2019· International Journal of Applied Data Science & Modern Computing· Vol 2, pp. 01-17· 0 citations
TL;DR
The role of predictive maintenance in optimizing manufacturing processes is explored, focusing on how data analytics can be harnessed to streamline operations, improve workforce productivity, and reduce costs.
Abstract
Predictive maintenance (PdM) has become an essential strategy in modern manufacturing, enabling industries to shift from traditional, reactive maintenance methods to data-driven, proactive approaches. By leveraging advanced data analytics, such as machine learning, artificial intelligence, and Internet of Things (IoT) technologies, manufacturers can predict equipment failures before they occur, thus reducing unplanned downtime and enhancing resource management. This paper explores the role of predictive maintenance in optimizing manufacturing processes, focusing on how data analytics can be harnessed to streamline operations, improve workforce productivity, and reduce costs. Case studies across various industries illustrate the practical applications and challenges of implementing PdM systems. Additionally, the paper examines the future trends shaping predictive maintenance and resource management, emphasizing the ongoing advancements in AI, IoT, and big data technologies. The paper concludes with insights into the broader implications for manufacturers looking to stay competitive in an increasingly data-driven manufacturing landscape.
The findings reveal that ML algorithms significantly improve fault detection, Remaining Useful Life (RUL) estimation, and maintenance decision-making, but challenges related to data quality, model interpretability, cybersecurity, and integration with legacy systems continue to affect implementation effectiveness.
P. Siva, Sankar Shunmuga, Sundaram et al.· Stanzaleaf International Jou...· 0 citations
In today's competitive business environment, organizations face the constant challenge of optimizing operational efficiency while managing limited resources. Traditional resource allocation methods often fall short in addressing the complexity and dynamic nature of modern operations. This paper explores the transformative role of Artificial Intelligence (AI) and Predictive Analytics in revolutionizing resource allocation processes. AI techniques, including machine learning and optimization algorithms, enable businesses to make data-driven decisions, while predictive analytics provides insights into future demand and resource needs. By integrating AI and predictive analytics, organizations can enhance decision-making accuracy, reduce costs, and improve overall operational efficiency. Through case studies and industry examples, this paper demonstrates the potential of these technologies to optimize resource allocation in various sectors, including manufacturing, healthcare, and logistics. The paper also discusses the challenges, ethical considerations, and future trends in leveraging AI and predictive analytics for operational optimization.
E. F. Novseen· International Journal of App...· 0 citations
The manufacturing sector is undergoing a paradigm shift with the integration of Industry 4.0 technologies, particularly Industrial Big Data Analytics (BDA), which leverages high-velocity data from IoT sensors, PLCs, and production systems to enable real-time decision-making. This paper presents a novel edge-cloud analytics framework designed to address critical challenges in modern manufacturing, including data heterogeneity, latency bottlenecks, and cybersecurity risks. By implementing a hybrid architecture, the system processes sensor data at the edge (e.g., vibration spectra, thermal images) with <50ms latency for time-sensitive tasks like defect detection, while cloud-based machine learning models (e.g., LSTMs) perform long-term predictive maintenance with 89% accuracy. A large-scale case study conducted at an automotive assembly line demonstrated a 20% increase in production throughput and 15% reduction in unplanned downtime, translating to $2.7M annual cost savings. Key innovations include: (1) a dynamic data normalization pipeline (Eq. 1) that handles skewed industrial datasets; (2) a comparative analysis of ML models, showing Random Forest outperforms ANN/SVM in defect classification (92.4% F1-score); and (3) a priority-based edge processing system that reduces cloud bandwidth usage by 60%. Despite these advancements, the study identifies persistent hurdles such as legacy system interoperability (resolved via OPC UA gateways) and adversarial robustness in edge ML models. The paper concludes with a roadmap for future work, including federated learning for multi-plant scalability and digital twin integration for simulation-driven analytics. These findings validate BDA as a transformative tool for smart manufacturing, offering a 5.2-month ROI and actionable insights for practitioners adopting Industry 4.0 solutions.
