The emergence of Industry 5.0 (I5.0) underscores the importance of integrating sustainability and human‐centricity into organizations. This requires combining corporate social responsibility (CSR) and circular economy (CE) within sustainable supply chain management (SSCM). However, the interplay between CSR, CE, and SSCM remains underexplored, especially in the context of I5.0. Therefore, this article proposes a framework for integrating CSR and CE principles within SSCM, serving as a transition to I5.0. The framework is derived from a comprehensive scoping review and experts' input, with a backdrop of the business legitimacy theory. It comprises several key constructs, including the supply chain structure and promoters, organized around four pillars representing the components of CE and embracing the triple bottom line dimensions. With 12 guidelines, the framework stands as a practical tool for organizations aiming to seamlessly integrate CSR and CE within SSCM, facilitating the transition to I5.0 and promoting a sustainable, socially responsible ethos.

Motivated by the project uncertainties of complex realities brought by the fourth industrial revolution, Industry 4.0 (I4.0), researchers must look at Lean project management (LPM) soft skills as a new mindset to have a holistic view of customer needs and to improve value to the organization. In the digital transformation era, it is essential that Lean practitioners develop socio-technical thinking while also prioritizing interpersonal soft skills that directly affect their ability to solve problems in order for the company to grow sustainably. This paper aims to identify the critical soft skills (CSSs) to implement LPM in the I4.0 era and to investigate, from the perspective of organizations, the latent factors for LPM implementation that generate sustainable value. For this purpose, a mixed-method approach was used, combining literature review and a survey with 166 Brazilian professionals. This empirical study intends to set the CSSs from a practice perspective and explore the benefits they generate throughout the organization. Data analysis was conducted with descriptive statistics and factorial analysis. Research findings revealed convergences regarding the practitioners’ perception regarding the ten CSSs identified in the literature, and portray two latent factors, human and process factors, which can assist decision-makers in implementing LPM by offering a better perspective of the key factors that add sustainable value for companies in the digital transformation context. This study contributes to the debate on rethinking traditional skills, accentuates the need for adjustments, and proposes a socio-technical framework that can be used in a project manager’s routine to enable better Lean project execution, help with decision making, and increase the understanding and meeting of customer needs.

This study fills a crucial gap in the research on Micro, Small, and Medium Enterprises (MSMEs), with the main objective of proposing a framework to support the integration of Industry 4.0 (I4.0) and sustainability into the supply chains of MSMEs. This integration is accomplished by people, process, and technology (PPT) mechanisms, all while adopting the triple bottom line (TBL) perspective. A mixed-method approach was employed for this study, which included a scoping review of 147 publications, expert panels, focus groups, and a survey with 55 valid responses. The findings identified 32 key indicators linked to the primary barriers and enablers of I4.0 and sustainability integration (S-I4.0) in MSMEs’ supply chains. This study also highlighted the dominant barriers and enablers within the PPT mechanisms and TBL dimensions, and their causal relationships and influences. The results were synthesized into a novel S-I4.0 framework, separately applied to Micro and Small Enterprises (MSEs) and Medium Enterprises (MEs) due to company size-related variations. This study offers valuable insights for academics and provides practical guidelines to assist MSMEs in integrating I4.0 and sustainability into their supply chains. An important contribution concerns to the need to treat MSEs and MEs differently. These research results provide relevant and novel guidance for MSEs and MEs to accelerate the S-I4.0 adoption process, with an immediate impact on their supply chains.

