Supply Chain Resiliency and Transparency Assessment Using Graph Analytics and Stress Testing
グラフ分析とストレステストを用いたサプライチェーンの強靭性と透明性の評価 (AI 翻訳)
Kemalcan Aydogdu, Sebnem Duzgun
🤖 gxceed AI 要約
日本語
本論文は、国際貿易データとHSコードを活用し、サプライチェーンの透明性と強靭性を評価する手法を提案。透明性は4段階に分類し、Scope 3 GHG排出量の推定や高排出ノードの特定を可能にする。強靭性はグラフ分析とモンテカルロシミュレーションで評価。Cr-13 Steel Drill Pipeの事例では、スクラップ材料の持続可能性メリットとトレーサビリティ課題を示した。
English
This paper proposes a data-driven methodology using global trade data and HS codes to assess supply chain transparency and resiliency. Transparency is classified into four levels, enabling Scope 3 GHG estimation and identification of high-emission nodes. Resiliency is evaluated through graph analytics, GCC, and Monte Carlo simulations. A case study on Cr-13 Steel Drill Pipe highlights sustainability benefits of scrap materials and challenges in traceability.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本手法は、SSBJが求めるScope 3開示やサプライチェーン全体の排出量把握に直接貢献する。日本企業がサプライチェーンの透明性向上と排出削減を両立するための実践的ツールとして有用。
In the global GX context
This methodology directly supports global Scope 3 disclosure requirements under ISSB and CSRD by providing a scalable approach to trace raw materials, estimate emissions, and identify risks. It advances supply chain transparency and resilience, key for climate transition planning.
👥 読者別の含意
🔬研究者:Novel graph-based transparency classification and stress testing framework for supply chain GHG analysis.
🏢実務担当者:Actionable method to improve Scope 3 accounting and identify critical nodes for emission reduction.
🏛政策担当者:Insights for designing regulations that enhance supply chain traceability and climate resilience.
📄 Abstract（原文）
This paper presents a comprehensive methodology for assessing supply chain transparency and resiliency using a data-driven approach. Leveraging global trade data and Harmonized System (HS) codes, the methodology maps each stage of the supply chain to enhance regulatory compliance and mitigate operational risks. Transparency is evaluated using a novel classification system that categorizes branches as fully transparent, highly transparent, moderately transparent, or non-transparent. This enables raw material traceability, Scope 3 greenhouse gas (GHG) emission estimation, and identification of high-emission nodes for targeted reductions. Resiliency is assessed through graph analytics and stress testing, incorporating metrics such as the Giant Connected Component (GCC) and probabilistic simulations to analyze vulnerabilities and develop recovery strategies. A case study on the Cr-13 Steel Drill Pipe supply chain highlights the benefits of incorporating scrap materials for sustainability, alongside challenges related to traceability due to regulatory gaps and non-transparent networks. Monte Carlo simulations identify critical nodes whose disruption significantly affects network connectivity; therefore, resiliency, and transparency. This methodology delivers actionable insights to improve supply chain resiliency, sustainability, and operational efficiency. It is scalable across industries, enabling stakeholders to optimize management strategies, align with global climate initiatives, and build resilient and transparent networks.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/mining6020031first seen 2026-05-15 16:58:53
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