Explainable Artificial Intelligence for Industrial and Supply Chain Systems
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 4251
Special Issue Editor
Interests: aircraft maintenance; virtual reality in training; Industry 4.0; explainable AI (XAI); decision analytics; education and engineering management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of Artificial Intelligence (AI) into industrial and supply chain systems has significantly enhanced operational efficiency, forecasting accuracy, and decision-making processes. However, the practical deployment of AI in these high-stakes environments remains constrained by the interpretability gap—the challenge of understanding how complex models arrive at their conclusions. Explainable AI (XAI) has thus emerged as an essential enabler of trust, transparency and actionable insight.
This Special Issue, “Explainable Artificial Intelligence for Industrial and Supply Chain Systems,” aims to gather cutting-edge research that bridges the gap between AI performance and interpretability in real-world industrial and logistics settings. We seek contributions that develop or apply XAI methods to enhance model transparency, support human–AI collaboration, and facilitate regulatory compliance—ultimately leading to more reliable and effective intelligent systems.
Topics of interest include but are not limited to the following:
- Interpretable machine learning models for industrial fault diagnosis and predictive maintenance.
- Explainable AI applications in demand forecasting and inventory optimization.
- Model-agnostic and post hoc explanation techniques for supply chain decision support.
- Human-in-the-loop XAI systems for logistics and production planning.
- Transparent reinforcement learning for autonomous industrial control.
- XAI in quality inspection and computer vision-based industrial systems.
- Causal inference and explainability in supply chain risk management.
- Evaluations of user trust and acceptance of explainable systems in industrial environments.
- XAI for sustainable and green supply chain operations.
We welcome the submission of theoretical, empirical, and applied research from both academic and industrial researchers. Submissions should clearly demonstrate how the proposed explainable methods enhance understanding, trust, or operational performance within industrial or supply chain contexts. Interdisciplinary contributions that integrate perspectives from computer science, engineering, and operations management are especially encouraged.
Dr. Yu-Cheng Wang
Guest Editor
Manuscript Submission Information
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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. Information 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
- explainable AI (XAI)
- industrial artificial intelligence
- supply chain analytics
- predictive maintenance
- demand forecasting
- human–AI collaboration
- sustainable supply chains
- decision-making
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