Too Many Rats eating up your Resources?Are you aware of how many oil and gas organizations find it challenging to quantify their spend on many goods and services because of improper categorization? You would be surprised to know that these companies have several hundred million dollars of mis-classified or mis-grouped data that prevents them from effectively managing their spend.
The usual methods are to classify spend manually, or to use a rules-based software on supplier names and keywords in the free text. These processes are labor-intensive and typically result in accurate classification of up to 85 percent of spend. This approach also limits sub-categorization due to reliance on supplier names vs. material descriptions.From a total of 800,000 to 1.2 million line items of a typical set of annual spend data, one can easily find 200,000 to 400,000 unclassified items which take an average of two to four years to bucket them in appropriate categories. This is owing to the labor-intensive nature of categorization process often hindering steady access to spend visibility for informed decision-making..
For large enterprises, now imagine the problem being more complex and almost impenetrable. People from various regions and business units use different nomenclature for vendor names, material, line-item descriptions, etc., which contribute to the primary challenge with mis/un -classified spend data that is not “text” rich or consistent.
Bring the Cat to your RescueMachine learning algorithm automates categorization of rich text-based data from fields including material, supplier or line-item description. Data is fish for ML; the more data you feed your ML algorithm, the better accuracy in spend classification can be expected. . This way, companies can improve their spend-visibility up to 95 percent or even more depending on the successful implementation of an ML based spend-analytics tool that captures data in real time. Apart from a surge in visibility percentage, imagine saving up to $110 million for every $1 billion of spend.
- Machine Learning slims down the process of data-examination by avoiding manual intervention and freeing up more time strategic work
- It contributes to expedited decision making by leveraging classified spend data
- As more categories are classified, a right estimation of buying channels helps decide if there’s a need in increasing these channels or streamlining them down to a few
- ML-based spend analytics helps decrease maverick spend, higher contract coverage, and supplier rationalization
- ML-based spend analytics helps avoid duplication of stock-keeping units (SKUs)