How to Mint Millions from your Unclassified Spend Data?

What is not utilized is a waste. Today, when you have access to vast amount of data, not leveraging it to its potential is a waste. Data contributes to the making of both strategic and tactical decisions thereby facilitating trust between internal stakeholders and customers. Without this trust, procurement loses its value. In this context, machine learning, an exercise of artificial intelligence, transforms data-based decision-making more extensive and accurate.  With Machine Learning (ML), computers learn from the surrounding without the need of any explicit programming. ML algorithms mine from data-rich fields—public, private, and company-owned information (e.g. spend data, contracts, and market intelligence) to the advantage of procurement organizations.

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 Rescue

Machine 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.

Key Takeaway:

• 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)

If you are a procurement professional in the oil and gas industry, by now you have learnt how to obtain fast and granular visibility into global spend and unlock cost-saving opportunities with innovative strategies. In fact, procurement professionals from any industry keen on minting millions per billion spend, should consider ML-based spend analytics tool which can process yearlong classification tasks in a matter of few hours. Need help? Ask me.

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