To better understand the use of generative AI in procurement, you must first understand agent-based modeling and strand commonality.

EDITOR’S NOTE: The following is my comment to a LinkedIn post by Kimberly-Clark‘s Rajesh Kumawat

Timing is everything. In another discussion stream, I just provided the following response that aligns with Tamera Fenske‘s “backbone of the company” comment:

NOW you have given me something to consider, e.g., Google Gemini.

Multimodal sounds similar to the “strand commonality” theory I developed in the late 1990s.

At the time, the government’s Scientific Research & Experimental Development program funded my research to develop a web-based self-teaching algorithm platform for the Department of National Defence. – https://procureinsights.com/2023/10/02/are-reverse-auctions-good-or-bad-the-debate-continues/

Strand commonality is based on the theory that seemingly unrelated strands or streams of data events have related attributes.

By recognizing that these “attributes” exist and are related, you can link them to achieve a collective optimal outcome using advanced self-learning algorithms. In a production environment with multiple shifting variables – both historical and real-time, the optimal outcome was achieved 97.3% of the time.

More to come . . .

RIDDLE: What do abacuses, calculators, and generative AI have in common?

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