Ardent Partners’ recent research report, Sourcing and Procurement: The 2016-2017 Technology and Innovation Outlook Reportchronicles the evolution of sourcing and procurement technologies over the last two decades to where they are today and poised to go tomorrow. This report, which is chock full of great insights on many advanced, distinct sourcing and procurement technologies, has spawned a series here on CPO Rising that looks in depth at each of the innovative solutions coming onto the market today and in the near future. Today’s installment: machine learning.

Like other facets of business, sourcing and procurement solutions have benefited and will continue to benefit from the wave of innovation sweeping across the technology landscape. One of the innovative technologies to hit the solution market has been machine learning, which is a function of powerful data engines, advanced algorithms, and user behavior that combine to take processes, user interfaces (UI), and user experiences (UX) to the next level. Sourcing and procurement technologies that are enabled with machine learning have the potential to revolutionize the discipline.

Machine learning starts off like many other technologies today: with the end user. Take supplier information management (SIM) for example. A SIM solution driven by machine learning will observe UI patterns, particularly when the user first begins to interface with the solution, and after a few transactions, will quickly begin to “learn” user behavior patterns. Over time, the SIM solution will “know” the most common suppliers that the user manages, the most common data sources and formats, the locations of the data, and the next step in the process – whether it is data enrichment, analysis, or dissemination. Machine learning will then adapt, or be able to “guide” the user through his / her workflow in a streamlined manner.

Eventually, solutions enabled with machine learning will be able to suggest ways to improve the process – whether by new data sources (e.g., from ISM), further enrichment or analysis (e.g., with ISM data or reporting), or by “checking” the user’s work before completion. Machine learning will be able to push alerts or recommendations to the end user, unprompted, based on what it “believes” the end user will need under those circumstances, which are ultimately based on previous transactions. Here, machine learning begins to merge with artificial intelligence (AI), another technological advancement taking the business solution market by storm.

Final Thoughts

Supply management solutions driven by machine learning capabilities will never fully remove the “man in the loop” in favor of a completely autonomous, AI-driven robot. Instead, they will augment the “man in the loop” by being his (or her) “wingman” on the journey towards a data-driven, holistic operating environment where tactical processes are not only automated but are also learned, and strategic value is achieved by unencumbered procurement practitioners.

Interested in learning more? Join us next Wednesday, December 14th at 2 PM EST for a complimentary webinar where Ardent’s analysts, Matthew York and Andrew Bartolini share their outlook for sourcing and procurement technology in 2017, including machine learning. Register here.

RELATED ARTICLES

Advanced Sourcing and Procurement Technologies: Optimization-based Sourcing Tools

Advanced Sourcing and Procurement Technologies: “Moneyball for Procurement”

Advanced Sourcing and Procurement Technologies – A Series Introduction

Innovation Continues to Drive the Evolution of Procurement Technology (Report Preview!)

Announcing the Sourcing and Procurement: 2016-2017 Tech and Innovation Outlook Report!

Tagged in: , , , ,

Share this post