EDITOR’S NOTE: “Strand Commonality” in procurement is a 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 and NYCTA – https://procureinsights.com/2023/10/23/are-you-chasing-solutions-or-solving-problems-part-1-of-3/
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.
STRAND COMMONALITY AND THE INTERNAL & EXTERNAL METAPRISE
The concept of strand commonality is integral to the Metaprise model, as it emphasizes the interconnectedness of seemingly disparate data streams within an enterprise. Here’s how they are linked:
1. Strand Commonality:
- Definition: This theory posits that related attributes exist within different data streams, and these attributes collectively influence desired outcomes.
- Application: By identifying and analyzing these common strands, organizations can uncover hidden patterns and relationships that inform decision-making and process optimization.
2. Metaprise Model:
- Definition: A Metaprise refers to a digitally interconnected enterprise framework that seamlessly integrates people, processes, data, and technologies across the entire business ecosystem, including internal operations, external partners, and customers.
- Function: It aims to break down organizational silos, enabling unified collaboration and real-time data sharing among all stakeholders.
Linkage Between Strand Commonality and the Metaprise Model:
- Data Integration: The Metaprise model leverages strand commonality to integrate diverse data sources, ensuring that related data attributes are connected and accessible across the enterprise.
- Enhanced Decision-Making: By recognizing and utilizing these common strands, the Metaprise framework facilitates more informed and holistic decision-making processes.
- Process Optimization: Understanding the interconnectedness of data streams allows for the optimization of workflows and processes, leading to increased efficiency and effectiveness.
In essence, strand commonality provides the analytical foundation for the Metaprise model, enabling enterprises to harness the full potential of their interconnected data and resources.
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Procurement Insights
What Is The Relationship Between Strand Commonality, The Metaprise And ProcureTech Success?
Posted on February 21, 2025
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EDITOR’S NOTE: “Strand Commonality” in procurement is a 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 and NYCTA – https://procureinsights.com/2023/10/23/are-you-chasing-solutions-or-solving-problems-part-1-of-3/
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.
STRAND COMMONALITY AND THE INTERNAL & EXTERNAL METAPRISE
The concept of strand commonality is integral to the Metaprise model, as it emphasizes the interconnectedness of seemingly disparate data streams within an enterprise. Here’s how they are linked:
1. Strand Commonality:
2. Metaprise Model:
Linkage Between Strand Commonality and the Metaprise Model:
In essence, strand commonality provides the analytical foundation for the Metaprise model, enabling enterprises to harness the full potential of their interconnected data and resources.
30
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