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Industrial Data Analytics for Intelligent Manufacturing Decisions

Author(s) Prof. Pang James
Country Singapore
Abstract Industrial manufacturing environments are increasingly characterized by interconnected machines, automated production systems, complex supply chains, high-volume data generation, and rapidly changing market requirements. Under these conditions, conventional decision-making approaches based primarily on historical reports and managerial experience may not provide sufficient responsiveness or analytical depth. Industrial Data Analytics provides an important technological and analytical foundation for transforming manufacturing data into actionable intelligence. This study examines the role of Industrial Data Analytics in supporting intelligent manufacturing decisions and evaluates its contribution to production optimization, predictive maintenance, quality management, resource allocation, supply-chain coordination, energy optimization, and operational performance. A qualitative descriptive research design based on secondary academic literature was adopted to investigate major analytical technologies and their industrial applications. The study considers descriptive, diagnostic, predictive, and prescriptive analytics alongside artificial intelligence, machine learning, Industrial Internet of Things technologies, cloud computing, edge computing, digital twins, and real-time visualization.
The analysis demonstrates that industrial data analytics can improve decision-making by providing timely information about production conditions, equipment performance, product quality, material movement, resource consumption, and operational risks. Descriptive analytics enables organizations to understand historical and current performance, while diagnostic analytics assists in identifying potential causes of operational problems. Prescriptive analytics can support the selection of appropriate operational responses by evaluating alternative decisions and their potential consequences. Predictive maintenance represents a significant application because data from sensors and equipment can be analyzed to identify early indicators of mechanical deterioration. Quality analytics can identify relationships between production parameters and defect occurrence, enabling earlier corrective intervention. Industrial Data Analytics can also contribute to energy management by identifying inefficient equipment and processes and monitoring energy consumption in real time. Despite these advantages, organizations face challenges involving data quality, interoperability, cybersecurity, analytical skills, technology costs, legacy infrastructure, organizational resistance, and model interpretability.
Keywords Industrial Data Analytics, Intelligent Manufacturing, Manufacturing Decisions, Predictive Analytics, Artificial Intelligence, Machine Learning, Industrial IoT, Smart Manufacturing, Predictive Maintenance, Decision Support
Field Engineering
Published In Volume 3, Issue 6, November-December 2021
Published On 2021-11-30
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