In today's manufacturing era, ignoring innovation and technology adoption is like a race team leaving their best mechanics on the sidelines. It's no longer a choice, it's a necessity for survival. It may seem all very daunting, with constant strife for improving efficiency, improving customer experience all while keeping up with sustainable practices and dealing with unpredictable market fluctuations. It seems like a complex battle where you are just trying to keep up for surviving.
Generative AI is here to help you tackle these challenges
Generative AI (GenAI) has emerged as a game changer, ushering in the next Industrial Revolution with its transformative capabilities across sectors, including manufacturing. Think of it as a creative problem-solver trained on vast amounts of data. It can design intricate components, predict equipment failures, and even suggest entirely new production processes. Unlike its analytical cousins, generative AI does not just analyse, it creates.
GenAI functions by leveraging neural networks to generate new data that mimics patterns observed in existing datasets. These networks, often trained on large datasets, learn to understand the underlying structure and characteristics of the data to which they are exposed. The basic processes which make up GenAI –
- Data Ingestion and Processing – Understanding structured as well as unstructured data (e.g., contracts, customer feedback, research data, policies)
- Pattern Recognition and Learning – Identifying statistical relationships and underlying rules within the data.
- Content Creation based on Existing Data (e.g., simulations, test cases) – Creating entirely new content based on the learned knowledge and patterns, not just replicating existing data.
- Continuous Learning and Improvement – Using feedback and evaluation to refine the model's ability to generate better content over time.
Due to these abilities which help in generating creative solutions, GenAI finds numerous applications throughout the manufacturing value chain.
Idea to Design
Product design blends engineering, user experience, marketing, and manufacturing to turn ideas into solutions that serve specific needs. Design for Manufacturing (DFM) optimizes product designs during their early phases to minimize production costs. This proactive approach streamlines manufacturing, saving time and money by identifying and correcting potential design flaws before production begins. GenAI revolutionizes this process. It allows manufacturers to explore a vast universe of design possibilities by analysing massive datasets and identifying innovative materials, functionalities, and solutions.
- Exploring new material and functionalities
Traditional material discovery, relying on time-consuming trial-and-error, is being transformed by startups using AI. These startups are not only finding alternative materials but also developing AI-assisted models that streamline the computational process, saving time, money, and effort. This is especially important as sustainability drives changes in product design. GenAI can suggest alternative materials that meet both product requirements and critical ESG criteria. - Detecting design constraints
Generating hypothetical scenarios of how distinctive design choices might impact functionality, cost, and sustainability. Use of simulation models to test different design iterations and predict their performance under various conditions can predict the design constraints more effectively. - Visualizing initial concepts
Building high-fidelity prototypes with 3D printing or virtual reality, accelerating design testing and feedback loops can shorten the iterative process in design phase. Analysing existing products, artwork, and natural patterns to generate new design ideas and aesthetic concepts can give a much realistic picture of end product in terms of looks, functionality, and constraints.
This isn't just theoretical – automotive majors have already incorporated these techniques in their processes. They leverage generative design algorithms to optimize parts for their vehicles, leading to lighter, stronger, and more cost-effective components. These algorithms generate multiple design options, each rigorously evaluated and tested under simulated real-world conditions before the optimal choice is made. Automotive manufacturers are actively leveraging AI-based generative design to develop zero-emission vehicles, a crucial step towards meeting tightening environmental regulations and shaping the future of mobility.
The benefits include: faster design cycles, reduced design bias, and improved design quality.
Design to Production
Manufacturing thrives on smooth operation, but unforeseen challenges can throw a spanner in the works, impacting everything from scheduling, production, inventory management, dispatch. Equipment maintenance, quality control, complex supply chains are the critical areas that are often susceptible to unexpected disruptions, leading to costly downtime, production delays, and even reputational damage. The ability of generative AI to ingest data and simulate scenarios tackle these challenges head-on.
- Predictive maintenance:
While traditional AI monitors factory machines, generative AI takes it further, predicting maintenance needs. By analysing various data points, it can forecast when servicing, shutdowns, or backup machines are necessary. This personalized approach (considering individual machine limitations) allows manufacturers to improve scheduling, collaborate with suppliers, and extend machine lifespans.
- Supply chain optimization:
GenAI acts as your supply chain advisor, offering a clear view into your entire network. It recommends the best suppliers based on your needs, considering factors like material specifications, availability, delivery schedules, and even sustainability. Its ability to analyse legal documents helps extract key information (using natural language processing) and provide real-time insights, leading to smarter decision-making across your supply chain.
- Quality control:
By analysing vast test case data, GenAI predicts potential issues, identifying recurring problems and failure points in new or modified products. This translates to increased process accuracy, improved efficiency and scalability, broader risk assessment, and ultimately, enhanced product quality.
The benefits include: improved scheduling, significant cost savings, and faster time to market.
Generative AI's potential in manufacturing extends far beyond what we've seen here. It can revolutionize everything from process design to growth strategies. However, understanding its strengths and strategically applying them to your specific needs is key. While the technology is still evolving, organizations that explore and implement it now will be well-positioned to unleash its transformative power.
About the Author:
Ankita Singh,
Business Consultant
Ankita Singh is a business consultant at Tech Mahindra’s digital transformation office with experience in strategy consulting, leading growth programs, and sales for manufacturing enterprise clients. She is responsible for creating compelling digital transformation narratives and helping clients navigate the rapidly evolving digital landscape.
