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The paper focuses on the issue of manual rebar-binding tasks in the construction industry, which are marked by high labor intensity, high costs and inefficient operations. The rebar-binding robots that are currently available are not fully mature. Most of them can only bind one or two nodes in one position, which leads to significant time wastage in movement. Based on a new type of rebar-binding robot, this paper aims to propose a new movement and binding control that reduces manpower and enhances efficiency.

The robot is combined with photoelectric sensors, travel switches and other sensors. It is supposed to move accurately and run in a limited area on the rebar mesh through logical judgment, speed control and position control. Machine vision is used by the robot to locate the rebar nodes and then adjusts the binding-gun position to ensure that multiple rebar nodes are bound sequentially.

By moving on the rebar mesh with accuracy, the robot meets the positioning accuracy requirements of the binding module, with experimental testing accuracy within 5 mm. Furthermore, its ability to bind four rebar nodes in one place results in a high efficiency and a binding effect that meets building standards.

The innovative design of the robot can adapt itself to the rebar mesh, move accurately to the target position and bind four nodes at that position, which reduces the number of movements on the mesh. Repetitive and heavy rebar-binding tasks can be efficiently completed by the robot, which saves human resources, reduces worker labor intensity and reduces construction overhead. It provides a more feasible and practical solution for using robots to bind rebar nodes.

With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

This study aims to analyze the factors that drive or prevent interorganizational data sharing in the context of digital transformation (DT). Data sharing appears as a precondition for companies to capture emerging opportunities in supply chain management and for product-related servitization; however, there are ongoing concerns, and data are often perceived as the “new oil.” It is thus important to gain a better understanding of the determinants of firms’ decisions.

The authors develop an embedded case study analysis involving 16 firms within an extended supply network in the automotive industry. The authors focus on the peculiarities of the new context, as opposed to elements highlighted by research prior to the advent of the latest technologies. Abductive reasoning is applied to the theoretical foundations of the resource-based view, resource dependence theory and the complex adaptive systems perspective.

Data sharing is largely underpinned by factors identified prior to DT, such as data specificity, dependence dynamics and protection mechanisms and the dynamism of the business context. DT, however, can influence the extent of data sharing. New factors concern complementarities whenever data are pooled from different sources and digital platforms, as well as different forms of data ownership protection.

This study stresses that data sharing in the context of DT can be explained through established theoretical lenses, providing the integration of elements accounting for new technological opportunities.

The purpose of this paper is to investigate the surface integrity features, including surface roughness (SR), recast layer (RL), material migration, topography and wire wear pattern in rough and trim-cut wire electric discharge machine (WEDM) of hybrid composite (Al6061-90%/SiC-2.5%/TiB₂-7.5%).

Effects of four important factors, namely, rough-cut history (RCH), pulse on time (T_on), peak current (IP) and wire offset (WO) have been assessed on the responses of interest for trim-cut WEDM. Box–Behnken design (RSM) was used to formulate the experimentation plan. Quantitative indices of surface integrity, namely, SR and RL, and selected samples have been investigated for qualitative analysis, namely, surface topography, material migration and wire wear pattern.

T_on and IP are found to be most significant, whereas RCH and WO are found insignificant for SR. T_on and WO were found to be the most significant factors affecting RL. After trim cut, an RL of thickness 8.26 µm is observed if the initial rough cut has been accomplished at high discharge energy setting. Whereas the best value of RL thickness, i.e. 5.36 µm, can be realized with low level of RCH. A significant decrease in the presence of foreign materials is recorded, indicating its strong correlation with the discharge energy used during machining.

Investigation on surface integrity features for machining of hybrid composite through rough and trim-cut WEDM has been reported by only a limited number of researchers in the past. This study is attempted at fulfilling few vital gaps by addressing the issues such as evaluation of the efficacy of trim cutting under different discharge energy conditions (using RCH), analysis of wire wear pattern in both rough and trim-cut modes and investigation of the wire breakage phenomenon during machining.

As the global community increasingly directs its attention towards sustainable urban development, integrating artificial intelligence (AI) into circular economy (CE) management within smart cities has become a potent strategy. This study aims to examine the potential influence of AI-based technologies on optimizing resources and minimizing waste, which constitute critical components of the principles underpinning the CE. The focus is mainly on applying these technologies within smart city environments. Artificial Intelligence can significantly enhance the processes of gathering, analyzing and decision-making by integrating internet of things (IoT) sensors, data analytics, machine learning algorithms and predictive analytics. This chapter explores the potential of AI in predicting trends, optimizing circular supply chains, improving waste management and recycling practices, facilitating sustainable product design, fostering citizen engagement and aiding policy development. The current research presents a comprehensive examination of the interrelated connection between the principles of CE and the advanced technology of AI. Doing so contributes significantly to our holistic comprehension of how these advancements might collectively influence the development of a more sustainable and resilient future for urban populations.

The purpose of this study is to explore the relationship between Lean manufacturing and Industry 4.0 for small and medium size of enterprise in Japan and Brazil.

The authors conducted a quantitative survey (20 companies in Japan and 30 companies in Brazil) combined with a qualitative interview (2 companies in Japan and 15 companies in Brazil).

