Second-life battery systems offer to prolong the usable life of electric vehicle lithium-ion batteries by repurposing them for stationary storage. Their repurposing in a variety of grid and energy services may increase infrastructure flexibility, but lack of reliable performance data and standardized health certifications prevents widespread deployment. This paper presents a comprehensive framework that combines a distributed ledger battery passport, deep learning health certification, and contract-based allocation. The digital battery passport stores production data, first-hand use data, repurposing test results, and signed health certifications, maintaining provenance and enabling stakeholders to share information in a trusted environment. To verify data authenticity, high-rate telemetry is stored off-chain, and integrity anchors are added on-chain. A deep learning model is trained to forecast the state of health and remaining useful life via exponential smoothing and metaheuristic hyperparameter tuning, yielding time-indexed state-of-health trajectories with quantifiable uncertainties. These health certifications are included in the passport to indicate the approved usable capability. Using these characteristics, this study develops a mixed-integer linear program to allocate batteries across various power system applications, optimizing a weighted health capacity value while satisfying application demand criteria. The model incorporates slack factors and penalties for unfulfilled demand. Assignments are recorded as smart contract transactions in the ledger, providing an auditable trail.

The responsibility of suppliers within value chains has become increasingly critical as EU regulations impose stringent sustainability requirements across environmental, social, and economic dimensions. In the photovoltaic (PV) sector, where end-of-life management and circular economy practices are pivotal, suppliers face growing pressure to demonstrate compliance while traditional assessment tools remain complex and inaccessible for rapid self-evaluation.

This paper addresses this gap by developing a streamlined methodological framework for sustainable supplier selection tailored to PV supply chains. Through systematic literature review, expert-driven relevance scoring, and Analytic Hierarchy Process (AHP) weighting, the study identifies and prioritizes 63 Evaluation Criteria (ECs) organized by Triple Bottom Line dimensions, culminating in a synthetic Sustainability Score.

The proposed model provides actionable sustainability indicators across environmental, economic, and social layers, enabling suppliers to conduct rapid self-assessments, identify corrective priorities, and pursue structured certifications while supporting buying firms in strategic sourcing decisions.

The increasing integration of artificial intelligence into product design workflows enables rapid generation of design alternatives but introduces new challenges in ensuring data integrity, traceability, and accountability within collaborative engineering environments. Traditional Product Lifecycle Management (PLM) and Product Data Management (PDM) systems rely on centralised repositories that are vulnerable to unauthorised modifications and lack transparent audit mechanisms. To shed light on the above matters, a bibliometric analysis and literature review are conducted to evaluate current research trends in blockchain-enabled engineering data management. Based on the findings, this paper proposes a decentralised framework that integrates blockchain technology with PLM/PDM systems to provide immutable traceability of CAD assembly iterations during generative AI-assisted product development. The framework employs a hybrid architecture where design metadata and structural hierarchies are recorded on-chain, while large CAD files are stored in decentralised off-chain storage. Future research will focus on building and validating the proposed framework through a prototype to bridge the gap between theoretical design and industrial implementation.

Transdisciplinary knowledge has demonstrated societal and environmental gains. This research explores the notion of a sustainable competitive advantage (SCA) by performing a use case analysis (UCA). The UCA is used to evaluate an integrated knowledge between industrial engineering (IE) and environmental sustainability (ES). This integrated knowledge recommends mechanisms for South Africa, and in this research, generalised for other countries. These components are specific strategies, theories, and methods and practices associated with the three business execution levels. The UCA primarily aligns features of the framework with international and South African literature, with conformance mapped against the knowledge components, Sustainable Development Goals, VRIO framework and three stages of Dynamic Capabilities. These demonstrate that the integration of IE and ES can best advance sustainable competitive advantages for South Africa and the likes of many other developing countries.