From Waste to Value: How Gen AI is transforming smart manufacturing

Waste management has emerged as a strategic priority in the boardroom as manufacturers are facing increasing pressure to enhance operations while maintaining sustainability commitments. Material waste can account for up to 30% of manufacturing costs in certain sectors. The next leap in industrial transformation will come from preventing waste at source, not just managing it after it is created.

Generative AI (GenAI) helps manufacturers to simulate, model, and optimize resource usage. It supports manufacturers in implementing sustainable manufacturing practices, helping reduce waste, improve energy efficiency, and accelerate progress toward the circular economy and net-zero targets.

GenAI models simulating waste reduction scenarios pre-deployment

A large portion of manufacturing waste results from product design and development. Conventional methods usually require multiple physical prototypes and testing, which increases materials consumed. GenAI is transforming this paradigm by allowing it to simulate many different designs and performances before production begins. This reduces the number of prototypes and helps cut down on materials consumed through extensive experiments.

GenAI can anticipate defects or performance issues before it is in full production, enabling manufacturers to address any problems before they become serious. Consequently, manufacturers can achieve lower scrap rates and less rework, and less material waste across manufacturing operations.

Achieving material savings via optimized processes

GenAI helps manufacturers in making informed and accurate decisions by analyzing the performance requirements, operating conditions and constraints in manufacturing. This enables us to choose materials that meet functional requirements while minimizing unnecessary use of resources.

Manufacturers can now improve their operational efficiency through real-time detection of faulty parts and the optimization of their overall manufacturing process to decrease excess material and rework. This means that instead of depending only on batch-level quality checks, errors identified at the individual piece level throughout the manufacturing cycle can be eliminated, resulting in fewer defective products, less wasted material, and reduced rework of materials during the manufacturing process.

Digital twins for energy efficiency and emissions reduction

Digital twins help manufacturers model and optimize energy use across equipment and production lines before changes are implemented on the shop floor. Manufacturers using digital twins have a greater level of visibility into energy consumption for their various production processes, which leads to more opportunities to improve efficiency and performance of the overall operations.

Combining GenAI with digital twins allows for digital replicas of actual working environments to improve manufacturing processes while they are still in the planning phase and identify potential issues before deployement. This provides organizations with greater visibility to their energy usage while optimizing their use of resources, as well as enhancing overall performance.

Ties to India’s net-zero goals with circular manufacturing focus & EPR compliance

Indian companies are becoming less dependent on new raw materials as they adopt a circular manufacturing strategy for reaching net-zero emissions and creating sustainable economies. AI can facilitate this by analyzing product characteristics to recommend products which can be manufactured utilising recycled or repurposed materials to provide the same level of functionality and safety as products manufactured with virgin (newly sourced) materials. It can also identify the most appropriate allocation of resources and recovery of materials from existing stock material through evaluating the material properties, characteristics of product specifications, and lifetime analysis of the materials.

With the increasing focus on creating circular economies through the scrapping of vehicles policy in India, AI can be used to assist manufacturers determine the most efficient method of manufacturing with recycled or repurposed materials. This will improve efficiency and eliminate reliance on newly sourced raw materials.

As EPR regulations are evolving, this means that manufacturers will be expected to be increasingly accountable to end-users and consumers for the entire life cycle of their products. GenAI can help organizations with product traceability, resource management, and visibility throughout the life cycle of products. These insights will enable manufacturers to make more informed decisions related to sourcing, recycling, and end-of-life management, while also aiding in compliance efforts.

Indian manufacturers embracing Industry 5.0

Industry 5.0 signifies a change from automated-centric, manufacturing-centric, and automation-laden production systems to an approach that emphasizes human-centric approaches while being both sustainable and resilient. GenAI is expected to serve as an intelligent partner in the collaborative environment of human expertise and advanced technology.

GenAI will provide manufacturers with real-time information to identify bottlenecks, improve workflows, and better respond to fluctuations in demand and operational changes, enabling the more efficient use of resources, minimising excessive inventories and material wastage, and increasing the resiliency of the supply chain.

As manufacturers look to create a balance between operational efficiencies and sustainable goals, their ability to make better data-based decisions will be a critical area of competitive advantage. Manufacturers that take a forward-looking view in this regard, through the intentional and strategic use of embedding intelligence and other capabilities into their entire supply chain, will maximise their efficiency, utilise and conserve resources sustainably, minimise waste, and create a foundation for sustainable long-term growth.

Author: Anup S S, Practice Head, Artificial Intelligence, Tata Elxsi