How can demand forecasting approach real time responsiveness? Vertex AI makes it possible
Director of Product Management, Cloud AI and Industry Solutions
Everyone wishes they had a crystal ball—especially retailers and consumer goods companies looking for the next big trend, or logistics companies worried about the next big storm.
With a veritable universe of data now at their fingertips (or at least at their keyboards), these companies can now get closer to real-time forecasting across a range of areas when they leverage the right AI and machine learning tools.
For retailers, supply chain, and consumer goods organizations, accurate demand forecasting 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 locations.
Customers don’t like to see items out of stock, but too much inventory is costly and wasteful. Retailers lose over a trillion dollars a year in mismanaged inventory, according to IHL Group, whereas a 10% to 20% improvement in demand forecasting accuracy can directly produce a 5% reduction in inventory costs and a 2% to 3% increase in revenue (Notes from the AI Frontier, McKinsey & Company).
Yet, inventory management is only one of the applications among many that demand forecasting can support—retailers need to also staff their stores and their support centers for busy periods, plan promotions and evaluate different factors that can impact store or online traffic.
As retailers’ product catalog and global reach broaden, the available data becomes more complex and more difficult to forecast accurately. Unconstrained activities through the pandemic have only accentuated supply chain bottlenecks and forecasting challenges as the pace of change has been so rapid.
Retailers can now infuse machine learning into their existing demand forecasting to achieve high forecast accuracy, by leveraging Vertex AI Forecast. This is one of the latest innovations born of Google Brain researchers and being made available to enterprises within an accelerated time frame.
Top performing models within two hours
Vertex AI Forecast can ingest datasets of up to 100 million rows covering years of historical data for many thousands of product lines from BigQuery or CSV files. The powerful modeling engine would automatically process the data and evaluate hundreds of different model architectures and package the best ones into one model which is easy to manage, even without advanced data science expertise.
Users can include up to 1,000 different demand drivers (color, brand, promotion schedule, e-commerce traffic statistics, and more) and set budgets to create the forecast. Given how quickly market conditions change, retailers need an agile system that can learn quickly. Teams can build demand forecasts at top-scoring accuracy with Vertex AI Forecast within just two hours of training time and no manual model tuning.
The key part of the Vertex AI Forecast is model architecture search, where the service evaluates hundreds of different model architectures and settings. This algorithm allows Vertex AI Forecast to consistently find the best performing model setups for a wide variety of customers and datasets.
Google has effectively built the brain that is applied towards demand forecasting in a non-intrusive and contextual way, to merge the art and (data) science of accurate demand forecasting. In benchmarking tests based on Kaggle datasets, Vertex AI Forecast performed in the highest 3% of accuracy in M5, the World’s Top Forecasting Competition.
Leading retailers are already transforming their operations and reaping the benefits of highly accurate forecasting.
"Magalu has deployed Vertex AI Forecast to transform our forecasting predictions, by implementing distribution center level forecasting and reducing prediction errors simultaneously” said Fernando Nagano, director of Analytics and Strategic Planning at Magalu.
“Four-week live forecasting showed significant improvements in error (WAPE) compared to our previous models,” Nagano added. “This high accuracy insight has helped us to plan our inventory allocation and replenishment more efficiently to ensure that the right items are in the right locations at the right time to meet customer demand and manage costs appropriately."
From weather to leather, Vertex AI can handle all kind of inputs
With the hierarchical forecast capabilities of Vertex AI Forecast, retailers can generate a highly accurate forecast that works on multiple levels (for example, tying together the demand at the individual item, store level, and regional levels) to minimize challenges created by organizational silos. Hierarchical models can also improve overall accuracy when historical data is sparse. When the demand for individual items is too random to forecast, the model can still pick up on patterns at the product category level.
Vertex AI can ingest large volumes of structured and unstructured data, allowing planners to include many relevant demand drivers such as weather, product reviews, macroeconomic indicators, competitor actions, commodity prices, freight charges, ocean shipping carrier costs, and more. Vertex AI Forecast explainability features can show how each of these drivers contributes to the forecast and help the decision makers understand what drives the demand to take the corrective action early.
The demand driver attributions are available not only for the overall forecast but for each individual item at every point. For instance, planners may discover that promotions are the main drivers of demand in the clothing category on weekdays, but not during the holidays. These kinds of insights can be invaluable when decisions are made on how to act on forecasts.
Vertex AI Forecast is already helping Lowe's with a range of models at the company’s more than 1,700 stores, according to Amaresh Siva, senior vice president for Innovation, Data and Supply Chain Technology at Lowe's.
“At Lowe's, our stores and operations stretch across the United States, so it’s critical that we have highly accurate SKU-level forecasts to make decisions about allocating inventory to specific stores and replenishing items in high demand,” Siva said. “Using Vertex AI Forecast, Lowe's has been able to create accurate hierarchical models that balance between SKU and store-level forecasts. These models take into account our store-level, SKU-level, and region-level inventory, promotions data and multiple other signals, and are yielding more accurate forecasts.”
Key retail and supply chain partners, including o9 Solutions and Quantiphi, are already integrating Vertex AI Forecast into to provide value added services to customers. To learn more about demand forecasting with Vertex AI, please contact your Field Sales Representative, or try Vertex AI for free here.