Goal: The purpose of this study is to develop a decision support framework to select critical success factors (CSFs) and benefits for the implementation of BIM in the public works sector. Design / Methodology / Approach: The research methodology integrates multiple fuzzy multicriteria decision making (MCDM) approaches to propose a composite framework of CSFs and benefits using qualitative and quantitative approaches, with the application of two semi-structured questionnaires with CSFs and benefits identified in previous studies, through a systematic literature review (SLR). Results: The study identified 42 CSFs and 31 benefits for the implementation of BIM for public works, and the relative importance of the identified factors and benefits was measured according to the four main BIM perspectives: legal, people, process, and technology. The result of the applied MCDMs showed the relationship between the factors and benefits of the four most influential BIM perspectives according to the proposed framework. The most influential CSFs and benefits were: BIM adoption requires individual and group motivation in the organization, Establishment of BIM and Lean construction standards, codes, rules, and regulations, Financial support from the government to establish BIM system, Availability of quality, schedule, and cost information during construction with BIM Accuracy and reliability of documents and data and BIM provides knowledge sharing among stakeholders, Workflow, productivity, and efficiency are affected by the transition to BIM, BIM technology reduces cost and time, Conflict detection, integration, coordination, and design validation, and Creating more efficient projects with stakeholder involvement, coordination, and oversight. Limitations of the investigation: The research sample consisted exclusively of Brazilian professionals and academics with experience in the implementation of BIM in public works. Practical implications: The proposed framework can be applied in implementation in public construction projects that aims to implement BIM considering the selection of critical success factors and benefits. Originality / Value: BIM methodology is widely used in the construction industry. However, there are few studies on CSFs and the benefits of BIM for the public sector, and there still lack of implementation frameworks proposal with empirical evidence.

Goal: This study aims to use artificial intelligence, specifically a random forest model, to predict the annual corrosion rate on FPSO offshore platforms in the oil and gas industry. Corrosion is a significant cause of equipment failure, leading to costly replacements. The random forest model, a machine learning technique, was developed using climatic and other relevant data to forecast corrosion trends based on selected variables. Design/methodology/approach: The methodology involved four steps: identifying influential factors affecting corrosion, selecting factors based on reliability and accessibility of measurements, applying the machine learning model to predict annual corrosion progression, and comparing the random forest model with other ML models. Results - The results showed that the random forest regression model successfully predicted corrosion rates, indicating an average yearly increase of 2.43% on the analyzed platforms. The main factors influencing this increase were wind speed, percentage of measured corrosion, and platform operating time. Regions with higher incidence of these factors are likely to experience higher corrosion rates, necessitating more frequent maintenance. Limitations of the investigation - The research sample consisted exclusively of 4 platforms located in the offshore region of Rio de Janeiro, Brazil. Thus, the results obtained must be interpreted as representative of these platforms and respective climate conditions. Practical implications – The use of data science tools to improve corrosion management allows managers to have knowledge of which areas has a greater or lesser tendency to corrode, helping to prioritize maintenance activities over time. Originality/value - This study aims to fill gaps regarding the use of random forest techniques for regression focused on predicting the rate of increase in corrosion. It offers a novel approach to assist decision-making in maintenance planning, providing insights into influential corrosion factors and facilitating more effective painting plans to preserve industrial unit integrity.

Background: Due to the growing integration between the various logistics entities and other internal operations, packaging management in the automotive industry is becoming increasingly important from the strategic point of view of the logistics operations of automakers. Performance evaluation of reverse operations is also necessary for managers to know their efficiency, avoid unnecessary resource use and promote circular thinking, enabling more sustainable supply chains. Methods: This research proposes a group decision-making (GDM) approach to evaluate packaging performance in automakers to assist return activities in developing countries. The reverse flow in an automaker was mapped, and by combining literature and empirical views of a packaging engineering team of a Brazilian company, a multicriteria indicator for performance evaluation of packaging was elaborated. It was prioritized through the analytic hierarchy process (AHP)-GDM method, combining judgments to establish a structured technical consensus. Results: It was possible to integrate multiple views of packaging engineering specialists within the same company to know which packaging deserves greater attention from managers when implementing reverse operations from a circular perspective. Conclusions: To demonstrate applicability, this composite indicator also aims to be a quick application approach, considering the restricted time and availability of the specialists in their daily routines.