According to the quantitative study, 90% of them practice Lean manufacturing and 40% of them practice Industry 4.0. In the qualitative study in Brazil, four managers responded that the Lean manufacturing is a prerequisite for Industry 4.0 since any production process with waste cannot be productive, even with sophisticated digitalization technology.

The authors explored further the relationship between “defensive Digital Transformation (DX),” which is based mainly on Lean manufacturing, and “offensive DX,” which relates to customer value creation through Industry 4.0. This study clarifies the relationship and plays as a roadmap to develop better the manufacturing from current status to the vision of Industry 4.0.

Charles Sanders Peirce (1839–1914), recognized as the father of philosophical pragmatism, has been described as a philosopher’s philosopher. Eliyahu Moshe Goldratt (1947–2011), considered the father of the management philosophy theory of constraints (TOC), has been described as being, first and foremost, a philosopher. The TOC body of knowledge is mainly preserved as concrete methodologies used in the management discipline. By examining the foundational elements of synechism and the TOC, the purpose of this study is to investigate the intellectual connections between the arguments and legacies of Goldratt and Peirce. Although this connection is worthy of much further investigation, the research emphasizes the possible implications from a management philosophy perspective.

Based on a “review with an attitude,” the authors first examined the foundations of Goldratt’s TOC through the lens of Peirce’s synechism. Next, the authors then examined how the study of Peirce combined with a selection of contemporary research in the management and organizational studies domain could point out a direction toward completing Goldratt’s unfinished intellectual work to establish a unified science management while addressing some of the current gaps in the TOC body of knowledge.

Major findings show that synechism’s growth may extend TOC knowledge, improving managerial practice in organizations. Findings on the convergent ideas of both also reveal that Goldratt valued all synechism categories, emphasizing the importance of not overlooking Firstness. Furthermore, the study analyzes the abductive inference demonstrated in the two use cases, introducing an additional metaphor to the management of organizational systems inspired by Peirce’s philosophical concepts. The research concludes that incorporating TOC and synechism principles can enhance management and organizational practices and enrich management philosophy and theories.

This pioneering research opens promising opportunities to draw parallels between Peirce and Goldratt. Interdisciplinary collaboration will enhance the rigor and validity of integrating synechism and TOC. Experts in organizational behavior, systems theory and complexity science can provide valuable insights into this debate, while practitioners and consultants could help identify barriers and opportunities for integrating synechistic principles.

The study proposes a novel abductive approach using Peirce’s cable metaphor as an initial framework to build a unified science of management based on evolutionary stages: TOC, common sense and connectedness.

This research reinforces the argument that contemporary management practices need philosophical thinking. The authors argue that re-evaluating the foundations of management thought enriches the decision-making process in organizations and the understanding of contemporary theories in management and organizational studies.

Implementing artificial intelligence (AI) in healthcare organizations involves the entire organization. This groundbreaking technology is becoming central to achieve the goals of the new healthcare through the ongoing commitment to sustainability despite the severe lack of resources. Decision-makers in healthcare need knowledge and skills to prepare for the changes in many professional activities in the years ahead. Furthermore, chief medical officers and clinical leaders need to act on the opportunities that AI can bring, starting from its integration into the reality of healthcare settings while working with those responsible for managing and implementing AI in compliance with current legislation in Europe and the United States. Finally, stakeholders need to know how to leverage AI capabilities and how to recognize its limitations and its opportunities in administrative applications (admin AI) to optimize day-to-day operations and clinical applications (non-admin AI). In this view, clinical leaders and health care decision-makers may appreciate AI as a new way to provide sustainable social and healthcare services.

The concept of lean and ISO 14001 as a combined approach is an evolving strategy for streamlining operational processes and attaining environmental sustainability in the manufacturing context. This paper explores the critical success factors (CSFs) for a combined lean and ISO 14001 implementation in the manufacturing industry for achieving the operational and environmental benefits.

A systematic literature review (SLR) based on Scopus and Web of Science databases is conducted to present peer-reviewed articles on the CSFs for lean and ISO 14001 implementation in manufacturing operations. This article applies the CSF theory to classify the CSFs for a joint lean and ISO 14001 adoption.

Numerous CSFs are synthesised from the SLR across seven theoretical contexts of industry, competitive strategy, managerial position, environmental, temporal, internal/external, monitoring and building/adapting factors for a combined lean and ISO14001 implementation.

Numerous CSFs are synthesised from the SLR across seven theoretical contexts of strategic direction, competitive strategy, leadership and management, environmental, temporal, internal/external, monitoring and continuous process improvement factors for a combined lean and ISO 14001 implementation.

This paper contributes to academic scholarship by providing a theoretical perspective through classification of CSFs for a combined lean and ISO 14001 implementation to achieve operational and environmental performance. This paper also contributes to practitioners and policymakers who can use the emergent theoretical framework for application in practice for a more efficient and effective deployment of both strategies in the manufacturing industry.

To the best of author's knowledge, this study is the first to propose a theoretical framework of CSFs for a combined lean and ISO 14001 implementation based on the CSF theory and SLR findings in the manufacturing industry.