Industry
Manufacturing
Future-Ready Manufacturing: Partnering in your journey to a sustainable manufacturing enterprise
Highlights
- Product development in manufacturing industries has traditionally been a long-drawn process. AI interventions in product development would lead to faster development cycles and improved product quality.
- While the need for AI interventions is evident, organizations are slow in in strategizing and architecting AI adoption in product development.
- Bringing effective AI practices in product development will help organizations shift their focus toward innovation and creativity, resulting in customer-centric products and services.
On this page
Model-based strategies in product development
The manufacturing sector is witnessing the confluence of multiple transformative trends.
Product development teams must navigate increasing product complexity, growing demand for faster time-to-market, evolving customer expectations for customized, fit-for-purpose products, and reducing environmental impact. All these objectives should be met while ensuring lower cost and high quality.
Model-based approaches leverage data-rich digital models that integrate requirements, design, testing, and validation information. These strategies improve collaboration, enable concurrent design, reduce rework, and improve decision-making throughout the product lifecycle.
By incorporating AI, these models can learn and adapt dynamically, allowing enhanced capabilities for hyper-automation, design optimization, and intelligent decision-making.
AI-driven model-based strategy
AI augmented model-based approaches can transform product development for superlative outcomes.
A model-based framework encompasses multiple approaches, which help enhance product development. AI can augment these approaches, improving automation, decision-making, and optimization.
Design exploration: Augment generative design systems to automate design exploration and expand the design envelope. This would allow manufacturers to recommend an optimal design based on performance criteria by comparing the output from a vast number of simulations.
Predictive analytics: Machine learning algorithms can be trained with large amounts of historical data and then used to analyze real-time data to forecast product performance and identify failure points. This will help to improve critical parameters such as performance, uptime, and sustainability impact of products.
Optimization algorithms: Multi-modal optimization techniques can be enhanced with evolutionary or gradient-based algorithms to provide design solutions, which can optimize multiple objectives including performance, sustainability, manufacturability, serviceability, and cost.
Natural language processing (NLP): AI can assist in interpreting, comparing, summarizing, and organizing complex requirements documents from various sources such as customers, regulations, and standards. It can also be applied to quickly draft reports and documents needed for various stakeholders.
While AI encompasses a vast array of techniques, certain methods are particularly suited to enhance model-based approaches.
Key AI techniques are given below:
Deep learning: Machine learning (ML), a subset of AI, enables learning from data and allows making predictions. Within ML, deep learning models, which leverage neural networks, have evolved rapidly in recent years. These would enable manufacturers to recognize patterns and support predictive analytics with visibly high accuracy.
GenAI: GenAI automates the process of generating new ideas, refining designs, and creating design parameters that explore design possibilities within a large design space. Combined with generative design systems, this will allow manufacturers to improve product design considerably.
Digital twin: AI-enabled digital twin models can monitor sensor data from the field in real-time, run simulations, and adapt embedded control parameters to optimize product performance.
Knowledge graphs and NLP: Together, knowledge graphs and NLP offer a powerful ability to contextualize the generation and analysis of data. Knowledge graphs will feed organizational and external data of industry and domain specific entities and their relationships providing context to NLP output.
Edge AI and distributed AI: Edge computing allows the processing of product usage and performance data closer to the device, enabling real-time feedback to optimize performance and avoid fault situations. Distributed AI allows you to optimize and coordinate across multiple systems operating on the edge.
How AI will play out across the product development lifecycle
AI will significantly accelerate design and prototyping processes while enabling data-driven personalization.
Integration of AI technologies will streamline development cycles, ensuring efficient, customer-centric products. AI can be applied across the entire product lifecycle—from concept and design to development, verification, validation, manufacturing, quality, and after-sales support, including end-of-life management.
Let us examine key use cases:
Concept development and ideation
- AI-driven generative design tools explore multiple design options based on engineering constraints and objectives such as cost, weight, material, and durability.
- NLP-based AI models analyze market trends and customer feedback to identify innovative product features. They can customize the product options, features, and functionality to create tailored solutions that improve customer experience.
- Automating CAD-related activities, performing feasibility analysis, and complying with design standards can create initial designs quickly.
Solution modeling and simulation
- Physics-informed neural network-based AI models can reduce the simulation iterations for traditional physics-based models by improving cycle time and reducing the high costs of expensive computation capacity.
- AI-assisted simulations for multiple domains like structural, thermal, and electromagnetic analysis can improve both accuracy and efficiency.
Testing, validation, and verification
- AI will enhance automated testing frameworks by optimizing the required testing scenarios based on the impact of changes to design, reducing manual test cycles.
- Predictive analytics models will help manufacturers identify potential design failures before physical prototyping.
