AI and Cloud fuels India’s automotive sector to top gear

This article is authored by Subramaniam Thiruppathi, Director of Sales for India and Sub-Continent, Zebra Technologies.
The rapid expansion of India's public cloud services market, projected to reach approximately $24.2 billion by 2028, is catalysing the adoption of Artificial Intelligence (AI) and data-driven solutions across various sectors, particularly in automotive manufacturing.As the number of automotive plants in India increases, the sector is witnessing an equity inflow from foreign direct investments worth approximately $1.5 billion in fiscal year 2024, as manufacturers are increasingly looking to leverage advanced cloud platforms.
This is primarily to support their digital transformation goals, especially when implementing AI-powered solutions like deep learning machine vision across multiple sites. New facilities and the modernisation of existing sites to support electric vehicle production are prime opportunities to rethink tooling and processes to maximise efficiency, quality and labour.
According to Zebra’s Automotive Ecosystem Vision study, 64% of APAC automotive manufacturers say a top priority is to build strategic partnerships with tech companies for their next generation of production. This underscores a growing recognition that collaboration with tech firms is crucial for advancing manufacturing capabilities. By forging these alliances, automotive manufacturers aim to leverage cutting-edge technologies, such as advanced robotics, AI-driven analytics, and Internet of Things (IoT) solutions, to enhance production efficiency, innovate their processes, and stay competitive in a rapidly evolving market.
We know that when it comes to developing new and existing factories and procuring solutions, there is a site-level focus with input and sign-off shared at the site and corporate levels. But there is always the possibility of different sites using different solutions for similar workflows, and the risk of expertise and data not being shared across sites, including when using newer AI-powered solutions in which data quality is essential. This can also be true for visual inspection teams using machine vision systems for quality and compliance.

AI, particularly deep learning, thrives on data – volume, variety and velocity of good quality data is key to training and testing deep learning models, so they deliver the outcomes expected when deployed in real life. Data needs to be stored, annotated, and used for training models, with other data sets needed for model testing. It makes no sense for company data in these cases to remain siloed, to the detriment of better training for machine vision models.

Machine vision teams across manufacturing industries need new ways to leverage deep learning machine vision, which should include using the cloud. A cloud-based machine vision platform would allow teams to securely upload, label, and annotate data from multiple manufacturing locations across site, country, and region. A larger, more diverse range of pooled data in a cloud-based platform from across sites and environments is better for deep learning training. Such a platform would allow defined users to work together in real time, collaborate on annotation, training and testing projects, and share their expertise.

With a cloud-based platform, users with defined roles, rights and responsibilities could train and test deep learning models in the cloud. Powered by much better training and testing data, they may deliver much higher levels of visual inspection analysis and accuracy beyond conventional, rules-based machine vision for certain use cases. These outcomes are sought by manufacturers in the automotive, electric battery, semiconductor, electronics and packaging industries, to name a few.
A software as a service model would give machine vision teams the flexibility and ease of investing in a cloud-based platform with a subscription while new features, models, and updates are seamlessly added by the technology partner. Deep learning cloud-based platforms will allow for model edge deployment on PCs and devices to support flexible, digitised workflows on the production line, on a PC or device wherever a user or team is located.
In conclusion, the surge in AI adoption, coupled with a strong emphasis on digital transformation, highlights the intent of manufacturers to enhance data management and harness new technologies that improve visibility and quality throughout the production process. One of the most pressing challenges in quality management today is the integration of data. As automotive manufacturers plan new facilities and focus on AI and data-driven objectives, the opportunity to leverage advanced cloud solutions and deep learning machine vision has never been greater. By adopting these technologies, manufacturers can address integration issues, optimize operations, and fully realize the benefits of AI, setting the stage for substantial improvements in efficiency and product quality.
Disclaimer: Views and opinions expressed in this article are solely those of the original author and do not represent any of The Times Group or its employees.