CO₂ Calculations of Complex Supply Chain Networks

Structured collection of emission data based on the SCOR model

Dieter Uckelmann ORCID Icon, Johannes Tonio Alt, Isabel Andujo
JournalIndustrie 4.0 Management
Issue Volume 35, 2019, Edition 6, Pages 16-20
Open Accesshttps://doi.org/10.30844/I40M_19-6_S12-16
Bibliography Share Cite Download

Abstract

As a result of the progressively climate change, the environmental impact of corporate activities is becoming the focus of internal and external shareholders. At the same time more complex supply chain networks are being developed in the industry. The majority of the processes involved in a supply chain network are executed by globally spread partners. This trend makes it difficult to quantify the impact of individual corporate-level activities on the overall ecological performance of the supply chain. The following document evaluates the scope of application on the SCOR model for a structured range of data emissions from global supply chains, based on a detailed literature research.

Keywords

, , ,

Bibliography

[1] Herzig, C.; Schaltegger, S.: Nachhaltigkeit in der Unternehmensberichterstattung. Gründe – Probleme – Lösungsansätze; Diskussionspapier zum Fachdialog des Bundesumweltministeriums (BMU) am 13. November 2003, Berlin. Lüneburg 2004.
[2] Bickhoff, N.: Quintessenz des strategischen Managements. Was Sie wirklich wissen müssen, um im Wettbewerb zu überleben. Berlin Heidelberg 2009.
[3] APICS: Supply Chain Operations Reference (SCOR) model. URL: www.apics.org/apics-for-business/frameworks/scor, Abrufdatum 08.05.2019.
[4] Bolstorff, P. A.; Rosenbaum, R. G.; Poluha, R. G.: Spitzen- leistungen im Supply Chain Management. Ein Praxishandbuch zur Optimierung mit SCOR, 1. Aufl. Berlin Heidelberg New York 2007.
[5] D ́heur, M.: CSR und Value Chain Management. Profitables Wachstum durch nachhaltige gemeinsame Wertschöpfung. Berlin 2014.
[6] APICS: Supply Chain Operation Reference Model Version 12.0. Chicago 2017.
[7] Cash, R.; Wilkerson, T.: GreenSCOR: Developing a Green Supply Chain Analytical Tool. McLean, USA 2003.
[8] Greenhouse Gas Protocol: Overview of GHG Protocol scopes and emissions across the value chain. URL: https://ghgprotocol.org/sites/default/files/standards_supporting/Diagram%20of%20scopes%20and%20emissions%20across%20the%20value%20chain.pdf, Abrufdatum 18.05.2019.
[9] Supply Chain Council: Supply Chain Operation Reference (SCOR) model. Overview – Version 10.0. Cypress, USA 2010.
[10] KPMG: Handbuch zur Nachhaltigkeitsberichterstattung. URL: https://assets.kpmg/content/dam/kpmg/pdf/2014/06/handbuch-nachhaltigkeitsbericherstattung.pdf, Abrufdatum 18.05.2019.
[11] GRI: Konsolidierter Satz der GRI-Standards für die Nachhaltigkeitsberichterstattung. Amsterdam 2016.
[12] Gabriel, A.: Freiwillige Veröffentlichung und Prüfung von GRI-Nachhaltigkeitsberichten. Eine empirische Analyse auf dem europäischen Kapitalmarkt. Wiesbaden 2015.
[13] IHK Nürnberg: Lexikon der Nachhaltigkeit. Drei Säulen Modell. URL: www.nachhaltigkeit.info/artikel/1_3_a_ drei_saeulen_modell_1531.html, Abrufdatum 14.06.2019.
[14] WBCDS, WRI: The greenhouse gas protocol. A corporate accounting and reporting standard, Rev. ed. Washington, DC, Conches-Geneva 2004.
[15] United States Environmental Protection Agency: Under- standing Global Warming Potentials. URL: www.epa.gov/ghgemissions/understanding-global-warming-potentials, Abrufdatum 14.06.2019.
[16] Global Reporting Initiative: GRI 305: Emissions 2016. Background. URL: www.globalreporting.org/standards/grstandards-download-center/gri-305-emissions-2016, Abrufdatum 19.05.2019.
[17] Schmied, M.; Knörr, W.: Berechnung von Treibhausgasemissionen in Spedition und Logistik. Bonn 2013.
[18] Supply Chain Council: Supply Chain Operation Reference Model Version 9.0. USA 2008.
[19] WWF, CDP: Unternehmerisches Klimamanagement entlang der Wertschöpfungskette – eine Sammlung guter Praxis. 2016. URL: https://www.globalcompact.de/wAssets/ docs/Umweltschutz/Publikationen/gute-praxis-sammlung_klimamanagement.pdf, Abrufdatum 23.09.2019.
[20] Uckelmann, D.; Bogenreuther, T.; Bräutigam, I.: Grüne IT für eine grüne Logistik – Umweltorientierter Einsatz von Informationstechnologien für eine nachhaltige Logistik. Digitalen Wandel gestalten. Wiesbaden 2019. S. 141-150.

