Industry
Manufacturing
Future-Ready Manufacturing: Partnering in your journey to a sustainable manufacturing enterprise
Highlights
- Synthetic data eliminates barriers created by real-world data by providing scalable, cost-effective ways to train AI models on rare and critical scenarios, making it a game-changer for industries striving to innovate.
- Platforms like NVIDIA Omniverse Replicator take synthetic data generation to the next level. Omniverse offers photorealistic rendering capabilities and seamless integration with 3D models, enabling industries to simulate highly realistic and diverse scenarios at scale.
- By solving these ethical and privacy challenges, synthetic data enables faster innovation while maintaining fairness and compliance. When aligned with organizational goals, synthetic data becomes a key differentiator in building future-ready AI systems.
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Synthetic Data
Synthetic data is transforming AI in mobility and manufacturing, solving challenges like data scarcity, high costs, and privacy concerns.
Synthetic Data is the Key to Innovation
By accelerating development cycles by 30%, cutting inspection costs by 60%, and enhancing AI’s ability to handle real-world edge cases, synthetic data delivers measurable results. For businesses seeking faster innovation, reduced costs, and market leadership, adopting synthetic data is essential to remain competitive.
Breaking Barriers in AI with Synthetic Data
AI models for autonomous driving and industrial inspection demand accuracy and scalability to ensure safety, efficiency, and operational excellence. However, reliance on real-world data often creates bottlenecks due to high costs, ethical concerns, and insufficient edge-case scenarios.
Synthetic data eliminates these barriers by providing scalable, cost-effective ways to train AI models on rare and critical scenarios, making it a game-changer for industries striving to innovate.
Framework for Synthetic Data Generation (SDG)
Real-World Data
Why Real-World Data Falls Short
While enterprises understand the importance of data, its limitations often hinder AI progress.
Rare Events Are Hard to Capture
Edge cases, such as detecting micro-defects in manufacturing or handling unpredictable traffic hazards, occur infrequently. This leaves AI models under-trained for critical but rare scenarios, reducing their reliability.Expensive and Time-Consuming
Data collection and cleaning processes consume up to 80% of project timelines in AI development. These labor-intensive tasks delay deployments and inflate costs, making it difficult to scale AI systems efficiently.Limited Diversity
Real-world datasets often lack variability in key factors like lighting, material properties, or environmental conditions. This limits AI models’ ability to generalize and adapt to new or unseen scenarios, reducing their effectiveness.Privacy and Ethical Challenges
Regulations and proprietary restrictions can severely limit the use of real-world data. Concerns over privacy, ownership, and ethical use further complicate access, slowing innovation in regulated industries.
By addressing these limitations, synthetic data unlocks the potential for AI systems to scale faster and operate more effectively in diverse conditions.
NVIDIA Omniverse
How Synthetic Data with NVIDIA Omniverse Helps Solves the Problem
Synthetic data provides a scalable solution to these challenges by simulating real-world conditions using advanced rendering and simulation tools. Unlike real-world data, synthetic data is designed to meet specific training requirements, making it a versatile and cost-effective alternative.
At TCS, we are integrating NVIDIA Omniverse Replicator, a framework for building custom synthetic data generation pipelines, into our existing workflows.
Replicator is a key component of The NVIDIA Omniverse, a platform of application programming interfaces, software development kits, and services that enable developers to easily integrate Universal Scene Description (OpenUSD) and NVIDIA RTX rendering technologies into existing software tools and simulation workflows for building AI systems
One of the key features of Omniverse Replicator is domain randomization.
Domain randomization introduces variability in datasets, enabling AI models to handle diverse real-world conditions. For example:
Lighting Variations: Simulating day/night, indoor/outdoor, and dim/bright settings.
Weather Effects: Generating fog, rain, snow, or overcast conditions.
Defect Variations: Creating micro-cracks, scratches, or material inconsistencies.
Camera Perspectives: Simulating different angles, distances, and viewpoints.
By leveraging domain randomization, synthetic data helps ensure that AI models are robust, adaptable, and capable of performing in unpredictable environments for a variety of domains.
Platforms like NVIDIA Omniverse Replicator take synthetic data generation to the next level. Omniverse offers photorealistic rendering capabilities and seamless integration with 3D models, enabling industries to simulate highly realistic and diverse scenarios at scale.
