Revolution in the Manufacturing Sector through AI

(General Studies Paper-3: Science & Technology – Developments and Applications and Their Effects in Daily Life)

Context

The Fourth Industrial Revolution (Industry 4.0) includes cyber-physical systems such as the Internet of Things (IoT) and Artificial Intelligence (AI), which enable the creation of "smart factories" where machines self-adapt, take autonomous decisions, and collaborate with humans.

Use of AI in the Manufacturing Sector

Predictive maintenance and fault detection: Sensors and AI are used to predict issues like vibrations or temperature anomalies.
Prescriptive maintenance: Diagnoses problems and recommends corrective actions.

Benefits in Manufacturing Sector

Time and Cost Reduction

Global companies suffer annual losses of up to $1.4 trillion due to breakdowns.
AI significantly reduces these losses, saving about 23% in service costs.

Enhanced Safety and Efficiency

Robotic drones/AI systems (e.g., Gecko Robotics) inspect hazardous infrastructure, improving safety and enabling real-time analysis.

Assembly Line Optimization

AI-powered robotics can fine-tune and adjust operational speeds, increasing productivity.
Example: Ford's torque converter plant saw a 15% increase in speed.

Data-Driven Quality Control

AI helps in quick defect detection, reducing wastage and ensuring consistent product quality.

Status of AI Usage in India

Growing AI Market: India's AI market in manufacturing is expected to reach ₹12.6 billion by 2028 (CAGR ~59%).
Sectoral Applications: AI and IoT are being used for predictive quality evaluation, maintenance, and supply chain optimization in key industries like dairy (Amul), automotive (Maruti, Bajaj), pharma, and textiles.
Government Initiatives: India’s National AI Mission (e.g., AI4ICPS, IndiaAI Mission) is investing heavily in computing infrastructure to support AI R&D in industrial applications.

Challenges

High Upfront Costs: Smart sensors and AI tools require significant capital expenditure.
Skill Gaps and Resistance: Traditional workers often lack AI/data skills and fear job loss. However, AI is being positioned as a collaborator.
Interoperability and Data Issues: Fragmented data silos, lack of standardization, and cybersecurity vulnerabilities hinder seamless AI adoption.
Lack of Trust: Black-box AI models reduce employee confidence.

Black Box AI

Black Box AI refers to artificial intelligence models whose decision-making processes are not transparent—i.e., it’s unclear how the AI arrived at a particular outcome or decision. When such systems are used in workplaces or organizations, they may negatively impact employee trust.

For instance, if an employee is denied a promotion by an AI system without explanation, they might perceive it as discrimination or bias.

Government Initiatives

Incentive Schemes: Encouragement to startups and Micro, Small, and Medium Enterprises (MSMEs) through schemes like PLI (Production-Linked Incentive), subsidies for AI technologies, and tax exemptions to reduce adoption barriers.
Skill Development Programs: Focused training in AI, robotics, and data analytics to reskill/upskill the workforce.
Standardization and Frameworks: Establishment of standardization and certification norms, Algorithm Impact Assessment (AIA), and data-sharing frameworks to build trust.
Infrastructure Investment: Continued public investment in indigenous GPU farms, AI hubs, and national data platforms.
Promotion of Ethical AI: Promoting explainability, model audits, and algorithmic fairness in industrial AI systems.