Numerous studies and reports have been published on the various methods (e.g., Monte Carlo) used to determine supply chain optimization.
I would have to say that my preferred method has been to use the heuristic approach under an agent-based model in which the unique operating attributes of each stakeholder is understood separately before a collective outcome is identified and achieved.
It is this latter “twist” if you can call it that, that has enabled the optimization process to extend beyond the limitations of executional boundaries referred to in a May 2007 article that appeared in Supply Chain Digest titled Supply Chain Optimization versus Simulation. Specifically the author’s assertion that “Mathematical” optimality is not used, and is not required or likely even feasible. Optimization almost always takes at least some minutes to process (and in some cases hours), and hence isn’t generally usable in an execution environment.”
With partial funding from the Government of Canada’s Scientific Research and Experimental Development (SR&ED) Program, I developed a theory that I refer to as “strand commonality” in which disparate and seemingly unrelated data streams can be linked through the use of advanced algorithms to produce a “positive or beneficial” collective outcome.
It is really quite fascinating in that production models have consistently produced the correct results in terms of real-world applicability approximately 98.2% of the time.
It gets really interesting when you introduce multiple tier factors that include intangible elements such as the value of technician certification versus x number of years of practical experience.
My First Procurement Insights Post on Similarity Heuristics and Iterative Methodologies (Excerpt) – Is Ford’s auto-xchange the “Real Deal?” (April 11th, 2008)
Using an agent-based model, in which the unique operating attributes of individual stakeholders are first identified and then understood, the emphasis of my research in 1998 focused on bringing together the seemingly disparate streams of unique stakeholder attributes to produce a “collective” best result or best value outcome. This formed the basis for my development of the theory I call strand commonality.
Referring once again to another of my more recent posts titled Similarity Heuristics, Iterative Methodologies and the Emergence of the Modern Supply Chain I also indicated that “The subject or attributes in a supply (chain) practice are in a state of constant motion and change.”
I went on to say that to “effectively capture the dynamic elements within this kind of environment, a different methodology such as strand commonality . . . must be employed to ensure that an accurate picture is captured on an ongoing basis, thereby bridging or synchronizing the chasms between multiple transactional (stakeholder) streams.”
ChatGPT (Feb. 2025) on The links between Agentic AI, Strand Commonality, Similarity Heuristics, Iterative Methodologies
The integration of Agentic AI, strand commonality, similarity heuristics, and iterative methodologies offers a comprehensive framework for enhancing decision-making and problem-solving in complex systems.
Agentic AI refers to autonomous systems capable of making decisions and performing tasks without human intervention. These AI agents utilize sophisticated reasoning and iterative planning to address complex, multi-step problems, continuously learning and adapting from their interactions. blogs.nvidia.com
Strand commonality, a theory developed by Jon Hansen, posits that seemingly unrelated data streams possess interconnected attributes. Identifying these hidden connections allows for a collective influence on desired outcomes, enhancing strategic planning and operational efficiency. procureinsights.com
Similarity heuristics are cognitive shortcuts where individuals assess the likelihood or value of an event, object, or person by comparing it to existing prototypes or memories. This heuristic enables quick judgments based on perceived resemblances, facilitating efficient decision-making.
Iterative methodologies involve a cyclical process of prototyping, testing, analyzing, and refining a product or process. This approach allows for continuous improvement and adaptation to changing requirements or feedback, ensuring that solutions evolve effectively over time.
Interconnections:
Agentic AI and Iterative Methodologies: Agentic AI systems employ iterative processes to refine their decision-making capabilities. Through continuous cycles of action and feedback, these agents enhance their performance and adapt to dynamic environments.blogs.nvidia.com
Strand Commonality and Similarity Heuristics: Both concepts focus on recognizing patterns and connections. Strand commonality seeks to uncover hidden relationships between disparate data streams, while similarity heuristics rely on perceived resemblances to inform judgments. Together, they facilitate a deeper understanding of complex systems by highlighting both overt and covert linkages.
Agentic AI and Strand Commonality: Agentic AI can leverage the principles of strand commonality to identify and analyze interconnected attributes within data. By doing so, AI agents can make more informed decisions, considering the broader context and the subtle influences of related data streams.
Similarity Heuristics and Iterative Methodologies: In iterative processes, similarity heuristics can guide the development and refinement stages. By comparing current iterations to previous prototypes or known models, teams can quickly identify areas of improvement and make informed adjustments.
