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

Roger Strange, A. Zucchella

L. Bibby, Benjamin Dehe

Abstract Firms do not currently fully appreciate the complex characteristics of Industry 4.0 and as a result are uncertain about what it represents for them. In this study, an assessment model is developed to measure the level of implementation of Industry 4.0 technologies, around three dimensions: ‘Factory of the Future’, ‘People and Culture’, and ‘Strategy’. The ‘Factory of the Future’ is the main dimension and is composed of eight attributes: Additive Manufacturing, Cloud, Manufacturing Execution System, Internet of Things and Cyber Physical Systems, Big Data, Sensors, e-Value Chains, and Autonomous Robots. The study uses a defence manufacturing firm to develop, test and validate the model and report on 12 partners. We concluded that the focal firm has an Industry 4.0 maturity level of 59.35, above the sector average of 55.58. This research contributes by empirically developing a model and providing an analysis of major firms in the Defence supply network.

Yuqian Lu, X. Xu

Abstract Making manufacturing as on-demand cloud services is a transformative paradigm to achieve the required business flexibility in the context of Industry 4.0 via enabling rapid configuration of loosely-connected manufacturing devices to develop highly customized products. The research in this paper aimed to fill the gap that there is a lack of a feasible solution for cloud-based manufacturing equipment that can provide on-demand manufacturing services accessible via the Internet. The technical challenges in developing cloud-based manufacturing equipment and the enabling technologies are discussed. A generic system architecture for cloud-based manufacturing equipment based on cyber-physical production systems and big data analytics is proposed, allowing manufacturing equipment to be connected to the cloud and made available for the provision of on-demand manufacturing services. An industry implementation in a world-leading machinery solution provider confirms that the proposed system architecture for cloud-based manufacturing equipment can successfully enable on-demand manufacturing services provisioned via the Internet and can be extended to businesses that endeavor to transform legacy production systems into cloud-based cyber-physical production systems.

Wenjin Yu, T. Dillon, Fahed Mostafa et al.

Artificial intelligence, big data, machine learning, cloud computing, and Internet of Things (IoT) are terms which have driven the fourth industrial revolution. The digital revolution has transformed the manufacturing industry into smart manufacturing through the development of intelligent systems. In this paper, a big data ecosystem is presented for the implementation of fault detection and diagnosis in predictive maintenance with real industrial big data gathered directly from large-scale global manufacturing plants, aiming to provide a complete architecture which could be used in industrial IoT-based smart manufacturing in an industrial 4.0 system. The proposed architecture overcomes multiple challenges including big data ingestion, integration, transformation, storage, analytics, and visualization in a real-time environment using various technologies such as the data lake, NoSQL database, Apache Spark, Apache Drill, Apache Hive, OPC Collector, and other techniques. Transformation protocols, authentication, and data encryption methods are also utilized to address data and network security issues. A MapReduce-based distributed PCA model is designed for fault detection and diagnosis. In a large-scale manufacturing system, not all kinds of failure data are accessible, and the absence of labels precludes all the supervised methods in the predictive phase. Furthermore, the proposed framework takes advantage of some of the characteristics of PCA such as its ease of implementation on Spark, its simple algorithmic structure, and its real-time processing ability. All these elements are essential for smart manufacturing in the evolution to Industry 4.0. The proposed detection system has been implemented into the real-time industrial production system in a cooperated company, running for several years, and the results successfully provide an alarm warning several days before the fault happens. A test case involving several outages in 2014 is reported and analyzed in detail during the experiment section.

Ishaan Kaushal, Amaresh Chakrabarti

This paper presents the Unified Smart Factory Model (USFM), a comprehensive framework designed to translate high-level sustainability goals into measurable factory-level indicators with a systematic information map of manufacturing activities. The manufacturing activities were modelled as set of manufacturing, assembly and auxiliary processes using Object Process Methodology, a Model Based Systems Engineering (MBSE) language. USFM integrates Manufacturing Process and System, Data Process, and Key Performance Indicator (KPI) Selection and Assessment in a single framework. Through a detailed case study of Printed Circuit Board (PCB) assembly factory, the paper demonstrates how environmental sustainability KPIs can be selected, modelled, and mapped to the necessary data, highlighting energy consumption and environmental impact metrics. The model's systematic approach can reduce redundancy, minimize the risk of missing critical information, and enhance data collection. The paper concluded that the USFM bridges the gap between sustainability goals and practical implementation, providing significant benefits for industries specifically SMEs aiming to achieve sustainability targets.

