Applying Business Intelligence to Minimize Food Waste across U.S. Agricultural and Retail Supply Chains

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Abstract

In the United States, food waste remains a significant challenge, with roughly one-third of all food produced for human consumption going to waste. This not only exacerbates issues related to food insecurity but also leads to economic inefficiency and environmental damage. Artificial Intelligence (AI) offers promising solutions to address these concerns by improving predictions of food spoilage and optimizing supply chain management. AI technologies, including machine learning models, predictive analytics, and advanced algorithms, can accurately forecast spoilage, thereby reducing waste. Key innovations include systems for early detection of spoilage indicators, dynamic algorithms that adjust storage conditions, and predictive models for waste forecasting based on real-time environmental data. Case studies, such as those from Shelf Engine and Afresh, show notable improvements, with a 14.8% reduction in food waste per store and a decrease of 26,705 tons of CO2 emissions. IKEA also achieved a 30% reduction in kitchen food waste within a year using AI-powered monitoring systems. However, challenges remain in data collection, model training, and integrating AI with existing food management systems. These include issues with data quality, legacy system compatibility, and regulatory hurdles. The paper concludes by offering recommendations for future research, advocating for collaboration across disciplines to create standardized data protocols, enhance real-time monitoring, and address the ethical concerns surrounding AI adoption in the food sector. By pursuing these strategies, AI can play a pivotal role in minimizing food waste in the U.S. and globally.

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