Supply Chain Resilience Model Data for Agricultural and Logistics Systems

A dataset supporting a framework using finite transformation semigroups to model supply-chain state evolution under cascading disruptions and resilience interventions. The data was authored by Job Agba Opue and last updated on 2026-05-28. The file is 5.5 KB in size and available in XLS format.

Use Cases

• Modeling supply-chain collapse conditions based on the described semigroup-theoretic criteria.
• Identifying minimal collapse-inducing sets and redundancy in networks using the described basis-pruning algorithms.
• Assessing shock containment strategies in real-world networks based on the described measurable resilience indicators.
• Analyzing state evolution and idempotent stabilization in logistics systems as described in the framework.

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• The underlying framework is described with explicit semigroup-theoretic criteria and algorithmic procedures.
• The data is licensed under CC-BY-4.0, allowing for open sharing and adaptation.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The description is theoretical; the actual data content and scale require manual inspection.

Provenance

Source

figshare, authored by Job Agba Opue.

Collection Method

Likely generated as part of a research framework for modeling supply-chain disruptions.

Freshness

Last updated 2026-05-28 17:39:58; freshness should be verified.