AI summary · arXiv: 1909.08021v2 · cs.GT

Strategic Formation and Reliability of Supply Chain Networks

Victor Amelkin, Rakesh Vohra

Original paper · DOI Submitted to arXiv on 17 Sep. 2019 revision 1 License: NONEXCLUSIVE-DISTRIB 1.0

The essentials

  • Supply chains are crucial for the global economy, but disruptions can be costly
  • Centrally managed supply chains invest in resilience, while decentralized supply chains rely on self-interest
  • Authors examine incentives of self-interested agents in forming resilient supply chain networks
  • Model considers yield uncertainty and congestion in supplier-retailer relationships
  • Networks tend to be sparse when only yield uncertainty is considered
  • Retailers concentrate links on a single supplier instead of diversifying to mitigate yield uncertainty
  • Networks become denser and resemble bipartite expander graphs when congestion is considered
  • Investments by suppliers to improve yield can make them worse off due to market saturation
  • Research sheds light on how self-interested agents form resilient supply chain networks under uncertainties and competition dynamics

Key points written by AI: they may contain errors, check them against the paper.

AI-generated keywords: Supply Chain Resilience Uncertainty Competition Incentives

Read further

  • Comprehensive Summary
  • Layman's Summary
  • Blog article
  • Original abstract
Supply chains are crucial for the functioning of the global economy, but disruptions to these networks can be costly. While centrally managed supply chains invest in ensuring their resilience, decentralized supply chains rely on the self-interest of individual components to maintain overall resilience. In this study, authors Victor Amelkin and Rakesh Vohra examine the incentives that independent self-interested agents have in forming a resilient supply chain network in the face of production disruptions and competition. The authors develop a model where competing suppliers face yield uncertainty (delivering less than ordered) and congestion (lead time uncertainty or "soft" supply caps). On the other hand, competing retailers must make decisions about which suppliers to link with based on both price and reliability. The researchers find that when there is only yield uncertainty, resulting supply chain networks tend to be sparse. Retailers concentrate their links on a single supplier instead of diversifying their supply base to mitigate yield uncertainty. This counterintuitive behavior occurs because retailers benefit from supply variance, suggesting that competition amplifies output uncertainty. When congestion is also considered in the model, the resulting networks become denser and resemble bipartite expander graphs proposed in previous supply chain literature. This finding provides an example of endogenous formation of resilient supply chain networks without explicitly encoding resilience in payoffs. Finally, the study reveals that investments made by suppliers to improve yield can actually make them worse off. High production output saturates the market, leading to lower prices and profits for all participants involved. Overall, this research sheds light on how self-interested agents form resilient supply chain networks under various uncertainties and competition dynamics. It highlights important considerations for both suppliers and retailers in managing disruptions and maintaining efficient operations within decentralized supply chains.

Was this synthesis useful?

Score: 0

Why do we need votes?

Votes are used to determine whether we need to re-run our summarizing tools. If the count reaches -10, our tools can be restarted.

Ask questions about this paper to our AI assistant

Compare with the PDF

You can also chat with multiple papers at once here.

⚠The license of the paper does not allow us to build upon its content and the AI assistant only knows about the paper metadata rather than the full article.

Figures and plots

Ask about a figure by number or use its question button to request visual analysis. Up to two figures and four image panels per question. Values read from plots are approximate; check the original data.

AI assistant instructions?

Tools for this paper

Explain it differently 3 free rewrites left today
Save to a collection Group papers in collections to compare them, chat with them and export them. Create a free account Log in

Advanced tools Premium

For researchers who follow a field closely: citation alerts, journal-club slides, version comparison and Zotero export. Discover Premium

Zotero
Citation alerts
Journal club Premium+ Slides to present it
For journalists Premium+ Press kit and hype check

Similar papers summarized with our AI tools

Navigate through even more similar papers through a

tree representation

The previous summary was created more than a year ago and can be re-run (if necessary) by clicking on the Run button below.

⚠The license of this specific paper does not allow us to build upon its content and the summarizing tools will be run using the paper metadata rather than the full article. However, it still does a good job, and you can also try our tools on papers with more open licenses.

Disclaimer: The AI-based summarization tool and virtual assistant provided on this website may not always provide accurate and complete summaries or responses. We encourage you to carefully review and evaluate the generated content to ensure its quality and relevance to your needs.