Artificial Intelligence and google cloud solutions

Artificial intelligence and google cloud solutions. Google Cloud launches a new artificial intelligence solution to forecast retail demand. Vertex AI Forecast, the new Google Cloud solution, offers retailers an artificial intelligence-based demand forecasting tool.

With the aim of helping retailers to optimize the task of provisioning, Google has launched a new tool based on artificial intelligence that allows to obtain real-time forecasts on demand, offer planning, and, in general, all those factors that can influence sales traffic in online and physical channels.

According to Craig Wiley, director of AI product management and industry solutions at Google Cloud, for the retail sector, accurate demand forecasting has always been a key factor for good business planning, inventory management, rationalization of logistics, and customer satisfaction. “Accurate forecasts are essential for delivering the right product to the right place in the right quantity.”

But this planning is not easy

According to data provided by Wiley, retailers lose more than $1 trillion a year due to poor inventory management. And a 10-20% improvement in the accuracy of demand forecasting can reduce warehousing costs by 5% and increase revenue by 2-3%.

This difficulty in forecasting also becomes more complex as the retailer gains geographic reach and expands the number of available SKUs. “Activities that have not been constrained during the pandemic have only aggravated bottlenecks in supply chains and increased the difficulty of forecasting, as changes have been rapid,” he says.

Against this backdrop, the technology firm has launched the Vertex AI Forecast

A new artificial-intelligence-based tool that can automatically process up to 100 million rows containing thousands of product lines from BigQuery or CSV files and evaluate hundreds of different models to find the most efficient one.

Its algorithm makes it possible to systematically find the most efficient configurations for a wide variety of customers and data sets, and thanks to its hierarchical forecasting capabilities, retailers can generate accurate forecasts that work at multiple levels (e.g., linking demands at the item, store, and regional levels) while minimizing the problems created by organizational silos.

The tool can also ingest large volumes of unstructured data to understand the most relevant signals, such as weather, commodity prices, freight, and shipping costs.

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