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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.06492 |
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| _version_ | 1866913829612421120 |
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| author | Shyalika, Chathurangi Prasad, Renjith Ghazo, Alaa Al Eswaramoorthi, Darssan Kaur, Harleen Muthuselvam, Sara Shree Sheth, Amit |
| author_facet | Shyalika, Chathurangi Prasad, Renjith Ghazo, Alaa Al Eswaramoorthi, Darssan Kaur, Harleen Muthuselvam, Sara Shree Sheth, Amit |
| contents | In the dynamic landscape of Industry 4.0, achieving efficiency, precision, and adaptability is essential to optimize manufacturing operations. Industries suffer due to supply chain disruptions caused by anomalies, which are being detected by current AI models but leaving domain experts uncertain without deeper insights into these anomalies. Additionally, operational inefficiencies persist due to inaccurate production forecasts and the limited effectiveness of traditional AI models for processing complex sensor data. Despite these advancements, existing systems lack the seamless integration of these capabilities needed to create a truly unified solution for enhancing production and decision-making. We propose SmartPilot, a neurosymbolic, multiagent CoPilot designed for advanced reasoning and contextual decision-making to address these challenges. SmartPilot processes multimodal sensor data and is compact to deploy on edge devices. It focuses on three key tasks: anomaly prediction, production forecasting, and domain-specific question answering. By bridging the gap between AI capabilities and real-world industrial needs, SmartPilot empowers industries with intelligent decision-making and drives transformative innovation in manufacturing. The demonstration video, datasets, and supplementary materials are available at https://github.com/ChathurangiShyalika/SmartPilot. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06492 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SmartPilot: A Multiagent CoPilot for Adaptive and Intelligent Manufacturing Shyalika, Chathurangi Prasad, Renjith Ghazo, Alaa Al Eswaramoorthi, Darssan Kaur, Harleen Muthuselvam, Sara Shree Sheth, Amit Artificial Intelligence In the dynamic landscape of Industry 4.0, achieving efficiency, precision, and adaptability is essential to optimize manufacturing operations. Industries suffer due to supply chain disruptions caused by anomalies, which are being detected by current AI models but leaving domain experts uncertain without deeper insights into these anomalies. Additionally, operational inefficiencies persist due to inaccurate production forecasts and the limited effectiveness of traditional AI models for processing complex sensor data. Despite these advancements, existing systems lack the seamless integration of these capabilities needed to create a truly unified solution for enhancing production and decision-making. We propose SmartPilot, a neurosymbolic, multiagent CoPilot designed for advanced reasoning and contextual decision-making to address these challenges. SmartPilot processes multimodal sensor data and is compact to deploy on edge devices. It focuses on three key tasks: anomaly prediction, production forecasting, and domain-specific question answering. By bridging the gap between AI capabilities and real-world industrial needs, SmartPilot empowers industries with intelligent decision-making and drives transformative innovation in manufacturing. The demonstration video, datasets, and supplementary materials are available at https://github.com/ChathurangiShyalika/SmartPilot. |
| title | SmartPilot: A Multiagent CoPilot for Adaptive and Intelligent Manufacturing |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.06492 |