In order to track industry 4.0 status, readiness models can be used to analyze the state of indus-try 4.0 technologies’ implementation allowing the quantification and qualification of its readiness level, focusing on different dimensions. To this matter, there are companies unable to relate the industry 4.0 with their business models, leading to a lack of a correct self-assess in order to understand the reached readiness level. Not all companies are adopting these new technologies with the same ease and with the same pace. Into this purpose, it is important to understand how to assess the industry 4.0’ readiness so far and what are the barriers on the adoption of these enabling technologies by the industry. This paper aims to assess the industry 4.0’ readiness level of companies, understand the perception of companies due to the barriers on the adoption of industry 4.0 enabling technologies and bring new barriers for discussion on academic community. To this end, empirical data was collected on a sample of 15 companies belonging to an important industrial cluster in Portugal.

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.