Stradigi AI Collaborates with Seculus to Substantially Improve their Demand Prediction Capabilities
Stradigi AI, a leading North American SaaS business AI platform provider, has announced the deployment of its Kepler platform to help Seculus, a Brazilian designer, manufacturer and vendor of watches, to better predict its demand. Seculus chose the Kepler platform to build accurate prediction models across their multiple brands and their large volumes of rotating SKUs.
With the Kepler platform, Seculus quickly developed several demand prediction models that significantly out-performed existing forecasting methods. The Kepler platform’s advanced automated workflows enabled them to create a very highly correlated demand prediction model within their first week on the platform.
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Oswaldo Moreira Neto, Director of Marketing and Innovation at Seculus said: “We needed to add more certainty to our demand predictions. Getting a product from concept to shelf can take between 9 and 12 months. Often, if we don’t stock the right amount of inventory, by the time we realize it, it’s too late to fix. Improving our demand forecasting is a top priority, and we see the Kepler platform as a critical tool in building more accurate demand prediction models so we can actively drive down shortfalls and excess inventory.”
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“The Kepler platform is redefining data-driven intelligence and is positively impacting businesses’ top and bottom lines through AI-powered decision-making capabilities”, said Per Nyberg, Chief Commercial Officer at Stradigi AI. “In the case of Seculus, they had a complex yet clear use case they wanted to act upon and the advanced Artificial Intelligence and ML technologies of our business AI platform have brought fast, accurate, and tangible results that directly translate to financial gains.”
By using the Kepler platform delivered over Microsoft Azure cloud, Seculus now has access to powerful Automated Data Science Workflows that can be leveraged to address 100s of use cases across their business, allowing them to bridge the gap between their data and real, tangible results.
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