Machine Learning, and Natural Language Processing in Contract Management
Does your procurement team have access to the data that it needs to make informed decisions and develop effective strategies? One common roadblock to accessing valuable data is that it is hidden in contracts that can be difficult to find, difficult to read once they are found, and in some instances, hundreds of pages long. In the past, procurement leaders have dedicated resources to manually enter data from these contracts into a database, instead of dedicating these resources to complete value-add work. Today, software that combines machine learning (ML) and natural language processing (NLP) are able to process and analyze the large amount of natural language data found in contracts and categorize this data into specific groups that are relevant to procurement. The following examples include a few of the many applications of ML and NLP in contract management.
Once the ML and NLP program has organized the data from a company’s contracts, it will be significantly easier for procurement teams to track contract compliance. For example, once the program has organized the contract end dates for every supplier, it will be easy to compare these end dates to payable data to determine which suppliers are being paid outside of their contracts. Additionally, for services spend, the software could pull the job classifications and billing rates from each contract and organize them for further analysis. Once this data is organized, analysts could compare these rates to all of a company’s suppliers or against market intelligence reports and determine where there is opportunity for cost savings.
Another area where ML and NLP can improve a procurement teams’ performance is in payment term management. In the past, when an executive has decided that a company needs to improve their payment terms, procurement leaders have had to dedicate valuable resources to read through contracts or search every contractor in an ERP system. However, ML and NLP programs could quickly pull payment terms data from every contract and organize the data on a simple spreadsheet. After this data is organized, procurement resources are free to spend their time negotiating improved payment terms with suppliers instead of spending their time looking for data.
Another application of ML and NLP is to help keep important definitions and clauses consistent in all contracts. For example, in company’s with multiple business units, it can be difficult to maintain consistent definitions across all contracts. However, contract managers could utilize ML and NLP programs to quickly search all contracts for a specific definition, determine if a specific definition is accurate for their contract, and simply copy and paste a previous definition into their new contract.
These are just a few of the many ways that ML and NLP can give your procurement team access to more data and more time to use that data to drive value to the business. Where would your procurement team add value if they had more data and time? We would love to hear from you!