The growing dependence on high quality data in industrial environments drives the adoption of artificial data generation techniques, especially in the development and implementation of Digital Twins (DTs). This article presents a critical review of the main approaches to creating synthetic data, with an emphasis on their application in Industry 4.0 intelligent cyber-physical systems. Initially, traditional techniques such as Random Oversampling (ROS), SMOTE and its variants are analyzed, as well as statistical models such as the Gaussian Mixture Model (GMM). Next, Deep Learning-based methods are explored, namely Autoencoders, Variational Autoencoders and Generative Adversarial Networks (GANs), highlighting their ability to produce realistic and diverse data. The study also includes the analysis of practical cases in which DTs have been developed using synthetic data, covering domains such as wind energy, aviation and urban infrastructure. In this way, the aim of this study is to critically explore the different techniques of artificial data generation with an integration with the technology of DTs. The results suggest that the appropriate use of synthetic data can not only overcome limitations related to privacy and the scarcity of real data, but also improve the robustness and effectiveness of Digital Twins models. The article concludes by discussing the current challenges and future opportunities in integrating these techniques into smart industrial environments.

This study evaluates the implementation of the Lean, Agile, Resilient, and Green (LARG) approach in the electric motorcycle industry in Indonesia using the Bayesian Best Worst Method (BWM). The main focus of the study is to identify and determine the weight of the most relevant LARG indicators to improve the competitiveness and sustainability of the industry. From the analysis results, the Resilience indicator has the highest weight, while the Lean indicator has the lowest weight. Important sub-indicators identified include the ability to take corrective action when disruptions occur, waste management according to regulations, flexibility in collaboration with industry partners, and component quality testing. Recommended priority strategies include developing a standards-based safety system, consistent technology transfer, and provision of adequate infrastructure. The results of this study provide data-based strategic guidance to improve efficiency, flexibility, durability, and environmental sustainability in the electric automotive industry in Indonesia.

Lean manufacturing (LM) aims to improve production efficiency by systematically reducing waste and fostering continuous improvement. This research analyzes how LM strategies are carried out, what is important for them to succeed and the benefits they bring to the manufacturing industry in Morocco’s north. Using a comprehensive questionnaire survey, the author shows that Kaizen and similar incremental improvement techniques matter and detailed their effect on top performance metrics, like how much a company produces and its cost efficiency. Using advanced statistical methods, the study investigated whether employee engagement, managerial support, and process adaptability act as mediators between lean practices and performance outcomes. The results confirm that adopting LM significantly enhances operational performance, emphasizing its central role in achieving manufacturing excellence. This paper illustrates the importance of LM as well as the practical advice to industries that wish to pursue sustainable competitive advantage by adopting lean transformation.

This article’s primary objective is to demonstrate the influence of the human-centric technology adoption factor on JIT4.0 implementation by displaying the best practices used in Moroccan JIT4.0 organizations and the benefits obtained. By analyzing the critical success factors (CSFs) or activities that manufacturing organizations perform when implementing Just-In- Time (JIT) and Industry 4.0, three latent variables are identified: production strategy (PS), relation with suppliers (RS), and human-centric technology (HCT). Based on the benefits obtained from JIT4.0 implementation, three latent variables are identified and analyzed: the benefits of the production process (BPP), the benefits of inventory management (BIM), and economic benefits (EB). The study also proposes a structural equation model that considers the human-centric technology adoption factor as the leading factor in the implementation of JIT4.0. Additionally, it demonstrates that this factor interacts with other CSFs and benefits as the primary independent latent variable.

This paper examines whether retrofitting a 1997 four-color offset press, Heidelberg Speedmaster SM74-4P, with Industrial Internet of Things (IIoT) solutions, preserves its performance compared to a 2021, digitally integrated Heidelberg Speedmaster CX75-4. For this purpose, observational studies with data collection were conducted. The study calculated descriptive statistics and Mann-Whitney U tests for print volume, job set-up time, production speed measures, waste, and overall equipment effectiveness (OEE). The results show that retrofitting an older press achieves comparable print volumes, and even the 1997M press had an average gross throughput that was 9.73% higher and a median that was 22.86% higher than the 2021 press. However, the new press achieves 63.14% higher average run length as well as significantly reduces make-ready time and waste. Despite the benefits that new machines offer due to technical advances, modernizing older machinery through IIoT solutions can therefore be a cost-effective strategy. In the discussed case, adaptation to the requirements of work in the modern IIoT environment by a relatively cheap modernization kit compared to a new machine, allowed for better operational efficiency, thus reducing costs and contributing to the sustainable development of the company without the need to invest in a new machine.

The rise of end-of-life vehicles (ELVs) in Malaysia highlights the urgent need for a sustain able recovery industry centered on reuse, recycling, and remanufacturing. Ineffective ELV management exacerbates waste generation, adversely affecting Sustainable Development Goal (SDG) 12 on responsible consumption and production. This study evaluated the current state of ELV reuse, recycling, and remanufacturing in Malaysia by selecting ten companies and employing a qualitative approach. Data were gathered through semi-structured interviews with industry stakeholders, supported by qualitative content analysis conducted in collaboration with a research team of industry experts. Responses were triangulated with direct observations during site visits. Key enablers for a sustainable framework were identified, including design strategies, adequate facilities, skilled labour, and efficient processes. The findings culminated in a sustainable ELV recovery framework emphasizing operational and stakeholder-driven perspectives. This framework is a roadmap for policymakers and industry players to align with regional sustainability goals.

