm to fork, click here.
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