Author
Listed:
- Lech Bukowski
(Department of Management Engineering, WSB University, 1c Zygmunta Cieplaka Street, 41-300 Dabrowa Gornicza, Poland)
- Sylwia Werbinska-Wojciechowska
(Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Wyspianskiego 27, 50-370 Wroclaw, Poland)
Abstract
The growing digitalization of supply chains and increasing sustainability requirements create the need for structured tools that assess organizational readiness for Cognitive Twin (CT) adoption. However, existing digital twin and sustainability maturity models rarely integrate technological architecture, governance, and circularity within a unified framework. To address this gap, the study proposes the Supply Chain Twin Sustainability–Cognitive Maturity Model (SCT-SCMM), a novel framework that explicitly integrates governance structures, sustainability objectives, and a hierarchical system architecture into the assessment of Cognitive Twin readiness. Unlike existing models, the proposed framework captures the interdependencies between technological capabilities, decision intelligence, and governance mechanisms across multiple system layers, providing a systemic perspective on sustainable digital transformation. The framework structures organizational readiness through five interdependent layers: Physical, Control, Communication, Decision-making, and Governance, and defines staged maturity levels reflecting progression toward sustainable cognitive autonomy. This layered architecture enables the simultaneous evaluation of operational automation, digital intelligence, and institutional governance as co-evolving dimensions of Cognitive Twin adoption. The model was developed through a structured literature review and operationalized using a hybrid multi-criteria and fuzzy-based evaluation approach, enabling the evaluation of complex socio-technical systems under uncertainty. The framework was applied in an automated product-to-human warehouse case study to evaluate technological, sustainability, and governance readiness. The results demonstrate the model’s ability to identify maturity gaps, reveal inter-layer dependencies, and prioritize transformation pathways toward more resilient and circular logistics systems. By integrating governance, sustainability, and system architecture into a single maturity model, SCT-SCMM extends existing digital twin maturity approaches and provides a transparent decision-support tool for guiding staged Cognitive Twin adoption in next-generation sustainable supply chains.
Suggested Citation
Lech Bukowski & Sylwia Werbinska-Wojciechowska, 2026.
"Maturity Model for Cognitive Twin-Enabled Sustainable Supply Chains,"
Sustainability, MDPI, vol. 18(7), pages 1-56, April.
Handle:
RePEc:gam:jsusta:v:18:y:2026:i:7:p:3635-:d:1915304
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:18:y:2026:i:7:p:3635-:d:1915304. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.