Era Of Industry Research Articles

A semantic axiomatic design for integrity in IoT

AbstractIn the complex, critical, and rapidly evolving era of Industry 4.0 manufacturing, mature engineering disciplines can be developed to validate system designs at an abstract level, enabling effective fault‐free environments. However, designing integrated IoT platforms presents significant challenges due to their inherent complexity and interoperability issues. To address this challenge, the proposed framework integrates axiomatic design principles with considerations for data integrity and domain ontology. Furthermore, this paper introduces a multi‐level integrity rule repository for IoT application to provides a robust foundation for designing and maintaining IoT systems. To validate the effectiveness of this approach, we conducted a study across four domains of smart cities. The design's quality was assessed using model quality parameters, including completeness, consistency and validity. Our analysis shows that the semantic‐aware axiomatic design framework can effectively address the problem of inconsistencies and low‐quality design in scientific IoT environments, providing a more reliable and efficient solution.

Isosurface-based marching cube algorithm for smooth geometric topology optimization within adaptive octree SBFE approach

In the era of Industry 4.0, the prominence of 3D printing as a pivotal manufacturing technology has greatly expanded, particularly within the domain of additive manufacturing (AM). Among the thriving research applications tailored for integration with AM, topology optimization (TO) has emerged as a resounding success. Given the prerequisite of TO for high-resolution meshing to ensure visual clarity in result depiction, researchers have been consistently driven to develop advanced techniques to refine optimal designs, thus elevating the challenge and popularity within this research realm. This paper presents a novel approach integrating an adaptive image-based octree mesh scaled boundary finite element (SBFE) framework with an evolutionary methodology that can effectively address the persistent challenges inherent to TO. A novel hierarchical SBFE mesh analysis not only facilitates efficient and precise TO but also substantially reduces computational resource demands. Furthermore, the pre-conditioned conjugated gradient (PCG) method is adopted to process practical-scale problems, minimizing computer memory resources. Additionally, the proposed work incorporates a post-processing technique utilizing the isosurface function based on a marching cube algorithm, thereby smoothing the boundaries of optimal results. Consequently, this research extends the horizons of design possibilities, particularly in the creation of intricate 3D structures, which can be seamlessly realized through additive manufacturing and 3D printing.

Exploring innovation and collaboration in the leather processing industry through the case study of the KIWANGO Leather Cluster

In the era of Industry 4.0, fostering innovation through strong collaboration among universities, industry, and government is crucial. However, the Tanzanian economy has faced challenges due to, among others, weak links between these entities. To address this, Sida initiated the Innovation Systems and Cluster Development Program (ISCP-Tz) in partnership with the University of Dar es Salaam. This was followed by a program at COSTECH focused on fostering innovation for socio-economic development. As part of these programs, 15 clusters, including KIWANGO Leather, were selected for documentation of their experiences. KIWANGO Leather exemplifies successful collaboration between cluster firms, the university, local government, and research institutions. Operationalizing guidelines for collaboration led to cooperative innovations, knowledge exchange, internships, and long-term partnerships established through Memorandum of Understanding (MoU). The cluster's experiences highlight a less-linear, inclusive innovation process with positive outcomes. Thus, this paper not only illustrates a less linear, inclusive innovation process, but also its results can motivate actors in emerging innovation ecosystems in the Global South to adopt and scale up suggested approaches for knowledge co-creation benefitting sustainable development. By adopting these approaches and fostering collaborative networks, countries can leverage their resources and expertise to drive innovation, create economic growth, and address societal challenges.

Cooperatives amid the Wave of Industrial Revolution 4.0: Opportunities and Challenges

This study examines the roles and challenges faced by cooperatives in the era of Industry 4.0, focusing on adapting digital technologies to enhance member welfare and economic growth. The research employs a comprehensive literature review, case studies, and content analysis of publications from the past decade. Findings indicate that digitalization has increased operational efficiency in cooperatives by up to 40%, expanded market access, and improved transparency and accountability. However, key challenges include the persistence of the digital divide, high initial investment costs, and the need for cultural change within organizations to support digital transformation. Government support, including digital literacy training and access to financing, is crucial in accelerating this transformation process. The practical implications of this study highlight the necessity for broader digital training programs, access to low-interest financing for technology investments, and the development of adequate digital infrastructure. This research underscores significant progress in technology adoption among cooperatives and the positive socio-economic impacts that result from it. As a result, digital transformation within cooperatives holds substantial potential for fostering more inclusive and sustainable economic growth.

