Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies.
M. Tseng, Thi Phuong Thuy Tran, Hien Minh Ha
et al.
ABSTRACT This study supplies contributions to the existing literature with a state-of-the-art bibliometric review of sustainable industrial and operation engineering as the field moves toward Industry 4.0, and guidance for future studies and practical achievements. Although industrial and operation engineering is being promoted forward to sustainability, the systematization of the knowledge that forms firms’ manufacturing and operations and encompasses their wide concepts and abundant complementary elements is still absent. This study aims to analyze contemporary sustainable industrial and operations engineering in Industry 4.0 context. The bibliometric analysis and fuzzy Delphi method are proposed. Resulting in a total of 30 indicators that are criticized and clustered into eight study groups, including lean manufacturing in Industry 4.0, cyber-physical production system, big data-driven and smart communications, safety and security, artificial intelligence for sustainability, the circular economy in a digital environment, business intelligence and virtual reality, and environmental sustainability. Graphical Abstract
Digital transformation is no longer a future trend, as it has become a necessity for businesses to grow and remain competitive in the market. The fourth industrial revolution, called Industry 4.0, is at the heart of this transformation, and is supporting organizations in achieving benefits that were unthinkable a few years ago. The impact of Industry 4.0 enabling technologies in the manufacturing sector is undeniable, and their correct use offers benefits such as improved productivity and asset performance, reduced inefficiencies, lower production and maintenance costs, while enhancing system agility and flexibility. However, organizations have found the move towards digital transformation extremely challenging for several reasons, including a lack of standardized implementation protocols, emphasis on the introduction of new technologies without assessing their role within the business, the compartmentalization of digital initiatives from the rest of the business, and the large-scale implementation of digitalization without a realistic view of return on investment. To instill confidence and reduce the anxiety surrounding Industry 4.0 implementation in the manufacturing sector, this paper presents a conceptual framework based on business process management (BPM). The framework is informed by a content-centric literature review of Industry 4.0 technologies, its design principles, and BPM method. This integrated framework incorporates the factors that are often overlooked during digital transformation and presents a structured methodology that can be employed by manufacturing organizations to facilitate their transition towards Industry 4.0.
Chandan Trivedi, Pronaya Bhattacharya, Vivek Kumar Prasad
et al.
The Industrial Revolution has shifted toward Industry 5.0, reinventing the Industry 4.0 operational process by introducing human elements into critical decision processes. Industry 5.0 would present massive customization via transformative technologies, such as cyber-physical systems (CPSs), artificial intelligence (AI), and big data analytics. In Industry 5.0, the AI models must be transparent, valid, and interpretable. AI models employ machine learning and deep learning mechanisms to make the industrial process autonomous, reduce downtime, and improve operational and maintenance costs. However, the models require explainability in the learning process. Thus, explainable AI (EXAI) adds interpretability and improves the diagnosis of critical industrial processes, which augments the machine-to-human explanations and vice versa. Recent surveys of EXAI in industrial applications are mostly oriented toward EXAI models, the underlying assumptions. Still, fewer studies are conducted toward a holistic integration of EXAI with human-centric processes that drives the Industry 5.0 applicative verticals. Thus, to address the gap, we propose a first-of-its-kind survey that systematically untangles EXAI integration and its potential in Industry 5.0 applications. First, we present the background of EXAI in Industry 5.0 and CPSs and a reference EXAI-based Industry 5.0 architecture with insights into large language models. Then, based on the research questions, a solution taxonomy of EXAI in Industry 5.0 is presented, which is ably supported by applicative use cases (cloud, digital twins, smart grids, augmented reality, and unmanned aerial vehicles). Finally, a case study of EXAI in manufacturing cost assessment is discussed, followed by open issues and future directions. The survey is designed to extend novel prototypes and designs to realize EXAI-based real-time Industry 5.0 applications.
Community Question Answering (CQA) platforms can be deemed as important knowledge bases in community, but effectively leveraging historical interactions and domain knowledge in real-time remains a challenge. Existing methods often underutilize external knowledge, fail to incorporate dynamic historical QA context, or lack memory mechanisms suited for industrial deployment. We propose ComRAG, a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism designed for retrieval, generation, and efficient storage. Evaluated on three industrial CQA datasets, ComRAG consistently outperforms all baselines--achieving up to 25.9% improvement in vector similarity, reducing latency by 8.7% to 23.3%, and lowering chunk growth from 20.23% to 2.06% over iterations.
