The rapid evolution of industrial systems demands a careful reconciliation of sustainability goals, beyond organizational success and financial profitability. While Industry 4.0 (I4.0) has driven significant advances in automation and process efficiency, its impact on the Sustainable Development Goals (SDGs) remains limited and primarily focused on economic outcomes. Responding to the need for deeper integration between digital transformation and sustainability, this study aims to map the existing literature on Lean Six Sigma (LSS) in the contexts of I4.0 and Industry 5.0 (I5.0), but also, to clarify how sustainability dimensions can be embedded into LSS practices. To achieve this, a scoping review was conducted, including all scientific research published up to submission date and sourced from reliable databases (Scopus and others). Through this comprehensive review, a notable gap in how LSS addresses the various dimensions of SDGs was identified. To address this, the present work introduces a novel conceptual framework —Lean Six Sigma 5.0 (LSS 5.0)— which repositions traditional process improvement tools within a human-centric, resilient, and sustainability-oriented manufacturing paradigm. This innovative model advances Sustainable Manufacturing (SM) by embedding sustainability at the core of quality and continuous improvement strategies, enabling organizations to respond to the evolving expectations of I5.0. Beyond its theoretical contribution, this work offers practical insights for industries seeking to align LSS initiatives with the broader goals of sustainable development. Given its theoretical nature, future research should validate this model through empirical case studies and industrial applications, supporting innovation in SM. The findings directly address the review’s objectives by mapping existing knowledge and proposing actionable pathways for integrating sustainability within LSS practices in the I5.0 era.

Nowadays, the world is recognizing the water-food-energy (WEF) as a conceptual framework that aims to the implementation of the Sustainable Development Goals (SDGs). The WEF Nexus is being developed to support the integrity of national and more importantly local projects in the cross-sectoral partnerships and coordination to enhance the sustainable development outcomes from the different projects as well as avoiding the trade-offs. Most importantly, building a great synergy between the sectors is challenged by sectorial boundaries such as policy decisions, scaling investments, and cross-sectorial consequences, which leads to anticipation biases when it comes to social, economic, and environmental costs. This paper aims to review the latest integrated WEF Nexus modeling platform that has been developed in the NENA region during the project "Implementing the 2030 Agenda for Water Efficiency/Productivity and Water Sustainability in NENA Countries" (WEPS-NENA), led by the Food and Agriculture Organization of the United Nations (FAO) and supported by the Swedish International Development Cooperation Agency.

Achieving high fault-classification accuracy alone does not guarantee reliable autonomous operation under varying operating conditions, raising the need to assess prediction reliability and deployment readiness. This work proposes a resilience-oriented framework for fault diagnosis under varying operating conditions, characterizing diagnostic behavior under operating-condition shifts and providing complementary information on confidence, deployability, and supervision requirements. The framework fuses multi-sensor vibration and motor current signals within a Multi-Stage architecture combining a data-driven branch (DD-MSCNN) and a physics-aware branch (PA-MSCNN) integrating order-tracking descriptors, augmented by a confidence-aware deployability assessment layer. Evaluated on the Paderborn KAT dataset across six bidirectional shifts involving speed, torque, and radial force, the results reveal that operating-condition shifts are not equivalent and that their impact is strongly direction-dependent. Physical knowledge does not systematically guarantee superior performance, highlighting the complementary roles of the two representations. To formalize these observations, the Physics Contribution Index (PCI), the Shift Directionality Index (SDI), and a four-level deployability classification are introduced, providing quantitative insights into prediction reliability and autonomous operation readiness in dynamic industrial environments.

The transition toward Industry 5.0 (I5.0) is reshaping industrial systems around sustainability, Human-Centricity (HC), and resilience, yet most Predictive Maintenance (PdM) approaches remain focused on technical objectives inherited from Industry 4.0. To characterize the maturity of I5.0-aligned PdM research, a scoping review of the Scopus database was conducted, screening 238 articles and retaining 123 based on substantive-engagement criteria, followed by a weighted scoring protocol assessing the depth of engagement with the three I5.0 pillars. The review reveals a pronounced skew toward shallow integration, with only a minority of studies operationalizing sustainability, HC, and resilience jointly. Building on these findings, this paper proposes an implementation-oriented Predictive Maintenance 5.0 (PdM 5.0) framework structured around three interconnected layers. The framework introduces a structured Large Language Model-mediated human-in-the-loop interaction, an adaptive learning mechanism, and an energy and CO₂ estimation pathway linking equipment degradation to sustainability indicators. A preliminary industrial case study using operational data from a recycled-paper mill demonstrates the practical execution of three interconnected pathways of the framework: equipment health assessment, energy and CO₂ impact estimation, and human-in-the-loop interaction supported by LLM-mediated explanations. Layer-specific evaluation metrics are further proposed to support broader operationalization. This work contributes a scoping-review-grounded architecture that advances PdM from a purely technical function toward a holistic, I5.0-aligned paradigm, while providing initial empirical evidence of its operational feasibility and a structured basis for broader industrial validation.