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Adaptive Decision Making Across Industries with AI and Machine Learning:...
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Adaptive Decision Making Across Industries with AI and Machine Learning: Frameworks, Challenges, and Innovations

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 453

Special Issue Editor

Dr. Samiul Islam

E-Mail Website
Guest Editor
Shannon School of Business, Cape Breton University, Sydney, NS B1M 1A2, Canada
Interests: supply chain management; operations management; data science and predictive analytics; machine learning; text classification; sentiment analysis

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) are transforming decision-making processes across various industries, from manufacturing and supply chain management to healthcare and finance. By enabling data-driven insights, predictive analytics, and automation, AI-driven adaptive decision making enhances efficiency, reduces risks, and improves overall operational performance. However, challenges such as model reliability, ethical considerations, interpretability, and real-time adaptability remain critical in industrial applications. This Special Issue aims to explore cutting-edge AI and ML methodologies that drive adaptive decision making across industries. We invite original research articles, review papers, and case studies that address the advancements, challenges, and practical implementations of AI-driven decision systems. Submissions focused on optimization techniques, forecasting, decision making under uncertainty, real-time analytics, risk management, and ethical AI applications in industry are particularly encouraged.

Topics of interest include, but are not limited to, the following:

  • AI-driven predictive analytics for industrial decision making;
  • Optimization and reinforcement learning in industrial applications;
  • Ethical considerations and fairness in AI-driven decisions;
  • Real-time adaptive decision models in manufacturing and logistics;
  • AI in supply chain resilience and risk management;
  • Human-AI collaboration in industrial automation;
  • Scalable AI solutions for dynamic industrial environments;
  • Trust, explainability, and transparency in AI-based decision systems.

Dr. Samiul Islam
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. Computers 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 1800 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

  • artificial intelligence
  • deep learning in Industry 5.0
  • optimization
  • reinforcement learning
  • automation
  • predictive analytics
  • decision science

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Published Papers (2 papers)

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Research

23 pages, 1590 KiB  
Article
A Decision Support System for Classifying Suppliers Based on Machine Learning Techniques: A Case Study in the Aeronautics Industry
by Ana Claudia Andrade Ferreira, Alexandre Ferreira de Pinho, Matheus Brendon Francisco, Laercio Almeida de Siqueira, Jr. and Guilherme Augusto Vilas Boas Vasconcelos
Computers 2025, 14(7), 271; https://doi.org/10.3390/computers14070271 - 10 Jul 2025
Abstract
This paper presents the application of four machine learning algorithms to segment suppliers in a real case. The algorithms used were K-Means, Hierarchical K-Means, Agglomerative Nesting (AGNES), and Fuzzy Clustering. The analyzed company has suppliers that have been clustered using responses such as [...] Read more.
This paper presents the application of four machine learning algorithms to segment suppliers in a real case. The algorithms used were K-Means, Hierarchical K-Means, Agglomerative Nesting (AGNES), and Fuzzy Clustering. The analyzed company has suppliers that have been clustered using responses such as the number of non-conformities, location, and quantity supplied, among others. The CRISP-DM methodology was used for the work development. The proposed methodology is important for both industry and academia, as it helps managers make decisions about the quality of their suppliers and compares the use of four different algorithms for this purpose, which is an important insight for new studies. The K-Means algorithm obtained the best performance both for the metrics obtained and the simplicity of use. It is important to highlight that no studies to date have been conducted using the four algorithms proposed here applied in an industrial case, and this work shows this application. The use of artificial intelligence in industry is essential in this Industry 4.0 era for companies to make decisions, i.e., to have ways to make better decisions using data-driven concepts. Full article
(This article belongs to the Special Issue Adaptive Decision Making Across Industries with AI and Machine Learning: Frameworks, Challenges, and Innovations)
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23 pages, 1222 KiB  
Article
A Data Quality Pipeline for Industrial Environments: Architecture and Implementation
by Teresa Peixoto, Óscar Oliveira, Eliana Costa e Silva, Bruno Oliveira and Fillipe Ribeiro
Computers 2025, 14(7), 241; https://doi.org/10.3390/computers14070241 - 20 Jun 2025
Viewed by 294
Abstract
In modern industrial environments, data-driven decision-making plays a crucial role in ensuring operational efficiency, predictive maintenance, and process optimization. However, the effectiveness of these decisions is highly dependent on the quality of the data. Industrial data is typically generated in real time by [...] Read more.
In modern industrial environments, data-driven decision-making plays a crucial role in ensuring operational efficiency, predictive maintenance, and process optimization. However, the effectiveness of these decisions is highly dependent on the quality of the data. Industrial data is typically generated in real time by sensors integrated into IoT devices and smart manufacturing systems, resulting in high-volume, heterogeneous, and rapidly changing data streams. This paper presents the design and implementation of a data quality pipeline specifically adapted to such industrial contexts. The proposed pipeline includes modular components responsible for data ingestion, profiling, validation, and continuous monitoring, and is guided by a comprehensive set of data quality dimensions, including accuracy, completeness, consistency, and timeliness. For each dimension, appropriate metrics are applied, including accuracy measures based on dynamic intervals and validations based on consistency rules. To evaluate its effectiveness, we conducted a case study in a real manufacturing environment. By continuously monitoring data quality, problems can be proactively identified before they impact downstream processes, resulting in more reliable and timely decisions. Full article
(This article belongs to the Special Issue Adaptive Decision Making Across Industries with AI and Machine Learning: Frameworks, Challenges, and Innovations)
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