CAPABILITIES FOR DATA ANALYTICS IN INDUSTRIAL INTERNET OF THINGS (IIOT)

2023 | European Conference On Information Systems | Citations: 0

Authors: Bagheri, Samaneh; Dijkstra, Jesse

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Topics: analytical information system internet of things mobile system organizational value cloud computing

Methods: qualitative interview machine learning literature study personal interview computational algorithm

The Core Industry Manufacturing Process of Electronics Assembly Based on Smart Manufacturing

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Chen, Rongli; Chen, Xiaozhong; Wang, Lei; Li, Jianxin

Abstract: This research takes a case study approach to show the development of a diverse a ... Expand

Abstract: This research takes a case study approach to show the development of a diverse adoption and product strategy distinct from the core manufacturing industry process. It explains the development status in all aspects of smart manufacturing, via the example of ceramic circuit board manufacturing and electronic assembly, and outlines future smart manufacturing plans and processes. The research proposed two experiments using artificial intelligence and deep learning to demonstrate the problems and solutions regarding methods in manufacturing and factory facilities, respectively. In the first experiment, a Bayesian network inference is used to find the cause of the problem of metal residues between electronic circuits through key process and quality correlations. In the second experiment, a convolutional neural network is used to identify false defects that were overinspected during automatic optical inspection. This improves the manufacturing process by enhancing the yield rate and reducing cost. The contributions of the study built in circuit board production. Smart manufacturing, with the application of a Bayesian network to an Internet of Things setup, has addressed the problem of residue and redundant conductors on the edge of the ceramic circuit board pattern, and has improved and prevented leakage and high-frequency interference. The convolutional neural network and deep learning were used to improve the accuracy of the automatic optical inspection system, reduce the current manual review ratio, save labor costs, and provide defect classification as a reference for preprocess improvement. Collapse

Topics: artificial intelligence smart manufacturing production management Python internet of things

Methods: experiment deep learning case study convolutional neural network Bayesian network

Introduction to the Special Issue on Smart Systems for Industry 4.0 and IoT

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Chen, Mu-Yen; Thuraisingham, Bhavani; Egrioglu, Erol; Rubio, Jose De Jesus

Abstract: INTRODUCTIONThe development of big data applications is driving the dramatic gr ... Expand

Abstract: INTRODUCTIONThe development of big data applications is driving the dramatic growth of hybrid data, often in the form of complex sets of cross-media content including text, images, videos, audios, and time series. Tremendous volumes of these heterogeneous data are derived from multiple IoT sources and present new challenges for the design, development, and implementation of effective information systems and decision support frameworks to meet heterogeneous computing requirements. Emerging technologies allow for the near real-time extraction and analysis of heterogeneous data to find meaningful information.Machine-learning algorithms allow computers to learn automatically, analyzing existing data to establish rules to predict outcomes of unknown data. However, traditional machine learning approaches do not meet the needs for Internet of Things (IoT) applications, calling for new technologies. Deep learning is a good example of emerging technologies that tackle the limitations of traditional machine learning through feature engineering, providing superior performance in highly complex applications. However, these technologies also raise new security and privacy concerns. Technology adoption and trust issues are of timely importance as well.Industrial operations are in the midst of rapid transformations, sometimes referred to as Industry 4.0, Industrial Internet of Things (IIoT), or smart manufacturing. These transformations are bringing fundamental changes to factories and workplaces, making them safer and more efficient, flexible, and environmentally friendly. Machines are evolving to have increased autonomy, and new human-machine interfaces such as smart tools, augmented reality, and touchless interfaces are making interaction more natural. Machines are also becoming increasingly interconnected within individual factories as well as to the outside world through cloud computing, enabling many opportunities for operational efficiency and flexibility in manufacturing and maintenance.An increasing number of countries have put forth national advanced manufacturing development strategies, such as Germany's Industry 4.0, the United States' Industrial Internet and manufacturing system based on CPS (Cyber-Physical Systems), and China's Internet Plus Manufacturing and Made in China 2025 initiatives. Smart Manufacturing aims to maximize transparency and access of all manufacturing process information across entire manufacturing supply chains and product lifecycles, with the Internet of Things (IoT) as a centerpiece to increase productivity and output value. This manufacturing revolution depends on technology connectivity and the contextualization of data, thus putting intelligent systems support and data science at the center of these developments. ACM Reference format: Collapse

Topics: production management computer data storage smart manufacturing differential privacy privacy

Methods: computational algorithm convolutional neural network machine learning

Smart System: Joint Utility and Frequency for Pattern Classification

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Lin, Qi; Gan, Wensheng; Wu, Yongdong; Chen, Jiahui; Chen, Chien-Ming

Abstract: Nowadays, the environments of smart systems for Industry 4.0 and Internet of Thi ... Expand

Abstract: Nowadays, the environments of smart systems for Industry 4.0 and Internet of Things are experiencing fast industrial upgrading. Big data technologies such as design making, event detection, and classification are developed to help manufacturing organizations to achieve smart systems. By applying data analysis, the potential values of rich data can be maximized, which will help manufacturing organizations to finish another round of upgrading. In this article, we propose two new algorithms with respect to big data analysis, namely UFCgen and UFCfast. Both algorithms are designed to collect three types of patterns to help people determine the market positions for different product combinations. We compare these algorithms on various types of datasets, both real and synthetic. The experimental results show that both algorithms can successfully achieve pattern classification by utilizing three different types of interesting patterns from all candidate patterns based on user-specified thresholds of utility and frequency. Furthermore, the list-based UFCfast algorithm outperforms the levelwise-based UFCgen algorithm in terms of both execution time and memory consumption. Collapse

Topics: database system production management smart manufacturing operating system Microsoft Windows

Methods: computational algorithm experiment case study machine learning literature study

Application Massive Data Processing Platform for Smart Manufacturing Based on Optimization of Data Storage

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Ren, Bin; Chen, Yuquiang; Wang, Fujie

Abstract: The aim of smart manufacturing is to reduce manpower requirements of the product ... Expand

Abstract: The aim of smart manufacturing is to reduce manpower requirements of the production line by applying technology of huge amounts of data to the manufacturing industry. Smart manufacturing is also called Industry 4.0, and the platform for processing huge amounts of data has an indispensable role. The massive data processing platform is like the brain of the entire factory, receiving all data from production line sensors via edge computing, processing, and analyzing, and finally making feedback decisions. With the innovation of production technology, the data that the platform needs to process has become diverse and complex, and the amount has become increasingly large. As well, many precision manufacturing industries have begun to enter the field of Industry 4.0. In addition to the accuracy and availability of data processing, there is emphasis on the real-time nature of data processing. After the sensor receives the data, the platform must provide feedback within a short period of time. This article proposes a massive data processing platform based on the Lambda architecture, which has the coexistence of stream processing and batch processing to meet real-time feedback needs of high-precision manufacturing. To verify the effectiveness of the optimization, it is based on real data from the manufacturing industry. To generate a large amount of test data to confirm the optimization of the storage of pictures. The results show that it optimizes the storage and optimization of the image data generated by the Automated Optical Inspection technology used in manufacturing today and optimizes the query for data storage. It also reduces the consumption of a large amount of memory as expected, and the query for Hive reduced the time spent. Collapse

Topics: Apache HBase Apache Hadoop Hadoop Distributed File System stream processing Python

Methods: experiment

Analysing cooperatives’ digital maturity using a synthetic indicator

2023 | International Journal of Information Management | Citations: 0

Authors: Ribeiro-Navarrete, Belén; Martín Martín, José María; Guaita-Martínez, José Manuel; Simón-Moya, Virginia

Abstract: A company’s digitalisation journey spans multiple dimensions and processes. Howe ... Expand

Abstract: A company’s digitalisation journey spans multiple dimensions and processes. However, studies of digital maturity have focused only on certain parts of the process. This study presents a method capable of fully capturing a company’s level of digital maturity. A DP2 indicator of digital maturity was constructed. This in­ dicator is based on responses to a company survey of digitalisation in different business areas and processes. Companies with the highest degree of digitalisation are the oldest and largest (most employees). Differences between companies and sectors can be explained by the level of digitalisation in terms of digital management intensity, departmental agility and digital orientation. Collapse

Topics: IS maturity mobile system IS management innovation management cloud computing

Methods: survey field study literature study descriptive statistic linear regression analysis

A Prototypical Dashboard for Knowledge-Based Expert Systems used for Real-Time Anomaly Handling in Smart Manufacturing

2023 | HICSS | Citations: 0

Authors: Stahmann, Philip

Abstract: The use of machine learning in digitized production increases potentials for pro ... Expand

Abstract: The use of machine learning in digitized production increases potentials for production automation. A milestone on the path to autonomous production is real-time anomaly detection. However, increasing complexity of production makes autonomous decisions difficult to understand for humans as central stakeholders. In this paper, we create a dashboard that incorporates elements from knowledge-based systems, requirements for real-time anomaly detection, and design guidelines for dashboards. Using design science research, the dashboard is designed, implemented and comprehensively evaluated with 98 participants. After the second design science iteration, the dashboard is approved in terms of usefulness and ease of use. Our research primarily contributes to practice, as our implementation constitutes a starting point for designing the interface between humans and autonomous production. We also contribute to academia as the dashboard is an instantiation in the research field of interface design for knowledge-based systems, which can be further developed in future research. Collapse

Topics: digital dashboard expert system production management industry 4.0 internet technology

Methods: survey anomaly detection qualitative interview survey design design guideline

A Taxonomy and Archetypes of Business Analytics in Smart Manufacturing

2023 | ACM SIGMIS Database | Citations: 0

Authors: Wanner, Jonas; Wissuchek, Christopher; Welsch, Giacomo; Janiesch, Christian

Abstract: Fueled by increasing data availability and the rise of technological advances fo ... Expand

Abstract: Fueled by increasing data availability and the rise of technological advances for data processing and communication, business analytics is a key driver for smart manufacturing. However, due to the multitude of different local advances as well as its multidisciplinary complexity, both researchers and practitioners struggle to keep track of the progress and acquire new knowledge within the field, as there is a lack of a holistic conceptualization. To address this issue, we performed an extensive structured literature review, yielding 904 relevant hits, to develop a quadripartite taxonomy as well as to derive archetypes of business analytics in smart manufacturing. The taxonomy comprises the following meta-characteristics: application domain, orientation as the objective of the analysis, data origins, and analysis techniques. Collectively, they comprise eight dimensions with a total of 52 distinct characteristics. Using a cluster analysis, we found six archetypes that represent a synthesis of existing knowledge on planning, maintenance (reactive, offline, and online predictive), monitoring, and quality management. A temporal analysis highlights the push beyond predictive approaches and confirms that deep learning already dominates novel applications. Our results constitute an entry point to the field but can also serve as a reference work and a guide with which to assess the adequacy of one's own instruments. Collapse

Topics: production management smart manufacturing analytical information system industry 4.0 database system

Methods: cluster analysis literature study survey literature sample machine learning

Theories: fuzzy set theory

Digital transformation solutions of entrepreneurial SMEs based on an information error-driven T-spherical fuzzy cloud algorithm

2023 | International Journal of Information Management | Citations: 0

Authors: Yang, Zaoli; Chang, Jinping; Huang, Lucheng; Mardani, Abbas

Abstract: The digital transformation of enterprises has become an inevitable development t ... Expand

Abstract: The digital transformation of enterprises has become an inevitable development trend and one of the key driving forces that promotes the sustainable development of enterprises. However, due to the many obstacles of financial burdens, technical thresholds, and talent shortages, digital transformation has become a challenging task for entrepreneurial Small and Medium-Sized Enterprises (SMEs). Additionally, many competitive digital trans­ formation solutions on the market cause confusion when enterprises must choose one. This study drew a new information error-driven T-spherical fuzzy cloud algorithm to evaluate digital transformation solutions of entrepreneurial SMEs and support its selection. First, an evaluation index system for the digital transformation solution of entrepreneurial SMEs was established from four aspects. Then, a new concept of a T-spherical fuzzy cloud was defined to represent the evaluation information of the indicators. Additionally, a T-spherical Fuzzy Cloud Weighted Heronian Mean (T-SFCWHM) operator was used to aggregate the evaluation information. Af­ terward, an evaluation and selection decision framework for the digital transformation solution of entrepre­ neurial SMEs based on the T-SFCWHM operator was developed. Further, a practical example was given to illustrate the effectiveness of the proposed method. Finally, a discussion of the findings in our study was con­ ducted, and the conclusions were summarized. Collapse

Topics: digital transformation cloud computing IT strategic planning decision making smart manufacturing

Methods: computational algorithm expert evaluation design science theoretical contribution synthesis

Theories: fuzzy set theory

TOWARDS DOMAIN-SPECIFIC KNOWLEDGE ON DIGITAL TRANSFORMATION - THE CASE OF THE FURNITURE INDUSTRY

2023 | European Conference On Information Systems | Citations: 0

Authors: Zeiher, Simon; Richter, Alexander; Wagner, David

Abstract: An abundance of individual cases have explored and explained the Digital Transf ... Expand

Abstract: An abundance of individual cases have explored and explained the Digital Transformation and, ultimately, proven its capability to create new value propositions. Moreover, various meta-studies have provided us with classifications and frameworks to synthesize existing knowledge on the topic. Yet, there is a lack of studies integrating industry-specific knowledge and providing orientation in the context of the relevant value-creation processes. Following a systematic literature search on Digital Transformation in the furniture industry, we analyse insights from 85 papers and suggest a theorydriven framework consisting of 5 focal areas: sources of disruption, strategic responses, value creation transformation, technical application, and obstacles to the implementation. Based on the framework, we structure and elaborate on the existing body of research and identify avenues for further research. Collapse

Topics: digital transformation production management database system industry 4.0 value creation

Methods: literature sample qualitative coding qualitative content analysis literature study structured literature research