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Industry 4.0—New Entrepreneurial Opportunities: How to Make Money with AI...
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Industry 4.0—New Entrepreneurial Opportunities: How to Make Money with AI in Manufacturing

A special issue of Sci (ISSN 2413-4155).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 17808

Special Issue Editors

Dr. Johannes Winter

E-Mail Website
Guest Editor
Head of Technology Department, National Academy of Science and Engineering, Munich, Germany
Interests: artificial intelligence; data science; industrial AI; industry 4.0; business models; SME
Special Issues, Collections and Topics in MDPI journals
Dr. Alexander Werbik

E-Mail Website
Guest Editor
Institute of Management and Regional Economics, Lucerne University of Applied Sciences and Arts, Lucerne, Switzerland
Interests: innovation; technology management and entrepreneurship

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) into manufacturing, marking the era of Industry 4.0, is revolutionizing the entrepreneurial landscape. Our next Special Issue, “Industry 4.0 – New Entrepreneurial Opportunities: How to Make Money with AI in Manufacturing”, aims to explore the economic implications of this technological revolution, by emphasizing how AI can be leveraged to create new business opportunities in the manufacturing sector.

This issue is set to explore the transformative impact of AI on manufacturing, showcasing how smart manufacturing and AI-driven processes not only enhance efficiency but also pave the way for new business models and sources of revenue. We are inviting contributions that explore successful concepts and strategies for data-driven services and servitization, monetization, innovative business models, and the path towards sustainable growth in AI-enhanced manufacturing ecosystems.

The shift to AI-driven manufacturing also requires addressing challenges such as the costs of initial investment, skill development, and the rise of new competitors on the market. Additionally, this transition sparks a broader discussion on the impact of AI automation, on the workforce and on local ecosystems. It suggests profound changes to the dynamics of regional industries and the fabric of community life.

We are calling for submissions that offer a blend of empirical research, theoretical insights, and practical case studies. Topics of interest include but are not limited to strategies for cost reduction, innovations in generating revenue, business process management and reengineering, and in-depth analyses of the economic effects of adopting AI in manufacturing.

This Special Issue aims to serve as a vital resource for researchers, industry professionals, and policymakers. It provides a clear, evidence-based exploration of the economic opportunities AI introduces to the manufacturing sector. We are excited to explore how AI can foster economic growth and transform traditional manufacturing practices.

Dr. Johannes Winter
Dr. Alexander Werbik
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. Sci is an international peer-reviewed open access quarterly 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 1200 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

  • Industry 4.0
  • smart manufacturing
  • smart factory
  • digital transformation of industry
  • automation
  • digitalization
  • customization
  • business model innovation
  • smart services

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Published Papers (5 papers)

