Accurate demand forecasting for retailers, supply chain and consumer goods organizations has always been a key driver of efficient business planning, inventory management, streamlined logistics, and customer satisfaction. Accurate forecasting is critical to ensure that the right products, in the right volumes, are delivered to the right places.

Customers do not like to see products that are out of stock, but keeping excess inventory is also costly and wasteful. According to IHL Group, while retailers lose more than a trillion dollars a year due to mismanaged inventory, a 10% to 20% improvement in demand forecast accuracy could lead to a 5% reduction in direct inventory costs and a 2% to 3% increase in revenue (Notes from the AI Frontier, McKinsey & Company).

Still, inventory management is just one among the many applications that demand forecasting can support; retailers also need to staff their stores and support centers for peak periods, schedule promotions, and consider different factors that may affect in-store or online traffic. As retailers' product catalog and global reach expand, available data becomes more complex and more difficult to predict accurately.

Retailers can now solve these problems by incorporating machine learning into existing demand forecasts to achieve high forecast accuracy by leveraging Vertex AI Forecast.

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Models that perform best in two hours

Vertex AI Forecast can retrieve datasets of up to 100 million rows of data covering years of historical data for thousands of product groups from BigQuery or CSV files. The powerful modeling engine will automatically process data and evaluate hundreds of different model architectures, packaging the best of them into a single model that is easy to manage even without advanced data science expertise.

Users can include up to 1,000 different demand factors (color, brand, promotion program, ecommerce traffic statistics, and more) and set budgets to generate estimates. Given how quickly market conditions change, retailers need an agile system that can learn fast. With Vertex AI Forecast, teams can create demand forecasts with the highest accuracy in just two hours of training time and without manual model adjustment.

The most important part of Vertex AI Forecast is the model architecture research, where the service examines hundreds of different model architectures and settings. This algorithm enables Vertex AI Forecast to consistently find best-performing model setups for a wide range of customers and datasets.

Leading retailers are already transforming their operations and taking advantage of high-accuracy forecasts.

“Magalu used Vertex AI Forecast to transform our forecast forecasts by implementing forecasting at the distribution center level while reducing forecast errors,” said Fernando Nagano, Director of Analytics and Strategic Planning, Magalu (Magazine Luiza), a retail company.

“The four-week live forecast showed significant improvements in error (WAPE) compared to our previous models,” Nagano added. “This high-accuracy insight has helped us plan inventory allocation and procurement more efficiently to ensure that the right products are in the right places at the right time to meet customer demand and manage costs appropriately.”

Vertex AI can handle all kinds of inputs

With Vertex AI Forecast's hierarchical forecasting capabilities, retailers can create a highly accurate forecast that works at multiple levels (e.g., linking demand at individual product, store level, and regional levels) to minimize the challenges created by organizational silos. Hierarchical models can also improve overall accuracy in cases where historical data is sparse. When demand for individual products is too random to predict, the model can still capture patterns at the product category level.

Vertex AI can take large volumes of structured and unstructured data and allows planners to incorporate many relevant demand factors, such as weather, product reviews, macroeconomic indicators, competitor actions, commodity prices, shipping charges, shipping carrier costs, and more.

Vertex AI Forecast explainability features can show how each of these factors contribute to the forecast and help decision makers understand what drives demand to take corrective action early. For example, planners may discover that the main driver of demand in the clothing category on weekdays is promotions, but this is not the case on holidays. Such insights can be invaluable when making decisions about how to act on forecasts.

You can contact us to learn more about demand forecasting with Vertex AI.

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