Stanley, Black & Decker Selects DataRobot to Supersize Data Science Efforts
Summary
Prior to selecting DataRobot, Stanley, Black & Decker had a data science team that used a wide range of open source code to solve a variety of analytics problems across more than 30 brands. The team also needed a tool that would quickly and accurately build the best machine learning models for a specific problem, including demand forecasting, which was previously handled using an outdated and extremely manual approach. The team will use DataRobot for a number of use cases across different business units; examples include demand forecasting, inventory optimization, supply chain management, customer acquisition, and logistics efficiencies. They needed a solution that would not only allow their data science team to build and deploy models, but also inspire a culture of change within the organization,” said Paul Winsor, General Manager of Retail, DataRobot. With a library of hundreds of the most powerful open source machine learning algorithms, the DataRobot platform encapsulates every best practice and safeguard to accelerate and scale data science capabilities while maximizing transparency, accuracy, and collaboration.
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