E. C. Overes, F. M. Santoro

Business Process Visualisations (BPVs) have become indispensable tools for organisations seeking to enhance their operational efficiency, decision-making capabilities, and overall performance. The burgeoning interest in process modeling and tool development, coupled with the rise of data visualisation field, underscores the significant role of visual tools in leveraging human cognition. Unlike traditional models, data visualisation approaches graphics from a novel angle, emphasising the potency of visual representations. This review aims to integrate the domains of BPV and data visualisation to assess their combined influence on organisational effectiveness comprehensively. Through a meticulous analysis of existing literature, this study aims to amalgamate insights on BPVs impact from a data visualisation standpoint, advocating for a design philosophy that prioritises user engagement to bolster organisational outcomes. Additionally, our systematic review has unveiled promising avenues for future research, identifying underexplored variables that influence the efficacy of BPVs, thereby charting a path for forthcoming scholarly inquiries.

Di Liao, Ruijia Liang, Ziyi Ye

With the deepening of digital transformation, business process optimisation has become the key to improve the competitiveness of enterprises. This study constructs a business process optimisation model integrating artificial intelligence and big data to achieve intelligent management of the whole life cycle of processes. The model adopts a three-layer architecture incorporating data processing, AI algorithms, and business logic to enable real-time process monitoring and optimization. Through distributed computing and deep learning techniques, the system can handle complex business scenarios while maintaining high performance and reliability. Experimental validation across multiple enterprise scenarios shows that the model shortens process processing time by 42%, improves resource utilisation by 28%, and reduces operating costs by 35%. The system maintained 99.9% availability under high concurrent loads. The research results have important theoretical and practical value for promoting the digital transformation of enterprises, and provide new ideas for improving the operational efficiency of enterprises.

Zhipeng Ma, Bo Nørregaard Jørgensen, Michelle Levesque et al.

Digitalization is challenging in heavy industrial sectors, and many pi-lot projects facing difficulties to be replicated and scaled. Case studies are strong pedagogical vehicles for learning and sharing experience & knowledge, but rarely available in the literature. Therefore, this paper conducts a survey to gather a diverse set of nine industry cases, which are subsequently subjected to analysis using the business model canvas (BMC). The cases are summarized and compared based on nine BMC components, and a Value of Business Model (VBM) evaluation index is proposed to assess the business potential of industrial digital solutions. The results show that the main partners are industry stakeholders, IT companies and academic institutes. Their key activities for digital solutions include big-data analysis, machine learning algorithms, digital twins, and internet of things developments. The value propositions of most cases are improving energy efficiency and enabling energy flexibility. Moreover, the technology readiness levels of six industrial digital solutions are under level 7, indicating that they need further validation in real-world environments. Building upon these insights, this paper proposes six recommendations for future industrial digital solution development: fostering cross-sector collaboration, prioritizing comprehensive testing and validation, extending value propositions, enhancing product adaptability, providing user-friendly platforms, and adopting transparent recommendations.

Cielo Gonzalez Moyano, Finn Klessascheck, Saimir Bala et al.

Business Process Management (BPM) is mostly centered around finding technical solutions. Nudging is an approach from psychology and behavioral economics to guide people's behavior. In this paper, we show how nudging can be integrated into the different phases of the BPM lifecycle. Further, we outline how nudging can be an alternative strategy for more sustainable business processes. We show how the integration of nudging offers significant opportunities for process mining and business process management in general to be more human-centric. We also discuss challenges that come with the adoption of nudging.

Yang Su, Jiuxue Tian, Jiadan Wei

Based on the Zhengbao Financial Cloud Sharing Center, the Smart Finance Industry College integrates enterprises into the educational setting. Aligned with the requirements for talent cultivation in big data and financial management programs, it establishes a three-tier job system comprising fiscal and tax accounting, business-finance analysis, and strategic finance. Through systematic training and practical operations, it cultivates professional competencies for financial positions. Leading enterprises, such as Beijing Dongda Zhengbao, Xiamen Wangzhongwang, and Kingdee Jingyi, delegate industry mentors with extensive real-world experience to implement dual-subject teaching, bringing authentic enterprise business and financial scenarios into classrooms. Students thus engage in “real accounts and real operations” on campus, gaining solid professional skills and comprehensive competencies. Additionally, the Industry College promotes the development of “dual-qualification” teacher innovation teams, constructs course teaching resources, and pilots both the 1+X certificate system and a Chinese-style apprenticeship program. Practice shows that in-depth integration of industry and education significantly enhances students’ professional practice skills and employability, thereby promoting the cultivation of high-quality financial professionals.

