Pedro Ponce, Brian Anthony, Russel Bradley, Wenhao Xu, Juana Isabel Méndez, Arturo Molina
The manufacturing industry continually seeks advanced technologies to enhance performance per evolving customer requirements. Machine learning (ML) emerges as a pivotal assistive technology essential for strategic integration with Key Performance Indicators (KPIs). Traditionally, KPIs monitor and measure industrial system performance. This paper proposes a framework leveraging KPIs to integrate ML across the automation pyramid in Industry 5.0. The framework enables early detection of malfunctions and areas for improvement, preventing productivity loss. Validated across various industries, the framework demonstrates enhanced operational efficiency, sustainability, and human-centric benefits. Information and Communication Technologies advancements facilitate real-time data collection and analysis, aligning with ISO 22400 standards for manufacturing operations management. ML techniques generate actionable insights crucial for sustainable development in industries such as automotive, which require holistic goal assessments. Industry 4.0 marked a significant shift towards automation and data exchange, leveraging IoT, cloud computing, and big data analytics. Industry 5.0 emphasizes human-machine collaboration, customization, and sustainability, evolving KPIs to include worker satisfaction, customization capabilities, and social and environmental impact metrics. This evolution spans various sectors: manufacturing, pharmaceuticals, retail, e-commerce, high-energy-use industries, and consumer goods. ML minimizes downtime, enhances product quality, optimizes supply chains, and improves worker safety by analyzing data from wearables and sensors. Integrating ML with KPIs in Industry 4.0 and 5.0 enables industries to be more efficient, adaptive, and responsive to market and environmental changes, improving decision-making, operational efficiency, and alignment with business and sustainability goals.
Conference
Innovative Intelligent Industrial Production and Logistics. IN4PL 2024.
DOI
10.1007/978-3-031-80760-2_25
Publication Date
2025-2-13
Available online https://doi.org/10.1007/978-3-031-80760-2_25
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