Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.8
2.3
Journals
Systems
Special Issues
Digital Transformation and Processes Innovation
share announcement

Digital Transformation and Processes Innovation

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Engineering".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 10646

Special Issue Editors

Dr. Francisco J. G. Silva

E-Mail Website
Guest Editor
Department of Mechanical Engineering, ISEP–School of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
Interests: tribology; coatings; manufacturing processes
Special Issues, Collections and Topics in MDPI journals
Dr. Luís Pinto Ferreira

E-Mail Website
Guest Editor
Department of Mechanical Engineering, ISEP–School of Engineering, Polytechnic of Porto, 4200-465 Porto, Portugal
Interests: lean manufacturing; manufacturing systems; discrete event simulation; simulation and optimization
Special Issues, Collections and Topics in MDPI journals
Prof. José Carlos Sá

E-Mail Website
Guest Editor
Department of Mechanical Engineering, ISEP—School of Engineering, Polytechnic of Porto, 4200-465 Porto, Portugal
Interests: industrial management; lean; six-sigma; processes improvement; safety; sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Teresa Pereira

E-Mail Website
Guest Editor
Institute of Mechanical Engineering and Industrial Management (INEGI), School of Engineering, Polytechnic of Porto, 4200-465 Porto, Portugal
Interests: operational research; decision support models; supply chain management; logistics; transportations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The digitalization of processes has gained great prominence in research essentially due to the added advantages it provides in improving efficiency and automating control. Furthermore, digital transformation implies a deeper analysis of installed systems, leading to the identification of gaps in processes, which, in turn, can give rise to innovations in the way these processes are conducted. The need imposed by the market to make processes more flexible implies that there is a strong innovation component, which can be accelerated by digital transformation. Therefore, this Special Issue aims to bring together cutting-edge work in the investigation of new methodologies for accelerating digital transformation and innovation in processes, both industrial and in the services sector. Original research or review work, properly structured, will be welcome, as well as case studies of success in the development of digital transformation or innovation in processes with a view to increasing efficiency.

Dr. Francisco J. G. Silva
Prof. Dr. Luís Pinto Ferreira
Prof. José Carlos Sá
Dr. Maria Teresa Pereira
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. Systems is an international peer-reviewed open access monthly 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

  • digital transformation
  • innovation
  • processes
  • Industry 4.0
  • smart manufacturing
  • manufacturing
  • services

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

25 pages, 1154 KiB  
Article
Digital Transformation and Innovation: The Influence of Digital Technologies on Turnover from Innovation Activities and Types of Innovation
by Anca Antoaneta Vărzaru and Claudiu George Bocean
Systems 2024, 12(9), 359; https://doi.org/10.3390/systems12090359 - 11 Sep 2024
Viewed by 5733
Abstract
In today’s competitive and globalized world, innovation is essential for organizational survival, offering a means for companies to address environmental impacts and social challenges. As innovation processes accelerate, managers need to rethink the entire value-creation chain, with digital transformation emerging as a continuous [...] Read more.
In today’s competitive and globalized world, innovation is essential for organizational survival, offering a means for companies to address environmental impacts and social challenges. As innovation processes accelerate, managers need to rethink the entire value-creation chain, with digital transformation emerging as a continuous process of organizational adaptation to the evolving societal landscape. The research question focuses on how digital technologies—such as artificial intelligence, Big Data, cloud computing, industrial and service robots, and the Internet of Things—influence innovation-driven revenues among enterprises within the European Union (EU). The paper examines, using neural network analysis, the specific impact of each digital technology on innovation revenues while exploring how these technologies affect various types of social innovation within organizations. Through cluster analysis, the study identifies patterns among EU countries based on their digital technology adoption, innovation expenditures, and revenues and the proportion of enterprises engaged in innovation activities. The findings highlight the central role of digital technologies in enhancing innovation and competitiveness, with significant implications for managers and policymakers. These results underscore the necessity for companies to strategically integrate digital technologies to sustain long-term competitiveness in the rapidly evolving digital landscape of the EU. Full article
(This article belongs to the Special Issue Digital Transformation and Processes Innovation)
►▼ Show Figures

Figure 1

26 pages, 1081 KiB  
Article
Can Digital Transformation Restrain Corporate ESG Greenwashing—A Test Based on Internal and External Joint Perspectives
by Shiwei Xu, Siyuan Zhang, Yilei Ren, Qijun Jiang and Dan Wu
Systems 2024, 12(9), 334; https://doi.org/10.3390/systems12090334 - 30 Aug 2024
Viewed by 1117
Abstract
Digital technology has the function of information governance, and digital transformation of enterprises may be the key way to identify and restrain ESG greenwashing. Based on the theory of digital empowerment, this study analyzes the influence and mechanism of digital transformation on restraining [...] Read more.
Digital technology has the function of information governance, and digital transformation of enterprises may be the key way to identify and restrain ESG greenwashing. Based on the theory of digital empowerment, this study analyzes the influence and mechanism of digital transformation on restraining corporate green washing behavior from the perspective of internal and external factors. This study takes A-share listed companies in 2012–2022 as research samples and tests the effectiveness of digital transformation. Research has found that (1) digital transformation can significantly suppress corporate greenwashing behavior, and this conclusion still holds after a series of endogeneity and robustness tests. (2) In the context of high environmental awareness among executives, the inhibitory effect of digital transformation on corporate ESG greenwashing is more pronounced. (3) Mechanism analysis shows that digital transformation has inhibited the company’s greenwashing behavior by increasing the attention of investors. (4) Heterogeneity analysis shows that in state-owned enterprises, non-heavily polluting industries, high-tech industries, and enterprises located in the eastern region digital transformation has a more effective inhibitory effect on corporate greenwashing behavior. This study examines the impact of digital transformation on corporate ESG greenwashing, expands the research on the non-economic effects of digital transformation, and may provide empirical evidence for improving the quality of ESG information disclosure and sustainable development of enterprises. Full article
(This article belongs to the Special Issue Digital Transformation and Processes Innovation)
►▼ Show Figures