Zhengxiong Mao, Fu Bao, Yuan Tian et al.

In view of the increasing data volume and the increasingly difficult data analysis in the power industry, an intelligent and efficient analysis and mining framework for power big data is designed to quickly obtain valuable information. Analyze the overall framework of the power big data center, mainly including the service layer, verification layer, data source layer, and feature analysis layer. In addition, through analyzing the process of data mining, it is found that the business needs to be strengthened And realize expansion. The framework design of power big data intelligent analysis and mining technology mainly includes power market demand, customer analysis, high-performance data analysis, service system, data security governance and other modules. Through the analysis of an example of intelligent power big data mining, the analysis results show that the intelligent power data mining has good analysis effect and high mining accuracy

Shu Xiong, Ouyang Xue, W. Xiong

The continuous development of GPS-enabled equipments and location-based services has brought new challenges to the storage and management of trajectory big data. Traditional database system such as PostgreSQL/PostGIS to cope with this demand. Meanwhile, distributed trajectory management systems based on NoSQL have attracted increasing attention from both industry and acdemia. However, to the best of our knowledge, there are not many reseach discussing the comparision of these systems. This paper aiming at filling this gap by comparing the performance of the representitive relational database component PostGIS and the distributed NoSQL data management system GeoMesa in various range query and multi concurrent access scenarios through experimental analysis. The experimental results show that GeoMesa outperforms the relational spatial database component PostGIS in the large number of access results and multi-user access scenarios.

Djabir Abdeldjalil Chekired, L. Khoukhi, H. Mouftah

Industry 4.0 or industrial Internet of things (IIoT) has become one of the most talked-about industrial business concepts in recent years. Thus, to efficiently integrate Internet of things technology into industry, the collected and sensed data from IIoT need to be scheduled in real-time constraints, especially for big factories. To this end, we propose in this paper a hierarchical fog servers’ deployment at the network service layer across different tiers. Using probabilistic analysis models, we prove the efficiency of the proposed hierarchical fog computing compared with the flat architecture. In this paper, IIoT data and requests are divided into both high priority and low priority requests; the high priority requests are urgent/emergency demands that need to be scheduled rapidly. Therefore, we use two-priority queuing model in order to schedule and analyze IIoT data. Finally, we further introduce a workload assignment algorithm to offload peak loads over higher tiers of the fog hierarchy. Using realistic industrial data from Bosch group, the benefits of the proposed architecture compared to the conventional flat design are proved using various performance metrics and through extensive simulations.

M. Poltavtseva, M. Kalinin

Big data management systems are in demand today in practically all industries, and they are also the foundation for artificial intelligence training. The use of heterogeneous poly-stores in big data systems has led to the fact that tools within the same system have different data granularity and access control models. Harmonization of such components by the security administrator and implementation of common access-policy is now done manually. This leads to an increasing number of customization vulnerabilities, which in turn serves as a frequent cause of data leaks. Analysis of works in the area of automation and analysis of access control in big data systems shows the lack of automation solutions for poly-store based systems. This paper poses the problem of automating the analysis of access control analysis in big data management systems. The authors formulate the main contradiction, which consists, on the one hand, in the requirement of scalability and flexibility of access control, and on the other hand – in the growth of the burden on the security administrator, aggravated by the use of different data models and access control in the system components. To solve this problem, we propose a new automated method for analyzing security policies based on a graph model of data processing, which reduces the number of possible vulnerabilities resulting from incorrect administration of big data systems. The proposed method uses the data life cycle model of the system, current settings and the desired security policy. The use of two-pass analysis (from data sources to recipients and back) allows to solve two tasks: analyzing the access control system for possible vulnerabilities and checking compliance with correctness of business logic. The paper gives an example of analysis of security policies of the big data management system using the developed software prototype and analyzes the obtained results.

