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Does a 2008 answer to a question on “optimization modeling” still stand in 2024?

Procurement Insights

EDITOR’S NOTE: More than two decades ago, the foundational elements for successfully utilizing advanced, self-learning algorithms to optimize the procurement process using an agent-based model were well established. Monte Carlo) used to determine supply chain optimization. If so, what was it? I hope this helps.

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The Oldest Procurement Blogs in the Procurement Industry

Procurement Insights

Sourcing Innovation Launch Date: June 2006 Overview: Authored by Michael Lamoureux, a Computer Science PhD, Sourcing Innovation focuses on procurement technology, strategic sourcing, and supply chain management. Procurement Insights: Broad trends and ethical sourcing guidance for global supply chain management.

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What does a 2015 article have to do with Hershey’s 2025 acquisition of LesserEvil for $750

Procurement Insights

What are some best practices to promote optimal adoption of the governing policies and procedures in the absence of spend management technology? Note: my August 3rd, 2007 post titled Procurements expanding role and the executive of the future reviewed the results of a panel discussion hosted by CPO Agenda.

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What is continuous, self-cleaning data?

Procurement Insights

Cited as a success in Procurement Insights (2007-2014), it embodied Hansens agent-based approach, likely a precursor to the Metaprise model, prioritizing the shared relationship between human agent experience and expertise and technology-based AI agents. supplier vetting patterns) to ensure cleaned data to optimize desired outcomes.

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Comparison of SAP’s Joule Feedback Loops and Hansen’s Learning Loopback Process (Post 1 of 3 Today)

Procurement Insights

2007, 2014, and The GenAI Metaprise, Oct 11, 2024)involves observing data, acting, assessing results, and adapting, aligning with self-learning concepts but prioritizing practical outcomes over technical specifics. cosmetics and coffee supply chains), and highlight technicality, adaptability, and application differences.

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Why is an agent-based model within a metaprise framework better for procurement than an equation-based Intake and orchestration model?

Procurement Insights

MODEL #2 An agent-based model within a metaprise framework outperforms traditional equation-based intake and orchestration models in procurement because it better addresses the complexity, unpredictability, and multi-stakeholder nature of modern supply chains. Complex, multi-tiered supply chains Superior (e.g.,

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What is Jon W. Hansen’s description of the learning loopback process for self-learning algorithms? (Post 2 of 3 Today)

Procurement Insights

Hansens Context and the Absence of a Learning Loopback Process Metaprise Model Overview: Hansens Agent-based Metaprise model (2007 post) uses autonomous agents to represent stakeholders (e.g., supplier performance, tariff costs) to optimize decisions. Adaptation (Optimization): Process: Agents continuously adapt to new data (e.g.,