Databricks and NIQ Collaborate on Retail Supply Chain Forecasting

Databricks and NIQ have teamed to introduce a supply chain solution for retailers.

The tool is built on the Databricks Data Intelligence Platform and allows for real-time collaboration, the companies said in a Tuesday (Jan. 16) news release, describing this as a step up from on-premise solutions that use outdated information.

“The initiative targets critical pain points historically plaguing the retail industry across supply chain processes,” the companies said.

“Beginning with modernizing the demand forecasting process, this solution focuses on elevating accuracy and efficiency. The approach involves adopting generative AI techniques, promising a groundbreaking enhancement of forecasting accuracy by an average of 10% to help retailers lower carrying costs, maintain fewer out of stocks, and have less markdowns.”

According to the release, the solution employs “advanced datasets to supercharge machine learning tasks” by using detailed panel data. The companies say this tactic greatly improves personalized services and optimizes multichannel strategies for manufacturers and retailers.

“The roadmap outlines a progressive trajectory for further retail innovation throughout 2024,” the release said. “This includes prioritizing refined strategies for personalization, aiming to create tailored consumer experiences as NIQ continues to invest in modern practices.”

PYMNTS examined the challenges that come with determining weaknesses in retail supply chains earlier this month in a conversation with Michael Falck, co-founder of RELEX Solutions.

“Many businesses, for example in retail, that are vertically integrated are facing exactly the same challenges,” he said.

What companies need, he told PYMNTS, is a comprehensive solution for the entire consumer goods supply chain, linking demand and planning so companies can optimize production plans and schedules, adjust inventory levels, and adapt to market changes.

Falck underlined the challenges faced by companies in sectors like meat and dairy, where supply and demand uncertainties require effective supply-demand balancing.

“Fresh goods demand requires forecasting and optimization because you have uncertainties regarding both the material supply and the demand, which leads to different opportunities in terms of how to use the raw material to meet the customer needs,” he said, adding that this leads to “less shrink and waste” throughout the entire supply chain.

Meanwhile, recent reporting by PYMNTS shows that more than three-quarters of retailers rank generative AI as the most important emerging technology.

“Integrating generative AI into the retail technology stack is about much more than automation — it’s about reshaping the consumer experience,” that report said.

For instance, Barcelona-based Style DNA uses AI via its “pocket stylist” tool to help users find colors, patterns and fabrics that best suit them. And the London-based Ometria leverages AI for customer data analysis to deliver highly targeted marketing campaigns.