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

Alfred Theorin, Kristofer Bengtsson, Julien Provost et al.

Shreyas Kasture, Gurpreet Kour Khalsa, Sudhanshu Maurya et al.

This research focused on real-time decision making in uncertain and volatile business context by creating a cloud-based, big data analytics framework supported by artificial intelligence. To address these issues, this study intended to incorporate stream processing techniques, distributed machine learning algorithms, and cloud service architectures. An architecture based on microservices was adopted with an emphasis on using containers and Kubernetes for supervision. It included Apache Kafka for streaming ingestion, Apache Flink for stream processing, and Apache Spark for batch analysis. Both ensemble methods and deep learning algorithms were used with TensorFlow on Kubernetes. The architecture showed twice the performance gains over traditional approaches in terms of data processing and analysis, while the data ingestion rates were higher by a factor of ten. Machine learning models achieved 94% accuracy in different aspects of prediction, using dynamic learning models to update their models based on current trends in the flow of data. The research was useful for extending the scientific community’s theory in multiple fields, such as integrating deep learning with distributed stream processing, adopting closed-loop control systems for adaptive analytics, and microservices for big data platforms. The study also gave some consideration to ethical issues around decision-making through the AI utilization of explainable AI methods. The research focused on the high level of security addressing techniques, organizing end-to-end encryption, and creating service key management for interaction with hardware security modules. A chaos engineering framework was also adopted into the system to test the overall stability of the system under different failure conditions in order to further strengthen the system. The discoveries indicated that the proposed model effectively improved decision-making in volatile surroundings, which provided ideas for future studies in real-time large dataset applications.

Aleksandr Karnauhov, Yuriy Kozhubaev, A. Ilin et al.

A variant of the system of end-to-end intelligent management of business processes of the oil and gas industry of Russia based on an integrated digital platform is proposed, priority variants of implementation of digital technologies and investments in management and technological business processes of the oil and gas complex (OGC) are defined: Big Data (big data), artificial intelligence (AI) and the Internet of things (IoT technologies). Keywords: digital technologies, platform, big data, intelligent systems, artificial intelligence.

N. Noor, Farah Arzu, Shah E Yar Qadeem et al.

The exponential growth of financial, operational, and market data has transformed the landscape of enterprise financial management, exposing critical limitations in conventional forecasting, budgeting, and risk assessment practices. Traditional financial systems characterized by fragmented data silos, rule-based decision routines, and delayed reporting cycles are no longer capable of supporting the speed, complexity, and granularity required in dynamic business environments. To address these challenges, this study proposes a big data–driven optimization framework that leverages large-scale data integration, advanced analytics, and intelligent optimization models to strengthen predictive decision-making, enhance enterprise-wide risk control, and achieve high computational efficiency. The proposed framework is structured around four interconnected layers. First, a multi-source data acquisition and integration layer consolidates heterogeneous data streams including ERP financial records, transactional logs, market indicators, supply chain data, customer interactions, regulatory updates, and alternative external datasets. This unified data repository provides comprehensive contextual visibility required for both micro-level financial insights and macro-level strategic planning. Second, a scalable big data infrastructure layer is designed using distributed storage, parallel computing, and streaming architectures capable of processing high-volume and high-velocity financial workloads with minimal latency. Third, an analytics and optimization intelligence layer integrates machine learning models for cash flow forecasting, anomaly detection, credit risk scoring, and cost prediction, combined with optimization algorithms for liquidity allocation, capital structure management, investment decisions, and real-time risk mitigation. Finally, a decision-support and visualization layer translates analytical outputs into actionable insights through interactive dashboards, predictive alerts, scenario analyses, and explainable AI modules tailored for CFOs, controllers, auditors, and risk management units. Through the fusion of predictive analytics, real-time data orchestration, and algorithmic optimization, the framework enables enterprises to transition from descriptive financial reporting toward proactive, predictive, and prescriptive financial management. This transition improves forecasting accuracy, strengthens resilience against market shocks, identifies early risk signals, and supports data-driven financial governance. The study further highlights the computational efficiency benefits achieved through distributed processing, workload balancing, and model optimization, significantly reducing processing time and enabling real-time decision flows. Overall, this research contributes a robust conceptual foundation and a scalable architectural pathway for deploying intelligent, big data–powered financial management systems. The framework offers a transformative direction for organizations seeking to enhance financial agility, strengthen risk governance, and achieve operational excellence in increasingly volatile and data-intensive business ecosystems

Elahe Mansouri, A. H. Kashan, Jalil Heidary Dahooie et al.