With the development of the digital transformation of the tobacco industry, the industrial control system is facing new security challenges. This paper studies the construction scheme of industrial control network security based on the trusted list, By sorting out the operating environment and parameters of industrial controlled assets, Build a "credible environment" for industrial control network security, Combined with the implementation of industrial control construction, Further enhance the enterprise industrial control network security technology management measures, Implementation: the device is trusted, In the industrial control network to rest assured to use; Content is trusted, Confidentiality, complete and tamper-proof; Application system can be trusted, Perform reliable industrial production control instructions, Building a comprehensive prevention and control system for "great industrial safety", Form a three-level safety management structure of headquarters, factory level and workshop level, Based on the multi-source heterogeneous technology, Integrate the equipment from different manufacturers, Forming a unified large security centers.
Kenya’s economy is predominantly agriculture-based, with tea representing one of the most significant cash crops contributing to national income, foreign exchange, and rural livelihoods. This study assessed the socio-economic impact of the tea industry on rural livelihoods in Kenya, focusing on three key tea-growing counties: Kericho, Kisii, and Kiambu. The research employed a descriptive survey design and stratified random sampling to select 60 smallholder tea farmers, complemented by secondary data from the Kenya Tea Development Agency (KTDA) and government records. Data were collected through structured questionnaires and analyzed using descriptive statistics and thematic interpretation. Findings revealed that 68.3% of respondents relied exclusively on tea farming for income, while 25% combined agriculture and service-based activities, and 1.7% integrated agriculture, service, and private business. County variations were notable, with Kisii farmers demonstrating the highest dependence on agriculture (90%), while Kericho farmers exhibited greater income diversification (45% combining agriculture and service). The tea industry emerged as a critical employer, generating over one million direct and indirect jobs nationally and significantly supporting rural economies through KTDA factories, transport, warehousing, and auction-related services. Beyond income, tea farming was linked to improved rural infrastructure, housing standards, education, and food security, contributing to enhanced living conditions and rural industrialization. However, emerging challenges such as mechanization in large-scale estates threaten employment levels, raising equity and sustainability concerns. The study concludes that tea remains a cornerstone of rural socio-economic development, but sustaining its benefits requires targeted interventions. Recommendations include promoting income diversification, balancing mechanization with job protection, strengthening rural infrastructure, enhancing farmer capacity building, and fostering value addition initiatives to maximize earnings. Policy frameworks ensuring fair pricing and timely payments are also essential to stabilize incomes and improve livelihoods for smallholder farmers. These insights provide a foundation for policymakers, industry stakeholders, and researchers seeking to optimize the socio-economic contributions of Kenya’s tea sector amid evolving market and technological dynamics. Keywords: Socio-Economic, Tea Industry, Rural Livelihoods, Kenya
On factory shop floors, in the context of Industry 4.0, there are growing problems of unplanned downtime, energy wastage, and inconsistent motor operation with resultant overall productivity loss. The work discusses a suggested smart and scalable IoT-based conveyor motor automation system relying on real-time monitoring and machine learning-based predictive maintenance, which aims to improve operational efficiency and dependability. The approach involves the utilization of an ESP8266 microcontroller with ACS712 (current), LM35 (temperature), and SW-420 (vibration) sensors to monitor motor health in real-time. The information is transferred wirelessly to Blynk and Power BI dashboards for simple visualization, fault detection, and remote monitoring. Historical sensor data are employed to train a Random Forest Regression model, which forecasts likely failures and performance trends. The core results are unscheduled downtimes reduced, energy used more efficiently, and system responsiveness improved. The novelty of the system is the harmonious combination of IoT sensing, cloud-based analytics, and machine learning algorithms into an integrated, low-latency platform. This feature makes it very suitable and practical for deployment in large-scale smart factories, thus making proactive maintenance and optimal motor utilization possible with minimal human intervention.