Your downloads


Solutions: Logistics

You might also be interested in

Competence Assurance for the Verification and Validation of Simulation Models

Competence Assurance for the Verification and Validation of Simulation Models

Requirements for AI-supported assistance in production and logistics
Sigrid Wenzel ORCID Icon, Felix Özkul ORCID Icon, Robin Sutherland ORCID Icon
Can artificial intelligence be used to assess the validity and credibility of simulation models and data for production and logistics? And what requirements must AI assistance meet to effectively support experts in verification and validation while simultaneously safeguarding their competence? In an online survey, simulation experts were asked regarding their expectations and needs.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 24-33 | DOI 10.30844/I4SE.26.5.3
Data-Driven Supplier Selection in Industry 4.0

Data-Driven Supplier Selection in Industry 4.0

Towards an industrial platform for selection and configuration
Purushothaman Ganesh ORCID Icon, Baris E. Albayrak ORCID Icon, Jörg Franke ORCID Icon, Till Sindel ORCID Icon, Jens Fürst, Sebastian Lang ORCID Icon
Industrial supply chains must repeatedly reconfigure sourcing strategies in response to disruptions, yet supplier capability information remains heterogeneous and difficult to operationalize. Existing research addresses supplier selection, simulation, and interoperability standards separately, treating discovery, evaluation, and optimization as disconnected steps requiring manual data transformation. This paper presents a framework for a data-driven industrial platform integrating three components: Asset Administration Shell-based supplier profiles structured by an Actor-Service ontology for semantic discovery, automatic discrete-event simulation model generation from layout data for performance evaluation, and KPI-driven configuration using optimization algorithms. The main contributions are an end-to-end interoperable workflow, a two-tier supplier profile concept separating semantic descriptions from simulation parameters, and a standardized KPI interface maintaining consistency ...
Industry 4.0 Science | Volume 42 | 2026 | Edition 4 | Pages 50-61 | DOI 10.30844/I4SE.26.4.6
Enabling Digital Trust in Green Hydrogen Markets

Enabling Digital Trust in Green Hydrogen Markets

Trust-Building Information Systems and Mechanisms
Johanna Voß, Jens Pöppelbuß ORCID Icon
Building a global hydrogen economy requires more than technology and investment; it requires trust. As information systems (IS) increasingly mediate collaboration among unfamiliar, distributed actors, the question of how trust can be deliberately built becomes critical. This study systematically maps how IS enable different types of trust and reveals how these mechanisms can support trusting collaboration in emerging hydrogen value chains.
Industry 4.0 Science | Volume 42 | 2026 | Edition 4 | Pages 82-90 | DOI 10.30844/I4SE.26.4.9
Classification of Digital Supply Chain Management Platforms

Classification of Digital Supply Chain Management Platforms

Review of existing classification approaches from a circular economy perspective
Sophia Botsch ORCID Icon, Eva Mante ORCID Icon, Marcel Papert ORCID Icon, Alexander Pflaum ORCID Icon
Assessing the impact of digital industrial platforms on the dynamics and resilience of supply chains requires clear classification of such platforms. This article examines the extent to which a current classification proposal from the field of supply chain management must be further developed in the context of digital platforms for implementing the circular economy (CE). The authors conclude that a fundamental revision is not necessary, as the digital CE platforms under consideration fit well into the existing classification system. However, new research questions arise regarding the distinction between digital service platforms and digital data-oriented platforms, as well as the link between the circular economy and supply chain management—particularly in connection with supply chain control towers, which are becoming increasingly established in supply chain management practice.
Industry 4.0 Science | Volume 42 | 2026 | Edition 4 | Pages 62-70 | DOI 10.30844/I4SE.26.4.7
Digital Factory Planning for Startups

Digital Factory Planning for Startups

A simulation-based production structure design
Herwig Winkler ORCID Icon, Tobias Isau
With the increasing complexity of production and logistics systems, traditional factory planning approaches are reaching their limits. In this context, digital factory planning offers a promising solution for enabling well-informed decisions, particularly during the early planning phases. For startups, the optimal planning of a production facility is challenging, as they often operate with limited financial and infrastructural resources. This paper presents a methodological approach to digital factory planning that utilizes VR simulation for the layout planning of a factory hall for a young company in the solar industry. The proposed approach demonstrates how simulations can support the design of flexible production structures, particularly in startup environments.
Industry 4.0 Science | Volume 42 | 2026 | Edition 3 | Pages 68-75
Site Assessment for Flexible Intralogistics in Brownfield Sites

Site Assessment for Flexible Intralogistics in Brownfield Sites

Innovative decision support in practice
Jolanda Schierbaum, Carsten Feldmann, Lars Renhof
Due to its dynamic environment, space planning in intralogistics is not a one-time task but a recurring decision-making process subject to numerous constraints imposed by existing infrastructure. Decisions are often based on incomplete data, resulting in a high risk of poor planning decisions and inefficient use of space. This paper presents a practice-oriented process model for space evaluation in brownfield projects. The proposed approach improves the standardization and consistency of space evaluation and promotes best practices among all stakeholders. By supporting systematic decision-making, the process model contributes to optimized planning and resource allocation, thereby reducing risks and avoiding costly implementation errors.The process model is demonstrated through a case study conducted at a commercial vehicle manufacturer.
Industry 4.0 Science | Volume 42 | 2026 | Edition 3 | Pages 124-133
Christoph Brocks