In recent years, the shortage of forest resources and the increase in demand for wooden products have faced severe challenges in the wood and paper industry. According to the surveys conducted, the branches and waste from palm pruning can be used for conversion industries, including the production of chipboard and medium-density fiberboard (MDF). The surface of Iran has significant coverage of palm trees, and currently, a large amount of waste from these trees is thrown away and burned. Therefore, the topic chosen in this research is to determine the location for building the wooden products production factory, aiming at optimal use of palm waste and helping to compensate for the lack of wooden production in the country. First of all, suitable criteria for building a wooden products factory are determined through sources and experts“ opinions. Then, they are prioritised and weighted using a questionnaire based on the BWM method. In the next step, ArcGIS software is used to apply the criteria on the level under investigation. Decision options are ranked using TOPSIS, ARAS, COPRAS, WASPAS, MULTIMOORA, VIKOR, SAW and CODAS decision-making methods. Then the obtained results are collected using the CRITIC method, and the best construction places are determined. When different decision-making methods are combined, the accuracy and strength of the obtained results also increase.

Yanyang Liu, Huizhen Tang, Ou Pu et al.

This study, based on an electricity big data platform, conducts optimization analysis and dynamic prediction of carbon flow deconstruction and sectoral carbon emissions in Guangxi. By integrating multi-source data, a carbon-electricity coupling analysis system encompassing 600 million data points was constructed, highlighting the significant correlation between electricity consumption and carbon emissions in energy-intensive industries (e.g., metal smelting and chemical manufacturing), with correlation coefficients reaching as high as 0.86 in some sectors. Using the ARIMA model and trend extrapolation methods, the study predicted the trends in the industry's carbon-electricity transmission coefficients for 2021–2023. Results indicate an upward trend in high-emission industries such as non-metallic mineral manufacturing, while primary industries and certain manufacturing sectors show a decline. High-frequency monitoring results reveal seasonal fluctuation patterns in carbon emissions, with daily emissions for some large industrial enterprises reaching 20,398.95 tons, peaking in March and August. The findings provide scientific support for precise carbon monitoring and dynamic management in Guangxi, offering a robust data foundation and methodological framework for formulating effective carbon reduction strategies.

Jianfeng Zhang, Yingying Li, Yunhao Liu et al.

Currently, the research field of Natural Language Generated SQL (NL2SQL) mainly focuses on generic datasets, aiming to build models that can parse natural language queries and automatically generate SQL statements. However, this generic exploration often ignores the complexity and idiosyncrasies of intra-enterprise data, such as industry-specific terminology, data structure differences, and security compliance requirements, resulting in the fact that the existing NL2SQL technology can only cover the more basic query requirements in practical applications, and is difficult to be deeply integrated into the business scenarios of enterprises. This paper aims to fill this research gap by focusing on the customized application of NL2SQL technology to specific internal enterprise environments, and adopting LLM+RAG+Reminiscence Engineering+Intelligent Body Feedback to design and implement a set of NL2SQL system that can be applied to internal enterprises. After practical project verification, the system’s ability to generate SQL in an enterprise-oriented financial data environment can be improved from 54% to about 70%, and the accuracy of multi-round dialog can be further improved. This system enables the seamless connection between natural language and enterprise database, which provides strong support for enterprise digital transformation.