Arthur Paul Christenson

This particular analysis explores the connection between firms' application of data analytics (specifically it's attributes) together with the revolutionary functionality of company. The other goal is assessing whether big amount of information is always better to get business innovation. The study collected information via questionnaire survey from control staffs of 250 businesses in both developed and developing economies. Statistical tools like Multiple regression methods and t-test were used to analyse the information. The study found suggestive evidence demonstrating that data analytics is a relevant determinant of a firm getting innovator and bring innovative services and products on the industry. The study even discovered that big volume of information isn't always better info to drive innovation. The results imply that firms are required to use big data analytics to remain imaginative and also have a competitive advantage. Unlike previous studies which approached large details as whole, this particular study addresses different ingredients of big data like variety, velocity, volume, and the individual impacts of theirs on innovation of organizations across the evolved economies.

Kuong Feng

The objective of this research paper is to analyze how Indian commercial banks handle big data, which refers to an extremely large data set that requires analysis, management, and validation through traditional data management tools. Purpose: Banks are one of the financial services industries that deal with a vast amount of transaction data, which must be managed, scrutinized, and utilized for the benefit of both the bank and its customers. This study will examine the factors that have a greater impact on banks when handling big data and how analytics can create value for the business.Research methods: Secondary data was collected from various sources such as articles, journals, and websites. The study focuses on big data management, risk management, fraud detection, customer segmentation, and the business value of banking industries. A conceptual framework has been developed to highlight the factors that have a higher impact on big data management in the banking industry. Findings: The findings indicate that big data analytics has a significant impact on the business value of banks, and the factors influencing business value have been identified. Conclusion: By utilizing big data and embracing emerging technologies, companies can enhance the worth of their organization.

Praveen Kantha, V. Sinha, Durgesh Srivastava et al.

Access to the internet can significantly enhance the capabilities and opportunities in the field of data mining. It provides a vast source of data, tools, and resources that can be leveraged to improve the data mining process. The Internet offers access to a wide range of data sources, including social media, websites, online databases, and more. The effectiveness of the data mining process depends on the ability to extract from a large dataset meaningful patterns and models. The goal of data mining is to uncover previously unknown information inside large databases. However, the information in the current datasets is not always unified and clean. Despite extensive work on the part of developers and fine-tuners, data mining models remain highly dependent on the quality of the data they are fed. The focus of this research is on the steps taken before feeding data into a machine-learning system. Any machine learning algorithm's major success is predicated on the caliber of the input data it uses. Even though many aspects influence how well Machine Learning (ML) performs a job, the representation and quality of the instance data remain key components in the algorithm's overall effectiveness. The process of knowledge discovery becomes increasingly challenging during the training phase when there is an abundance of irrelevant and duplicated information, along with noisy and unreliable data. Data preparation and filtering steps in ML problems are well known to cost a substantial amount of processing time. Data preprocessing produces the final training set. Access to data, secure data handling, a robust network infrastructure, and the support of the IT industry are all critical components of successful data mining endeavors. Hence, this article offers strategies for optimizing data collection performance at every stage of data preprocessing.

Mojtaba Eshghie, Wolfgang Ahrendt, Cyrille Artho et al.

Logical flaws in smart contracts are often exploited, leading to significant financial losses. Our tool, HighGuard, detects transactions that violate business logic specifications of smart contracts. HighGuard employs dynamic condition response (DCR) graph models as formal specifications to verify contract execution against these models. It is capable of operating in a cross-chain environment for detecting business logic flaws across different blockchain platforms. We demonstrate HighGuard's effectiveness in identifying deviations from specified behaviors in smart contracts without requiring code instrumentation or incurring additional gas costs. By using precise specifications in the monitor, HighGuard achieves detection without false positives. Our evaluation, involving 54 exploits, confirms HighGuard's effectiveness in detecting business logic vulnerabilities. Our open-source implementation of HighGuard and a screencast of its usage are available at: https://github.com/mojtaba-eshghie/HighGuard https://www.youtube.com/watch?v=sZYVV-slDaY

Hanjia Lyu, Arsal Imtiaz, Yufei Zhao et al.

Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups - using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.

J. K., A. R.

Developments in information technology and its prevalent growth in several areas of business, engineering, medical, and scientific studies are resulting in information as well as data explosion. Knowledge discovery and decision making from such rapidly growing voluminous data are a challenging task in terms of data organization and processing, which is an emerging trend known as big data computing. Big data has gained much attention from the academia and the IT industry. A new paradigm that combines large-scale compute, new data-intensive techniques, and mathematical models to build data analytics. Thus, this chapter discusses the background of big data. It also discusses the various application of big data in detail. The various related work and the future direction would be addressed in this chapter.