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Research

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14 pages, 492 KiB  
Article
Coming Home in the Age of Industry 4.0? The Effects of Offshoring and Backshoring on Manufacturing Companies’ Success
by Alexander Werbik, Julien Nussbaum and Johannes Winter
Sci 2024, 6(4), 58; https://doi.org/10.3390/sci6040058 - 1 Oct 2024
Viewed by 903
Abstract
This study explores the effects of offshoring and backshoring on value creation per employee within the manufacturing sector by investigating the difference between firms that did and those that did not engage in corresponding relocation activities. Historically, offshoring has been a strategy to [...] Read more.
This study explores the effects of offshoring and backshoring on value creation per employee within the manufacturing sector by investigating the difference between firms that did and those that did not engage in corresponding relocation activities. Historically, offshoring has been a strategy to reduce costs and increase efficiency. However, the rise of advanced digital technologies and changing market dynamics have sparked a countertrend toward backshoring. Using data from the European Manufacturing Survey, this research examines how these strategies impact value creation, also taking into account the roles of sales growth and export intensity as potential moderators. The results of hierarchical regression analysis indicate that neither firms that have engaged in offshoring of production nor firms that have engaged in offshoring of R&D have significantly higher or lower value creation per employee than those that did not. In contrast, firms that have engaged in backshoring of production realize higher value creation when coupled with high sales growth. Firms that have engaged in backshoring of R&D, on the other hand, realize higher value creation when combined with high export intensity. These findings highlight the importance of aligning strategic decisions with both technological capabilities and market conditions to enhance productivity. The study suggests that a balanced and holistic approach, integrating both offshoring and backshoring strategies tailored to specific business contexts, can optimize value creation and maintain competitiveness in a rapidly evolving global landscape. Full article
(This article belongs to the Special Issue Industry 4.0—New Entrepreneurial Opportunities: How to Make Money with AI in Manufacturing)
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14 pages, 405 KiB  
Article
The Effect of the Acquisition Rate on Post-Acquisition Innovation
by Yingmei Li, Yona Kwon and Seungho Choi
Sci 2024, 6(3), 37; https://doi.org/10.3390/sci6030037 - 1 Jul 2024
Viewed by 1197
Abstract
Technology acquisitions are one of the most common growth strategies for firms. Firms that have made multiple acquisitions in the past are more likely to make new ones. With previous M&A experience, firms are more likely to make acquisitions. The acquisition rate is [...] Read more.
Technology acquisitions are one of the most common growth strategies for firms. Firms that have made multiple acquisitions in the past are more likely to make new ones. With previous M&A experience, firms are more likely to make acquisitions. The acquisition rate is the total number of acquisitions a firm has made at a given time. In technology acquisition, the acquisition rate affects innovative firm performance. The more frequent acquisitions a firm makes, the less innovative performance will occur. A high acquisition rate negatively affects post-acquisition performance by dominating the attention of decision-makers and overloading the firm. During the process, there needs to be structural integration between the acquirer and the target firm. This study empirically analyzes 380 cases of technology acquisitions of U.S. publicly traded companies from 1990 to 2005. The results show that a high acquisition rate is negatively related to the post-acquisition innovation performance of the acquirer. Although structural integration has no impact on the negative relationship between post-acquisition performance and acquisition rate, considering the acquisition rate when pursuing M&A allows acquiring firms to avoid detrimental consequences. Full article
(This article belongs to the Special Issue Industry 4.0—New Entrepreneurial Opportunities: How to Make Money with AI in Manufacturing)
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19 pages, 5407 KiB  
Article
Decision Making in Service Shops Supported by Mining Enterprise Resource Planning Data
by Shaun West, Daryl Powell, Fabian Ille and Stefan Behringer
Sci 2024, 6(2), 27; https://doi.org/10.3390/sci6020027 - 5 May 2024
Viewed by 1801
Abstract
This research examines the application of Enterprise Resource Planning (ERP) systems in service shops, focusing on the specific challenges unique to these environments compared to those in the manufacturing sector. Service shops, distinguished by their smaller scale and variable demands, often need different [...] Read more.
This research examines the application of Enterprise Resource Planning (ERP) systems in service shops, focusing on the specific challenges unique to these environments compared to those in the manufacturing sector. Service shops, distinguished by their smaller scale and variable demands, often need different functionalities in ERP systems compared to manufacturing facilities. Our analysis is based on detailed billing records and monthly cash flow data to deliver critical insights into businesses’ performance for service shop managers. This study analyses ERP data from 27 service shops over 35 months. It is based on detailed billing records and monthly cash flow data to deliver critical insights into businesses’ performance for service shop managers that support managerial decision making. Our findings emphasise the importance of incorporating additional contextual information to augment the effectiveness of ERP systems in service contexts. Our analysis shows that simple, standardised data mining methods can significantly enhance operational management decision making when supported with visuals to support understanding and interpretation of the data. Moreover, this study suggests potential directions for future research aimed at improving business analytics and intelligence practices to optimise the use of ERP systems in service industries. This research contributes to the academic discourse by providing empirical evidence on utilising ERP data in service shops and offers practical recommendations for ongoing operational improvements. Full article
(This article belongs to the Special Issue Industry 4.0—New Entrepreneurial Opportunities: How to Make Money with AI in Manufacturing)
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Review

