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Big Data: A State-of-the-Art within the Application Area of Smart Factory and Industry 4.0

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 39887

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Prof. Dr. Carsten Felden

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Institute of Business Computer Science / Information Economy, Technische Universität Bergakademie Freiberg, 09559 Freiberg, Germany
Interests: big data; analytics; artificial intelligence; data warehouse; data lake design science and behavioristics
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Prof. Dr. Nicola Castellano

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Department of Economics and Management, Università di Pisa, 10 - 56124 PISA, Italy
Interests: management; business; strategic management; entrepreneurship; business development; strategic planning; innovation; information technology; project management; strategic analysis
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Prof. Dr. Henning Baars

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Business Administration Institute, Universität Stuttgart, 70174 Stuttgart, Germany
Interests: business Intelligence & analytics (BIA); BIA and big data; BIA in the cloud; BIA in the internet of things
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Prof. Dr. Bruno Maria Franceschetti

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Department of Economics and Law, Universita di Macerata, 62100 Macerata, Italy
Interests: economic and financial analysis; bankruptcy prediction models; earnings quality and earnings management; social innovation; circular economy
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Prof. Michiyasu Nakajima

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Faculty of Business and Commerce, Kansai University, Suita 564-8680, Japan
Interests: management accounting; cost accounting; accounting theory; environmental management accounting
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M.Sc. Fanny-Eve Bordeleau

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Institute of Business Computer Science / Information Economy, Technische Universität Bergakademie Freiberg, 09559 Freiberg, Germany
Interests: performance management systems; change management; Industry 4.0

M.Sc. Trinks Sebastian

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Assistant Guest Editor

Institute of Business Computer Science / Information Economy, Technische Universität Bergakademie Freiberg, 09559 Freiberg, Germany
Interests: real time analytics; Industrie 4.0; image mining and image processing

Special Issue Information

Dear Colleagues,

While enterprises have a good understanding of the impact of digital transformation, based on the advent of big data and their requirements, they struggle to apply this transformation, notably when integrating analytical/digital solutions in their core business and identifying new governance models, according to a study of 4000 CIO and IT managers. Initially introduced as a way to leverage new technologies to face modern challenges, such as resource scarcity, global competition, and demographic changes, the concept of Industry 4.0 was quickly complemented by the notion of a digital transformation of organizations, to emphasize the organizational changes needed to reach the aforementioned goals. One part is the Smart Factory—a term from research in the field of production technology. This is usually a part of the high-tech strategy as part of the future Industry 4.0 project.

In addition to technological drivers, digital strategies and business model innovations are notably mentioned as key drivers of the digital transformation. Nevertheless, literature reviews on the Industry 4.0 highlight that most publications on the subject are technology-centric. Liao et al. identified, in their literature review of 224 papers the need “for a more detailed roadmap regarding the realisation of Industry 4.0”. Meanwhile, Kamble et al. highlighted the lack of studies on the guidelines for the successful implementation of Industry 4.0. These two literature reviews show there are few publications studying the Industry 4.0 as a digital transformation process.

To address this research need, we seek to identify gaps and research on the big-data-related digital transformation process, especially concerning the methods and guidelines to support organizational changes in the context of Industry 4.0, especially the Smart Factory.

Prof. Dr. Carsten Felden
Prof. Dr. Nicola Castellano
Prof. Dr. Henning Baars
Prof. Dr. Bruno Maria Franceschetti
Prof. Michiyasu Nakajima
Fanny-Eve Bordeleau
Trinks Sebastian
Guest Editors

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Keywords

Smart Factory in the Industry 4.0
Digital transformation
Organizational change
Dynamic capabilities
Image mining and computer vision
Usage of big data in the smart factory
Machine learning in Industry 4.0
Artificial intelligence in Industry 4.0
Edge computing in Industry 4.0
Architectural consequences in the smart factory

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Published Papers (8 papers)

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Research

12 pages, 684 KiB

Open AccessArticle

Digital Transformation of Terrestrial Radio: An Analysis of Simulcasted Broadcasts in FM and DAB+ for a Smart and Successful Switchover

by Przemysław Falkowski-Gilski

Appl. Sci. 2021, 11(23), 11114; https://doi.org/10.3390/app112311114 - 23 Nov 2021

