Dynamic Closed-Loop Supply Chain Simulation Data from a Differential Game Model

A 9.1 KB dataset from a paper analyzing a three-level dynamic closed-loop supply chain differential game model. The data, authored by Qing Ma and last updated in May 2026, likely contains results from numerical simulations exploring the impact of consumer purchase and replacement regret on wholesale price, retail price, and return effort.

Use Cases

• Modeling optimal pricing strategies based on wholesale and retail price paths mentioned in the description
• Analyzing the impact of consumer regret on supply chain performance based on the described theoretical framework
• Simulating long-term profit dynamics for manufacturers and retailers based on the described numerical results
• Studying the effect of consumer behavior on product lifecycle length in the market as described

Strengths

• Dataset is openly licensed under CC-BY-4.0, permitting reuse and modification.
• Data is associated with a specific research paper and model, providing clear academic context.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 9.1 KB, indicating limited scope or highly aggregated results.

Provenance

Source

figshare, authored by Qing Ma.

Collection Method

Likely generated from numerical simulations of a differential game model.

Time Range

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Freshness

Last updated 2026-05-22 17:31:25; freshness should be verified.

Geography

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