The rise of cloud computing has resulted in a massive data explosion. By 2025, it is estimated the amount of data will double every 12 hours. Organizations have become more data-driven for decades, but research shows that about 70% of data collected and stored by companies goes unused. AI can consume data far more efficiently than humans, delivering accurate insights and predictions and improving the bottom line.
Effectively responding to complexity and volatility is increasingly dependent on an organization’s ability to absorb data — internal and external — and promptly process the data into intelligent insights driving decisions.
In a world now dominated by data and AI, many organizations are stuck in the past, operating with a mixed bag of legacy technologies incapable of efficiently processing and integrating the massive data sets needed for accurate predictive intelligence and decisions. These companies struggle to stay afloat in their respective industries as competitors use modern solutions that leverage AI for demand forecasting and supply chain management.
How do organizations harness the power of data and algorithms for better demand forecasting? With the adoption of a cloud-native, modern solution that uses predictive models fed by a foundational data layer, powered by AI, and enabled with real-world scenario testing.
Here are some ways AI for demand forecasting transforms organizations’ decision-making process by leveraging large amounts of data.
AI Pattern Recognition
By applying a modern solution that leverages AI for demand forecasting, companies obtain a clear and unbiased view of their historical data. These solutions use AI and machine learning to digest complex demand patterns (such as seasonality, non-linear trends, lags, etc.) and prescribe the appropriate forecasting model. As AI-powered solutions drive the decision-making process, they monitor and adapt to changes in the data.
Internal and External Data Sources
Once patterns from historical data are identified, modern solutions employ AI to enhance demand signals and quantify demand drivers by bringing in both internal and external data sets. Internal or 1st party data may include price, promotions, point-of-sales, and product lifecycle intelligence, and external data can come in the form of both 2nd and 3rd parties. 2nd party data sources include data from partners, distributors, retailers, etc., and 3rd party data might consist of macroeconomic indicators such as GDP and Consumer Price Index, regional weather data, logistics data, or demographic trends.
The ability to model what-if scenarios is the key to accurate demand forecasting. What-if scenarios enable planners to accurately predict how hypothetical situations impact production and the movement of supplies through the supply chain. Modern solutions, such as Vanguard Predictive Planning™, offer complete support for unlimited what-if scenarios. These scenarios can be saved and shared across the platform, enabling lightning-fast calculations, so users have no downtime.
AI plays a vital role in understanding patterns and delivering insights for short-term forecasts. Probabilistic forecasting determines the likelihood of a range of possible outcomes by using advanced algorithms to analyze multiple demand variables. It creates more reliable forecasts in situations when demand patterns are lumpy and unpredictable, where there’s limited order history, or when factors like seasonality affect demand. Modern solutions with AI for demand forecasting make forecasts more accurate by considering and analyzing outcomes across a vast amount of data.
Organizations are seeing the benefits that reinventing their planning process with AI for demand forecasting hold. The resulting insights provided by AI’s ability to digest large data sets enables an organization to adapt to shifting market conditions and accelerate decisions about supply chain scalability, sourcing strategies, product portfolio, and capacity planning.
Unpredictability is the new normal, and now is the time for market leaders to take an AI-powered approach to forecast demand.
Want to integrate AI into your planning process but don’t know how? Download the recording of our webinar: Breaking up with Excel with Concord Foods. Hear from Jen Moore, Sales Planning Manager at Concord Foods, as she discusses the challenges to digital transformation, the advantages of an automated, collaborative, AI-based planning platform, and how they achieved success with Vanguard in less than three months.