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Digital Transformation in Manufacturing Industry
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Digital Transformation in Manufacturing Industry

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

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 61636

Special Issue Editors

Dr. Radmehr P. Monfared

E-Mail Website
Guest Editor
School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: digitalized technologies; supply chain management; business process analysis and simulation; blockchain technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Diana Segura-Velandia

E-Mail Website
Guest Editor
Lecturer in ICT for Manufacturing, Loughborough University, Loughborough LE11 3TU, UK
Interests: artificial intelligence; decision-making; materials; sensors; modeling; simulation; Systems Engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Daniel A. Vera

E-Mail Website
Guest Editor
WMG (Warwick Manufacturing Group), International Manufacturing Centre, University of Warwick, Coventry, UK
Interests: digital manufacturing; virtual engineering; robotics and automation

Special Issue Information

Dear Colleagues,

During the last decade, the world has been transformed by new digital technologies. The current digital transformation has vast potential to change consumers’ lives and meet their new expectations, add further value to data-driven businesses and create unique societal benefits.

The rate of technological innovation is exponential, to the point that change is becoming almost a new normal now, with many innovations having passed the proof-of-concept stage and entered the investment phase.

Although it has become apparent that industrial digitalisation is potentially capable of improving productivity, and the predictability of businesses, nonetheless, manufacturing industries are lagging behind and failing to keep pace with other sectors such as finance and media. This is probably due to the need for proven technological robustness and demonstratable benefits.

This Special Issue is aimed at disseminating advanced research in the theory and application of digitalisation in the area of manufacturing industries (also known by some experts as Industry 4.0).

The scope of this Special Issue is focused on new digital technologies that can directly impact on various lifecycle stages of manufacturing industries. These could include marketing, design, production, quality control, resource management, supply chain, product and process tracking, and product recycling.

The potential themes include but are not limited to the application of the following technologies within manufacturing industries: cloud computing, big data, Internet of Things (IoT), blockchain in manufacturing, virtual engineering and digital twining (virtual reality and augmented reality), simulation, machine learning for analytics and predictions in industrial businesses, and industrial cybersecurity.

We also invite articles investigating the human role in digitalised manufacturing industries, the need for resource upskilling, and new business models for manufacturing industries.

Dr. Radmehr P. Monfared
Dr. Diana Segura-Velandia
Dr. Daniel A. Vera
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • manufacturing digital transformation
  • digital technology
  • blockchain in manufacturing
  • new business models in manufacturing

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Published Papers (7 papers)