Cited by 1 | Viewed by 3297

Abstract

The process of digitizing radio is far from over. It is an important interdisciplinary aspect, involving Big Data and AI (Artificial Intelligence) when it comes to classifying and handling content, and an organizational challenge in the Industry 4.0 concept. There exist several methods for delivering audio signals, including terrestrial broadcasting and internet streaming. Among them, the DAB+ (Digital Audio Broadcasting plus) system is one of the leading standards of terrestrial digital radio transmission. Compared with analog FM (frequency modulation) radio, it is more bandwidth efficient and offers greater possibilities when it comes to delivering content and forming an ensemble and multiplex. Currently, many countries worldwide, particularly European States, are still making adjustments in order to perform an efficient switchover from analog FM to digital DAB+ radio. This paper presents the current situation of the digital radio market as well as the results of a subjective quality evaluation study and questionnaire concerning broadcasting in both digital and analog techniques. It involves radio programs, transmitting both speech and music signals, simulcasted in DAB+ and FM standards. It also presents the development of the national multiplex. The results of this study may help both researchers and scientists as well as policy makers and professionals active in the field of broadcasting and electronic media and not to mention the consumption of multimedia content. Full article

(This article belongs to the Special Issue Big Data: A State-of-the-Art within the Application Area of Smart Factory and Industry 4.0)

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22 pages, 2196 KiB

Open AccessArticle

Big Data in the Metal Processing Value Chain: A Systematic Digitalization Approach under Special Consideration of Standardization and SMEs

by Marcel Sorger, Benjamin James Ralph, Karin Hartl, Manuel Woschank and Martin Stockinger

Appl. Sci. 2021, 11(19), 9021; https://doi.org/10.3390/app11199021 - 28 Sep 2021

Cited by 16 | Viewed by 4372

Abstract

Within the rise of the fourth industrial revolution, the role of Big Data became increasingly important for a successful digital transformation in the manufacturing environment. The acquisition, analysis, and utilization of this key technology can be defined as a driver for decision-making support, process and operation optimization, and therefore increase the efficiency and effectiveness of a complete manufacturing site. Furthermore, if corresponding interfaces within the supply chain can be connected within a reasonable effort, this technology can boost the competitive advantage of all stakeholders involved. These developments face some barriers: especially SMEs have to be able to be connected to typically more evolved IT systems of their bigger counterparts. To support SMEs with the development of such a system, this paper provides an innovative approach for the digitalization of the value chain of an aluminum component, from casting to the end-of-life recycling, by especially taking into account the RAMI 4.0 model as fundament for a standardized development to ensure compatibility within the complete production value chain. Furthermore, the key role of Big Data within digitalized value chains consisting of SMEs is analytically highlighted, demonstrating the importance of associated technologies in the future of metal processing and in general, manufacturing. Full article

(This article belongs to the Special Issue Big Data: A State-of-the-Art within the Application Area of Smart Factory and Industry 4.0)

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17 pages, 2019 KiB

Open AccessArticle

Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning

by Swetha Chittam, Balakrishna Gokaraju, Zhigang Xu, Jagannathan Sankar and Kaushik Roy

Appl. Sci. 2021, 11(18), 8596; https://doi.org/10.3390/app11188596 - 16 Sep 2021

Cited by 7 | Viewed by 3151

Abstract

There is a high need for a big data repository for material compositions and their derived analytics of metal strength, in the material science community. Currently, many researchers maintain their own excel sheets, prepared manually by their team by tabulating the experimental data collected from scientific journals, and analyzing the data by performing manual calculations using formulas to determine the strength of the material. In this study, we propose a big data storage for material science data and its processing parameters information to address the laborious process of data tabulation from scientific articles, data mining techniques to retrieve the information from databases to perform big data analytics, and a machine learning prediction model to determine material strength insights. Three models are proposed based on Logistic regression, Support vector Machine SVM and Random Forest Algorithms. These models are trained and tested using a 10-fold cross validation approach. The Random Forest classification model performed better on the independent dataset, with 87% accuracy in comparison to Logistic regression and SVM with 72% and 78%, respectively. Full article

