Data-Driven Decision Making in Industrial Automation

Authors

  • Muhammad Waseem Assistant Professor, Department of Mechanical & Industrial Engineering, University of Engineering and Technology, Lahore, Pakistan
  • Ayesha Khalid Lecturer, Department of Electrical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
  • Ahmed Raza Associate Professor, Department of Mechatronics, NED University of Engineering and Technology, Karachi, Pakistan

Keywords:

Data analytics, Industrial automation, Industry 4.0, Machine learning, Predictive maintenance, Smart manufacturing, Artificial intelligence, Decision optimization

Abstract

The rapid integration of data analytics and automation technologies has revolutionized industrial operations, ushering in the era of smart manufacturing and Industry 4.0. Data-driven decision-making (DDDM) enables industries to enhance efficiency, reduce downtime, and optimize processes through real-time insights derived from large data streams. This paper explores how big data, artificial intelligence, and machine learning are transforming industrial automation by enabling predictive maintenance, intelligent control systems, and adaptive production planning. The study also examines challenges such as data integrity, integration complexity, and workforce readiness while offering recommendations for sustainable implementation. Through a comprehensive review and sectoral analysis, the article highlights that data-driven strategies are key enablers of competitiveness, resilience, and sustainability in modern industrial ecosystems

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Published

2022-06-30

Issue

Jan-Jun 2022

Section

Articles

License

All articles published in the International Journal of Advanced Sciences and Computing (IJASC) are open access and licensed under the Creative Commons Attribution (CC BY 4.0) license. Authors retain copyright, and users may read, share, reuse, and adapt the content with proper attribution.