Climate change and food insecurity are intrinsically linked. Droughts, floods, and extreme weather affect our soil and water, farming communities, and agricultural labor force, pushing food producers to make the most of scarce resources. Still, between 30-40 percent of the food supply is wasted in the U.S. alone.
Food waste not only contributes to food insecurity but also adds to greenhouse gas emissions (GHG). A recent UNEP 2024 report showed that in one year, food waste alone accounted for an estimated 8-10% of GHG emissions. The good news is that by reducing waste, we can also reduce emissions.
The path to zero waste
Many CPG brands and distributors have established ambitious targets for reducing food waste, with some even outlining specific timelines for achieving better waste metrics. While zero waste may never be possible, near zero waste is certainly achievable. To get there, industry supply chain partners need to embrace modern technologies that enable real-time communication and visibility across every step of the supply chain. Only then will we be able to root out inefficiencies, effectively predict demand, make agile production supply and distribution decisions, and craft marketing and merchandising campaigns that facilitate the purchase of at-risk products.
CPG brands and their partners have invested in a myriad of technologies over the past decade, but due to the disparate nature of supply chains, many of these software platforms have created data silos that limit end-to-end visibility. Data exists in hundreds of systems that have been primarily designed to dump raw data into massive data lakes. What should be the foundational knowledge to drive better decision-making has become so complex that it leads to best-guess decisions or delayed actions that offer only partial benefits to the business.
This is where AI enters the picture.
Addressing waste reduction
AI can help CPG companies reduce waste through data analysis and streamlined processes that present actionable, real-time insights that enables faster decision making and unlocks optimization across a brand’s manufacturing, distribution, inventory management, and marketing functions.
For example, AI can analyze sales patterns, market trends, and external factors such as weather conditions and large-scale events to enhance demand forecasting for products. This allows for more precise planning and reduces the risk of overstocks which lead to waste. AI can also identify inefficiencies and problems within supply chains, such as spotting delays or bottlenecks, and suggest real-time adjustments to minimize food spoilage.
Unlocking value across the supply chain
Unactionable and unstructured hold CPG companies back from optimizing their business processes. AI can transform data into actionable insights that drive waste reduction, but its effectiveness depends on clean, normalized, and well-structured data. When AI is integrated on top of a semantic layer—a structured framework that unifies disparate data sources, creating a common language for business metrics, aggregations, and key performance indicators—brands can unlock insights that reduce waste across every stage of the supply chain. This helps teams make better decisions during forecasting, logistics, assortment planning, marketing, and even new product development.
A semantic layer catalogs, organizes and translates complex datasets while standardizing metrics, such as sales, inventory turnover, and demand forecasting. It acts as a translator, mining what exists within disparate data sources to build a unified, business-friendly language around it. Moreover, normalized retailer and distributor data sets can be seamlessly extended with external data sources like syndicated market reports, EDI and weather data, and much more. The semantic layer supports AI in creating more accurate analyses to make the right decisions in real time, which can substantially reduce food waste and related emissions.
Without proper data ingestion, normalization, and a semantic layer, gaining these insights would require hours of manual effort to download, comb through, and analyze vast amounts of data. When partnered with AI, these same insights can be delivered in seconds.
AI use cases within striking distance
One practical application for AI involves using data to forecast demand and optimize logistics based on weather. AI uses machine learning models to predict how variables such as temperature, humidity, and extreme weather affect spoilage rates. Higher seasonal temperatures, extreme heat, and droughts make it more challenging to store, process, transport, and sell food safely, often leading to significant volumes of food being wasted. With this knowledge, companies can adjust inventory, suggest restocking, or even recommend shifting inventory to other locations. This real-time analysis helps inventory managers optimize logistics and distribution down to the store level, reducing waste by ensuring products are sold before they spoil.
AI can also predict demand shifts by analyzing previous sales data and recommending dynamic pricing strategies, targeted promotions, or marketing campaigns for products nearing expiration. This predictive capability helps prevent overstock and out-of-stock issues, allowing retailers to maintain optimal inventory levels.
Another application for AI is optimizing supply chain logistics. AI can monitor transportation routes using sensors and IoT devices to track conditions. By feeding data into predictive models, it can forecast risks to product quality during transit. For example, if a particular route frequently experiences delays, AI can recommend alternative routes or adjust delivery schedules to minimize risks. AI could even adjust cooling systems within transport vehicles in response to real-time data, ensuring that optimal storage conditions are maintained. This reduces the likelihood of spoilage due to environmental factors, ensuring that products arrive at their destination in prime condition.
These examples demonstrate how AI uses predictive analytics, real-time monitoring, and decision-making algorithms to optimize operations. Through continuous learning and new data, AI models improve over time, becoming more accurate in forecasting demand and identifying potential disruptions in the supply chain. Real-time, data driven decisions are key to minimizing the risk of spoilage and loss.
The broader impact
Addressing food waste is critical to reducing climate change. By leveraging AI to break down barriers to data sharing and analysis, CPG brands and retailers can make more efficient decisions in real-time, optimizing supply and demand management, reducing waste, and meeting customer expectations.
AI will play a critical role as the industry moves toward zero waste. With the right partnerships, tools, and the strategic application of AI, CPG brands, retailers, and their supply chain partners can enhance their ability to unlock insights from data and more effectively achieve their waste reduction goals.
