How TealBook Uses Machine Learning to Ensure a More Efficient Supply Base 1110x860

How TealBook Uses Machine Learning to Ensure a More Efficient Supply Base

By Jervis Pinto

Machine learning is what makes TealBook able to deliver such a significant change to procurement processes. It enables our tool to continually improve your vendor master, allowing you to focus on strategic activities.

What is machine learning?

Machine learning is a form of artificial intelligence that makes it possible for systems to automatically learn and improve based on the data they encounter. It teaches an AI system how to teach itself. Machine learning algorithms study patterns in massive amounts of data—images, words, numbers, or anything else—and use those patterns to determine the best option and apply it to the other AI systems in place.

This grasp of patterns makes for better data because the machine learning algorithm can tackle vast quantities of information that would be impossible for humans or much other technology to sort through in a reasonable amount of time.

How does machine learning help your business?

Machine learning helps businesses turn massive amounts of data into actionable intelligence. It enables you to do more with less, saving you time and money. It also provides insight into your business, your customers, and your suppliers, which allows you to future-proof your organization’s performance.

TealBook’s innovative machine learning technology uses data to improve procurement-supplier relationships by giving easy access to supplier information, eliminating the need for multiple supplier profile platforms, and automatically cleansing data of duplicates while highlighting diverse suppliers and sanctioned companies.

Machine learning saves procurement departments time, money, and helps evaluate risk.

  • It empowers you to automate routine IT tasks, allowing your team to focus on more strategic aspects of their work.
  • Data cleansing and maintenance are automatic rather than manual.
  • Supplier discovery is quick and easy, with automatically updating products and services for existing suppliers, and customized recommendations for your supply chain needs.
  • It enables you to consolidate or extend contracts with existing suppliers for new or more products and services.
  • It give you a complete view of all the variables that contribute to supplier decisions, including previous and current sanctions.
  • Personalized recommendations, diversity and environmental certifications, and predictive modeling based on previous patterns can provide strategic insight for future transactions.

Final Thoughts

If you think about your vendor master and the amount of time and energy you’ve had to put into it to build it, and the daunting task of trying to keep it updated and correct, you’re likely going to be overwhelmed quickly. And, if you’ve had to sort through a collection of dirty data full of errors or duplicated but slightly different entries—and had to spend time tracking down the correct information instead of being able to quickly connect with and complete an order with your vendor—you know the frustration of incorrect information. Machine learning removes the need for all of this manual labor and provides so many more advantages.

At TealBook, we have an in-house team of data scientists, developers, and supply chain experts working together to create and perfect the technology needed for innovative solutions for today’s digital global economy. To learn more and see how we can help your business, connect with us.

About the Author

Jervis Pinto serves as the VP, Data and AI at Tealbook. Jervis has 10+ years of academic and industry experience working at the intersection of machine learning, software engineering, big data and the cloud. Jervis was most recently at EA, where he worked on deep learning methods for synthesizing ultra-realistic dialogue and speech. Prior to that, he was at FiscalNote, using machine learning algorithms to analyze millions of documents. He has a PhD in machine learning and computer science from Oregon State University and serves as a program committee member and reviewer for leading ML and AI conferences (ICML, NeurIPS, AAAI, etc.).

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