Integrating AI into circular supply chain decision-making: a systematic review and conceptual meta-framework

NEW TRENDS IN CONTEMPORARY ECONOMICS, BUSINESS AND MANAGEMENT

A Layered Decision Architecture for Circular Construction Supply Chains: Integrating Capabilities, Constraints, and Alignment

Construction supply chains are pivotal to circular economy transitions but remain structurally fragmented, limiting the scalability of resource-efficient solutions. At the same time, digital technologies and life cycle assessment are often deployed in isolation, constraining their ability to enable system-level circularity. Using a theory-building literature synthesis of 141 publications across circular economy, sustainable supply chain management, digitalization, and life cycle sustainability assessment, this study develops an integrated conceptual framework that explains how circular performance may be shaped by AI-enabled decision capabilities, lifecycle sustainability constraints operationalized through PESI-LCA, and system-level alignment conceptualized through DCAM. AI is conceptualized as a dynamic capability for prediction and optimization, while PESI-LCA is positioned as an operationalized LCSA-based constraint system that embeds environmental, social, and economic criteria into decision architectures. DCAM defines the alignment conditions required across digital infrastructure, circular strategies, business models, and institutional enablers. The framework advances a non-additive logic: circular outcomes depend on how sustainability constraints shape AI-driven decision-making and how alignment enables coordinated implementation across supply chains. A key theoretical contribution is the identification of structural distortion as a failure mode in which digital optimization reinforces linear resource flows. The study advances sustainable supply chain theory and offers testable propositions and governance implications for scaling circular construction systems.

AI-Driven Leadership in Manufacturing Supply Chains: A Systematic Literature Review, Research Gaps Analysis, and Exploratory Study of Egyptian Manufacturing Firms

This study conceptualizes AI-driven leadership as a higher-order dynamic capability through which leaders sense AI-enabled opportunities and threats, seize them through strategic resource orchestration and governance, and reconfigure organizational and supply-chain capabilities to enhance intelligence, resilience, agility, and adaptability.

Governing AI-Driven Sustainable Supply Chains for SME Participation in Africa: A Scoping Review

Artificial intelligence (AI) is increasingly embedded in sustainable supply chains through traceability systems, supplier scoring, risk analytics, demand forecasting, compliance monitoring and procurement platforms. These tools can improve transparency, reduce waste and strengthen resilience, but they can also convert sustainability into a data-intensive gatekeeping regime that disadvantages small and medium-sized enterprises (SMEs). This scoping review maps peer-reviewed evidence published between February 2021 and January 2026 on AI-enabled sustainable supply chain governance, with attention to SME participation and African or comparable emerging-market relevance. A PRISMA-aligned search identified 892 records, screened 624 titles and abstracts, assessed 186 full texts and retained 60 DOI-bearing journal studies. The synthesis finds that AI is most often framed as a performance and resilience tool, whereas governance questions of proportional evidence, explainability, supplier appeal rights and tiered compliance remain underdeveloped. The review proposes an inclusion-oriented framework linking digital infrastructure, algorithmic mechanisms and governance safeguards to sustainability and SME participation outcomes.

Decision support systems to facilitate circular economy at the supply chain level: a systematic literature review

The manufacturing industry fulfils essential societal needs, but it creates substantial environmental pressure. Decision support systems serve as valuable tools to steer manufacturing toward sustainability. However, manufacturers are currently limited to a structured decision-support system for integrating circular economy principles across the product, process, and system levels. This systematic literature review examines decision support systems for circular economy implementation in manufacturing through bibliometric and qualitative content analysis. The analysis reveals significant fragmentation: only 5% of studies integrate product, process, and system levels simultaneously, while 52% address a single level. The developed decision support systems were evaluated primarily by using life cycle assessment and life cycle costing. Digital technologies enable predictive capabilities, but implementation barriers remain substantial. Critical gaps include limited integration across decision levels, end-of-life bias, and inadequate focus on business model innovation. The discussion emphasises developing an integrated decision support system by converging system-level thinking with a circular economy framework and digital capabilities, creating an early-stage tool that provides feedback during design phases, and enabling organisations to proactively position sustainability.

Exploring AI governance for sustainable decision-making: evidence from six case studies

Examining how artificial intelligence (AI) governance supports sustainable decision-making across organizational contexts in Europe reveals that governance increasingly aligns with formal frameworks through policies, dedicated structures, human oversight and Environmental, Social and Governance oriented indicators, enhancing transparency and reliability.