In the first two blogs of this series, we focused on the foundational pillars of Unique Vendor Identification and Taxonomy. We saw how the continuous intelligence gleaned from real-time data elevates Procurement from a purely transactional to a strategic perspective. We will now talk about Categorization - the third pillar of Spend Intelligence.

Categorizing your spend into a predefined taxonomy provides an understanding of where your money goes - who spent it, on what, and with whom? Such visibility can help you spot inefficiencies and find savings opportunities.

Categorization – No longer a daunting task!

Classifying spend into various procurement categories has always been a challenge in Procurement. It involves consolidating spend data from different sources and then categorizing them into a predetermined taxonomy. It is an essential step as accurate and organized spend data is crucial in developing spend management strategies. However, it is both a tedious and repetitive process, eating up a massive chunk of time that can be spent on more productive work.

Machine learning algorithms can automatically classify spend data into procurement taxonomy with speed and accuracy. The pre-built models can organize a significant chunk of the data into the taxonomy, leaving a smaller subset of data for qualitative assessment by the procurement staff. The system continuously learns from the human review process and improves its accuracy in subsequent runs. The more data you feed to your ML algorithm, the better the precision in spend classification. In addition, ML algorithms can also spot potential errors and produce an exception list. ML-driven Categorization is a continuous process and not a discrete and error-prone activity.

The result: Improvement in strategic sourcing, cost reduction opportunities, and gain in efficiency leading to business advantage.

ElectrifAi’s SpendAi: 2-4% savings in 6-8 weeks

ElectrifAi is one of the US’ leading ML products providers with an extensive library of pre-built products enabling our clients to capture tangible benefits quickly. We work with the C-suite to understand and solve business problems through machine learning. The insights generated from SpendAi help our clients realize 2-4% savings in 6-8 weeks. In addition, it provides specific recommendations to mitigate supply chain risks. All this is done with zero data quality requirements and zero need for a data science team.

Our product does not require investment in a new platform or infrastructure or a data science team. Instead, we leverage the data existing in your system to power the ML models to deliver business outcomes.

We are the last mile product that sits on the top to solve specific business problems and bring about savings.

A leading Fortune 100 chemical manufacturer accelerated visibility into their vendor spend categories and delivered on client expectations for cost reductions. The results exceeded targeted cost reduction by 7.5%, tangible results seen within 30 days, and 98% classification of vendor spend in organized categories.