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Digital Process Passport: A Conceptual Model for Information Collection and Sharing In Sustainable Manufacturing

デジタルプロセスパスポート:持続可能な製造における情報収集と共有のための概念モデル (AI 翻訳)

Marija Glišić, Charles Møller, Badrinath Veluri, Devarajan Ramanujan

Procedia CIRP📚 査読済 / ジャーナル2024-01-01#開示インフラOrigin: EU経営インパクト: 調達リスク対象セクター: manufacturing
DOI: 10.1016/j.procir.2024.10.209
原典: https://doi.org/10.1016/j.procir.2024.10.209

🤖 gxceed AI 要約

日本語

本論文は、Industry 4.0/5.0で収集される製造データを統合し、環境影響の定量化・共有・削減を可能にする「デジタルプロセスパスポート」という概念モデルを提案する。EUのデジタルプロダクトパスポート規制に対応するため、情報収集の粒度や検証可能性など、製造現場での実装上の論点を整理している。

English

This paper proposes a conceptual model, the Digital Process Passport, for collecting and sharing manufacturing information to support sustainable manufacturing. It integrates data scattered across enterprise systems to quantify, monitor, and mitigate environmental impacts, and aligns with the EU's evolving Digital Product Passport regulation by addressing granularity, accuracy, and verifiability of environmental data.

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

EUのデジタルプロダクトパスポート(DPP)規制は域外企業にも適用されるため、欧州に輸出する日本企業にとって、製造データの収集・共有の枠組みを設計する本モデルは示唆に富む。特にサプライチェーン全体で環境情報を検証可能にする点は、今後の日本企業の対応に有用である。

In the global GX context

As the EU Digital Product Passport regulation takes shape, this model offers a structured approach to gathering verifiable environmental information across manufacturing value chains. It contributes to the emerging disclosure infrastructure for product-level sustainability data, complementing TCFD/ISSB-style corporate reporting with process-level data.

👥 読者別の含意

🔬研究者:Provides a conceptual foundation for integrating manufacturing data with environmental impact assessment and DPP implementation.

🏢実務担当者:Offers a template for structuring internal manufacturing data to prepare for EU DPP compliance and customer data requests.

🏛政策担当者:Highlights open design questions (granularity, verifiability) that DPP rule-making must resolve.

📄 Abstract(原文)

The transition towards smart manufacturing, aided by the concepts of Industry 4.0 and Industry 5.0, is enabling manufacturers to gather extensive amounts of data from machinery, systems, and operations. Access to manufacturing data can improve information flow and data sharing, enabling the possibility of reducing costs, saving material, and improving process and quality of production, while leading to fewer wastes and emissions. Typically, manufacturing information is spread across multiple enterprise information technology systems, and structured based on traditional product development paradigms (e.g., product design, process planning, manufacturing execution, quality control, etc.). However, realizing sustainable manufacturing requires integrating this information into a unified frame allowing for quantifying and subsequently mitigating the environmental impacts of production systems. Furthermore, the growing focus on standards-based reporting of product environmental footprints, e.g., through the European Union’s Digital Product Passport (DPP) regulation, necessitates that manufacturers and their value chains share verifiable information on environmental impacts. Given that the structure and use cases for DPPs are still evolving, there is a need for manufacturing value chains to investigate, what manufacturing information needs to be collected, how to conduct information collection in complex manufacturing systems, what the needed level of granularity, and how to ensure the needed level of accuracy and verifiability. To support these goals, our work proposes a conceptual model for information collection and sharing in sustainable manufacturing titled Digital Process Passport. The goal of a Digital Process Passport is to enable systematic estimation, monitoring, sharing, and mitigating manufacturing-related environmental impacts, by providing a common template for representing circularity- and sustainability-focused information of production systems.

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。