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 204

Special Issue Editors


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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

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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

Published Papers (1 paper)

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Research

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 (registering DOI) - 05 May 2024
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
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