Post-launch optimization
- Digital twins, combined with AI, enable real-time monitoring and adaptive enhancements. It can prescribe changes to design parameters or even automatically change them to improve performance in real time.
- Neural networks-based AI models can identify underlying patterns in usage data that would allow for design changes to improve user experience and launch improvement packages to extend the life of products.
Key benefits of adopting AI-driven model-based strategies in product development include:
- Increased efficiency: Automating the product development process—including design iterations, testing, and data analysis—frees up critical engineering resources for strategic tasks and accelerates development time.
- Improved product quality: Analyzing large datasets to identify potential design flaws and predict issues before they occur will lead to high-quality products with fewer defects.
- Faster time-to-market: By streamlining the development process and identifying potential issues in design and development early, manufacturers will be able to significantly reduce the time to conceptualize and produce new products.
- Personalized customer experience: Manufacturers can analyze customer data to suggest design options and considerations for tailoring products and services that would elevate customer experience.
- Innovation: AI will assist in generating new product ideas and design concepts through generative models, fostering innovation and creativity.
- Cost reduction: With AI, product teams will be able to optimize designs, identify inefficiencies, and reduce errors to minimize design and development costs.
Some challenges to tackle
Comprehensive AI adoption requires a solid process and data foundation.
Despite its potential, AI adoption presents critical challenges.
Data quality and availability: AI results are as good as the quality of data used to train the models. Inconsistent product attribute data (such as dimensions, material, and standards), proliferated designs pose challenge to the accuracy of AI models. Also, this data should be available at significant volume for AI use. Hence, investment in data digitalization and governance is essential.
Value-based repercussion: The outcome of any AI model is based on the volume of data used to train the models. It is crucial to consider biases in AI algorithms and ensure accurate decision-making. Since product designs are governed by IP, regulatory and security policies of an organization, it may limit the use of right data sets.
System interoperability: Integrating AI solutions with existing systems and infrastructure can become a bottle neck for adoption. AI in model-based product development demands diverse data sources from across engineering platforms (for instance CAD, PLM, and simulation), which may pose limitations to successful adoption. In addition, organizations need AI expertise to understand and manage AI systems effectively.
Cost of implementation: The cost to implement AI is evolving as rapidly as the technology. This creates difficulty in defining measurable returns on investment (ROI) for AI initiatives. Specifically in product development domain, the cost to value ratio grows proportionately with the maturity of AI capabilities.
Regulatory compliance: Navigating complex regulations surrounding AI usage in specific industries and geographies is another challenge manufacturers would face. For instance, medical and food industries have defined product standards and regulations which can limit the full value of AI adoption. Similarly, countries have institutionalized security policies to regulate the use of AI and its outcome.
An approach to a successful adoption
Organizations should adopt a structured, phased approach to AI integration.
AI has many use cases that will drive leaps in performance improvement. Business leaders are increasing investments in AI. Based on research, most organizations have AI implementation in process or have completed implementing use cases in one function. As the rate of adoption increases, organizations are looking to AI for more intelligent use cases and higher gains from investments. Yet, only few companies today are leveraging AI as a transformation agent for business.
Organizations should adopt a plan big, start small approach. They should have a long-term AI strategy supported by a business case and roadmap and adopt an agile implementation approach. This will allow opportunities to learn and adapt the AI strategy as technology evolves.
Organizations must factor interoperability and scalability in their architecture decisions. AI strategy should set guidelines for the adoption of technologies that are interoperable to gain benefits as AI scales across the enterprise and extended value chain. Model interoperability and leveraging open-source AI tools are key. This will also help accelerate AI initiatives by leveraging the ready resources available. To ensure scalability for future needs, cloud and edge computing should be part of the foundational reference architecture to build AI capabilities.
Lastly, people’s perspective is one of the most important factors that need to be considered for the responsible use of AI. Business users should be trained in the potential and risks associated with embedding AI in business processes. To ensure smooth adoption, AI should become part of the execution process by embedding it in the tools leveraged for executing tasks. AI expertise should be built into the organization. It should be augmented with business experts to identify areas of potential and become evangelists. Along with building inhouse skillset to guide the development and execution of AI strategy, external help from domain and technology experts should be taken to learn from their experience and avoid expensive mistakes.
Accelerating innovation, optimizing performance
AI-driven model-based strategies will fundamentally transform conventional design philosophies to address dynamic market needs.
With rapid advancements in AI and enabling technologies, product development will be supported by a high degree of automation and intelligence to optimize operations and improve decision making. Adoption of complementary technologies such as omniverse, extended reality, 6G, and quantum computing will exponentially increase the benefits. Taking a strategic approach to AI adoption at scale within the enterprise, leading adopters will stand to gain benefits from a higher innovation rate, product quality, and efficient operations.
