Industrial Data Mining and Machine Learning Applications

Dear Colleagues, 

In the era of Industry 4.0, the fields of data mining and machine learning have evolved at a breakneck pace, surpassing human intelligence in uncovering data patterns and refining decision-making processes. Data mining techniques have shown promise in discerning trends and patterns that yield valuable insights for business and management. Conversely, machine learning—with its spectrum spanning supervised, unsupervised, and reinforcement learning—offers diverse functionalities such as reasoning, clustering, and optimization. These tools are instrumental in making well-informed decisions to tackle complex industrial engineering challenges. 

A plethora of research endeavors in recent years have sought to harness these advanced techniques to address industrial problems, including demand forecasting, customer relationship management, inventory control, and fleet management. Despite a robust theoretical framework and extensive exploration, the practical adoption of these technologies is not widespread, particularly within small and medium-sized enterprises (SMEs). In sectors with a high concentration of SMEs, the benefits reaped from the advancements in data mining and machine learning remain modest. Consequently, the disparity in capabilities between enterprises that do and do not employ these technologies is widening, potentially impeding the sustainable growth of entire industries. 

This Special Issue aims to address the following critical questions: (i) Why have industrial data mining and machine learning applications not achieved widespread adoption across industries? (ii) How can these applications be effectively implemented within industrial settings? When data mining and machine learning tools are put into practice, the resultant value—be it in terms of sustainability, resilience, or human-centric approaches—can be substantial, fostering the shift towards the next industrial revolution, Industry 5.0. 

We invite original research and review articles that probe and capitalize on industrial applications by leveraging big data and machine learning. Submissions should focus on both the exploration and the exploitation of these technologies within an industrial context. 

Topics of interest for this Special Issue include but are not limited to the following:

• Industrial data mining applications;
• Big data mining in industrial settings;
• Machine learning applications in industry, including supervised, unsupervised, and reinforcement learning;
• Engineering education of industrial data mining and machine learning;
• Drivers and barriers to implementing industrial applications;
• The impact of industrial applications on sustainability, resilience, and human centricity;
• Solutions to industrial engineering problems in sectors such as manufacturing, logistics, supply chain management and healthcare.

We look forward to your contributions that will help bridge the gap between theoretical research and practical implementation, ultimately steering industries towards a more innovative and sustainable future.

Dr. Yung Po Tsang
Dr. C. H. Wu
Prof. Dr. Kit-Fai Pun
Guest Editors

Manuscript Submission Information

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• industry 4.0
• data mining
• machine learning
• industrial applications
• supervised learning
• unsupervised learning
• reinforcement learning
• small and medium-sized enterprises (SMEs)
• industry 5.0
• higher teaching
• engineering education