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Recent Advances in Data Mining for Industrial Engineering Applications

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2126

Special Issue Editor


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Guest Editor
Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan
Interests: data mining; social networking; quality management; customer relationship management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of big data and machine learning technologies, the field of industrial engineering is undergoing a digital revolution. The advancement of Industry 4.0 has made data mining methods a powerful tool for industrial engineers, enabling them to optimize production processes, enhance quality, reduce costs, and increase competitiveness. We particularly welcome submissions on the following topics (but they are not limited to):

  • The application of data mining in the manufacturing industry, including production process monitoring, quality control, and supply chain management;
  • Data analysis methods for industrial forecasting and predictive maintenance;
  • The application of data mining in energy management and environmental protection;
  • Intelligent decision support systems in industrial engineering;
  • The use of data mining in the context of the Internet of Things (IoT);
  • Machine learning algorithms and models in industrial engineering.

Prof. Dr. Long-Sheng Chen
Guest Editor

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

  • Industry 4.0
  • deep learning
  • decision making
  • forecasting and predictive maintenance
  • production and marketing

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Published Papers (1 paper)

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Research

22 pages, 7366 KiB  
Article
Analyzing the Corporate Business Intelligence Impact: A Case Study in the Financial Sector
by Serap Akcan Yetgin and Hilal Altas
Appl. Sci. 2025, 15(3), 1012; https://doi.org/10.3390/app15031012 - 21 Jan 2025
Viewed by 1592
Abstract
Business intelligence is the process and methods that enable businesses to effectively analyze large amounts of data and transform it into meaningful information, helping to increase efficiency and productivity in businesses, thus enabling businesses to gain competitive advantage. In this context, business intelligence [...] Read more.
Business intelligence is the process and methods that enable businesses to effectively analyze large amounts of data and transform it into meaningful information, helping to increase efficiency and productivity in businesses, thus enabling businesses to gain competitive advantage. In this context, business intelligence improves data management and decision-making processes and plays a critical role in strategic management. The main purpose of this study is to analyze the transition process of business intelligence solutions in financial institutions in detail, to increase efficiency in reporting processes, and to optimize decision-making processes. The study examines the ‘Cheque Report’, which reports the status of cheques in XY Financial Institution. Within the scope of the study, the transition process to business intelligence in the financial institution examined the ‘Cheque Report’ in three stages: in the first stage, reports were prepared manually; in the second stage, they were prepared with PL/SQL, and in the last stage, they were prepared with a business intelligence solution, and their outputs were compared. As a result, it was observed that with the use of business intelligence, fast and direct access to reports, data security, freedom from person dependency, and efficiency in internal information sharing are provided. Full article
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