(This article belongs to the Special Issue Big Data: A State-of-the-Art within the Application Area of Smart Factory and Industry 4.0)

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12 pages, 260 KiB

Open AccessFeature PaperArticle

Digital Marketing Platforms and Customer Satisfaction: Identifying eWOM Using Big Data and Text Mining

by Fotis Kitsios, Maria Kamariotou, Panagiotis Karanikolas and Evangelos Grigoroudis

Appl. Sci. 2021, 11(17), 8032; https://doi.org/10.3390/app11178032 - 30 Aug 2021

Cited by 48 | Viewed by 10877

Abstract

Big data analytics provides many opportunities to develop new avenues for understanding hospitality management and to support decision making in this field. User-generated content (UGC) provides benefits for hotel managers to gain feedback from customers and enhance specific product attributes or service characteristics in order to increase business value and support marketing activities. Many scholars have provided significant findings about the determinants of customers’ satisfaction in hospitality. However, most researchers primarily used research methodologies such as customer surveys, interviews, or focus groups to examine the determinants of customers’ satisfaction. Thus, more studies must explore how to use UGC to bridge the gap between guest satisfaction and online reviews. This paper examines and compares the aspects of satisfaction and dissatisfaction of Greek hotels’ guests. Text analytics was implemented to deconstruct hotel guest reviews and then examine their relationship with hotel satisfaction. This paper helps hotel managers determine specific product attributes or service characteristics that impact guest satisfaction and dissatisfaction and how hotel guests’ attitudes to those characteristics are affected by hotels’ market positioning and strategies. Full article

(This article belongs to the Special Issue Big Data: A State-of-the-Art within the Application Area of Smart Factory and Industry 4.0)

18 pages, 2029 KiB

Open AccessArticle

Cooperative Approaches to Data Sharing and Analysis for Industrial Internet of Things Ecosystems

by Henning Baars, Ann Tank, Patrick Weber, Hans-Georg Kemper, Heiner Lasi and Burkhard Pedell

Appl. Sci. 2021, 11(16), 7547; https://doi.org/10.3390/app11167547 - 17 Aug 2021

Cited by 16 | Viewed by 4273

Abstract

The collection and analysis of industrial Internet of Things (IIoT) data offer numerous opportunities for value creation, particularly in manufacturing industries. For small and medium-sized enterprises (SMEs), many of those opportunities are inaccessible without cooperation across enterprise borders and the sharing of data, personnel, finances, and IT resources. In this study, we suggest so-called data cooperatives as a novel approach to such settings. A data cooperative is understood as a legal unit owned by an ecosystem of cooperating SMEs and founded for supporting the members of the cooperative. In a series of 22 interviews, we developed a concept for cooperative IIoT ecosystems that we evaluated in four workshops, and we are currently implementing an IIoT ecosystem for the coolant management of a manufacturing environment. We discuss our findings and compare our approach with alternatives and its suitability for the manufacturing domain. Full article

(This article belongs to the Special Issue Big Data: A State-of-the-Art within the Application Area of Smart Factory and Industry 4.0)

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20 pages, 4280 KiB

Open AccessArticle

Adaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine Data

by Oscar Serradilla, Ekhi Zugasti, Julian Ramirez de Okariz, Jon Rodriguez and Urko Zurutuza

