Business Problem
A MNC lacked effective supportfor classifying vendor spend, particularly for services and indirectmaterials. Manual text entries and inconsistent descriptions led toincorrect spend categorization, unreliable reporting, and limited visibilityfor budgeting and accounting.
Actions Taken
· Implemented an AI-based spendclassification framework using text analytics and neural network–basedmodels.
· Designed training, scoring, and feedbackloops to continuously improve classification accuracy.
· Applied hierarchical classification logic tohandle diverse and unstructured vendor descriptions.
· Leveraged semantic understanding capabilitiesto interpret free-text procurement data.
Outcomes Achieved
· Achieved 90–95% classification accuracyon business-validated data samples.
· Improved reliability of spend reportingacross services and indirect materials.
· Increased operational efficiency within theglobal procurement organization.
· Enabled more accurate budgeting andaccounting through consistent vendor classification.
