Digital transformation is widely considered the most significant structural challenge for companies’ business models. In this sense, it is important to assess the evolution of the digital transition, analyzing patterns, challenges, and trends. This research methodology is composed of three phases: the first is a survey to assess the readiness of Portuguese industrial companies, the second phase is a literature review to collect relevant studies, and the third is a qualitative comparison of the sample analysed with the literature. Comparing 18 Portuguese industrial companies with 12 previous studies, the study shows that digitalization has progressed little, mainly at the operational level, but average readiness remains intermediate, with integration and advanced analysis below the standard reported in the literature. The results show that Portuguese industrial companies must strengthen systems integration, data management, and analytical skills to advance in digital maturity. The findings offer a clear diagnosis that can guide managers and decision-makers in prioritizing investments and defining more strategic digital transformation roadmaps. By integrating empirical evidence with a comparative analysis of 12 international studies, this research offers an original and in-depth perspective on the assessment of industrial digital maturity and a global qualitative comparison.

Maintenance management increasingly depends on effective information flow and coordination between internal teams and external service providers. This study investigates the use of Business Process Model and Notation (BPMN) to support the formalization of Computerized Maintenance Management System (CMMS) workflows and improve transparency, decision-making, and interorganizational coordination. A single case study was conducted in the maintenance department of an electricity distribution company characterized by tacit knowledge, informal communication practices, and limited process formalization. Existing corrective maintenance workflows were analyzed and modeled using BPMN to identify inefficiencies, decision points, and opportunities for improvement. The proposed BPMN models were aligned with CMMS operational states associated with anomaly management and work-order execution processes and supported by a procedural manual. Results obtained during a three-month observation period suggest reductions in training time, email communications, and dependence on individual decision-makers, together with increased use of CMMS workflow functionalities and improved process traceability. These findings provide preliminary evidence, derived from operational indicators within a single case study, that BPMN-supported process formalization may contribute to workflow standardization, operational clarity, and knowledge management in maintenance-intensive environments. Given the single-case design and limited observation period, the results should be interpreted as context-specific and not directly generalizable to the broader energy sector.

Industrial maintenance has increasingly evolved into a strategic function for improving asset reliability, extending asset lifecycle, and supporting sustainability objectives. However, the literature remains fragmented, with limited integration between digital twins, maintenance practices, and sustainability-oriented decision-making. To address this gap, the study performs a systematic review of 49 publications indexed in Scopus and Web of Science and introduces an integrative conceptual framework for Sustainable Maintenance 4.0. The analysis explores the role of digital twins, as a key enabling technology within the Industry 4.0 landscape, in supporting the shift from reactive and schedule-based maintenance toward predictive and prescriptive strategies. The findings suggest that digital twins can enhance maintenance decision-making, improve asset reliability, and contribute to lifecycle optimization. The reviewed studies also report improvements in operational and energy performance, although these effects vary according to digital maturity, system configuration, and implementation scope. In addition, digital twins may support safer operations and workforce development through data-driven and immersive environments. Despite these benefits, challenges remain, including high investment requirements, interoperability limitations, cybersecurity risks, and the need for interdisciplinary skills. The proposed framework positions digital twins as a mediating element between physical assets, data acquisition, advanced analytics, maintenance services, and sustainability outcomes.

Paper aims To design and validate a mechanically integrated error-proofing mechanism capable of preventing incorrect center drill positioning during setup operations in Computer Numerical Control (CNC) turning environments, reducing scrap generation, and improving process reliability. Originality This study provides empirical industrial evidence on the implementation and quantitative validation of a mechanically integrated Poka-Yoke mechanism in CNC machining operations, preventing setup errors at the source through an intervention. Research method An industrial case study was conducted in an automotive component production line. The research included process observation, root-cause identification of setup variability associated with center drill positioning, and the design and implementation of a mechanical error-prevention device. Operational performance was evaluated through scrap rate and Overall Equipment Effectiveness (OEE) indicators using a before-and-after approach. Main findings Scrap was reduced from 1.18% to 0.56% (52.5%), while OEE increased from 85% to 90% (5 percentage points), indicating improvements in process stability and operational performance. Implications for theory and practice The study contributes empirical evidence to the literature on Poka-Yoke, preventive quality engineering, and source-level error prevention, while demonstrating the applicability of low-cost integrated interventions in machining environments. Findings also suggest that such mechanisms may complement digital quality systems within hybrid quality strategies.