Appl. Sci. 2021, 11(16), 7376; https://doi.org/10.3390/app11167376 - 11 Aug 2021

Cited by 41 | Viewed by 5913

Abstract

Predictive maintenance (PdM) has the potential to reduce industrial costs by anticipating failures and extending the work life of components. Nowadays, factories are monitoring their assets and most collected data belong to correct working conditions. Thereby, semi-supervised data-driven models are relevant to enable PdM application by learning from assets’ data. However, their main challenges for application in industry are achieving high accuracy on anomaly detection, diagnosis of novel failures, and adaptability to changing environmental and operational conditions (EOC). This article aims to tackle these challenges, experimenting with algorithms in press machine data of a production line. Initially, state-of-the-art and classic data-driven anomaly detection model performance is compared, including 2D autoencoder, null-space, principal component analysis (PCA), one-class support vector machines (OC-SVM), and extreme learning machine (ELM) algorithms. Then, diagnosis tools are developed supported on autoencoder’s latent space feature vector, including clustering and projection algorithms to cluster data of synthetic failure types semi-supervised. In addition, explainable artificial intelligence techniques have enabled to track the autoencoder’s loss with input data to detect anomalous signals. Finally, transfer learning is applied to adapt autoencoders to changing EOC data of the same process. The data-driven techniques used in this work can be adapted to address other industrial use cases, helping stakeholders gain trust and thus promote the adoption of data-driven PdM systems in smart factories. Full article

(This article belongs to the Special Issue Big Data: A State-of-the-Art within the Application Area of Smart Factory and Industry 4.0)

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36 pages, 7706 KiB

Open AccessArticle

Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods

by Pavel Raska, Zdenek Ulrych and Miroslav Malaga

Appl. Sci. 2021, 11(16), 7315; https://doi.org/10.3390/app11167315 - 9 Aug 2021

Cited by 5 | Viewed by 2236

Abstract

The paper presents possible approaches for reducing the volume of data generated by simulation optimisation performed with a digital twin created in accordance with the Industry 4.0 concept. The methodology is validated using an application developed for controlling the execution of parallel simulation experiments (using client–server architecture) with the digital twin. The paper describes various pseudo-gradient, stochastic, and metaheuristic methods used for finding the global optimum without performing a complete pruning of the search space. The remote simulation optimisers reduce the volume of generated data by hashing the data. The data are sent to a remote database of simulation experiments for the digital twin for use by other simulation optimisers. Full article

(This article belongs to the Special Issue Big Data: A State-of-the-Art within the Application Area of Smart Factory and Industry 4.0)

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25 pages, 2749 KiB

Open AccessArticle

Research Hotspots and Frontiers of Product R&D Management under the Background of the Digital Intelligence Era—Bibliometrics Based on Citespace and Histcite

by Hongda Liu, Yuxi Luo, Jiejun Geng and Pinbo Yao

Appl. Sci. 2021, 11(15), 6759; https://doi.org/10.3390/app11156759 - 23 Jul 2021

Cited by 32 | Viewed by 3808

Abstract

The rise of “cloud-computing, mobile-Internet, Internet of things, big-data, and smart-data” digital technology has brought a subversive revolution to enterprises and consumers’ traditional patterns. Product research and development has become the main battlefield of enterprise competition, facing an environment where challenges and opportunities coexist. Regarding the concepts and methods of product R&D projects, the domestic start was later than the international ones, and many domestic companies have also used successful foreign cases as benchmarks to innovate their management methods in practice. “Workers must first sharpen their tools if they want to do their jobs well”. This article will start from the relevant concepts of product R&D projects and summarize current R&D management ideas and methods. We combined the bibliometric analysis software Histcite and Citespace to sort out the content of domestic and foreign literature and explore the changing trends of research hotspots. Finally, combined with the analysis of confirmed cases in domestic masters and doctoral dissertations to test the theory, the literature review of the product R&D project management theme was carried out from the dual perspectives of comprehensive theory and practice. This study uses the core collection library of Web of Science as the object of document extraction. Based on the search conditions of “Product development” or “Intergrat* product development”, 8998 sample documents were initially retrieved. The search deadline was June 2019, with a time range from 2000 to June 2019. Then, using the record number of 50 as the critical condition, 5007 analysis samples were deleted, refined, and cleaned. Through the review and measurement of 5007 papers, the analysis showed that: (1) in the last ten years, sustainability, consumer focus, new approaches to product development management, and organizational design have become critical considerations in the product development process stage; (2) at this stage, researchers are paying more attention to the innovation, design, product development, identification, simultaneous engineering, consequence, and stage/gate model aspects of product development; and (3) factors such as long development cycles, high costs, and poor organizational design are now common problems in the product development process. Full article

(This article belongs to the Special Issue Big Data: A State-of-the-Art within the Application Area of Smart Factory and Industry 4.0)

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