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Research

21 pages, 6016 KiB  
Article
Establishing the Digital Transformation Strategies for the Med-Tech Enterprises Based on the AIA-NRM Approach
by I-Ching Fang, Peng-Ting Chen, Hsin-Hui Chiu, Chia-Li Lin and Fong-Chin Su
Appl. Sci. 2020, 10(21), 7574; https://doi.org/10.3390/app10217574 - 27 Oct 2020
Cited by 8 | Viewed by 3635
Abstract
The medical technology (Med-Tech) industry turnover has reached a record high, attracting a great deal of capital investment, while mergers and acquisitions continually increase. In order to move to a higher value-added segment, traditional Med-Tech manufacturers have to transform into digital smart manufacturers. [...] Read more.
The medical technology (Med-Tech) industry turnover has reached a record high, attracting a great deal of capital investment, while mergers and acquisitions continually increase. In order to move to a higher value-added segment, traditional Med-Tech manufacturers have to transform into digital smart manufacturers. This development trend promotes industrial operators of Med-Tech to consider how to strengthen professional competence, expand their market, and determine the future direction. This study proposed the value-driving forces of Med-Tech enterprise, based on five aspects: Professional competence (PC), operation management (OM), critical resources (CR), regulatory system (RS), and market expansion (ME). Then, the acquisition and importance analysis (AIA) and the network relation map (NRM) approaches were proposed and implemented to find an optimal pathway for a Med-Tech enterprise to implement digital transformation. Our findings suggest that Med-Tech enterprises should treat RS as the priority in transformation. Finally, based on small- and medium-sized Med-Tech enterprise scenarios, we propose four development strategies (direct acquisition, strategic alliance, maintenance status, and in-house development) should be decided in the digital transformation process. Full article
(This article belongs to the Special Issue Digital Transformation in Manufacturing Industry)
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12 pages, 1951 KiB  
Article
Content and Privacy Protection in JPEG Images by Reversible Visual Transformation
by Xin Cao, Yuxuan Huang, Hao-Tian Wu and Yiu-ming Cheung
Appl. Sci. 2020, 10(19), 6776; https://doi.org/10.3390/app10196776 - 27 Sep 2020
Cited by 12 | Viewed by 2545
Abstract
With the popularity of cloud computing and social networks, more and more JPEG images are stored and distributed. Consequently, how to protect privacy and content in JPEG images has become an important issue. Although traditional encryption schemes can be employed, the file format [...] Read more.
With the popularity of cloud computing and social networks, more and more JPEG images are stored and distributed. Consequently, how to protect privacy and content in JPEG images has become an important issue. Although traditional encryption schemes can be employed, the file format of JPEG images is changed so that their usage may be affected. In this paper, a reversible visual transformation algorithm is proposed to protect content in JPEG images. Specifically, the DC coefficient in each user-selected block is modified, while the information required to recover it is reversibly hidden into AC coefficients. Then the signs of AC coefficients in the selected blocks are flipped and the blocks are further scrambled with a secret key. By embedding the location information of the selected blocks in a transformed image, the original image can be exactly recovered when needed. Besides, regions to be protected can be arbitrarily chosen without substantially affecting the rest of the image. The experimental results on a set of JPEG images validate the efficacy and reversibility of the proposed algorithm. In addition, good performance is achieved in terms of invisibility of the protected content, image quality, file size preservation and security. Full article
(This article belongs to the Special Issue Digital Transformation in Manufacturing Industry)
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34 pages, 5423 KiB  
Article
Print Management System Model in a Large Organization
by Vincent Karovič, Jr., Filip Kováč, Vincent Karovič and Peter Veselý
Appl. Sci. 2020, 10(12), 4193; https://doi.org/10.3390/app10124193 - 18 Jun 2020
Cited by 1 | Viewed by 11249
Abstract
This article is focused on the analysis and solution of the issue of a printing system model in a large organization. It provides an overview of the current stat29e of the organization and its current printing system. Based on the information about strengths [...] Read more.
This article is focused on the analysis and solution of the issue of a printing system model in a large organization. It provides an overview of the current stat29e of the organization and its current printing system. Based on the information about strengths and weaknesses, the most suitable solution for the given organization was designed and subsequently implemented. The created design meets all the requirements required by the company, while minimizing the threats that the deployment of the new system and the resulting changes may have. The work also describes various ways of dealing directly with change, whether when dealing with the old printing system or preparing employees for its change. The new system brings clear unification of the press, its monitoring and administration under the supervision of its own employees without the need for external companies or support. Full article
(This article belongs to the Special Issue Digital Transformation in Manufacturing Industry)
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15 pages, 3381 KiB  
Article
Enterprises’ Servitization in the First Decade—Retrospective Analysis of Back-End and Front-End Challenges
by Natalia Kryvinska, Sebastian Kaczor and Christine Strauss
Appl. Sci. 2020, 10(8), 2957; https://doi.org/10.3390/app10082957 - 24 Apr 2020
Cited by 28 | Viewed by 4273
Abstract
The concept of servitization provides major benefits both for the performing company and for the profiting customer. Thus, during the last years many companies were heading for this well-proven direction tempted by expectations, but have been possibly even not aware of some inherent [...] Read more.
The concept of servitization provides major benefits both for the performing company and for the profiting customer. Thus, during the last years many companies were heading for this well-proven direction tempted by expectations, but have been possibly even not aware of some inherent challenges. Since there are some issues even threatening the existence of companies, and some other rather easy to overcome, it is by all means necessary to consider and deal with this matter. Hence, this paper provides an insight into pitfalls in servitization named “service paradox” and addresses corresponding managerial issues. To operationalize the challenges of servitization, this paper suggests a separation among internal back-end challenges and customer-facing front-end issues, which represents the applied framework for their examination. Besides, the matter of appropriate pricing and the inherent shift of risks towards suppliers are discussed. Finally, the last part concludes the analysis outputs and gives suggestions for the future strategies in the enterprise servitization. Full article