TODAY’S TAKEAWAY – In summary, integrating Agentic AI with the principles of strand commonality, similarity heuristics, and iterative methodologies creates a robust framework. This synergy enhances the ability to navigate and manage complex systems, leading to more effective and adaptive solutions.
Procurement Insights
The Links Between Agentic AI, Strand Commonality, Similarity Heuristics, Iterative Methodologies And Why It is Important to Procurement
Posted on February 25, 2025
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My First Procurement Insights Post on Strand Commonality (Excerpt) – Optimization Modeling and the Modern Supply Chain (A PI Q and A) by Jon Hansen (March 2008)
Numerous studies and reports have been published on the various methods (e.g., Monte Carlo) used to determine supply chain optimization.
I would have to say that my preferred method has been to use the heuristic approach under an agent-based model in which the unique operating attributes of each stakeholder is understood separately before a collective outcome is identified and achieved.
It is this latter “twist” if you can call it that, that has enabled the optimization process to extend beyond the limitations of executional boundaries referred to in a May 2007 article that appeared in Supply Chain Digest titled Supply Chain Optimization versus Simulation. Specifically the author’s assertion that “Mathematical” optimality is not used, and is not required or likely even feasible. Optimization almost always takes at least some minutes to process (and in some cases hours), and hence isn’t generally usable in an execution environment.”
With partial funding from the Government of Canada’s Scientific Research and Experimental Development (SR&ED) Program, I developed a theory that I refer to as “strand commonality” in which disparate and seemingly unrelated data streams can be linked through the use of advanced algorithms to produce a “positive or beneficial” collective outcome.
It is really quite fascinating in that production models have consistently produced the correct results in terms of real-world applicability approximately 98.2% of the time.
It gets really interesting when you introduce multiple tier factors that include intangible elements such as the value of technician certification versus x number of years of practical experience.
My First Procurement Insights Post on Similarity Heuristics and Iterative Methodologies (Excerpt) – Is Ford’s auto-xchange the “Real Deal?” (April 11th, 2008)
Using an agent-based model, in which the unique operating attributes of individual stakeholders are first identified and then understood, the emphasis of my research in 1998 focused on bringing together the seemingly disparate streams of unique stakeholder attributes to produce a “collective” best result or best value outcome. This formed the basis for my development of the theory I call strand commonality.
Referring once again to another of my more recent posts titled Similarity Heuristics, Iterative Methodologies and the Emergence of the Modern Supply Chain I also indicated that “The subject or attributes in a supply (chain) practice are in a state of constant motion and change.”
I went on to say that to “effectively capture the dynamic elements within this kind of environment, a different methodology such as strand commonality . . . must be employed to ensure that an accurate picture is captured on an ongoing basis, thereby bridging or synchronizing the chasms between multiple transactional (stakeholder) streams.”
ChatGPT (Feb. 2025) on The links between Agentic AI, Strand Commonality, Similarity Heuristics, Iterative Methodologies
The integration of Agentic AI, strand commonality, similarity heuristics, and iterative methodologies offers a comprehensive framework for enhancing decision-making and problem-solving in complex systems.
Agentic AI refers to autonomous systems capable of making decisions and performing tasks without human intervention. These AI agents utilize sophisticated reasoning and iterative planning to address complex, multi-step problems, continuously learning and adapting from their interactions. blogs.nvidia.com
Strand commonality, a theory developed by Jon Hansen, posits that seemingly unrelated data streams possess interconnected attributes. Identifying these hidden connections allows for a collective influence on desired outcomes, enhancing strategic planning and operational efficiency. procureinsights.com
Similarity heuristics are cognitive shortcuts where individuals assess the likelihood or value of an event, object, or person by comparing it to existing prototypes or memories. This heuristic enables quick judgments based on perceived resemblances, facilitating efficient decision-making.
Iterative methodologies involve a cyclical process of prototyping, testing, analyzing, and refining a product or process. This approach allows for continuous improvement and adaptation to changing requirements or feedback, ensuring that solutions evolve effectively over time.
Interconnections:
TODAY’S TAKEAWAY – In summary, integrating Agentic AI with the principles of strand commonality, similarity heuristics, and iterative methodologies creates a robust framework. This synergy enhances the ability to navigate and manage complex systems, leading to more effective and adaptive solutions.
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