Hasil untuk "Large industry. Factory system. Big business"

Shiyong Wang, J. Wan, Daqiang Zhang et al.

Marlon Dumas, Fredrik Milani, David Chapela-Campa

Since the early 90s, the evolution of the Business Process Management (BPM) discipline has been punctuated by successive waves of automation technologies. Some of these technologies enable the automation of individual tasks, while others focus on orchestrating the execution of end-to-end processes. The rise of Generative and Agentic Artificial Intelligence (AI) is opening the way for another such wave. However, this wave is poised to be different because it shifts the focus from automation to autonomy and from design-driven management of business processes to data-driven management, leveraging process mining techniques. This position paper, based on a keynote talk at the 2025 Workshop on AI for BPM, outlines how process mining has laid the foundations on top of which agents can sense process states, reason about improvement opportunities, and act to maintain and optimize performance. The paper proposes an architectural vision for Agentic Business Process Management Systems (A-BPMS): a new class of platforms that integrate autonomy, reasoning, and learning into process management and execution. The paper contends that such systems must support a continuum of processes, spanning from human-driven to fully autonomous, thus redefining the boundaries of process automation and governance.

Muhammad Arshad, W. O. Choo, Ashfaq Ahmad et al.

As the trend in the current generation with the use of mobile devices is rapidly increasing, online video streaming has risen to the top in the entertainment industry. These platforms have experienced radical expansion due to the incorporation of Big Data Analytics and Artificial Intelligence which are critical in improving the user interface, improving its functioning, and customization of recommended content. This paper seeks to examine how Big Data Analytics makes it possible to obtain large amounts of data about users and how they view, what they like, or how they behave. While customers benefit from this data by receiving more suitable material, getting better recommendations, and allowing for more efficient content delivery, AI utilizes it. As a result, the study also points to the importance and relevance of such technologies to promote business development, and user interaction and maintain competitiveness in the online video streaming market with examples of their effective application. This work presents a comprehensive investigation of the combined role of Big Data and AI and presents the necessary findings to determine their efficacy as success factors of existing and future video streaming services.

A. Hermawati, Rizki Dwi Ariyanto, Abimanyu Tuwuh Sembhodo et al.

The tourism industry in East Java plays a strategic role in economic growth and Gross Regional Domestic Product (GRDP) . However, it still faces several challenges: 1) inconsistencies in human resource development , marketing strategies, and industrial policy strategies. 2) limited data-based evaluation systems, which hinder the sector's competitiveness . Therefore, a holistic, integrated approach with multiple management aspects based on big data technology is needed to strengthen the contribution to tourism industry performance and provide a strategic implementation solution. Identifying and analyzing factors that impact the performance of the tourism industry ; Identifying the gap between the importance and performance of factors that impact the integration of HRD, marketing, strategic management, and the East Java tourism industry ; Knowing the relationship between HRD factors, marketing strategies, and strategic planning on the performance of the tourism industry in East Java ; Designing and validating the PARIS (Performance Analysis and Recommendation for Integrated Strategy) system model as a data-based decision-making support tool in the integration of HRD, marketing, and strategic management in the East Java tourism industry. Analysis Tools: Systematic Literature Review; Survey & Focus Group Discussion . The population is all tourism industry employees in East Java. 200 tourism sector business samples, each with five employees as respondents, a total of 1000 respondents, proportionally from 11 selected cities/regencies: Malang, Batu, Surabaya, Banyuwangi, Blitar, Jombang, Ponorogo, Pacitan, Pasuruan, Probolinggo, Tulungagung. Due to limited time, the researcher chose one in the Batu city area.

Afrizal Zein, Fordiana Ekawati

The growth in volume, velocity, and diversity of data has driven the need for analytical systems that are not only capable of handling big data, but also capable of generating intelligent predictions and insights through the integration of machine learning. This study aims to design and analyze a comprehensive framework that integrates machine learning algorithms into big data analytical systems. The research approach is carried out through literature studies and evaluations of various platforms and architectures such as Hadoop, Spark, and TensorFlow, which enable efficient large-scale data processing. The proposed framework includes the stages of ingestion, preprocessing, model training, evaluation, deployment, and feedback loops that support continuous learning. This integration not only improves the predictive capabilities of the system but also enables organizations to respond proactively to real-time data dynamics. The results of this study are expected to be a strategic reference in the development of modern data-driven analytical systems.

