In late 1998, with funding from the Government of Canada’s Scientific Research & Experimental Development (SR&ED) program, I converted my “strand commonality” theory into one of the industry’s first web-based procurement platforms. Utilizing an agent-based model, RAM leveraged self-learning advanced algorithms within a nascent AI framework architecture to enable the Department of National Defence to select the right supplier 97.3% of the time.
“The concept of “strand commonality” in procurement is not widely recognized as a standard term in mainstream procurement literature, but based on available discussions, it appears to have been first articulated by Jon W. Hansen in 1998. Hansen, identified as a Chief Procurement Officer (CPO), introduced the idea in the context of linking seemingly unrelated data streams or “strands” to optimize procurement outcomes. He described strand commonality as a theory where unrelated strands of data events share related attributes that, when connected through advanced self-learning algorithms, can achieve optimal results—demonstrated in a production environment with a 97.3% success rate. This was noted in a 2024 article reflecting on procurement innovations, suggesting Hansen’s work laid an early foundation for leveraging data connectivity in procurement, even if the term itself didn’t become a widely adopted standard.” – What Are The Origins Of ProcureTech Implementation Success?
That same logic and architecture is now being applied to helping practitioners select the right ProcureTech solution provider. In short, instead of a 20% generational initiative success rate, there is no reason why, with this first iteration, practitioners cannot use RAM assessment logic to select the right solution provider partner to achieve a successful ProcureTech initiative rate of 90% or better.
“Hansen, a noted procurement expert, has discussed leveraging algorithms to optimize procurement processes, particularly during his work in the late 1990s and early 2000s. However, there’s no widely recognized, specific algorithm named “Hansen’s algorithm” in procurement literature akin to, say, Dijkstra’s algorithm in computer science. Instead, his contributions are more broadly tied to practical applications of algorithmic thinking in supply chain and procurement optimization.” – What Are The Origins Of ProcureTech Implementation Success?
Conversely, there is no reason why ProcureTech solution providers can’t use this same RAM assessment logic to select the right practitioner clients.
TODAY’S TAKEAWAY
ProcureTech solution providers are touting the power of AI to digitalize and optimize procurement performance and successful outcomes in areas like supplier selection and management. Why can’t this same approach work to enable practitioners to select the right ProcureTech solution provider partner?
30
Procurement Insights
If AI can help practitioners improve procurement performance, why can’t they use AI to select the right ProcureTech partner?
Posted on March 31, 2025
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In late 1998, with funding from the Government of Canada’s Scientific Research & Experimental Development (SR&ED) program, I converted my “strand commonality” theory into one of the industry’s first web-based procurement platforms. Utilizing an agent-based model, RAM leveraged self-learning advanced algorithms within a nascent AI framework architecture to enable the Department of National Defence to select the right supplier 97.3% of the time.
“The concept of “strand commonality” in procurement is not widely recognized as a standard term in mainstream procurement literature, but based on available discussions, it appears to have been first articulated by Jon W. Hansen in 1998. Hansen, identified as a Chief Procurement Officer (CPO), introduced the idea in the context of linking seemingly unrelated data streams or “strands” to optimize procurement outcomes. He described strand commonality as a theory where unrelated strands of data events share related attributes that, when connected through advanced self-learning algorithms, can achieve optimal results—demonstrated in a production environment with a 97.3% success rate. This was noted in a 2024 article reflecting on procurement innovations, suggesting Hansen’s work laid an early foundation for leveraging data connectivity in procurement, even if the term itself didn’t become a widely adopted standard.” – What Are The Origins Of ProcureTech Implementation Success?
That same logic and architecture is now being applied to helping practitioners select the right ProcureTech solution provider. In short, instead of a 20% generational initiative success rate, there is no reason why, with this first iteration, practitioners cannot use RAM assessment logic to select the right solution provider partner to achieve a successful ProcureTech initiative rate of 90% or better.
“Hansen, a noted procurement expert, has discussed leveraging algorithms to optimize procurement processes, particularly during his work in the late 1990s and early 2000s. However, there’s no widely recognized, specific algorithm named “Hansen’s algorithm” in procurement literature akin to, say, Dijkstra’s algorithm in computer science. Instead, his contributions are more broadly tied to practical applications of algorithmic thinking in supply chain and procurement optimization.” – What Are The Origins Of ProcureTech Implementation Success?
Conversely, there is no reason why ProcureTech solution providers can’t use this same RAM assessment logic to select the right practitioner clients.
TODAY’S TAKEAWAY
ProcureTech solution providers are touting the power of AI to digitalize and optimize procurement performance and successful outcomes in areas like supplier selection and management. Why can’t this same approach work to enable practitioners to select the right ProcureTech solution provider partner?
30
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