Food Waste, Food Insecurity and AI
The consumer projection is based on the store point of sale forecast as a starting point, but also takes into consideration meal size, special events and other variables that impact at home consumption. Then the analytics and decision making structure can build a more holistic view of inventory need from store-level data on a range of factors, including minimum display quantities to safety stock minimums.
Demand forecasting is far more accurate when trading partners make use of AI technology that considers those external factors. For example, they can use AI to better manage the demand for essentials like water and Pop-Tarts prior to and immediately after a hurricane, and plan for bumper crops so nothing is left rotting in the fields.
In addition, the data can help route product from where it is likely to be discarded to areas of food insecurity prior to shipment from the production facility.
Based on past performance, food companies using AI-based integrated forecasting and replenishment solutions see dramatic reductions in spoilage and inventory. Other benchmark improvements include reductions of out-of-stocks and correlated increases in product service levels.
New AI and machine learning technologies can also optimize transportation so suppliers and retailers are alerted when too much product is being sent to one outlet and divert the shipment to a food bank in need.
Millions of tons of food in the U.S. and abroad continue to go to landfills while millions of people go to bed not knowing where their next meal is coming from. Using AI and machine learning, companies can address both of these issues simultaneously by optimizing the alignment of supply and demand, and better understanding how to adjust inputs for distribution channels to deliver outcomes that dramatically reduce food waste while directly addressing food insecurity.
Sivakumar Lakshmanan is COO, AI forecasting and supply chain at Antuit.ai.