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The aim of this article states that in each stage of the industrial revolution, only a few initiatives have been real game changers. In Industry 3.0, “Internet of Information” has transformed the business landscape via connectivity and communications. Enterprises could come together to spur innovation in a cooperative or competitive manner. In Industry 4.0, the “Internet of Value” has shown considerable benefits; and, blockchain technology is expected to touch all layers of a business ecosystem, and the construction industry is not an exception.

This study aims to answer the “How do enterprise blockchain solutions contribute to the vibrancy of the construction ecosystem from social, economic, and environmental aspects?” Following a comprehensive literature review, the Grey Ordinal Priority Approach (OPA-G) is employed in multiple criteria decision analysis (MCDA). OPA-G can select functionally rich enterprise blockchain solutions that meet the needs of the future construction industry, while there is uncertainty in the input data.

The results from the case study show that organization under observation welcomes an enterprise blockchain solution that delivers services related to “renewable energy certificates” in the context of “smart cities and built environment”. Employing high-ranked blockchain solutions brings vibracy and sustainability to construction ecosystem in terms of “C₆. decentralized finance and investment,” “C₃. multi-party and cross-industry collaboration,” and “C₈. data-driven value creation”.

At the micro level, blockchain solutions automate processes, streamline operations, and build new capacities on a new business model. At the macro level, blockchain creates a vibrant ecosystem based on transparency, decentralization, consensus-based democracy, interoperability, etc. Indeed, the capability of blockchain solutions at an enterprise scale (enterprise blockchain solutions) can shape a new construction ecosystem. The practical implications of current research are preparing executives for a fundamentally different next normal in construction.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

This study aims to explore the integration of Industry 4.0 technologies with lean six sigma practices in the manufacturing sector for enhanced process improvement.

This study used a fuzzy decision-making trial and evaluation laboratory approach to identify critical Industry 4.0 technologies that can be harmonized with Lean Six Sigma methodologies for achieving improved processes in manufacturing.

The research reveals that key technologies such as modeling and simulation, artificial intelligence (AI) and machine learning, big data analytics, automation and industrial robots and smart sensors are paramount for achieving operational excellence when integrated with Lean Six Sigma.

The study is limited to the identification of pivotal Industry 4.0 technologies for Lean Six Sigma integration in manufacturing. Further studies can explore the implementation challenges and the quantifiable benefits of such integrations.

Integrating Industry 4.0 technologies with Lean Six Sigma enhances manufacturing efficiency. This approach leverages AI for predictive analysis, uses smart sensors for energy efficiency and adaptable robots for flexible production. It is vital for competitive advantage, significantly improving decision-making, reducing costs and streamlining operations in the manufacturing sector.

The integration of Industry 4.0 technologies with Lean Six Sigma in manufacturing has significant social implications. It promotes job creation in high-tech sectors, necessitating advanced skill development and continuous learning among the workforce. This shift fosters an innovative, knowledge-based economy, potentially reducing the skills gap. Additionally, it enhances workplace safety through automation, reduces hazardous tasks for workers and contributes to environmental sustainability by optimizing resource use and reducing waste in manufacturing processes.

This study offers a novel perspective on synergizing advanced Industry 4.0 technologies with established Lean Six Sigma practices for enhanced process improvement in manufacturing. The findings can guide industries in prioritizing their technological adoptions for continuous improvement.

Road freight transportation companies need to take advantage of information and communication technologies to develop capabilities. This study proposes a framework to guide road freight transportation companies to achieve data visibility in their operations by developing such capabilities. By proposing this framework, this research contributes to literature and practice, highlighting the capabilities and the respective supporting technologies for improved data visibility in road freight transportation.

A mixed-method approach is used to develop the framework, considering three methodological steps. In phase 1, the capabilities are identified in the literature and validated by experts. In phase 2, an empirical assessment of cause–effect relationships between capabilities is performed using a multiple case study and DEMATEL. Lastly, in phase 3, an analysis of the cause model and significant associations is conducted to enable the development of the framework. In addition, the proposed framework was validated by the experts interviewed.

The results provide a framework that explains the link between the technology-enabled data visibility capabilities in road freight transportation operations. In addition, a pathway was established that road freight transportation companies could follow to achieve data visibility in their operations by developing such capabilities.

This work develops the first framework that provides a path for data visibility in road freight transportation operations from adopting certain technologies. The insights are compelling for researchers and practitioners to optimize the decision-making process for adopting technologies and developing capabilities related to data visibility.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

Increased emphasis on offering quality education underscores the need for developing a rigorous process for assessing academic programs in higher education. In this chapter, we develop a practical and rigorous framework for comprehensive assessment of academic programs. This framework generates in-depth communication between the academic departments and the university administration. It provides a useful tool for advancing the university mission, setting priorities, allocating resources, and identifying future areas of potential growth. This data-driven framework covers a wide range of qualitative and quantitative variables. To ensure a smooth and efficient implementation of the assessment process we present the critical stages in the development of a successful program assessment framework − from determining the assessment criteria, establishing the organizational climate, appointing the assessment committee, preparing program self-studies, to collecting and analyzing data. We present real examples from the author’s home institution to illustrate and support the reader’s understanding of the framework.