I. A. Ajah, H. F. Nweke

Big data and business analytics are trends that are positively impacting the business world. Past researches show that data generated in the modern world is huge and growing exponentially. These include structured and unstructured data that flood organizations daily. Unstructured data constitute the majority of the world’s digital data and these include text files, web, and social media posts, emails, images, audio, movies, etc. The unstructured data cannot be managed in the traditional relational database management system (RDBMS). Therefore, data proliferation requires a rethinking of techniques for capturing, storing, and processing the data. This is the role big data has come to play. This paper, therefore, is aimed at increasing the attention of organizations and researchers to various applications and benefits of big data technology. The paper reviews and discusses, the recent trends, opportunities and pitfalls of big data and how it has enabled organizations to create successful business strategies and remain competitive, based on available literature. Furthermore, the review presents the various applications of big data and business analytics, data sources generated in these applications and their key characteristics. Finally, the review not only outlines the challenges for successful implementation of big data projects but also highlights the current open research directions of big data analytics that require further consideration. The reviewed areas of big data suggest that good management and manipulation of the large data sets using the techniques and tools of big data can deliver actionable insights that create business values.

S. Kahveci, Bugra Alkan, Mus'ab H. Ahmad et al.

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and cost efficient big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an electric vehicle battery module assembly automation system designed by the Automation Systems Group at the University of Warwick, the UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments.

Jiabing Xu, Jiarui Liu, Tianen Yao et al.

This study aims to transform the existing telecom operators from traditional Internet operators to digital-driven services, and improve the overall competitiveness of telecom enterprises. Data mining is applied to telecom user classification to process the existing telecom user data through data integration, cleaning, standardization, and transformation. Although the existing algorithms ensure the accuracy of the algorithm on the telecom user analysis platform under big data, they do not solve the limitations of single machine computing and cannot effectively improve the training efficiency of the model. To solve this problem, this article establishes a telecom customer churn prediction model with the help of backpropagation neural network (BPNN) algorithm, and deploys the MapReduce programming framework on Hadoop platform. Using the data of a telecom company, this article analyzes the loss of telecom customers in the big data environment. The research shows that the accuracy of telecom customer churn prediction model in BPNN is 82.12%. After deploying large data sets, the learning and training time of the model is greatly shortened. When the number of nodes is 8, the acceleration ratio of the model remains at 60 seconds. Under big data, the telecom user analysis platform not only ensures the accuracy of the algorithm, but also solves the limitations of single machine computing and effectively improves the training efficiency of the model. Compared with that of the existing research, the accuracy of the model is improved by 25.36%, and the running time is shortened by about twice. This business model based on BPNN algorithm has obvious advantages in processing more data sets, and has great reference value for the digital-driven business model transformation of the telecommunications industry.

M. Riesener, M. Kuhn, S. Schümmelfeder et al.

—Advancing digitalization and its impact on business models leads to various streams in research that emerge in parallel and provide different explanatory and systematization approaches of digital business models. The large number of contributions induces fragmented concepts and unclear terminologies. In particular, most existing conceptualizations neglect the examination of the business model concept taking into consideration the advancing digitalization, especially in the manufacturing industry, or only consider domain-specific aspects. Therefore, there is a lack of systematic approaches to structure the research area. This unclear understanding of the terminology leads to challenges in practice. In addition, in most cases, companies have only fuzzy ideas of what characterizes business model innovation in the digital age and which element form a digital business models. Thus, the following contribution is devoted to a systemization of the questioned research field in order to enable further research initiatives and to support companies to develop digital business models.