Fuzzy expert system for ergonomic assembly line worker assignment and balancing problem under uncertainty

ABSTRACT In the era of Industry 5.0, there is a significant gap in addressing ergonomic risks and imprecise task times in manufacturing systems. This study aims to fill this gap by extending the ergonomic assembly line balancing problem with worker assignment. It employs a novel two-phase framework combining a constructive heuristic for feasibility with a unique ergonomic assessment method developed through a fuzzy expert system. Validated using 96 synthesized numerical instances, the proposed method addresses the scarcity of fuzzy and ergonomic-oriented data in benchmarks. Then, computational results are thoroughly analyzed to evaluate the method’s performance and identify potential areas for further research. The proposed optimization method provided high-quality solutions with a majority demonstrating low ergonomic risk and high worker safety, contributing to an overall improvement in ergonomic conditions. The integration of fuzzy expert system and advanced optimization techniques yields a robust framework for achieving a safer and more efficient manufacturing environment.

The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review.

Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.

Pengaruh Pengembangan Karir dan Work Life Balance terhadap Turnover Intention dengan Retensi Karyawan sebagai Variabel Mediasi

In the era of competitive business and Industry 5.0, human resource management (HRM) becomes crucial for achieving competitive advantage through the development, reinforcement, and transformation of organizational culture. This study explores the influence of career development and work-life balance on the turnover intention of female employees at Bank Nagari. Employees are considered the most important assets because their contributions affect the company's reputation and profitability. The involvement of female workers provides valuable skills, diversity, and inclusivity, yet their representation in leadership roles remains low. This research uses a quantitative method with primary data collected through questionnaires from 67 female employees at Bank Nagari headquarters. The results show that career development does not significantly affect turnover intention, while work-life balance has a significantly negative influence on the intention to leave. Demographic factors such as age, marital status, length of service, and income level play a more dominant role in influencing employees' decisions to stay. Additionally, employee retention and job satisfaction act as mediators in the relationship between work-life balance and turnover intention, indicating that policies supporting work-life balance and effective career development programs can enhance retention and reduce turnover intention. Investing in career development programs and work-life balance is a crucial strategy for increasing employee satisfaction and loyalty.

A conceptual framework-based architecture strategy for implementing industry 4.0 enabling technologies in Indian commercial vehicle manufacturing plant

ABSTRACT From the first industrial revolution to the era of Industry 4.0 (I4.0), Indian manufacturing sectors have undergone significant transformations. The global market’s competitiveness has surged due to the rising demand for customized consumer goods, prompting industries to adopt advanced manufacturing models to maintain their edge. This paper introduces a novel Conceptual Framework-Based Architecture (CFA) using a real-life case study of an Indian commercial vehicle manufacturing plant. The proposed CFA aims to minimize barriers to implementing I4.0 technologies by developing a standardized architecture customized to the Indian manufacturing context. It addresses existing technological requirements and implementation challenges in three phases, considering existing production systems, current technologies, and system needs. The CFA enhances productivity, and transparency through real-time data monitoring, automated system communication, and responsiveness. It also tackles I4.0 implementation challenges between physical and cyber systems, employee adaptability to new technologies, the absence of global standards, and the need for uniform standards in tools, equipment, systems, and data services. The CFA elements were validated for system readiness in an existing manufacturing plant. This framework serves as a reference tool for industrial strategists, decision-makers, policymakers, and researchers, aiding in the integration and configuration of I4.0 technologies in the Indian manufacturing sector.

Deep learning-based vehicular engine health monitoring system utilising a hybrid convolutional neural network/bidirectional gated recurrent unit

Vehicles play a pivotal role in the current era of Industry 4.0 by providing passengers with excellent mobility, comfort, and safety while strengthening national and international economies. Unanticipated vehicular engine issues can hinder performance and lead to costly maintenance. As analytics processes become faster, more accurate, and more reliable, intelligent fault prediction and diagnosis for vehicles, particularly engines, is becoming increasingly popular. To date, hybrid deep learning approaches to vehicle engine diagnostics have been limited, and none have used engine health monitoring and categorisation based on vulnerability assessment and vehicle structural information. This paper introduces a hybrid deep learning-based vehicular engine health monitoring system (VEHMS) decision model using Deep CNN (convolutional neural network)-BiGRU (bi-directional gated recurrent unit). This model monitors a vehicle’s engine health in real-time and classifies its status as good, critical, moderate, or minor condition. Several advanced and hybrid deep learning algorithms were applied to monitor engine health and categorise its status by integrating sensor data with evaluated vulnerability information from an infrastructure vulnerability assessment model. The Deep CNN-BiGRU-based VEHMS decision model outperformed other techniques with an accuracy of 0.8897, ensuring minimal decision losses while classifying engine conditions. This study aims to contribute to developing comprehensive vehicle health monitoring systems and advance the automotive industry by incorporating more intelligent features. The proposed approach can enhance vehicle performance, reliability, and efficiency in the transportation sector by improving engine health monitoring.