Context and purpose:This study was conducted to study the performance of agricultural and industrial cooperatives in Zanjan province in the competitive market space.Methodology/approach:In this study, 157 agricultural cooperatives and 93 industrial cooperatives out of a total of 265 agricultural and 123 industrial active cooperatives were selected and analyzed by stratified sampling method. To assess the appropriateness of the competitiveness indicators of cooperative enterprises, four indices including economic, social-cultural, managerial and infrastructural-institutional indicators were examined and evaluated.Findings and conclusions:The results of this study showed that among the surveyed indicators, economic and infrastructure indicators play a more important role in the competitiveness of cooperative enterprises with an average value of 3.37 and 3.36, respectively. In addition, in terms of socio-cultural and managerial indicators, the challenges for industrial cooperatives are more than agricultural cooperatives, while infrastructure and institutional challenges are more in agricultural cooperatives compared to industrial cooperatives. In planning and policy sub-indicators; knowledge and awareness, experience and expertise; industrial cooperatives have more problems than agricultural cooperatives. Forming cooperatives, marketing, commercializing, modifying the law to comprehensively support cooperatives, enhancing culture in the society, creating and enhancing social capital and improving investment security and private investment play an important role in the competitiveness of agricultural and industrial cooperatives.Originality/innovationPrevious studies have examined the status of agricultural or industrial cooperative enterprises, but this study simultaneously investigates and compares the situation of these enterprises in terms of competitiveness.
The re-energization of electrical distribution systems in a post-disaster scenario is of grave importance as most modern infrastructure systems rely heavily on the presence of electricity. This paper introduces a method to coordinate the field teams for the optimal energization of an electrical distribution system after an earthquake-induced blackout. The proposed method utilizes a Markov Decision Process (MDP) to create an optimal energization strategy, which aims to minimize the expected time to energize each distribution system component. The travel duration of each team and the possible outcomes of the energization attempts are considered in the state transitions. The failure probabilities of the system components are computed using the fragility curves of structures and the Peak Ground Acceleration (PGA) values which are encoded to the MDP model via transition probabilities. Furthermore, the proposed solution offers several methods to determine the non-optimal actions during the construction of the MDP and eliminate them in order to improve the run-time performance without sacrificing the optimality of the solution.
Reza Khanmohammadi, Simerjot Kaur, Charese H. Smiley
et al.
This paper investigates the relationship between scientific innovation in biomedical sciences and its impact on industrial activities, focusing on how the historical impact and content of scientific papers influenced future funding and innovation grant application content for small businesses. The research incorporates bibliometric analyses along with SBIR (Small Business Innovation Research) data to yield a holistic view of the science-industry interface. By evaluating the influence of scientific innovation on industry across 10,873 biomedical topics and taking into account their taxonomic relationships, we present an in-depth exploration of science-industry interactions where we quantify the temporal effects and impact latency of scientific advancements on industrial activities, spanning from 2010 to 2021. Our findings indicate that scientific progress substantially influenced industrial innovation funding and the direction of industrial innovation activities. Approximately 76% and 73% of topics showed a correlation and Granger-causality between scientific interest in papers and future funding allocations to relevant small businesses. Moreover, around 74% of topics demonstrated an association between the semantic content of scientific abstracts and future grant applications. Overall, the work contributes to a more nuanced and comprehensive understanding of the science-industry interface, opening avenues for more strategic resource allocation and policy developments aimed at fostering innovation.
Given the continuous global degradation of the Earth's ecosystem due to unsustainable human activity, it is increasingly important for enterprises to evaluate the effects they have on the environment. Consequently, assessing the impact of business processes on sustainability is becoming an important consideration in the discipline of Business Process Management (BPM). However, existing practical approaches that aim at a sustainability-oriented analysis of business processes provide only a limited perspective on the environmental impact caused. Further, they provide no clear and practically applicable mechanism for sustainability-driven process analysis and re-design. Following a design science methodology, we here propose and study SOPA, a framework for sustainability-oriented process analysis and re-design. SOPA extends the BPM life cycle by use of Life Cycle Assessment (LCA) for sustainability analysis in combination with Activity-based Costing (ABC). We evaluate SOPA and its usefulness with a case study, by means of an implementation to support the approach, thereby also illustrating the practical applicability of this work.
Andrea Zugarini, Andrew Zamai, Marco Ernandes
et al.
Albeit Natural Language Processing has seen major breakthroughs in the last few years, transferring such advances into real-world business cases can be challenging. One of the reasons resides in the displacement between popular benchmarks and actual data. Lack of supervision, unbalanced classes, noisy data and long documents often affect real problems in vertical domains such as finance, law and health. To support industry-oriented research, we present BUSTER, a BUSiness Transaction Entity Recognition dataset. The dataset consists of 3779 manually annotated documents on financial transactions. We establish several baselines exploiting both general-purpose and domain-specific language models. The best performing model is also used to automatically annotate 6196 documents, which we release as an additional silver corpus to BUSTER.
