From Complexity to Clarity: How AI Is Transforming Forecast Accuracy

Inflation and economic uncertainty continue to put pressure on consumers. As a result, many are turning to private label products and smaller pack sizes in search of better value. This is adding new levels of complexity to CPG supply chains, particularly within demand forecasting.In this Expert Exchange episode, senior editor Liz Dominguez speaks with Tom Gregorchik, global vice president of CPG industry strategies at Blue Yonder, about how these consumer behavior shifts are challenging traditional demand planning in the industry.The rise in private label popularity is creating challenges not only for branded companies but also for private label manufacturers, which now face increased pressure to prioritize between retail partners as demand surges. Meanwhile, CPG companies are responding by optimizing their portfolios, focusing on products that consumers still value, even if it means downsizing formats to maintain affordability.Gregorchik emphasized that agility in the forecasting process is now more critical than ever. Companies need to quickly react to shifting demand signals and ensure they are accurately reflected throughout the supply chain.CPGs are adjusting their planning strategies in response to growing price sensitivity and reduced effectiveness of traditional promotions. Gregorchik noted that companies are also rethinking their approach to trade spend. He said the old playbook of running the same endcap promotions year after year no longer works in a retail environment where consumer behavior is more fluid. To stay effective, companies are testing promotions offline, analyzing lift and cannibalization, and optimizing trade investments using more advanced tools. New technologies enable them to understand how pricing and promotional strategies directly influence demand, allowing for a more dynamic and responsive planning process.Balancing short-term demand shifts, such as those driven by viral trends or economic pressures, with long-term brand strategy is another area where CPGs are evolving. Gregorchik pointed out that companies are now blending their sales and operations execution processes with longer-term integrated business planning. Enabled by tech, this approach allows organizations to run complex scenarios so companies can weigh short-term decisions against their long-term objectives.AI, of course, plays a major role. Gregorchik said that while traditional demand planning often relied on historical data and manual intervention, modern AI models incorporate internal and external variables. These include promotional activity, POS data, weather patterns, economic indicators and more. Also, AI tools are built with explainability in mind, allowing planners to see exactly what’s driving forecast changes, which builds trust and reduces reliance on manual overrides.Operational decisions are also shifting. When facing demand spikes, companies are using scenario planning to determine whether to increase internal production or outsource to external manufacturers. Gregorchik stressed the importance of evaluating these decisions through both short- and long-term lenses, considering costs such as equipment investment, inventory carrying and lead time variability.SKU rationalization is another key strategy. Gregorchik highlighted that CPGs are working more closely with retailers to customize store-level assortments, aligning with local demographics while managing complexity and cost. Rationalization allows companies to streamline offerings and focus resources where demand is strongest.As an example, Gregorchik pointed to a global beverage manufacturer that recently used AI to integrate factors such as pricing and promotions into its forecasting model. This resulted in a 6-point improvement in forecast accuracy and an 11% reduction in forecast bias for the company. Additionally, the beverage company experienced lower inventory levels and higher service rates driven by increased transparency from AI.

From Complexity to Clarity: How AI Is Transforming Forecast Accuracy

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