Services
IoT and Digital Engineering
Bringing Together IoT and Digital Engineering to Create Rich Experiences
Highlights
- Businesses are transitioning toward artificial Intelligence (AI)-based decision making, moving away from intuition-led or experience-centric decision making.
- An outcome-based approach focusing on product innovation, operational efficiency, productivity, and servitization can be a key differentiator in defining transformation interventions.
- There is significant potential for cross-leveraging AI applications across engineering, asset-intensive, manufacturing, and service-oriented value chains.
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Mega convergence
Devising a cohesive long-term AI strategy will require enterprises to take a multi-dimensional approach.
Software-enabled products, assets, and devices; digital twins; AI (including generative, industrial, and cognitive AI); and edge-to-cloud distributed computing with 5G connectivity are emerging as game-changers for successful strategic transformations. Propelled by these technology capabilities, there is also a significant shift in the typical characteristics exhibited by value chains across industries. Unlike conventional value chains that offer a linear structure, which is self-limiting and follow a more sequential left-to-right approach, industry value chains are now largely enabled by a data-driven approach, right-to-left outcome focus, and AI-based decision making.
Among other technologies, AI and GenAI are taking center stage with their transformational capabilities and effortlessly bringing a shift in every aspect of the business landscape. However, to drive sustainable business value from AI, enterprises will need to design a cohesive, long-term AI strategy. It will require them to take a multi-dimensional approach (see Figure 1) that enables:
- A convergence of industrial analytics, cognitive analytics, and generative AI.
- A combination of data science, business and functional domain context, and engineering (physics) tenets.
- Bringing together structured and unstructured data from information technology (IT), engineering technology (ET), and operational technology (OT) systems.
A multi-dimensional approach
TCS’ Bringing Life to ThingsTM framework provides a roadmap to achieve this and inspires businesses to explore exponential value propositions that touch upon business dimensions and core technical capabilities for realizing sustainable growth. Learn more about leveraging digital intelligence to build a truly connected enterprise.
Innovation catalyst
Generative AI is not just a technology; it’s an innovation catalyst.
Generative AI (GenAI) is making waves across various industries, transforming the way we create, operate, and consume information. The boundaries of what is possible with GenAI-driven creativity, precision, and efficiency are continually expanding. GenAI is opening new doors for innovation, optimizing operations, empowering creativity, and enhancing decision making across industries. Industry leaders are already embracing AI-powered technologies as ‘virtual soundboards’ for quick and accurate decision making.
In a recent TCS AI study, executives said that “enabling better compliance and monitoring” and “gaining real-time information” were among the top business objectives driving their AI investments. Such an approach seems to be paying off. Organizations using AI for the above goals were more likely to have had higher revenue growth, profit growth, or both, compared to their competitors, as seen recently.
With its ability to analyze data in multi-modal formats, GenAI holds the promise to reshape industries and drive efficiencies in unprecedented ways. From conceptualizing and creating designs and interactive videos to accelerating code development and building visual avatars, GenAI applications are challenging the status quo and transforming the way tasks were once done.
For instance, the healthcare industry is constantly evolving to provide better patient care and personalized treatments. With GenAI, healthcare professionals can now synthesize information from MRIs and create accurate reports, simulate scenarios on how an ailment may progress, and help pre-empt the same; thus, providing much-needed targeted personalized care/medicine.
Another application of GenAI is automated code generation—it significantly expedites product development, which is often a complex, time-consuming task. It is proving to be a game-changer in product development.
Innovate to execute
Bringing life to human-like intelligence in systems, products, and processes.
Imagine a future where machines do not just perform operational tasks but are also able to ‘connect in context’ and become ‘predictive’ and ‘self-aware’—the defining pillars of TCS Bringing Life to ThingsTM. And then imagine how generative AI can take these capabilities to the next level with the ability to process large volumes of data with greater accuracy and with human-like intelligence.
Organizations that have made digital technologies key to their core strategic agenda are deriving exponential value by responding to physical context with digital intelligence. Such organizations will benefit further, with GenAI taking away the cognitive load from humans and empowering machines to respond in context and drive efficiency, productivity, performance, and engineering innovation.
A GenAI-led journey for businesses can be carved out across three phases, with increasing order of maturity:
- Assist: In this phase, GenAI capabilities can be leveraged to assist humans in performing their tasks. GenAI models can be used as assistants for R&D, in product design, or for smart factory operations.
- Augment: In this phase, GenAI can empower employees with contextual knowledge that boosts their productivity. For example, it can support design engineers with data from multiple sources, such as past maintenance records and service records, to enhance product design.
- Transform: In the final phase, GenAI-powered, ‘context-aware’ systems and processes can operate autonomously to deliver human-like, high-quality, and consistent decision making. Such systems, can, for example, have the ability to automatically generate standard operating procedures for asset operations and autocorrect set point machine parameters to avoid failure.
Reshape value chains
Moving toward GenAI-enriched engineering, asset-intensive, manufacturing, and service value chains.
Clearly, GenAI will play a key role in reshaping the engineering, asset-intensive, manufacturing, and service value chains and moving them toward an intelligent and knowledge-driven future where the boundaries between man and machine are blurred. Let’s see how.
Pioneering next-gen engineering R&D
The rising demand for new and differentiated products has made product design more complex than ever. Engineering teams are under constant pressure to expedite projects while meeting the highest quality standards. GenAI brings the ability to generate and interpret information across diverse formats such as language and imagery and incorporate learnings from different sources and instances to rapid experimentation. It also provides ample opportunities to elevate model-based systems engineering (MBSE) techniques, from automating the way complex systems are designed and analyzed to optimizing system performance, thus promising a reimagined future of R&D.
