Industrial Generative AI for Manufacturing
Manufacturing, with its wealth of sensor data, could see enormous efficiency gains and better predictive maintenance by applying Industrial Generative AI and other advanced analytics. According to McKinsey, generative AI could have an $170-290B annual impact in advanced manufacturing.
Harness your live streaming sensor data to drive smarter decisions in real time.
Many manufacturers struggle to fully leverage their sensor data. In some cases, sensors measuring variables of interest cannot be placed due to physical limitations. In other cases, operators struggle to manage the sheer volume and velocity of incoming data. Other challenges include data quality issues, data cleaning, merging data from different sources, and processing data on the edge. Most importantly, manufacturers dealing with these challenges often miss the opportunity to apply AI and machine learning to gain insight from their live streaming sensor data and drive better decisions.
Solutions for Manufacturing’s most complex industrial-scale challenges — on the edge and in real-time.
Anomaly Detection
LLM Retrieval
Optimization
Predictive Modeling
Sensor Fusion
Anomaly Detection
LLM Retrieval
Optimization
Predictive Modeling
Sensor Fusion
Key Challenges
Zapata AI Solutions
Compliance Automation
Automate the detection of regulatory compliance violations in product and planning documentation, facility sensor data and distribution network data.
Predictive Maintenance
Prevent downtime by training an algorithm to proactively identify early warning signs of equipment breakdowns.
Quality Control
Detect faulty components or finished products more accurately than conventional or manual approaches.
Safety Hazard Detection
Detect potential hazards with greater accuracy by training the detection algorithm with synthetic data simulating possible workplace scenarios.
Optimizing Manufacturing Plant Scheduling with BMW
BMW and other global manufacturers face a complex optimization problem in scheduling their workers to hit production targets while minimizing idle time. In collaboration with The Center for Quantum Engineering at MIT, we applied quantum-inspired generative AI to generate new solutions to BMW’s plant scheduling problem. Our proprietary Industrial Generative AI technique tied or outperformed other state-of-the-art optimization solvers in 71% of problem configurations.
How can these solutions work for your enterprise?