AI Solutions
for Aerospace & Automotive

AI, quantum techniques and other Big Compute^TM promise to unlock new advances in fluid dynamics, autonomous driving, supply chain optimization and traffic management. According to McKinsey, even a 2-5% increase in productivity would create $10 billion to $25 billion in value for the industry each year.

Featured Use Case

Disruption Management

Challenge

Approach

Disruptions are common in the transportation industry, particularly among airlines. Since large logistics networks are closely interconnected — one plane may fly multiple routes in a single day — a small disruption in one place can lead to costly losses across the entire network. In fact, irregular operations (IROPs) can cost airlines as much as 8% of their revenue. Airline operators are often forced to weigh multiple competing considerations when rescheduling flights such as time, costs, further delays, and passenger compensation. The result is a complex multi-factor combinatorial optimization problem that needs to be solved in real-time.

Use Cases

Applications for Aerospace and Automotive

Vehicle Design

Vehicle Manufacturing

Fleet Operations

Vehicle Design

Vehicle Manufacturing

Fleet Operations

Key Challenges

High costs of wind tunnel aerodynamics testing
Time-consuming and expensive simulations for aerodynamics and heat transfer
Optimizing designs to balance many objectives for desired product functionalities
Diagnosing points of failure to explain a set of given observations

Zapata Solutions

Computational Fluid Dynamics (CFD) Simulation

Speed up data analysis, improve accuracy and reduce costs using quantum CFD simulations for aerodynamic design.

Heat Transfer Inverse Design

Improve heat transfer in designs for engines and other vehicle components using generator-enhanced optimization (GEO) with PDE constraints.

Failure Analysis

Identify failure modes through quantum-enhanced fault-tree analysis to improve safety in future vehicle designs.

Design Process Optimization

Balance various product functionalities with safety, reliability and costs in design process optimization models and solve them with GEO.

Key Challenges

Reducing waste and downtime in manufacturing processes
Training autonomous vehicles for rare scenarios
Reducing costs and delivery times in the supply chain
Preventing inventory stock-outs and buffer overflows

Zapata Solutions

Manufacturing Process Optimization

Increase efficiency by optimizing the design of factories, employee schedules, and machine processes using quantum or quantum-inspired prescriptive analytics.

Object Detection

Improve object recognition in autonomous vehicles using quantum-enhanced supervised learning.

Supply Chain Optimization

Optimize the location of facilities and selection of suppliers, distributors, and vendors for product quality, costs, delivery times, and demand coverage using GEO.

Inventory Optimization

Optimize the stocking of raw materials and manufacturing components to prevent overflows and reduce costs using quantum or quantum-inspired prescriptive analytics.

Key Challenges

Reducing fuel consumption to save costs and reduce emissions
Predicting maintenance needs for fleet vehicles
Mitigating delays and costs stemming from irregular operations (IROP)
Time-consuming calculations for routing optimization
Optimizing the scheduling and distribution of fleet vehicles to satisfy demand and reduce waste

Zapata Solutions

Fuel Optimization

Optimize the speed and route of airplanes, buses, and other vehicles to minimize fuel consumption using GEO.

Predictive Maintenance

Predict maintenance needs for fleet vehicles using variational quantum classifier and other quantum-enhanced machine learning techniques.

Disruption Management

Optimize the re-routing of planes and other logistics networks to react quickly to irregular operations using quantum-enhanced optimization techniques.

Network Planning Optimization

Optimize the scheduling and routing of fleet vehicles to account for demand, crew scheduling, fuel planning, and other concerns using GEO.

Use Case Timeline (est.)

Featured Case Study

Andretti Autosport

At over 200mph, tires degrade quickly: a typical Indy 500 race includes around five tire changes per car. A key part of race strategy for teams like Andretti Autosport is knowing when to take a pit stop — a problem similar to predictive maintenance problems faced by automotive manufacturers.

Since spring 2022, we’ve worked with Andretti Autosport to upgrade their analytics infrastructure and build machine learning models for race strategy. This includes models for tire degradation analysis and fuel savings optimization that translate directly to automotive and OEM use cases

Read Full Case Study

Our Platform

Orquestra^® Benefits for Aerospace and Automotive

Orchestration Across Environments

Leverage the heterogenous compute resources best suited for your tasks, without getting locked into any one hardware platform. Deploy across hybrid backends at enterprise scale.

Data Management & Velocity

Store, retrieve, and analyze large datasets. Streamline data management from ingestion to export to accelerate data velocity.

Open Framework

Keep what works now. Integrate existing and future solutions with a framework optimized for extensibility, interoperability, and innovation.

Workflow Development and Deployment

One unified platform to go from research to development to deployment with extensible, scalable, modular workflows.

AI Solutions for Aerospace & Automotive

Disruption Management

Challenge

Approach

Applications for Aerospace and Automotive

Use Case Timeline (est.)

Andretti Autosport

Orquestra® Benefits for Aerospace and Automotive

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AI Solutions
for Aerospace & Automotive

Orquestra^® Benefits for Aerospace and Automotive