A self-supervised learning method for fault detection of wind turbines

Abstract As promising solutions to condition-based maintenance of wind turbines, artificial intelligence-based techniques have drawn extensive attention in the era of industry 4.0. However, accurate fault detection is still challenging owing to volatile operating conditions in real-world settings. To handle this problem, a novel method is proposed for fault detection of wind turbines. Specifically, a data augmentation scheme is developed to simulate the effects of time-varying environments and noise. Then, a self-supervised proxy task of variant prediction is designed and conducted. In this way, valid data representations can be extracted to represent the health status of wind turbines. Additionally, the compactness of data representations is guaranteed by the directional evolution, which can relieve the confusion of health conditions. The effectiveness of the proposed method is verified with actual measurements. Using the proposed method, several faults can be detected more than 10 d earlier, and blade breakage can be identified more than 22 h earlier. Furthermore, the developed method outperforms several benchmark approaches.

Role and applications of advanced digital technologies in achieving sustainability in multimodal logistics operations: A systematic literature review

Integration of advanced digital technologies along with ensuring environmental sustainability in the logistics sector is becoming the need of the hour novel multimodal transportation concept. The rapid technological development in the era of industry 4.0 can not only create new opportunities in achieving highly efficient, intelligent, and smart logistics systems but also enable new business models for value addition, additionally, this novel system will also avoid environmental degradation and use of scarce natural resources that contribute to climate change. This paper summarizes the research trends of these technologies in multimodal logistics systems since 2005 through a systematic literature review. The results revealed a mixed picture of the situation, starting from emerging technologies like artificial intelligence, machine learning, and blockchain becoming key technological concepts for multimodal logistics systems to government policy hurdles and lack of impact breakthroughs in the industry. However, the results show that adaptation to these technologies improves the overall environmental, economic, and social sustainability of the system.

Driving Organic Customer Acquisitions through Community Marketing in Digital Banking Application

The objective of this research is to identify and analyze the key factors that influence customers on the reluctance of being loyal or become the advocator of J Bank and to propose the organic customer acquisitions strategies to be applied to J Bank through community engagement in J Bank. The era of Industry 4.0 has transformed production by integrating digital and internet technologies with traditional industries, enhancing efficiency and effectiveness in transactions. In Indonesia, the number of digitally active banking consumers has increased significantly, making up 32% of the banked population since 2014. This growth underscores the value of digital consumers to banks. The sector has become highly competitive, with fintech companies disrupting traditional banks by offering innovative solutions like e-wallets, loans, and digital payments. Established banks are investing in digital banking to keep pace with new startups. J Bank, Indonesia’s first digital banking application, offers comprehensive services such as account creation, transactions, and financial management via smartphone. Targeting tech-savvy individuals with middle to upper economic status, J Bank provides features like Send It for transactions, QRIS for touchless payments, and various saving and loan options. Its innovative approach has attracted millennials, Gen Z, and even seniors, positioning it as a leading digital banking solution. However, J Bank experienced a significant drop in organic acquisitions from 2020 to 2022, partly due to issues like fraud and cybercrime, which led to negative sentiment and user attrition. Using qualitative approach with 6 survey respondents that consist of existing user of J Bank and existing Co.Create member, and also former J Bank users and former Co.Create members. This research using thematic analysis with internal and external analysis to explore the potential solutions. For internal analysis, the author is using Marketing Mix analysis. Meanwhile for external analysis, the author is using Porter Five Forces analysis, Competitor analysis, and SWOT analysis. Based on the analysis, this research proposed several organic customer acquisitions activity to be implemented in J Bank through community marketing approach. J Bank offers a wide range of services, including account creation, transactions, and financial management via smartphone, targeting tech-savvy individuals from middle to upper economic backgrounds. Despite its initial success, attracting millennials, Gen Z, and even seniors, J Bank faced a decline in organic acquisitions from 2020 to 2022 due to issues like fraud and cybercrime, which led to negative sentiments and user attrition. The research utilized a qualitative approach, involving surveys of six respondents, including current and former users of J Bank and members of its Co.Create community. Thematic analysis was conducted, incorporating internal analysis with the Marketing Mix and external analysis with Porter’s Five Forces, competitor analysis, and SWOT analysis. The findings suggest that increasing community engagement and addressing security concerns are crucial for improving J Bank’s customer retention and acquisition. Based on these insights, several community marketing strategies are recommended to enhance organic customer acquisition.