As an important carrier and component of industrial clusters, industrial districts are an important way for regional economic growth, and the economic effects of industrial districts have attracted more and more attention. Therefore, how to identify high-quality districts with good development trends and obvious competitive advantages from a large amount of district information has become an increasingly important topic. This article takes industrial and intelligent manufacturing districts as an example and proposes an innovative industrial and intelligent manufacturing zone rating method that combines rule-based modeling, Analytic Hierarchy Process (AHP), and Artificial Intelligence (AI) models. Collect structured and unstructured data, including real-time operational data, historical records, market trends, and expert opinions, from multiple sources through big data technology. Apply rule-based models and Analytic Hierarchy Process (AHP) to prioritize and weight various factors that affect regional performance, and obtain district ratings; And use the Kmeans method for clustering to obtain district level classification. Finally, the paper combines the outputs of the rule-based models, AHP, and AI models to create an overall rating system. This system continuously monitors district performance, providing accurate and comprehensive ratings to support decision-making in industry and government.
China's prepared vegetable industry has achieved certain development, and the prepared vegetable market is huge and the market space is large, but it still faces some challenges and problems. The food quality of prepared dishes needs to be guaranteed by high-quality cold chain logistics. However, the development level of cold chain logistics in China is relatively insufficient, and the corresponding development level of cold chain logistics of prepared dishes is of low quality, which restricts the development of prepared dishes industry to a certain extent. With the development of artificial intelligence, cloud computing, big data, Internet of Things and other technologies, digital and intelligent technologies are increasingly applied in logistics. The application of intelligent logistics technologies such as artificial intelligence technology to build smart cold chain logistics system is conducive to improving the level of cold chain logistics of prepared dishes, ensuring the quality of prepared dishes, promoting the upgrading of prepared dishes industry, and expanding the market of prepared dishes industry.
The safety of hazardous chemicals is a key focus of safety production work, involving a large number of enterprises, multiple industry sectors, multiple regulatory departments, high potential safety risks and easily affecting public safety. There are safety hazards that cannot be ignored in various links such as production, operation, storage, transportation, usage and disposal. The research purpose of the whole process supervision information system of hazardous chemicals is to make full use of information and intelligent means and combine big data, cloud computing, artificial intelligence, Internet of Things, mobile Internet and other technologies to build a full coverage and whole process supervision information system of hazardous chemicals. This system plans to establish a hazardous chemical supervision information exchange platform based on the government data resource management platform, create a hazardous chemical data lake and use the entire life process of hazardous chemical operation, storage, transportation, use and disposal as the data bus. Through data aggregation, data governance, data integration, data verification and data presentation, it will achieve the full network interconnection of regional hazardous chemical data and establish a new mode of hazardous chemical data exchange and sharing among joint units.
The steady development of accounting informationization has become an inevitable requirement for the sustainable development of enterprises. The integration of industry and finance, as an effective way for enterprises to maintain business vitality and improve business efficiency in response to fierce market competition, is also gradually deepening. However, the current accounting informationization and industry financial integration still exist fragmentation, individualization, system is not strong and accounting personnel concept and quality and integration requirements gap between large and other issues. In this regard, this paper analyzes and discusses accounting informationization and business integration, mainly analyzes its functional role and specific implementation path. At the same time, it points out the implementation focus of the financial sharing center model to promote the integration of industry and finance, systematically explains the meaning of financial sharing and industry and finance integration, and further clariifies the relationship between the two and the promotion effect of financial sharing on industry and finance integration. On this basis, the problems existing in the process of enterprise industry-finance integration under the mode of financial sharing are deeply analyzed, countermeasures and suggestions are put forward to solve these problems, in order to provide certain guidance and reference for the development of enterprise industry-finance integration. At the same time, it is hoped that it can innovate financial management mode, help enterprises understand and promote the process of enterprise industry-finance integration management, keep up with the historical pace of data management and data operation, and realize the ultimate goal of maximizing enterprise value.
Aaron Friedrich Kurz, Timotheus Kampik, Luise Pufahl
et al.
In order to better facilitate the need for continuous business process improvement, the application of DevOps principles has been proposed. In particular, the AB-BPM methodology applies AB testing and reinforcement learning to increase the speed and quality of improvement efforts. In this paper, we provide an industry perspective on this approach, assessing requirements, risks, opportunities, and more aspects of the AB-BPM methodology and supporting tools. Our qualitative analysis combines grounded theory with a Delphi study, including semi-structured interviews and multiple follow-up surveys with a panel of ten business process management experts. The main findings indicate a need for human control during reinforcement learning-driven experiments, the importance of aligning the methodology culturally and organizationally with the respective setting, and the necessity of an integrated process execution platform.