This study aims to examine the necessity effects of big data analytics capabilities (BDAC) on decision-making performance (DMP), particularly in the public sector.

The authors used the combined methods of partial least square structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) to test the hypothesized relationships.

The findings show that the presence of all three BDAC (infrastructure, management and personnel) is significant and necessary to achieve higher levels of DMP. Specifically, the results revealed big data management capabilities to be of higher necessity to achieve the highest possible DMP. The findings provide public-sector practitioners with insights to support the development of their BDAC.

Time-sensitive domains such as the public sector require insight and quality decision-making to create public value and achieve competitive advantage. This study examined BDAC in light of the combined methods of (PLS-SEM) and NCA to test the hypothesized relationships in the public sector context.

Industry 4.0 is recognised as a competitive strategy that helps implementers optimise their value chain. However, its adoption poses several challenges. This study investigates and ranks the drivers and barriers to implementing Industry 4.0 in the Thai sugar industry, the world's second-largest sugar exporter. It also evaluates the industry's readiness for Industry 4.0.

The drivers and impediments were identified based on a systematic literature review (SLR) and further investigated using a questionnaire, expert interviews, Pearson's correlation and nonparametric statistical analyses. The IMPULS model was used to assess the industry's readiness.

Most companies expect to minimise costs, develop employees and improve various elements of operational performance and data tracking capability. Thai sugar producers are still at a low readiness level to deploy Industry 4.0. High investment is the major challenge. Small businesses struggle to hire competent employees, collaborate with a highly credible technology provider and adapt to new solutions.

The findings can serve as a benchmark or guide for sugar manufacturers and companies in other sectors, where Industry 4.0 technologies are not yet widely utilised, to overcome existing roadblocks and make strategic decisions. They can also assist governments in developing policies that foster digital transformation and increase national competitiveness.

There is a scarcity of research on Industry 4.0 execution in the sugar industry. This study addresses this gap by investigating the reasons for the hesitancy of sugar producers to pursue Industry 4.0 and proposing solutions.

The industrial revolution changed the market landscape significantly in all industrial sectors. It has a noteworthy impact on enhancing the quality of goods and services. The quality aspect is of utmost concern and determines the success or failure of any product. Therefore, the presented study analyses the key barriers and solutions of Quality 4.0.

Twenty barriers and fifteen solutions were identified using a literature review and investigated using a hybrid approach. Barrier weights were evaluated with the help of the fuzzy AHP method. Furthermore, the computed weights were used to perform computations in the next step using fuzzy-TOPSIS to prioritize the ranking of identified solutions.

The research results show that “Lack of applying advanced analytics to uncover Quality 4.0 initiatives” and “Lack of integrating data from various sources across the organization” are the topmost barriers. Furthermore, “Implement a leadership development program focused on Quality 4.0” and “Cross-departmental peer learning environment” are the topmost solutions.

Managers and industrialists can benefit from Quality 4.0 through improved decision-making, process efficiency, supply chain collaboration, agile quality management, enhanced customer experience and a culture of continuous improvement. This results in better quality, operational effectiveness and a competitive edge.

The solutions need to be mapped with barriers to adopting Quality 4.0. Furthermore, the research results involve novelty by prioritizing the solutions to overcome the anticipated barriers.

The research aims to develop an assessment framework that evaluates critical success factors (CSFs) for the Quality 4.0 (Q 4.0) transition among Indian firms.

The authors use the fuzzy-Delphi method to validate the results of a systematic literature review (SLR) that explores critical aspects. Further, the fuzzy decision-making trial and laboratory (DEMATEL) method determines the cause-and-effect link. The findings indicate that developing a Q 4.0 framework is essential for the long-term success of manufacturing companies. Utilizing the power of digital technology, data analytics and automation, manufacturing companies can benefit from the Q 4.0 framework. Product quality, operational effectiveness and overall business performance may all be enhanced by implementing the Q 4.0 transition framework.

The study highlights significant awareness of Q 4.0 in the Indian manufacturing sector that is acquired through various means such as training, experience, learning and research. However, most manufacturing industries in India still follow older quality paradigms. On the other hand, Indian manufacturing industries seem well-equipped to adopt Q 4.0, given practitioners' firm grasp of its concepts and anticipated benefits, including improved customer satisfaction, product refinement, continuous process enhancement, waste reduction and informed decision-making. Adoption hurdles involve challenges including reliable electricity access, high-speed Internet, infrastructure, a skilled workforce and financial support. The study also introduces a transition framework facilitating the shift from conventional methods to Q 4.0, aligned with the principles of the Fourth Industrial Revolution (IR).

This research exclusively examines the manufacturing sector, neglecting other fields such as medical, service, mining and construction. Additionally, there needs to be more emphasis on the Q 4.0 implementation frameworks within the scope of the study.

This may be the inaugural framework for transitioning to Q 4.0 in India's manufacturing sectors and, conceivably, other developing nations.