(This article belongs to the Special Issue Digital Transformation in Manufacturing Industry)
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30 pages, 11545 KiB  
Article
Scenario-Based Analysis of IT Enterprises Servitization as a Part of Digital Transformation of Modern Economy
by Natalia Kryvinska and Lukas Bickel
Appl. Sci. 2020, 10(3), 1076; https://doi.org/10.3390/app10031076 - 5 Feb 2020
Cited by 31 | Viewed by 5353
Abstract
The transition towards Servitization in the IT Business is extremely challenging because IT Enterprises must transform partly or entirely from hardware manufacturers into service providers. Moreover, Servitization does not occur all at once; it is a long and continuous process. Thus, in order [...] Read more.
The transition towards Servitization in the IT Business is extremely challenging because IT Enterprises must transform partly or entirely from hardware manufacturers into service providers. Moreover, Servitization does not occur all at once; it is a long and continuous process. Thus, in order to succeed, a company must determine which phase of this process it is in within a short time period, due to the dynamic competition in the modern IT Business. An examination of such a transition is crucial for accurate enterprise resource planning and for business success in general. Accordingly, to gain a better understanding of this process/transition in the IT industry, five major players were analyzed. We provide a foundation of the definitions and concepts regarding Servitization. Based on this foundation, every major player is analyzed by business segment. Then, those business segments are broken down into the offerings delivered to the customers. Depending on the offering, an analysis of the revenue is performed. In addition, we discuss the challenges and their effects on every company, and then we examine the similarities and differences in the process. We conclude with a brief statement of our primary achievements, and possible future investigation directions/topics are suggested. Full article
(This article belongs to the Special Issue Digital Transformation in Manufacturing Industry)
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17 pages, 1480 KiB  
Article
Low-Code as Enabler of Digital Transformation in Manufacturing Industry
by Raquel Sanchis, Óscar García-Perales, Francisco Fraile and Raul Poler
Appl. Sci. 2020, 10(1), 12; https://doi.org/10.3390/app10010012 - 18 Dec 2019
Cited by 125 | Viewed by 30662
Abstract
Currently, enterprises have to make quick and resilient responses to changing market requirements. In light of this, low-code development platforms provide the technology mechanisms to facilitate and automate the development of software applications to support current enterprise needs and promote digital transformation. Based [...] Read more.
Currently, enterprises have to make quick and resilient responses to changing market requirements. In light of this, low-code development platforms provide the technology mechanisms to facilitate and automate the development of software applications to support current enterprise needs and promote digital transformation. Based on a theory-building research methodology through the literature and other information sources review, the main contribution of this paper is the current characterisation of the emerging low-code domain following the foundations of the computer-aided software engineering field. A context analysis, focused on the current status of research related to the low-code development platforms, is performed. Moreover, benchmarking among the existing low-code development platforms addressed to manufacturing industry is analysed to identify the current lacking features. As an illustrative example of the emerging low-code paradigm and respond to the identified uncovered features, the virtual factory open operating system (vf-OS) platform is described as an open multi-sided low-code framework able to manage the overall network of a collaborative manufacturing and logistics environment that enables humans, applications, and Internet of Things (IoT) devices to seamlessly communicate and interoperate in the interconnected environment, promoting resilient digital transformation. Full article
(This article belongs to the Special Issue Digital Transformation in Manufacturing Industry)
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16 pages, 1969 KiB  
Article
Optimizing the Spectral Characterisation of a CMYK Printer with Embedded CMY Printer Modelling
by Qiang Liu, Zheng Huang, Michael R. Pointer and M. Ronnier Luo
Appl. Sci. 2019, 9(24), 5308; https://doi.org/10.3390/app9245308 - 5 Dec 2019
Cited by 7 | Viewed by 2948
Abstract
In the digital printing process, reliable colour reproduction is commonly achieved by printer characterisation, which defines the correspondence between the input device control values and the output colour information. The cellular Yule–Nielsen spectral Neugebauer model, together with its variants, is widely adopted in [...] Read more.
In the digital printing process, reliable colour reproduction is commonly achieved by printer characterisation, which defines the correspondence between the input device control values and the output colour information. The cellular Yule–Nielsen spectral Neugebauer model, together with its variants, is widely adopted in this topic because of its superb colorimetric and spectral accuracy. However, it seems that current studies have neglected an inconspicuous defect in such models when characterising printers equipped with black ink. That is, the cellular structure of these models overemphasises the sampling for dark-tone colours, and thus leads to relatively large errors in light tones. In this paper, taking a CMYK printer as an example, a simple and effective solution is proposed with no need of extra sampling. With the aid of a newly built cellular spectral Neugebauer model for the embedded CMY printer, this approach optimises the printer characterisation for light tones, slightly improves the precision for middle tones while it maintains the accuracy for dark tones. The performance of the proposed method was evaluated with regard to three different kinds of substrates and the experimental results validated its improvement in spectral printer characterisation. Full article
(This article belongs to the Special Issue Digital Transformation in Manufacturing Industry)
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