Systematic Review of Open-Source Software for Technical and Vocational Education and Training (TVET)

In the era of Industry 4.0, software is essential for connecting technologies, creating smart systems, and driving innovation across industries. There are several challenges and limitations related to software in Technical and Vocational Education and Training (TVET), such as software cost, software piracy, and limited modification access to the software. This demonstrates the demand for new approaches that may deal with these issues and offer alternatives by using the open-source software. The paper aims to explore on the utilization and impact of Open-Source Software (OSS) in TVET based on three main themes: (1) hardware compatibility using OSS, (2) teaching and learning aids using OSS, and (3) online learning practice using OSS. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, the research, based on 21 articles selected from Scopus and Eric databases, reveals that OSS is compatible with hardware used in TVET programs, offers diverse teaching and learning aids, and is suitable for online learning environment. These findings underscore OSS's potential as an alternative in meeting TVET requirements, fostering dynamic learning environments, and improving pedagogical approaches, accessibility, and cost-effectiveness in TVET institutions.

Tool Condition Monitoring in the Milling Process Using Deep Learning and Reinforcement Learning

Tool condition monitoring (TCM) is crucial in the machining process to confirm product quality as well as process efficiency and minimize downtime. Traditional methods for TCM, while effective to a degree, often fall short in real-time adaptability and predictive accuracy. This research work aims to advance the state-of-the-art methods in predictive maintenance for TCM and improve tool performance and reliability during the milling process. The present work investigates the application of Deep Learning (DL) and Reinforcement Learning (RL) techniques to monitor tool conditions in milling operations. DL models, including Long Short-Term Memory (LSTM) networks, Feed Forward Neural Networks (FFNN), and RL models, including Q-learning and SARSA, are employed to classify tool conditions from the vibration sensor. The performance of the selected DL and RL algorithms is evaluated through performance metrics like confusion matrix, recall, precision, F1 score, and Receiver Operating Characteristics (ROC) curves. The results revealed that RL based on SARSA outperformed other algorithms. The overall classification accuracies for LSTM, FFNN, Q-learning, and SARSA were 94.85%, 98.16%, 98.50%, and 98.66%, respectively. In regard to predicting tool conditions accurately and thereby enhancing overall process efficiency, SARSA showed the best performance, followed by Q-learning, FFNN, and LSTM. This work contributes to the advancement of TCM systems, highlighting the potential of DL and RL techniques to revolutionize manufacturing processes in the era of Industry 5.0.

Automated mobile robots routing and job assignment in automated factory

In the era of Industry 4.0, smart manufacturing and automated factories require advanced software and hardware resources. The decisions on AMR routing and job assignment are two of the key issues in automated factories. In this paper, we investigate the key issues by establishing a mixed integer linear programming model to minimize the makespan for a batch of products. As the problem considers the joint optimization of AMR routing, job-to-AMR assignments, job-to-machine assignments, and job sequencing on machines, the proposed model is comprehensive but complex, with million integer variables and constraints. To tackle this complexity, we introduce a hybrid heuristic approach based on variable neighborhood search and adaptive large neighborhood search algorithms. Three accelerating tactics, two specific neighborhood structures, and six tailored operators are devised to handle large-scale instances efficiently. Remarkably, our metaheuristic can handle 280 jobs within 800 s, resulting in twice the efficiency achieved by traditional factories. Some managerial implications are also obtained based on sensitivity analysis, which may be potentially useful to increase the operational efficiency in automated factory management.

Digitalization of talent planning in IT sector: Mediating role of HR policies and practices

In the highly dynamic digital era of the information technology industry, agile talent planning and human resources (HR) management strategies are essential for maintaining a competitive edge. This study aims to determine the impact of HR policies and practices as a mediator between the effectiveness of talent planning and digitalization tools and techniques within the IT sector. The research population comprised IT companies in Delhi (and its national capital region), India. Using the convenience sampling technique, a sample of 106 respondents was selected out of 168 contacted, including human resource professionals and managers. The study employed regression analysis and structural equation modeling. The results reject that digitalization tools and techniques do not significantly increase the effectiveness of talent planning in IT organizations (beta coefficient = 0.455 at p-value < 0.001). Additionally, the paper shows that digitalization tools and technologies significantly affect HR policies and practices (beta coefficient = 0.826 at p-value < 0.001). The findings reject the suggestion that there is no significant positive relationship between HR practices and policies and the effectiveness of talent planning in the IT sector (beta coefficient = 0.425 at p-value < 0.001). Furthermore, the study rejects the perception that HR policies and practices do not mediate the relationship between digitalization tools and techniques and the effectiveness of talent planning (beta coefficient = 0.351 at p-value < 0.001). These insights will contribute to developing effective HR strategies that align with technological advancements and foster organizational growth.

