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 a $170-290B annual impact in advanced manufacturing.
Harness live streaming sensor data
to drive smarter decisions in real time.
Many manufacturers struggle to fully leverage their sensor data in real time. In many cases, physical constraints prevent the placement of sensors where they are critically needed to measure certain variables of interest. In other cases, the sheer volume and velocity of the incoming data can be overwhelming – there’s too much data, too fast. Problems with data quality, merging datasets, and processing data on the edge can present further challenges. Most importantly, manufacturers dealing with these challenges often miss the opportunity to apply AI and machine learning to gain insights from their live streaming sensor data to inform real-time decision-making.
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
Leverage quantum techniques to detect unusual events more accurately than traditional algorithms, triggering alerts or annotating data.
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
Train an algorithm to proactively identify early warning signs of equipment breakdowns and prevent downtime.
Quality Control
Detect faulty components of finished products more accurately than conventional or manual approaches.
Safety Hazard
Simulate plausible workplace scenarios and detect potential hazards with greater accuracy.
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?