March 6, 2025 Harnessing AI to Modernize Manufacturing

AI is revolutionizing manufacturing, from predictive maintenance to smart automation, helping companies stay competitive.

By Venky Iyengar, Vice President and Global Head, Engineering and Cloud Services at Infosys

Manufacturers are under increasing pressure to enhance efficiency, reduce downtime, and drive innovation while navigating evolving market demands. Artificial intelligence plays a transformative role in modernizing manufacturing processes, from predictive maintenance and intelligent automation to real-time optimization. By strategically integrating AI, manufacturers can not only enhance productivity but also position themselves for long-term success in an increasingly digital and automated industrial landscape.

Understanding the Cost of Goods Manufactured

The typical breakdown of costs in manufacturing for the Cost of Goods Manufactured (COGM) includes three main components:

1. Direct Materials: This represents the cost of raw materials and components directly used in producing the finished goods. For example: raw materials (e.g., fabric for clothing manufacturing), components or parts (e.g., electronic components for gadget manufacturing) and packaging materials directly associated with the product.
2. Direct Labor: This covers the wages and salaries of workers directly involved in the production process. The wages of production line workers, salaries of factory managers and supervisors directly overseeing production and costs associated with quality assurance personnel are all included in this category.
3. Manufacturing Process and Overhead: This category encompasses all indirect costs associated with the production process. This includes indirect materials (e.g., supplies used in production but not part of the final product), indirect labor (e.g., maintenance staff, material handlers), factory utilities (electricity, water, etc.), rent or mortgage for the production facility, depreciation of manufacturing equipment, insurance and taxes related to the factory and factory supplies not directly associated with products.

The Importance of Manufacturing Efficiency

The exact proportion of these costs can vary significantly depending on the industry and specific manufacturing processes. The total cost of manufacturing as a percentage of revenue is a key metric used to evaluate manufacturing efficiency. However, this percentage can range significantly across different industries and companies.

Efficiency gained in the manufacturing will add to the competitive advantage for the companies. With the widespread availability of AI technologies in various aspects of manufacturing, the companies that are in the leading edge of leveraging AI will likely gain significant competitive advantage from a manufacturing perspective.

AI-Driven Cost Reduction Strategies

We will explore some of the ways AI will play a role in gaining efficiencies in each of these three areas:

Direct Materials Cost Reduction:

1. Optimized Material Selection: AI analyzes material properties, costs, and availability, enabling manufacturers to select the most cost-effective and sustainable materials for production. This balances cost and quality, ensuring competitiveness while maintaining high product standards.
2. Predictive Demand Forecasting: AI-powered tools like ChatGPT analyze historical data, market trends, and economic indicators to provide highly accurate demand forecasts. This allows manufacturers to optimize inventory levels and reduce unnecessary production costs, minimizing risks associated with inventory surplus.

By implementing these AI-driven strategies, manufacturers can achieve substantial reductions in material costs while maintaining or even improving product quality and operational efficiency.

Direct Labor Cost Reduction:

1. Robotic Process Automation (RPA): AI-driven robots perform repetitive tasks with high precision and speed, reducing labor costs and increasing productivity. For example, Foxconn employs AI-driven robots to assemble electronics faster and more accurately than human workers.
2. Workforce Optimization: AI-powered connected worker platforms analyze performance data to provide personalized learning paths and optimize onboarding, reducing training costs and improving efficiency. These systems can assess skills objectively, monitor performance continuously and predict future skill requirements.
3. Smart Scheduling: AI can optimize worker schedules based on demand forecasts, skills and availability, ensuring efficient labor allocation and reducing overtime costs.
4. Virtual Assistance: AI-powered chatbots and virtual assistants can handle routine inquiries and tasks, reducing the need for human customer service and support staff.

It’s important to note that we augment human workers rather than replace them, allowing employees to focus on higher-value tasks that require creativity, problem-solving and complex decision-making.

Manufacturing Process and Overhead Cost Reduction:

1. Design Optimization and Innovation: AI algorithms can explore vast design possibilities, generating optimal product configurations based on specified parameters and constraints. This approach enables designers to create innovative solutions that may not be obvious to human designers.
2. Rapid Prototyping: AI will accelerate the prototyping phase by quickly generating and testing designs in virtual environments leveraging digital technology before physical models are built.
3. Design for Manufacturing: AI ensures designs are optimized for manufacturability, considering factors like material usage, cost and production efficiency.
4. Sensor-Enabled Manufacturing: Different aspects of the actual manufacturing can be optimized leveraging AI techniques that work on sensor data. For example: FANUC has introduced the “AI path control function” to improve the precision of cutting and welding with robots.
5. Predictive Maintenance: AI systems analyze machinery data from connected devices to predict potential failures, reducing downtime and maintenance costs by preventing unexpected breakdowns. This approach minimizes the need for constant human monitoring and reactive maintenance work.
6. Quality Control: AI-powered inspection tools, particularly those using computer vision, can identify defects more accurately than human inspections, reducing the need for manual quality control labor.
7. Automated E2E view: Agentic AI transforms E2E business processes by enabling reasoning and connecting the dots that are not obvious to humans.
8. Energy Optimization: AI monitors and regulates energy usage, ensuring machines operate at their most efficient levels, leading to significant cost savings in energy-intensive processes.

Conclusion

AI-driven modernization is transforming the manufacturing landscape, enabling companies to enhance efficiency, reduce downtime and drive innovation. By strategically integrating AI technologies, manufacturers can streamline operations, improve supply chain resilience and accelerate product development.

While challenges exist, the potential benefits of AI in manufacturing are substantial, positioning companies for long-term success in an increasingly digital and automated industrial environment. As the manufacturing sector continues to evolve, those who embrace AI-driven modernization will be better equipped to navigate market uncertainties, meet changing customer demands and maintain a competitive edge.

About the Author:
Venkatesh Iyengar (Venky) has been with Infosys for 22+ years playing different global roles. He currently lives in Silicon Valley and is responsible for growing Cloud and Validation solutions business worldwide across industries. Venky has developed a Patented Testing Organization Maturity Assessment methodology while leading the validation consulting group and has managed multiple organization transformations for clients across industry verticals. He holds a Bachelor’s degree in Mechanical Engineering and MBA in Sales & Marketing.

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