Toward industry 5.0: Challenges and enablers of intelligent manufacturing technology implementation under the perspective of sustainability

The advancement of intelligent manufacturing technology in the era of Industry 5.0 has propelled the intelligence and automation of manufacturing production, while also exerting a significant impact on sustainable development of the manufacturing industry. However, the challenges and enablers faced by the transformation of intelligent manufacturing technology in the context of sustainable development of Industry 5.0 are still unclear. Based on literature review and expert opinions, this study uses the Likert scale to determine the challenges and enablers of the implementation of intelligent manufacturing technology in social, environmental and economic sustainability. The fuzzy-DEMETAL and AISM are used to analyze the logical relationship and hierarchical relationship between the above factors, and the MICMAC matrix is used to determine the key influencing factors. The research conclusions show that the most important challenges affecting the implementation of intelligent manufacturing technology are cost and funding, and the most important enabler is social benefits and public service improved. This research will provide insights for industry practitioners and decision makers in the management and decision-making process of implementing the transformation and upgrading of manufacturing intelligent manufacturing, thereby enhancing the sustainability of manufacturing development.

Supply chain digitalization and sports industry performance in Chinese context: a mediated moderated model

PurposeIn era of industry 4.0 all the organizations are investing their resources so they can adopt the latest technologies with no exception of supply chain. Accordingly, the present study attempted to examine the influence of digitalization of supply chain and performance in context of Chinese sports industry. Additionally, it has considered the supply chain integration and time management as a mechanism along with boundary condition of supply chain resilience.Design/methodology/approachThe study followed positivism philosophy and adopted quantitative methods. The data were collected by using the questionnaire from the Chinese sport industry organizations’ professionals related to logistics and supply chain. A total 746 questionnaires were distributed and 570 of them subjected to SPSS and Smart PLS for data analysis. Hypotheses were tested using PLS-SEM through the Smart-PLS 4.FindingsThe results of the study revealed that supply chain digitalization positively influences the both internal and external supply chain integration. Moreover, supply chain planning and scheduling influenced by both internal and external supply chain integration lead by supply chain digitalization. Ultimately resulting in improved sports industry performance. However, the supply chain resilience did not found as a significant moderator.Originality/valueThe study is few of the attempts that have tested a comprehensive mechanism through which the supply chain digitalization lead towards the higher performance. Also, the study results signify that the digitization can help the organizations to accomplish superior performance specifically in sports industry.

Peramalan Jumlah Kebutuhan Packaging Papper Photocopy (PPC) PT. Y Menggunakan Metode Moving Avarage dan Exponential Smoothing

In the era of globalization, industry and trade in Indonesia face fierce competition. Technological advances require quality human resources from formal and non-formal educational institutions with supporting facilities. This research focuses on solving safety stock problems using the Exponential Smoothing and Moving Average methods. Due to the current changing market demand, the results of data processing carried out using the Quality Management (QM) program for Windows show that the forecasting method with the Moving Average approach has the highest Mean Absolute Percentage Error (MAPE) value of 16.03%, while the Exponential Smoothing method has a MAPE value of 13.7%. So with this, the lowest MAPE value is in the forecasting method with the Exponential Smoothing approach of 13.7%. Keywords: Demand Forecasting, Exponential Smoothing Method, Moving Average Method, MAPE, Safety Stock

Application of Deep Learning Techniques for the Optimization of Industrial Processes Through the Fusion of Sensory Data

In the era of Industry 4.0, mastering the management and analysis of large volumes of data has become essential to foster technological advances and improve operations. Our work presents a methodology combining spatial and temporal data with deep learning techniques, specifically Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory and Gated Recurrent Units, and Deep Neural Networks. We aim to transform anomaly detection, perform predictive maintenance, and optimize industrial processes. Our work has found that intelligent integration of multiple data sources improves accuracy and other key indicators, such as F1 score and AUC, and enriches decision-making with more profound, detailed information about the operating environment. The results have been promising. We have seen an increase in anomaly detection accuracy by up to 92%, an improvement in early detection for predictive maintenance by 150%, and an improvement in operational efficiency from 70% to 85%. These advances validate our proposal and demonstrate its practicality in various industrial environments. This work proposes a guide for integrating data fusion technologies in Industry 4.0, highlighting the practical benefits of our methodology and opening new possibilities for innovation and improving operational efficiency.