Abdolhossein Jojam, Saeid Abdolmanafi, Abolfazl Baghbani- Arani

پژوهش حاضر با هدف بررسی تاثیر جهت‌گیری کارآفرینی سبز بر دیدگاه مصرف کننده و بهبود مزیت رقابتی پایدار انجام شد. جامعه آماری مورد مطالعه تعداد 150 شرکت تعاونی تولیدی کشاورزی استان خوزستان بود. از بین این شرکت‌ها، بر اساس روش نمونه‌گیری با استفاده از جدول مورگان، 111 شرکت به عنوان نمونه آماری انتخاب شدند. داده‌های تحقیق با استفاده از پرسشنامه شامل 30 سوال جمع‌آوری شد. به منظور تشریح رابطه بین جهت‌گیری کارآفرینی سبز، مزیت رقابتی پایدار و دیدگاه مصرف‌کننده از مدل معادلات ساختاری استفاده شد. داده‌های به دست آمده از پرسشنامه با استفاده از روش حداقل مربعات جزیی و نرم‌افزارSmart-PLS3 تحلیل شدند. پایایی پرسشنامه با استفاده از روش آلفای کرونباخ و ضریب پایایی ترکیبی اندازه‌گیری شد. مقدار هر دو ضریب برای سازه‌های تحقیق بیشتر از 9/0 بود. نتایج نشان داد جهت‌گیری کارآفرینی سبز در شرکت‌های تعاونی تولیدی کشاورزی استان خوزستان، بر دیدگاه مصرف کننده اثر مثبت و معناداری دارد. نتایج تحقیق، اثر جهت‌گیری کارآفرینی سبز بر دیدگاه مصرف کننده را با ضریب مسیر 707/0 و ضریب معناداری 43/17و نیز بر مزیت رقابتی پایدار را با ضریب مسیر 823/0 و ضریب معناداری 893/13تایید کرد. همچنین نتایج حاکی از تاثیر مثبت و معنادار دیدگاه مصرف‌کننده بر مزیت رقابتی پایدار با تاثیرپذیری از جهت‌گیری کارآفرینی سبز با ضریب معناداری 086/2 بود. با توجه به یافته‌های این پژوهش، شرکت‌های تعاونی کشاورزی می‌توانند با جهت‌گیری کارآفرینی و نوآوری سبز، علاوه بر بهبود عملکرد اقتصادی و اجتماعی شرکت، به کاهش پیامدهای مخرب زیست محیطی کمک کنند.

P. O'Donovan, K. Leahy, K. Bruton et al.

AbstractThe term smart manufacturing refers to a future-state of manufacturing, where the real-time transmission and analysis of data from across the factory creates manufacturing intelligence, which can be used to have a positive impact across all aspects of operations. In recent years, many initiatives and groups have been formed to advance smart manufacturing, with the most prominent being the Smart Manufacturing Leadership Coalition (SMLC), Industry 4.0, and the Industrial Internet Consortium. These initiatives comprise industry, academic and government partners, and contribute to the development of strategic policies, guidelines, and roadmaps relating to smart manufacturing adoption. In turn, many of these recommendations may be implemented using data-centric technologies, such as Big Data, Machine Learning, Simulation, Internet of Things and Cyber Physical Systems, to realise smart operations in the factory. Given the importance of machine uptime and availability in smart manufacturing, this research centres on the application of data-driven analytics to industrial equipment maintenance. The main contributions of this research are a set of data and system requirements for implementing equipment maintenance applications in industrial environments, and an information system model that provides a scalable and fault tolerant big data pipeline for integrating, processing and analysing industrial equipment data. These contributions are considered in the context of highly regulated large-scale manufacturing environments, where legacy (e.g. automation controllers) and emerging instrumentation (e.g. internet-aware smart sensors) must be supported to facilitate initial smart manufacturing efforts.

Tomomi Yamazaki, T. Miura, I. Sakata

Big data analysis is increasingly being used as a decision-making support tool for science and technology policy and technology management through the understanding of the trends and structure of innovation activities and the prediction of their changes. Since diversity and convergence are generally regarded as opportunities for groundbreaking innovation, analysis of academia-industry convergence, a type of fusion among entities, using big data analysis is expected to provide useful suggestions for policy formation and technology management. Nevertheless, little quantitative analysis has been conducted to date. With the development of academia-industry collaboration in a broad sense, it has become possible to observe a certain number of cases where researchers themselves bridge academia and industry with two faces, one in academia and the other in business. In this study, we focus on academic research conducted through academia-industry collaboration and researchers who conduct academia-industry collaboration themselves ("ambidextrous researchers") and clarify the magnitude and changes of their academic impact by analyzing article citation data. Specifically, using Scopus as a dataset, we extracted papers co-authored by academia-industry and "ambidextrous researchers" using our definition and analyzed their publication and citation numbers comprehensively and over time. The results showed, first, that the average number of citations of the academia-industry co-authored papers is higher than the overall average, indicating that academia-industry collaboration enhances the quality of academic research. On the other hand, it was found that, at least up to now, academia-industry collaboration is not necessarily a suitable environment for producing home-run papers, as we cannot find such papers at the top of the citation count. Second, for papers whose authors include "ambidextrous researchers" who belong to both academic institutions and companies, we found that the number of publications and the academic attention of papers whose authors include "ambidextrous researchers" is increasing over time, especially in the field of AI (artificial intelligence), where we can safely say that the attention is structurally higher. Third, as a system in which "ambidextrous researchers" act as intermediaries for continuous technology transfer to companies has been established, it was confirmed that companies, especially platform companies, have come to play a major role in advanced AI research. This is because research resources such as computational resources and data possessed by platforms are effective in further developing academic research and so are quite attractive for researchers at universities and other institutions, which can be an incentive for them to be "ambidextrous researchers."

