CASE STUDY 2 AI driven operational efficiency for aerospace components manufacturer A mid-sized manufacturing enterprise specializing in advanced composite parts for the aerospace and defence sectors was facing challenges with material waste and production planning. The company worked with costly carbon fibre prepreg materials, making even small inefficiencies financially significant. Additionally, the manual process of generating daily cut plans for production was complex, time-consuming and heavily reliant on engineering support. To address these issues, the company implemented an AI-powered industrial optimization solution. This system seamlessly integrated with the existing infrastructure and enabled automatic generation of dynamic cut plans. It allowed for efficient grouping of work orders using the same materials, significantly reducing the total number of plans needed and streamlining production processes. As a result, the company achieved a 4.5% reduction in material waste, improved flexibility in manufacturing and increased overall efficiency. The production team gained independence from engineering for routine planning, and the system’s automation capabilities made it easier to manage last-minute changes. The enhanced traceability and ability to handle recuts more precisely further contributed to improved productivity and material savings. 2 AI-enabled quality management The context SMEs that wish to integrate with global supply chains face quality challenges. Traditional manual inspections are labour-intensive and can miss defects, leading to customer dissatisfaction, rework and higher costs. AI-powered quality control changes this through computer vision and deep learning, which enables real-time defect detection and classification. The first movers in India’s steel and electronics industries report a 30% improvement in defect detection, reduced rework costs and greater customer satisfaction. A detailed look – AI-enabled quality management FIGURE 8 Current scenario AI-enabled scenario AI-enabled quality management Manual inspections lead to inconsistent quality control and limited defect detectionAI systems enable automated, high-precision defect detection across production cycles Real-time computer vision identiﬁes subtle defects, reducing rework and improving quality AI seamlessly processes visual data without affecting production speedTraditional inspections create bottlenecks in high-speed productionMicroscopic defects escape detection, causing rework and customer dissatisfaction Comprehensive analytics enable data-driven process enhancementPoor defect tracking prevents systematic quality improvement AI-powered quality control meets international standards, strengthening market positionLimited quality capabilities restrict access to global supply chains Source: World Economic Forum Transforming Small Businesses: An AI Playbook for India’s SMEs 29

Transforming Small Businesses 2025