Transformer-Based Time Series Forecasting for Smart Manufacturing and Industrial Data Analytics
Keywords:
transformer networks, time series forecasting, smart manufacturing, industrial data analytics, self-attention, edge-cloud architecture, model robustness, sustainability, fairnessAbstract
The adoption of transformer architectures for time series forecasting has catalyzed a paradigm shift in smart manufacturing and industrial data analytics. Unlike recurrent or convolutional approaches, transformers leverage self-attention mechanisms to capture long-range dependencies and intricate temporal patterns across multivariate sensor streams, production logs, and equipment states. This paper provides a comprehensive systems-level analysis of transformer-based forecasting frameworks deployed in industrial contexts. We examine the architectural trade-offs inherent in scaling self-attention to high-frequency, multi-sensor environments, including computational complexity, memory footprint, and temporal resolution management. A critical assessment of deployment infrastructure is presented, focusing on edge-cloud orchestration, latency constraints for real-time control loops, and data governance challenges arising from heterogeneous data sources. Furthermore, we explore structural considerations such as model interpretability for root-cause diagnostics, robustness to distribution shifts caused by equipment degradation or process changes, and fairness implications when forecasting models influence maintenance scheduling, resource allocation, or workforce planning. Sustainability dimensions, including energy consumption during training and inference, and the environmental footprint of large-scale transformer models, are evaluated in the context of industrial carbon accounting. Policy and ethical considerations are discussed in relation to proprietary data sharing, standardization of forecasting benchmarks, and regulatory frameworks for AI-driven decision support in manufacturing. By integrating insights from computer science, industrial engineering, and socio-technical systems theory, this paper offers a multi-faceted roadmap for responsibly deploying transformer-based time series forecasting in smart manufacturing ecosystems. We conclude with forward-looking recommendations for governance structures, model lifecycle management, and cross-domain research collaborations.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