Paulo Figueiras, Luís Lourenço, R. Costa et al.

Industry 4.0 is expanding to the entire manufacturing fabric. Such evolution entails the complete digitalization of industrial processes and products, through the deployment of cyber-physical systems and automation in the shop floors, logistics and business processes. Such digitalization is achieved by extracting value, in the form of insights, decision-supporting information and detailed virtual representations of the physical industrial processes. One prominent example of such digitalization is the advent of Digital Twins, accurate virtual representations of industrial processes and products in the physical world. This work presents the development and deployment phases and procedures of a Big Data-supported Digital Twin for logistics processes in the automotive sector. The Digital Twin enables planning and optimization of logistics processes as, for instance, the optimization of stock and inventory, and planning the arrival of new parts, in order for the production to be as efficient as possible, without the risk of stopping the shop floor, ultimately enabling savings in both idle stored parts and in supplier orders' reductions.

zahra athari

Universities and higher education centers are responsible for the missions of training the expert force, and expanding of technology, innovation and creativity. Student cooperatives can play a significant role in developing the skills and abilities necessary to prepare students to enter in the labor market. Therefore, this research devoted to identifying characteristics of successful student cooperative in College of Agriculture and Natural Resources at Razi University, through grounded theory methodology. The population for this study consisted of students majoring in agriculture who are members of the Pishgaman Student Cooperative in College of Agriculture and Natural Resources at Razi University. Interviewing was the main tool for gathering data and using purposeful sampling method and snowball technique. The results showed the characteristics such as a "Participation and Cooperation", "Necessary Facilities and Infrastructure", "Appropriate Economic Conditions", "Entrepreneurship and Creativity", "Implementation and Development of Sustainable Agriculture", "Successful Management" and "Appropriate Marketing" were the most important characteristics of successful student cooperative.

Rodrigo Contreras

Nicolás Schubert

Milad Rezaee, mojtaba sedaghatifard, Habib karimian

Since the role of elites in correcting, controlling, and cleaning the specific macro, in this way used as a guiding manager of the management component (system, environmental-cultural, special-exploitation, psycho-identification) in the implementation and failure of deputies in management and management, managerial features. A statistical population referring to the CEO of Cooperative, Labor, and Organizational Welfare who can select 160 people from other users using different sampling. You can use SPSS and Lisrel financial services licenses to confirm or deny the assumptions. Using descriptive and inferential statistics, the judge's research and interpretation of all four environmental-legal, psychological, systemic, and consulting factors, respectively, have a special impact on services and the failure of elites in managing and using political and social systems. Finally, suggestions for future research provided.

S. Liao, Yi-Shan Tasi

Purpose In the retailing industry, database is the time and place where a retail transaction is completed. E-business processes are increasingly adopting databases that can obtain in-depth customers and sales knowledge with the big data analysis. The specific big data analysis on a database system allows a retailer designing and implementing business process management (BPM) to maximize profits, minimize costs and satisfy customers on a business model. Thus, the research of big data analysis on the BPM in the retailing is a critical issue. The paper aims to discuss this issue. Design/methodology/approach This paper develops a database, ER model, and uses cluster analysis, C&R tree and the a priori algorithm as approaches to illustrate big data analysis/data mining results for generating business intelligence and process management, which then obtain customer knowledge from the case firm’s database system. Findings Big data analysis/data mining results such as customer profiles, product/brand display classifications and product/brand sales associations can be used to propose alternatives to the case firm for store layout and bundling sales business process and management development. Originality/value This research paper is an example to develop the BPM of database model and big data/data mining based on insights from big data analysis applications for store layout and bundling sales in the retailing industry.