As I’ve written about the last few weeks, Artificial Intelligence, Machine Learning, and Robotic Process Automation are having an impact on Accounts Payable (“AP”), and will continue to do so at an even greater pace moving forward. Today, we are going to take a look at another technology, predictive analytics, which holds great promise not only for AP but the entire supply chain. If you are not yet familiar with predictive analytics it is no doubt a technology that you will be hearing about much more in the coming months and years.
Predictive analytics is sometimes wrongly lumped in with discussions on spend analysis. To that end, I thought it would be helpful to differentiate the two. Spend analysis is the name of the process used to (a) collect the spend data from different enterprise source systems, validate it, and then place it in a standardized and usable format (b) organize the information into a standard classification structure and cleanse the information by eliminating any mistakes, discrepancies, and/or flaws in the data set (c) enrich the spend data with complimentary information that can provide greater context around the category, supplier, and usage within the enterprise and (d) analyze (i.e. “slice & dice”) the spend data to identify opportunities and support decisions.
Visibility into enterprise spend is the desired output of spend analysis, and helps AP, Finance, and Procurement departments understand what money is being spent with which suppliers and on what categories, by which business units, etc. Spend Analysis seeks to provide data in a usable format with context to help stakeholders make better, more-informed decisions.
Predictive analytics, on the other hand, is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, which analyze current and historical facts to make predictions about future or otherwise unknown events. Predictive analytics does not tell you what will happen in the future but rather tries to exploit patterns found in historical and transactional data to identify risks, opportunities, patterns, and lessons learned, and then apply those learnings to current, real-world conditions to enable users to predict future behaviors of individuals or businesses and make predictions, or educated guesses about potential business outcomes.
One area where predictive analytics has been used for quite some time is credit scoring. Everyone reading this blog has a credit score which is based upon personal credit history. This data is used to predict an individual’s ability or likelihood to be able to make future payments, and financial institutions then use this data to make decisions on extending credit and on what terms.
Predictive analytics applied to AP can be used to help predict a great many things including helping to identify which of your suppliers is most likely to accept an early payment discount, is having financial challenges, or is likely to increase prices in the future, to name just a few.
Conclusion
Ardent Partners’ Research consistently shows that visibility into enterprise spend is the foundation of AP, Finance, and Procurement excellence. Leaders who attempt to manage their operations without visibility into spend, simply cannot achieve Best-in-Class results, nor can they truly be a strategic function. Before predictive analytics can be implemented and leveraged fully, an organization needs to have a well-designed, and implemented, spend analysis initiative already in place. Predictive analytics holds great promise for AP, Finance, and Procurement but they must first have a good handle on their historical data. As George Santayana, writer and philosopher, said “Those who do not learn history are doomed to repeat it’ or in the case or predictive analytics…not be able to leverage it to make better business decisions and help the organization prosper.
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