Abstract
This paper advocates for Digital Twin (DT) technology as a pivotal solution to address the complexities of Complex Operations Environments (COEs). Recognizing the need for a thorough understanding of COEs and their DTs, a methodology is introduced to bridge existing gaps. Given the lack of a universal definition, the approach leverages the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Latent Dirichlet Allocation (LDA) to extract insights, facilitating the development of a comprehensive definition for COE and DT. The methodology integrates Ontology and Systems Modelling Language (SysML) to provide a semantic and conceptual model of COE and DT. Ontology enriches the semantic understanding, exploring existence and entity relationships, while SysML ensures clear and concise communication through standardized graphical representation. This paper aims to present a methodology to achieve a precise understanding of COEs and their corresponding DTs, providing a robust foundation for addressing operational complexities in dynamic environments.

Abstract
The recent past has shown that many companies stressed from the competition, have reduced manufacturing costs as well implemented sustainable practices as much as possible. And yet this effort has proved to be insufficient. This reality is forcing the companies and managers to address the problem of competitiveness and sustainability in a holistic way, by considering the entire supply chain. Due to this pressure from supply chain stakeholders to a comprehensive sustainability assessment of the entire network, extended “performance metrics” are required not only on the economic value of a business, but also in its environmental and social impacts. Increasing numbers of organizations report a massive volume of data, with low consistency and high variability in data quality, and a dispersion of indicators, making it necessary to develop and implement new approaches for Supply Cain Management (SCM) sustainable performance assessment. This paper focuses on this topic, presenting a new approach for performance and risk assessment within dynamic supply chain networks, supported in a new and comprehensive Sustainability Assessment Framework (SAF).

Abstract
This paper evaluates an Augmented Reality (AR) solution designed to support quality control in a assembly line inspection station before body marriage at a European automotive manufacturer. A three-phase methodology was applied: an AS-IS assessment, a formative evaluation of an intermediate prototype, and a summative evaluation under real production conditions. The AR solution aimed to improve task standardization, non-value-added time (NVAT), and enhance operator accuracy. The results showed that operators successfully developed inspections using the AR tool, identifying and correcting non-conformities (NOKs) while maintaining task duration. Participants valued having contextual information directly in their field of vision and reported increased rigor and consistency. However, usability and ergonomic improvements were noted, such as headset weight, gesture interaction, and visibility over dark components. The findings highlight AR’s potential to support operator autonomy and accuracy in industrial environments while emphasizing the need for human-centered design and integration to ensure long-term adoption. © 2025 Elsevier B.V., All rights reserved.

Abstract
Complex systems, characterised by high interconnectedness and unpredictability, demand structured approaches to support decision-making, system integration, and operational efficiency. This study aims to develop a comprehensive understanding of these systems to facilitate the implementation of Digital Twins (DTs) in Complex Operations Environments (COEs). A Systematic Literature Review (SLR), following PRISMA methodology, is conducted and complemented by a grounded theory approach to identify, organise, and synthesise system characteristics. The result is a hierarchical conceptualisation comprising eight core categories that capture the essential dimensions of complexity, including: Process and System Interoperability, Human Relationships, Behaviour/Nature, Products and Services, Multi-processes, Performance, System Structure, and Management and Skill Requirements. This framework offers a structured foundation for designing and implementing DT-based decision support in COEs.