GenAI is already accelerating the innovation process and driving improvements in automotive design by suggesting alternate materials and designs that enhance vehicle performance and safety through early tests, validation, and defect discovery.
GenAI can help in navigating the complexities faced by change specialists in conducting impact analyses across diverse data sets related to design, material specification, engineering, and quality. It can swiftly generate summaries across design and process-related documents to provide technical recommendations that encompass the entire hierarchy of items and documents impacted by a proposed change, therefore, resulting in increased productivity and efficiency gains.
Moving toward neural manufacturing
With its ability to support quality control, supply chain monitoring, optimizing production processes, and more, AI is already making significant impact on manufacturing. GenAI’s ability to summarize and contextualize information across formats raises AI-based decision making to the next level. It allows operators to work more productively by optimizing workflows, automating repetitive and time-consuming tasks such as maintenance, or troubleshooting through ‘copilots’.
Consider quality control, something that is central to manufacturing operations. It significantly impacts cost and on-time delivery and in its absence, factories may produce higher waste levels that result in increased labor costs and reduced efficiencies. In combination with industrial AI and cognitive AI, GenAI models that are pre-trained on huge amounts of product quality and visual inspection data can help analyze and identify anomalies in real time, thus reducing deviations and enhancing product consistency.
Similarly, a GenAI-based interactive system can transform a day in the life of a factory operator with its ability to interpret data from multiple sources and influence decision making, taking it from experience-based to data-driven. GenAI co-pilots can provide recommendations that can help workers to identify the best ways to perform specific tasks. It can also augment predictive maintenance capabilities by creating step-wise instructions in the form of text or images, which can help workers do the required fixes quickly—thereby increasing efficiency and productivity.
Enhancing asset performance and uptime
Effective asset management plays a pivotal role in maximizing efficiency and minimizing downtime in a typical plant. Manufacturing processes rely heavily on a wide range of assets, including machinery, equipment, and tools, which contribute to overall efficiency and productivity. A day in the life of an asset manager involves maintenance planning, enhancing asset or equipment reliability, and optimizing workflows to improve quality and productivity. AI-led technological advancements can help asset managers make data-driven decisions, detect anomalies, optimize asset utilization, and proactively address maintenance needs. GenAI foundation models, when combined with traditional AI, can further assist in bringing better control over complex asset environments that are often located in remote places and require travel to unsafe locations. These foundation models can be trained on vast amounts of structured and unstructured data to generate responses like text and images for prompt decision making.
Take the case of a coal steam gas producer, where the most critical asset is an electrical submersible pump. A predictive model with a deep understanding of data science, engineering technology and operational technology can help significantly reduce downtime and the distance traveled by technicians to unsafe locations, resulting in cost savings, improved worker productivity, and increased customer satisfaction.
Mastering new service-based business models
AI-powered virtual assistants and chatbots permeate our routine lives. There’s no escaping them—whether you are doing flight enquiry or seeking banking assistance. AI-powered virtual assistants and chatbots have been integrated across all industries. And companies are increasingly relying on them to manage customer inquiries about products, services, maintenance, and ensuring personalized experience and smoother operations. These virtual assistants can not only respond to user queries mimicking human intelligence but also assist in collecting meaningful and often difficult-to-gather data on customer’s inquiries, future preferences, and behavior patterns. Such contextual insights are helpful in devising new business strategies by analyzing interactions and identifying trends and customer needs, which can be used to enhance existing products, new product development, and craft marketing strategies.
In another example, smart farming has great potential to deliver a more productive, efficient, and sustainable form of agricultural production with a combination of AI techniques. A combination of AI and GenAI models can use different multi-modal data sets such as weather data, soil moisture data, water quality data to create more accurate, efficient, and effective irrigation systems, predict equipment failures, soil conditions, and crop water requirements benefiting OEMs, solution providers, and other ecosystem partners. They can gain insights to develop more efficient and effective irrigation systems, optimize operations, contribute to sustainable agriculture practices, and create new business models and revenue streams.
Eyeing outcomes
Key considerations to craft a winning AI strategy.
Globally, business leaders are realizing that AI-led technological investments are inevitable, not just to exist but also to thrive in a fast-paced, and highly competitive marketplace. At the same time, they are always challenged with the decision to invest in the right use cases and reflect upon the return on investment quickly. We recommend a right-to-left outcome-centric strategy to realize business gains that are focused on:
- Efficiency by using AI models for document search, compliance, and reporting.
- Productivity by applying AI for faster embedded code development and the simulation of test scenarios.
- Value creation, such as constraint-based dynamic planning for manufacturing supply chains, prescriptive maintenance, and product mix optimization with AI.
- Performance, such as using AI for testing, verification, and validation, and improving quality.
- Engineering innovation, such as new feature discovery and development.
Given the nature of GenAI models, it is important to be ‘secure by design’. Enterprises need to focus on:
- Data security – There is a large amount of data used for training AI models. User rights access, personal identifiable information (PII), and other privacy considerations need to be properly accounted for.
- Model security – Protecting the model is a must so that it performs or describes only the tasks it is intended for and does not hallucinate.
- Infrastructure security – This includes both physical and virtual infrastructure security.
While implementing GenAI models involves significant investments, organizations need to realize that GenAI is revolutionizing industries, redefining ways of working, and its role is only poised to expand further as technology evolves. Enterprises taking a step forward on this journey can collaborate with partners, research institutions, and other experts to craft and realize their short-term and long-term objectives.