Guang-zhen Wu, Yang Liu

With the rapid development of the mobile Internet, mobile phone SMS has become an essential means of contact in people's daily life and has been widely used in various fields by virtue of its advantages such as low price, convenient use and reliable delivery. However, the massive use of SMS has brought certain hidden dangers in terms of information security, such as the proliferation of spam SMS. Because of this, how to maintain the healthy development of the SMS business while avoiding the adverse effects caused by spam has become an urgent problem to be solved. First, extract text keywords as labels through SMS text segmentation, and classify and process the labels; second, from the spam SMS dataset, obtain the label classification combinations of spam SMS to generate a new dataset; then, build a model based on convolutional neural network and recurrent neural network, conduct recognition training on the new dataset, and generate a scoring algorithm; finally, build a big data platform to distributed processing a large amount of data to meet the actual enterprise needs.

Ali Abdulqader Al Qisi, Azli Bin Nawawi, Adel Muhsin Elewe

Big data in manufacturing fields present several challenges leads to reduce profitability and missed opportunity for innovation. One of the used strategies is the use of radio frequency identification system.  considered a business strategy to increase productivity, speed up decision-making, and enhance production monitoring and control while preserving the structure and integrity of current manufacturing systems. The present research compares five artificial inelegant algorithms based on RFID system in facility layout design to investigate the fitness of each algorithm in manufacturing big data processing. The objective functions have been used are the minimum number of required readers, minimum readers overlap, and maximum tags coverage. The contribution in this work is the workability of each algorithm in different facility design condition based on design alternatives. the results present that cuckoo search (CS) has the optimum fitness reach to 74.68% in big data and large area condition while particle swarm optimization (PSO) observed optimum fitness 74.46% in small data and large area. The simulation results illustrate the applicability and robustness of the proposed method, with the characteristics maintaining exceptional approximation capabilities even in high-dimensional spaces.

Md Mosleuzzaman, Imran Arif, Amir Siddiki

This systematic literature review examines the operational and organizational impacts of Industry 4.0 technologies on smart factories, drawing on insights from 120 peer-reviewed articles published between 2010 and 2024. The study follows the PRISMA guidelines to ensure a transparent and rigorous review process, focusing on the key enablers of smart manufacturing, including cyber-physical systems (CPS), the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and machine learning (ML). The findings reveal that smart factories offer significant benefits, including enhanced flexibility and customization, predictive maintenance that reduces downtime by up to 50%, and improved supply chain integration through real-time data sharing. Big data analytics plays a crucial role in optimizing operations by allowing factories to perform continuous real-time adjustments, improving efficiency and reducing resource waste. The review also highlights the evolving role of the workforce, with a growing need for technical skills and increased human-machine collaboration in smart manufacturing environments. However, challenges such as interoperability, cybersecurity, and the economic feasibility of large-scale smart factory implementations remain underexplored in the literature. Emerging technologies like blockchain and 5G offer promising solutions, but further research is required to assess their full potential. Overall, this review provides a comprehensive understanding of the current state of smart factory technologies and outlines key areas for future research, particularly in addressing gaps related to standards, workforce adaptation, and security concerns.

Hugh J. Watson, D. Larson

Companies are moving from a cottage industry to a factory approach to analytics, especially in regard to machine learning (ML) models. This change is motivating companies to adopt ML operations (MLOps) as a methodology for the timely development, deployment, and maintenance of ML models in order to positively impact business outcomes. The adoption of MLOps requires changes in processes, technology, and people, and these changes are informed by previous work on decision support systems (DSS), development operations (DevOps), and data operations (DataOps). The processes, technologies, and people needed for MLOps are discussed and illustrated using a customer purchase recommendation example. Current and future directions for MLOps practice driven by artificial intelligence (AI) are explored. Suggestions for further academic research are provided.

