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Manufacturing
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Future-Ready Manufacturing: Partnering in your journey to a sustainable manufacturing enterprise
As the manufacturing value chain faces disruptions, know how TCS’ framework drives innovation and AI technology in manufacturing for better agility and growth.
Related Insights
This POV focuses on the importance of building robust, intuitive analytical and visual models to highlight cost gaps by design with respect to the market ask (what the Customer is willing to pay, including the premium) at the feature level for the respective market, compare and contrast different features and models along with competitive data, navigate up and down the associated vehicle functional groups such as Infotainment, Seating, Lighting, Navigation and so on. These analytical and visual models will also include the Year-on-Year cost reduction beyond the Supply negotiations, using Value Analysis and Value Engineering, before the next major refresh - like the way it is institutionalized in the case of ICE vehicles today.
White Paper
08 Jan 2025
This white paper explores the integration of Artificial Intelligence (AI) and Quantum Intelligence in battery development for the electric vehicle industry. It highlights innovative approaches to enhance battery performance, efficiency, and sustainability, addressing key challenges in EV technology evolution.
White Paper
27 Dec 2024
As electric vehicles (EVs) become more prevalent, software is playing a crucial role in shaping their future. Advanced software solutions are enhancing EV performance, efficiency, and user experience. From intelligent energy management systems that optimize charging and discharging to smart charging schedules that consider grid load and energy prices, software is driving innovation in electric mobility. This transformation is paving the way for more sustainable, efficient, and user-friendly transportation solutions.
White Paper
16 Dec 2024
Highlights
- Better training for operators across manufacturing processes can improve the quality and safety of end products. However, the lack of cost-effective training options poses a challenge.
- Industries are looking at integrating artificial intelligence (AI) with image processing techniques to transform various aspects of manufacturing.
- TCS has partnered with NVIDIA to enhance the quality of operations across the manufacturing value chain by adopting the best practices and closed-loop feedback.
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Overview
There is room for manufacturing industry to boost operational efficiency by enhancing capabilities of its workforce, among other factors.
The manufacturing value chain is a complex network of processes. It includes operator training, quality inspection of tools, components, operations, and finished products at different stages of production. It also demands adherence, adaptation, and generation of standard operating procedures (SoPs).
Globally, manufacturers and distributors allocate large budgets to spend on learning tools for operators. The availability of training experts for interns or new operators poses a challenge. This may hamper the quality of finished products. In manufacturing, quality is also linked to the safety of end users of products. Traditional manufacturers often struggle to deal with changes in existing processes of production, assembly, and operations. This can affect customer satisfaction and lead to delays in turnaround time for quality products.
AI in manufacturing
Leveraging niche technologies to transform manufacturing value chain
In today's fast-paced and increasingly complex manufacturing landscape, there is a growing demand for a more efficient, reliable, and comprehensive approach to dealing with such operational challenges. Enterprises and public sector organizations around the world are developing AI agents to boost the capabilities of the workforce. Application of cognitive AI in image processing techniques has emerged as a transformative force in quality control. By leveraging computer vision and advanced image analysis algorithms, AI systems can automate, enhance, and sometimes, redefine quality control procedures.
TCS is integrating the NVIDIA AI Blueprint for video search and summarization (VSS) to build visual AI agents that can transform manufacturing processes. The technology helps build and deploy generative AI-powered visual agents that can ingest and analyze massive volumes of videos and extract insights for summarization and interactive Q&A. These AI agents can answer user questions, generate summaries, and enable alerts for a wide spectrum of scenarios.
Strategic approach
Converting hours of visual data from industrial cameras using VSS and Gen AI vision agent into useful insights.
AI agents are designed to help humans handle labor-intensive processes. A feature that stands out in the combination of the NVIDIA AI Blueprint and Gen AI vision agents is the stored memory of scenarios, which can be contextualized further by an array of vision language models (VLM). This strategic approach is poised to reduce bottlenecks in the manufacturing value chain.
- A key component could be checking the machine assembly for the right tools, the right sequence of operations, missing actions, and anomalies.
- Causality check is another area that is addressed by asking queries in simple language with an expectation from VSS to highlight anomalies that occurred during operations -for example, why is there noise inside the machine or why is assembly taking longer than expected etc.
- Another area of implementation could be training operators by rating the quality of operations against a benchmark and by providing real-time feedback during operations and assembly.
- Lastly, this approach can also be used to generate SoP documents by creating a comprehensive list of actions performed in front of the cameras in one or multiple zones in the plant.
Way forward
Paving the path towards improved quality of manufacturing operations
This approach can be applied across industries and varied scenarios like bolstering security in retail outlets and transportation, boosting airport and traffic operations, etc. In short, it has the potential to transform cities into smart cities. TCS is working to find compelling ways to streamline the quality of operations across the manufacturing value chain. Using NVIDIA NIM microservices and AI Blueprints, and following best processes will set the right environment to maximize yield and aids in creating closed-loop feedback for better training across operations.
Image of Dr. Apurba Das
Dr. Apurba Das
Dr. Apurba Das is the global technology head for the cognitive AI practice at TCS’ IoT and digital engineering unit. He advises companies across business verticals on how to use computer vision and generative AI to solve their business problems. He has more than 21 years of experience working across different industries and has been instrumental in developing innovative solutions and services for automotive, retail, logistics, manufacturing, and healthcare industries. He has filed 44 patents globally on super-resolution, adaptive compression, multi-modal micro-activity recognition, and autonomous vehicle systems and published more than 40 research papers in peer-reviewed journals. He has also authored five technical textbooks. Apurba is part of the editorial board of multiple SCI-indexed journals. He holds a PhD in computer vision and an M.Tech degree in intelligent automation and robotics.
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