Supply Chain Resilience Models Using Finite Transformation Semigroups

A 5.5 KB Excel file presents a framework using finite transformation semigroups to model supply-chain state evolution. The dataset, authored by Job Agba Opue and last updated in May 2026, likely contains analytical results and case studies for manufacturing, agricultural, and e-commerce logistics systems. It provides semigroup-theoretic criteria for analyzing collapse conditions, equilibria, and redundancy in networks.

Use Cases

• Modeling cascading disruptions in logistics networks based on the described finite configuration spaces.
• Identifying minimal collapse-inducing sets in supply chains using the described basis-pruning algorithms.
• Assessing shock containment strategies for e-commerce hubs based on the described semigroup properties mapping to resilience indicators.
• Analyzing redundancy and stabilization in manufacturing systems based on the described idempotent stabilisation criteria.

Strengths

• Framework is based on a rigorous mathematical model using finite transformation semigroups.
• Dataset is licensed under CC-BY-4.0, allowing for open sharing and modification.
• File size is 5.5 KB, indicating a concise and focused dataset.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Job Agba Opue via figshare

Collection Method

Likely contains results from the described analytical framework and case studies.

Freshness

Last updated 2026-05-28 17:39:59; freshness should be verified.