Industry
Manufacturing
Future-Ready Manufacturing: Partnering in your journey to a sustainable manufacturing enterprise
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.
On this page
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.