Artificial Intelligence (AI) has recently gained significant traction in the oil and gas manufac turing sector. This study focuses specifically on the oil and gas manufacturing sector. As a key technology, AI has the potential to revolutionize smart manufacturing and the fourth industrial revolution (Industry 4.0). Product Lifecycle Management (PLM) is integral in managing engineering, business, and operational processes for products throughout their lifecycle. By leveraging AI, companies can enhance decision-making processes that align with economic and environmental considerations. This study investigates AI’s role in the design phase of PLM within the oil and gas sector. The objective is to evaluate AI’s benefits in design optimization and its influence on decision-making. A Delphi-based survey was conducted among 62 experts from multinational oil and gas firms. The findings indicate that AI significantly improves design efficiency, visualization, and prototyping. However, the study highlights integration challenges, data security concerns, and limitations in current industrial communication proto cols. Further research should explore AI’s role in the remaining PLM phases and its integration with existing IT infrastructures. Future research should extend to the remaining phases of PLM not covered in this study and address limitations such as integration with existing IT systems and improvements in industrial communication protocols.

The paper focuses on creating a proposal for an innovative solution for technological changes in manufacturing and assembly processes in industrial production of incubators. The aim of the paper is to point out how the manufacturing and assembly processes can be made more efficient so that capacity of industrial production of incubators is maximized, enterprise resource planning is fully integrated with production plan and overall evaluation of machinery efficiency is acceptable to all business subjects involved and creates potential for economic added value. The empirical methods used were TOC method, data mining-based analysis, analysis of internal documents, process benchmarking, FMEA, Lean manufacturing, Kaizen, process audit, Six Sigma, guided interview, Pareto analysis, Ishikawa diagram, Practice, 5 Whys method and 8D report method. The application part of the paper describes changes and innovation in production of industrial incubators concerning increased efficiency of assembly processes targeted at product quality. By optimizing production of incubators in compliance with customer preferences, life functions of new-born babies were improved. According to analysis of input data implementation of investment projects concerning changes in mass production and assembly of machinery benefits of projects were upgrading manual manufacturing to semiautomatic. Increased performance and streamlining of manufacturing and assembly processes in integration with an increase capacity of production stations enabled reducing operating costs of main production activity by implementing cost-saving measures. This contributed to promoting competitiveness of industrial and production enterprises and stabilizing their position in national and international market by integrating strategic, commercial, labor, and other business development and growth opportunities. Key innovations and technological changes create potential for economic added value and stable growth. A better understanding and implementation of optimization scientific research methods in production processes has potential to ensure long-term competitiveness of industrial and manufacturing enterprises on the global market. Ambitions of further research is application of SMED (Single Minute Exchange of Die) in Lean manufacturing, which enables synchronization of production flows and reduction of machine working hours with an emphasis on waste minimization, operator space optimization and rejection rate reduction of non-value operations at machine changes.

Although there is a growing body of evidence supporting the significance of servant leadership as a basic component in shaping the execution of TQM practices, the research has not fully examined the framework associated with servant leadership in terms of “customer focus, people management, process management, strategic planning, information and analysis” in general and in the information technology industry. Therefore, the authors conducted this empirical research examining the associations among servant leadership and the abovementioned TQM practices in the information technology industry. This empirical study also examined the mediating role of interfunctional coordination in TQM practices in the context of servant leadership. We gathered data from 335 leaders in 57 software companies in the information technology industry with at least a capability maturity model (CMM) level 3 to CMM5 companies providing software development and services. The data were collected from team leaders, project heads, project managers, lead testers, and higher-level managers of the marketing and operations management area. We investigated how servant leadership interacts with the application of these five TQM practice components and the mediating role of the interfunctional coordination construct in this interaction. To accomplish this goal, we empirically investigated a structural model and mediation models by bootstrapping and SEM with IBM AMOS (IBM, 2023). The model fit indices CMIN/DF 1.724, CFI 0.953, TLI 0.938, NFI 0.920, SRMR 0.049, RMSEA 0.049, and PCl close 0.624 indicate excellent model fit, and further investigation was carried out. The findings showed that putting TQM practices into practice was positively and directly correlated with servant leadership. Interfunctional coordination partially mediates TQM practices through servant leadership, with a positive association. The results confirm the influence of servant leadership on TQM practices in the information technology industry.

The study employs a bibliometric approach, analyzing global literature from the Scopus database related to user acceptance based on emotional responses to products. Initially, 749 documents were identified, narrowed down to 378 final-stage journal articles published in English. The data collected comprises information from the last 10 years (2013–2023). These were analyzed using Vos Viewer for bibliometric network visualization and R-biblioshiny for additional analysis. The research, conducted in November 2023, used the following specific search strings (TITLE-ABS-KEY (“Emotional response”) AND TITLE-ABS-KEY (product* OR device*) AND TITLE-ABS-KEY (measur* OR assess* OR evaluat*)). The study highlights the increasing research on emotional responses to products, with a 9.6% annual growth in publications. While the USA, UK, Spain, and Australia lead in this field. Five topic clusters identified involve emotional response measurement, behavior related to product acceptability, the user of the product, behavioral symptoms, and emotion psychology. Emerging areas include consumer goods and product design, with a future focus on physiological assessment, emotional reaction’s impact on design, and consumer purchase intentions. Density visualization suggests further exploration in wearable technology, purchase intentions, and emotional response measurements like electroencephalography, electromyography, electrophysiology, facial expression, and skin conductance.