Case Study: EPAM optimizes merchandise decisions with KNIME

How EPAM built a recommendation engine for brand portfolio decision making

EPAM worked with KNIME to address the challenge of optimizing merchandise levels for retail decision making. EPAM needed a better way to predict which brands or products would sell well, reduce unsold stock, and support Merchandising and Brand Portfolio Managers with more reliable regional insights instead of relying on trial and error.

KNIME built a recommendation engine and Guided Analytics application using its data analytics platform and KNIME Server. The workflow included ETL and data preparation, then recommendation modeling, and finally deployment as an analytical application for business users. The case study highlights improved decision support for brand portfolio decisions, though it does not provide specific measurable impact figures.

EPAM