For Manufacturing: NVIDIA Omniverse allows developers to simulate defects such as scratches, cracks, and misalignments under varied lighting and material conditions. This improves defect detection accuracy and enhances quality control processes.
For Mobility: Omniverse lets developers replicate dynamic traffic scenarios, such as foggy nights, glare, and sudden obstacles. These simulations accelerate the training and validation of autonomous driving systems, helping ensure robust performance in edge cases.
By enabling high-quality synthetic data creation, NVIDIA Omniverse bridges the gap between synthetic and real-world data, empowering businesses to train more reliable and adaptable AI models.
Additionally, the advent of Generative AI is helping accelerate the creation of synthetic data by speeding up many tasks, from content creation to image augmentation.
Industry-Specific Impact: Mobility and Manufacturing
Autonomous vehicles (AVs) rely on AI systems that can respond to diverse traffic scenarios, including rare and hazardous edge cases. Synthetic data provides an efficient way to train AV models on these conditions.
Mobility: Autonomous Driving
Autonomous vehicles (AVs) rely on AI systems that can respond to diverse traffic scenarios, including rare and hazardous edge cases. Synthetic data provides an efficient way to train AV models on these conditions.
Example: A Japanese AV startup simulated 100,000+ traffic scenarios, including rare hazards like foggy nights, sudden pedestrian crossings, and obstructed road signage. This reduced validation cycles by 30%, accelerating deployment while improving safety and reliability.
Impact: Synthetic data enables AV companies to handle edge cases effectively, significantly improving safety and deployment speed.
Manufacturing: Defect Detection
In manufacturing, detecting micro-defects in components is critical to ensuring quality and reducing warranty claims. Synthetic data allows manufacturers to simulate surface anomalies and defects under controlled conditions.
Example: A leading U.S. railroad transportation company is revolutionizing vehicle inspections at its terminals by implementing an automated damage detection system. Vehicles pass through automated inspection portals, where cameras detect and classify damages in real-time. This eliminates manual inspections, reducing downtime and inspection costs while improving operational efficiency.
Impact: Synthetic data accelerates defect detection, reduces costs, and ensures consistent operational excellence.
Ethical and Privacy Benefits
Synthetic data addresses critical privacy and ethical concerns that often delay AI adoption:
Regulatory Compliance
Synthetic datasets eliminate ownership and intellectual property conflicts, ensuring adherence to data protection regulations. By removing sensitive information, synthetic data accelerates innovation while adhering to legal requirements.Bias Reduction
AI models trained on real-world data often inherit biases present in the original dataset. Synthetic data mitigates this issue by introducing controlled variability, ensuring datasets are diverse, balanced, and free from systemic bias.
By solving these ethical and privacy challenges, synthetic data enables faster innovation while maintaining fairness and compliance.
Synthetic Data Advantage: Faster, Smarter, More Efficient
Faster Development
Synthetic data reduces dependency on rare real-world data, cutting training timelines by 75% and enabling rapid iterations.Cost Savings
By automating defect detection and minimizing manual inspections, synthetic data lowers inspection costs by 60%, improving efficiency.Accelerated Time-to-Market
Synthetic data shortens development cycles by 30%, enabling businesses to accelerate products launches and enhance their competitive advantage.
These advantages make synthetic data a powerful tool for achieving operational excellence and driving innovation.
The Strategic Role of Synthetic Data
Synthetic data is more than a technical solution — it’s a strategic enabler for businesses striving to achieve:
Digital Transformation: Simulate complex scenarios that drive innovation and operational efficiency.
Faster Product Cycles: Shorten time-to-market while maintaining high quality.
Competitive Advantage: Stay ahead by preparing AI systems for real-world challenges with scalable, innovative solutions.
When aligned with organizational goals, synthetic data becomes a key differentiator in building future-ready AI systems.
The Future is Synthetic
Synthetic data isn’t just solving today’s challenges — it’s shaping the future of AI in mobility and manufacturing.
By reducing costs, improving AI adaptability, and ensuring faster product cycles, synthetic data is redefining what’s possible for AI-driven enterprises. To remain competitive and lead in innovation, businesses must harness the transformative potential of synthetic data today.
If your organization is ready to explore the possibilities of synthetic data, connect with us at autonomous.solutions@tcs.com to begin your journey.