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38 pages, 6522 KiB  
Review
Challenges and Opportunities in the Implementation of AI in Manufacturing: A Bibliometric Analysis
by Lorena Espina-Romero, Humberto Gutiérrez Hurtado, Doile Ríos Parra, Rafael Alberto Vilchez Pirela, Rosa Talavera-Aguirre and Angélica Ochoa-Díaz
Sci 2024, 6(4), 60; https://doi.org/10.3390/sci6040060 - 3 Oct 2024
Viewed by 10897
Abstract
This study explores the evolution and impact of research on the challenges and opportunities in the implementation of artificial intelligence (AI) in manufacturing between 2019 and August 2024. By addressing the growing integration of AI technologies in the manufacturing sector, the research seeks [...] Read more.
This study explores the evolution and impact of research on the challenges and opportunities in the implementation of artificial intelligence (AI) in manufacturing between 2019 and August 2024. By addressing the growing integration of AI technologies in the manufacturing sector, the research seeks to provide a comprehensive view of how AI applications are transforming production processes, improving efficiency, and opening new business opportunities. A bibliometric analysis was conducted, examining global scientific production, influential authors, key sources, and thematic trends. Data were collected from Scopus, and a detailed review of key publications was carried out to identify knowledge gaps and unresolved research questions. The results reveal a steady increase in research related to AI in manufacturing, with a strong focus on automation, predictive maintenance, and supply chain optimization. The study also highlights the dominance of certain institutions and key authors driving this field of research. Despite the progress, significant challenges remain, particularly regarding the scalability of AI solutions and ethical considerations. The findings suggest that while AI holds considerable potential for the manufacturing industry, more interdisciplinary research is needed to address existing gaps and maximize its benefits. Full article
(This article belongs to the Special Issue Industry 4.0—New Entrepreneurial Opportunities: How to Make Money with AI in Manufacturing)
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19 pages, 770 KiB  
Review
Fortifying Industry 4.0: Internet of Things Security in Cloud Manufacturing through Artificial Intelligence and Provenance Blockchain—A Thematic Literature Review
by Mifta Ahmed Umer, Elefelious Getachew Belay and Luis Borges Gouveia
Sci 2024, 6(3), 51; https://doi.org/10.3390/sci6030051 - 2 Sep 2024
Viewed by 1843
Abstract
Cloud manufacturing allows multiple manufacturers to contribute their manufacturing facilities and assets for monitoring, operating, and controlling common processes of manufacturing and services controlled through cloud computing. The modern framework is driven by the seamless integration of technologies evolved under Industry 4.0. The [...] Read more.
Cloud manufacturing allows multiple manufacturers to contribute their manufacturing facilities and assets for monitoring, operating, and controlling common processes of manufacturing and services controlled through cloud computing. The modern framework is driven by the seamless integration of technologies evolved under Industry 4.0. The entire digitalized manufacturing systems operate through the Internet, and hence, cybersecurity threats have become a problem area for manufacturing companies. The impacts can be very serious because cyber-attacks can penetrate operations carried out in the physical infrastructure, causing explosions, crashes, collisions, and other incidents. This research is a thematic literature review of the deterrence to such attacks by protecting IoT devices by employing provenance blockchain and artificial intelligence. The literature review was conducted on four themes: cloud manufacturing design, cybersecurity risks to the IoT, provenance blockchains for IoT security, and artificial intelligence for IoT security. These four themes of the literature review were critically analyzed to visualize a framework in which provenance blockchain and artificial intelligence can be integrated to offer a more effective solution for protecting IoT devices used in cloud manufacturing from cybersecurity threats. The findings of this study can provide an informative framework. Full article
(This article belongs to the Special Issue Industry 4.0—New Entrepreneurial Opportunities: How to Make Money with AI in Manufacturing)
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