Jin Wan

: Many countries in the world use 5G technology to develop the future society and accelerate the strategic composition of 5G. Each generation of mobile network standards is designed to respond to changes in the use of mobile communications. The 5G network features high speed, large capacity, low latency and high reliability, which meet people's needs for the network and bring great convenience to people. With the improvement of information technology, all walks of life have developed rapidly, but the education industry lags behind in the process of informatization, and most of them still follow the past education methods. For the evaluation of teaching quality, it is also a simple evaluation based on the quality of students' test results or the evaluation of teachers. The data collection process is cumbersome, and such evaluations lack systematic and comprehensive. Nowadays, the hidden value of big data and artificial intelligence (AI for short) has gradually become the focus of attention of all walks of life, which can bring about better development opportunities for the education system to be more rationally reformed and entrepreneurship education for college students. The purpose of this paper is to use big data and AI technology to study the quality evaluation system of entrepreneurship education for college students. This paper distributed 300 questionnaires through online questionnaires, face-to-face surveys, and email surveys. Finally, 211 colleges, 985 colleges, college students' entrepreneurial enterprise internships, and participation in young entrepreneurs' associations were investigated. In the final survey, 220 valid questionnaires were collected. According to the calculation, the questionnaire has high reliability and validity. In the overall quality index of entrepreneurship in various dimensions of college students, the survival, growth dimension and the internal benefit dimension belong to the middle level, and the innovation dimension and the external benefit dimension belong to the middle and lower level. The average score of the overall quality of college students' entrepreneurship is 2.904, which is lower than 3 points, indicating that the overall quality of college students' entrepreneurship is low. The mean values of the question items of the innovation dimension and the external benefit dimension are 2.836 and 2.529 respectively. In addition, this paper analyzed individual factors according to the gender, personality, educational background, and average annual income of the survey respondents, and concluded that gender has nothing to do with entrepreneurial quality, but personality, educational background, and average annual income had a certain positive correlation with entrepreneurial quality. Therefore, this paper used big data and AI to evaluate the quality of college students' entrepreneurship education and achieves good research results.

A.M. Balashov

Currently, digitalization and widespread adoption of digital technologies are significantly changing people's activities in many areas. Digital technologies provide automation of business processes, data management, analytics, they support strategic decision-making and dictate the need to introduce new approaches to doing business in order to increase its efficiency and profitability, as well as to ensure sustainability of companies' development in modern conditions. It needs to be especially mentioned how the big data processing and analysis technologies and other Industry 4.0 achievements are introduced in the mining industry. The use of big data analytical systems in modern production, including the mining industry, provides an integrated approach to processing and analyzing a large amount of information. It also provides organizations with significant advantages reflected at various levels of management and strategic decision-making. The prospects for implementation and development of these digital solutions currently look very encouraging. Effective management of these processes provides companies with significant opportunities and advantages, allowing them to increase competitiveness, optimize the use of resources and increase the efficiency of their business as a whole.

Idoia Berges, Víctor Julio Ramírez-Durán, Arantza Illarramendi

The growing trends in automation, Internet of Things, big data and cloud computing technologies have led to the fourth industrial revolution (Industry 4.0), where it is possible to visualize and identify patterns and insights, which results in a better understanding of the data and can improve the manufacturing process. However, many times, the task of data exploration results difficult for manufacturing experts because they might be interested in analyzing also data that does not appear in pre-designed visualizations and therefore they must be assisted by Information Technology experts. In this paper, we present a proposal materialized in a semantic-based visual query system developed for a real Industry 4.0 scenario that allows domain experts to explore and visualize data in a friendly way. The main novelty of the system is the combined use that it makes of captured data that are semantically annotated first, and a 2D customized digital representation of a machine that is also linked with semantic descriptions. Those descriptions are expressed using terms of an ontology, where, among others, the sensors that are used to capture indicators about the performance of a machine that belongs to a Industry 4.0 scenario have been modeled. Moreover, this semantic description allows to: formulate queries at a higher level of abstraction, provide customized graphical visualizations of the results based on the format and nature of the data, and download enriched data enabling further types of analysis.

F. Bolivar, Miguel A. Duran, A. Lozano-Vivas

This paper analyzes the relation between bank profit performance and business models. Using a machine learning-based approach, we propose a methodological strategy in which balance sheet components' contributions to profitability are the identification instruments of business models. We apply this strategy to the European Union banking system from 1997 to 2021. Our main findings indicate that the standard retail-oriented business model is the profile that performs best in terms of profitability, whereas adopting a non-specialized business profile is a strategic decision that leads to poor profitability. Additionally, our findings suggest that the effect of high capital ratios on profitability depends on the business profile. The contributions of business models to profitability decreased during the Great Recession. Although the situation showed signs of improvement afterward, the European Union banking system's ability to yield returns is still problematic in the post-crisis period, even for the best-performing group.

Xiaoming Sang, Hongyu Liu, Song Cheng et al.

This paper innovatively proposes the goal of research on the decision support system of the large-scale industrial agglomeration circle in Foshan. Based on the computer big data combined with the process of urban planning and management in Foshan, this paper proposes the construction content of the current planning decision support system for the new industrial agglomeration circle in Foshan. This paper proposes the relevant models for the analysis and construction of the development and evolution model and the construction of the auxiliary decision-making management system. The method of large-scale industrial agglomeration area in Foshan proposed in this paper provides services for the government to make rational and scientific decision-making. The computer big data industry circle planning model proposed in this paper has certain practical significance.

Carmine Garzia, F. Gentile, Edoardo Slerca

Prosecco wine has become one of the widest diffused sparkling wine with 627 million bottles produced in 2021 compared to 140 million bottles produced in 2010. The spread of the product is due to a rapid growth in production capacity that has allowed a large amount of product to be placed on the market at very competitive prices. The growth of the sector has led to a radical change in the characteristics of supply with the emergence of companies with a business model based on trading, not integrated in wine production, focused on bottling and selling the product. The paper analyzes, using a System Dynamics approach, the process that determined the affirmation of the trading business model by identifying the critical variables and the relevant feedback loops structures. Resource dynamic analysis allowed us to evaluate the long-term sustainability of the dominant business model.

Muhammad Umar, Abdul Aziz, Vivake Anand

The purpose of the current study is to examine that what big data analytics (BDA) may have a moderating effect on the relationship between supply chain management practices (SCMPs) and competitive advantage (CA) in Pakistani manufacturing companies. Data was gathered quantitatively from several manufacturing businesses located industrial in Karachi. The study hypothesis was tested using SPSS method and linear regression analysis, sample size is 154 persons working in small, medium and large sized manufacturing companies with multiple categorizations based on enterprise size, job & experience. The findings demonstrate that SCMPs have a major favorable impact on competitive advantage (CA). In particular, information quality (IQ), customer relationship management (CRM), and strategic supplier partnerships (SSP) all significantly improve competitive advantage (CA), information sharing (ISh) not significantly on competitive advantage (CA). Furthermore, investigates the association between SCMPs and CA in Pakistani manufacturing enterprises, as well as the moderating effect of big data analytics (BDA). The findings validate H1, H2, and H3 and demonstrate the beneficial effects of Supplier Strategic Partnership (SSP), customer relationship management (CRM), and information quality (IQ) on competitive advantage (CA). This study is to look at how big data analytics (BDA) may be able to moderate the relationship between supply chain management practices (SCMPs) and competitive advantage (CA) in Pakistani manufacturing companies. The findings demonstrate that SCMPs significantly improve CA. Information quality (IQ), customer relationship management (CRM), and strategic supplier partnerships (SSP) in particular significantly improve competitive advantage (CA). Information sharing (ISh), on the other hand, had no effect on competitive advantage (CA).  Some flaw in the way the firms under investigation handle big data analytics (BDA). It emphasizes that for big data analytics (BDA) systems to produce the intended outcomes, managers must integrate them with other business systems and capabilities.

Wenjin Yu, Yuehua Liu, T. Dillon et al.

With the rapid growth in the use of various smart digital sensors, the Internet of Things (IoT) is a swiftly growing technology, which has contributed significantly to Industry 4.0 and the promotion of IoT-based smart factories, which gives rise to the new challenges of big data analytics and the implementation of machine learning techniques. This article proposes a practical framework that combines IoT techniques, a data lake, data analysis, and cloud computing for manufacturing equipment health-state monitoring and diagnostics in smart manufacturing. It addresses all the required aspects in the realization of such a system and allows the seamless interchange of data and functionality. Due to the specific characteristics of IoT sensor data (low quality, redundant multisources, partial labeling), we not only provide a promising framework but also give detailed insights and pay considerable attention to data quality issues. In the proposed framework, an ingestion procedure is designed to manage data collection, data security, data transformation and data storage issues. To improve the quality of IoT big data, a high-noise feature filter is proposed for automated preliminary sensor selection to suppress noisy features, followed by a noisy data cleaning module to provide good quality data for unbiased diagnosis modeling. The proposed framework can achieve seamless integration between IoT big data ingestion from the physical factory and machine learning-based data analytics in the virtual systems. It is built on top of the Apache Spark processing engine, being capable of working in both big data and real-time environments. One case study has been conducted based on a four-stage syngas compressor from real industries, which won the Best Industry Application of IoT at the BigInsights Data & AI Innovation Awards. The experimental results demonstrate the effectiveness of both the proposed IoT-architecture and techniques to address the data quality issues.

Shahintaj Karimi, Ayatollah Karami, Fatemeh Alipanahiyan

Social capital is one of the influential components in the performance and success of cooperatives, including rural cooperatives, which is considered by experts. The purpose of this study is to investigate the role of social capital in the success of the rural cooperative in Dana women. The method of this research is descriptive-analytic and a questionnaire technique is used to collect information. The statistical population is 600 members of the rural women's rural cooperative in Dena County, according to Bartlett's table, 100 were identified. The questionnaire was the most important tool for collecting data. The results of the questionnaire were analyzed using Spss software. To test the hypothesis, t-test, Pearson correlation coefficient was used. In order to investigate the role of social capital in the success of rural women's cooperatives in Dena, indicators such as social capital, social trust, and social participation were measured. Based on the results obtained from the indicators of social capital research, social trust index the impact on the success of the DENA Women's Co-operative.

Reza Movahedi, Masoud Samian, Mohamad Mohamadi

Context and purpose. The aim of this study was to find factors affecting the training courses' effectiveness on handmade-carpet cooperatives in Zanjan Province of Iran, the research's type was an applied study in terms of aim and surveying study in terms of data collection. Methodology/approach. Data collection tool was a researcher made questionnaire. The study population included all members of Handmade-Carpet cooperative of Zanjan province. The number of all Handmade-Carpet cooperatives was 38 including 366 members. Of those members 181 people were selected as the samples through Morgans' sampling table. A proportionate selection method was used to select the fit samples from each cooperative. The validity of the questions was done by experts' views and recommendations. The reliability of the questions was tested through Alpha's test (alpha =0.874 to 0.909). Findings and conclusions. After gathering data, SPSS software was used to analyze and describe the data. In descriptive part, both frequency tables and central statistics such as mean, median and mode as well as discrepancy statistics such as standard deviation and coefficient of variation were used. In analytical part, correlation and regression analysis methods and Mann-Whitenny and Kruskal- Wallis tests were used. The correlation's results showed that there was a significant relationship between variables attendance into training courses, income's amount from the cooperatives, the level of content relevancy, the level of objectives relevancy, the level of educators' knowledge, the level of training methods relevancy and the the effectiveness of the training courses. In addition, the results of regression analysis by a stepwise method revealed that the variables the level of objectives relevancy, income's amount from the cooperatives, and the level of educators' knowledge were determined 81.6 percent of dependent variable (the training courses' effectiveness). Originality. Iranian hand-woven carpets have a special place in the economy, now considering the essential role of cooperatives in strengthening Iranian hand-woven carpets, this research examines the important factors affecting the educational effectiveness of these cooperatives.

M. Sazu, S. Jahan

Leading trends over the last couple of years tend to be the increasing value of big data and analyzing the information through analytics. The information has tremendous value; fast moving consumer goods (FMCG) businesses must capitalize on the assortment of information by proper and in-depth evaluation with the usage of big data analytics (BDA). Objective: This article seeks to spotlight the changing dynamics of the SC managing atmosphere, to recognize the way the two leading trends will influence supply chain management (SCM) in future, to demonstrate the advantages which may be derived, and to generate suggestions to provide SC managers if BDA is adopted. Method: A survey was done amongst workers of multinational FMCG businesses across the world: the Americas, Asia etc. Systemic situation modeling is used in the quantitative evaluation to analyze the information gathered from surveys. The process of deriving value from the large quantities of information within the SCM is defined. Results: The adoption of BDA technologies can develop extensive value-added as well as a financial gain for companies and can quickly be a regular during the entire market. It is demonstrated, through examples, the way SCM location might be influenced by these brand-new developments and trends. Within the examples, BDA have been adopted, utilized, and applied effectively. Big data and analytics to draw out value coming from the information can create a big influence. Conclusion: It is clearly suggested chain administrators pay attention to these 2 trends, since better usage of BDA can ensure they hold abreast with innovations modifications, which could help improve company competitiveness. Keywords: Supply chain management; Big data analytics; FMCG; Developing countries