Supply Chain Resilience Model Data Using Finite Transformation Semigroups

A theoretical framework and accompanying data model supply-chain state evolution under disruptions using finite transformation semigroups. The dataset, created by Job Agba Opue and last updated in May 2026, is a 5.5 KB Excel file illustrating the framework with examples from manufacturing, agriculture, and e-commerce logistics.

Use Cases

• Modeling cascading disruptions in supply networks based on the described semigroup framework.
• Identifying minimal collapse-inducing sets in logistics systems based on the described algorithmic procedures.
• Assessing shock containment strategies using semigroup-theoretic criteria for synchronizing collapse.
• Analyzing redundancy and stabilization in manufacturing stages based on the described idempotent stabilization concepts.

Strengths

• Dataset is small (5.5 KB), ensuring fast download and processing.
• Framework is explicitly linked to case studies in manufacturing, agriculture, and e-commerce, providing concrete application contexts.
• The model provides explicit semigroup-theoretic criteria for collapse and stabilization, offering a formal analytical foundation.

Limitations

• Row count is unknown, which may limit suitability assessment for large-scale empirical analysis.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality and content require manual inspection after download.

Provenance

Source

figshare, author Job Agba Opue

Collection Method

Developed as part of a paper presenting a theoretical framework, likely containing illustrative or synthetic data.

Freshness

Last updated 2026-05-28 17:39:57; freshness should be verified.