J. Arsac, Gérard Huet· International Journal of Dat...· 0 citations
This review examined how predictive analytics, supported by modern data engineering, can strengthen spare parts planning and supply chain reliability in semiconductor manufacturing. The study adopted a structured narrative review approach, synthesising evidence on intermittent-demand forecasting, predictive maintenance, equipment-failure modelling, feature engineering, inventory optimisation, data architectures, systems integration, governance, and organisational readiness. Particular attention was given to the operational realities of capital-intensive fabrication environments, where proprietary components, uncertain failure patterns, long replenishment lead times, equipment obsolescence, and production bottlenecks create substantial reliability risks.
The findings show that effective planning depends on combining sensor streams, maintenance histories, inventory transactions, procurement records, supplier performance, and production priorities within scalable and governed data pipelines. Machine-learning models, survival analysis, anomaly detection, remaining-useful-life estimation, and intermittent-demand methods can provide earlier and more accurate indications of component requirements. However, analytical accuracy alone is insufficient unless predictions are embedded within maintenance, inventory, procurement, and supplier-management systems. The review further identifies poor data quality, weak asset-to-part mapping, model drift, cybersecurity exposure, skills shortages, and fragmented decision ownership as major barriers to implementation.
The study concludes that predictive spare parts planning should be treated as an integrated reliability capability rather than a stand-alone analytical initiative. It recommends phased deployment beginning with bottleneck equipment and high-criticality components, standardised master data, confidence-based decision rules, continuous model validation, cross-functional governance, and supplier collaboration. Future progress should prioritise digital twins, uncertainty-aware forecasting, interoperable data platforms, and workforce development to reduce downtime, improve inventory productivity, strengthen resilience, and support more dependable semiconductor operations. These priorities provide a practical foundation for responsive planning across distributed facilities, suppliers, maintenance networks, and markets.
Ayokunle Olamide Ijagbemi, Stanley Nwakamma, Marudi Oyefuga· International Journal of Mul...· 0 citations
Unplanned downtime and suboptimal maintenance practices remain a significant challenge across most industrial sectors, leading to costly operational interruptions and reduced productivity. The Institute of Mechanical Engineers estimates that industrial downtime costs the global economy approximately $500 billion annually, with the majority attributed to unplanned maintenance and equipment failures. Predictive Maintenance (PdM) systems leveraging digital twin technology offer promising solutions for real-time monitoring, fault detection, and failure prediction. However, many industries find it challenging to apply these technologies effectively due to data accuracy issues, system complexity, and scaling difficulties. This paper proposes an integrated digital twin-based PdM framework that enhances fault detection and diagnosis capabilities for complex industrial machinery, particularly wind turbines. The system simulates equipment behavior, predicts potential failures, and proactively schedules maintenance to reduce unplanned downtime through the exploitation of real-time data from Internet of Things (IoT) sensors. Moreover, integrating blockchain technology ensures secure, transparent data sharing among stakeholders. The proposed framework is projected, under the stated assumptions to reduce downtime and maintenance costs by margins consistent with prior digital-twin predictive-maintenance literature, pending empirical validation. Thus, this paper contributes a novel predictive maintenance approach that integrates leading-edge technologies, providing practical insights for future deployments across diverse industrial sectors. This indicates the high potential of digital twin technology as a foundation for the applications of Industry 4.0 and will lay the ground for more robust and efficient industrial operations. This paper presents a conceptual and theoretical framework; it has not been empirically validated on physical equipment or real operational data, and the figures reported herein are illustrative projections rather than measured outcomes.
Kashif Iqbal, Sohaib Elahi, Mairaj Nawaz et al.· Journal of Information and C...· 0 citations
Artificial intelligence-driven predictive maintenance represents a critical enabler of operational excellence, resilient manufacturing systems, and sustainable industrial transformation in the era of Industry 4.0.
Banoth Samya, V. Ramesh, A. Vathsala et al.· Journal of Intelligent Decis...· 0 citations
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