Figure 1

31 pages, 645 KiB  
Article
A Mechanistic Study of Enterprise Digital Intelligence Transformation, Innovation Resilience, and Firm Performance
by Guangsi Zhang, Xuehe Wang, Jiaping Xie and Qiang Hu
Systems 2024, 12(6), 186; https://doi.org/10.3390/systems12060186 - 24 May 2024
Cited by 1 | Viewed by 1484
Abstract
Enterprise Digital Intelligence Transformation is based on the Digital Conversion of information and process service upgrading, further touching the enterprise’s core business, with the goal of building a new business model of Digital Intelligence Transformation at a higher level. Based on dynamic capability [...] Read more.
Enterprise Digital Intelligence Transformation is based on the Digital Conversion of information and process service upgrading, further touching the enterprise’s core business, with the goal of building a new business model of Digital Intelligence Transformation at a higher level. Based on dynamic capability theory, this paper conducts an in-depth study on the mechanism of enterprise Digital Intelligence Transformation and firm performance. This paper selects manufacturing companies listed in China’s Shanghai and Shenzhen A-shares from 2013 to 2022 as the research sample, and analyzes and tests the sample data using empirical research methods in order to explore the actual impact of Digital Intelligence Transformation on firm performance, including the specific pathways of action and moderating effects. This study helps enterprises to positively face the megatrend of Digital Intelligence Transformation and upgrading and the challenges it brings, and to grasp the new opportunities in the digital era. This study finds that enterprises carry out digital empowerment transformation and development strategies, and implement information Digital Conversion, service upgrading, and Digital Intelligence Transformation to promote firm performance to different degrees. Enterprise innovation resilience has a mechanism effect between information digitalization conversion enterprise performance and process service upgrading enterprise performance. The higher the environmental uncertainty, the greater the positive contribution of information digitalization to firm performance. Digital Conversion is the base and service upgrading is the process. The current sample enterprises have limited years of data collection, and most of them have only carried out the strategic implementation of Digital Conversion or servitization, and have not reached the high-level stage of digital and intellectual transformation. Therefore, it is found that enterprise innovation resilience has not yet shown a significant role mechanism effect between digital–intelligent transformation and enterprise performance at present. And environmental uncertainty has not yet shown a significant moderating effect in the stage of Servitization Upgrading and digital–intelligent transformation. The marginal contributions of this paper are mainly reflected in the following: (1) This study introduces the dynamic capability theory to explore the role of Digital Intelligence Transformation (Digital Conversion + service upgrading) on enterprise performance. (2) This paper investigates the role of Digital Intelligence Transformation in influencing the performance of enterprises from the Digital Conversion and service upgrading phases, and enriches the relevant role and regulatory mechanisms. (3) This study provides new ideas and strategic suggestions on Digital Intelligence Transformation for enterprises with different factor intensities, at different stages of development, and in different regions. Full article
(This article belongs to the Special Issue Digital Transformation and Processes Innovation)
►▼ Show Figures

Figure 1

10 pages, 450 KiB  
Article
Minimization of Costs with Picking and Storage Operations
by Cristina Lopes and Ana Oliveira
Systems 2024, 12(5), 158; https://doi.org/10.3390/systems12050158 - 1 May 2024
Cited by 1 | Viewed by 1441
Abstract
This work presents two mixed-integer programming models that intend to minimize the costs of the picking and storage operation through better planning and organization of the places occupied by the products in the warehouse. A large customer that stores frozen goods in a [...] Read more.
This work presents two mixed-integer programming models that intend to minimize the costs of the picking and storage operation through better planning and organization of the places occupied by the products in the warehouse. A large customer that stores frozen goods in a Portuguese cold chain logistics company was selected for the analysis of the allocation of the products in the warehouse and of the corresponding outbound movements. Data with 8525 movements that occurred during 2021 were collected for 228 different product references. For this case study, the products that had a picking place in the initial scenario now have pallets with all the goods in the reserve area, and vice versa. The mathematical models were permitted to obtain savings for the logistics operator costs of around 30.9%. The proposed models can, in the future, be applied in other warehouse scenarios to companies in completely different sectors of activity. Full article
(This article belongs to the Special Issue Digital Transformation and Processes Innovation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop