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AI’s Biggest Contribution to Automotive Development Will Be Reducing Turnaround Times: Tata Technologies

Artificial intelligence is playing an increasingly transformative role in vehicle engineering, with its biggest contribution being a drastic reduction in development timelines, says John Johnston, Chief Engineer — Body Structures at Tata Technologies. At Autocar Professional’s Vehicle Light weighting Conference, Johnston said that AI can be used to standardise design processes for certain areas of the vehicle. “We can accelerate the automation of design. The predictability will improve, drop tests, dynamic tests, AI will accelerate the predictability of new materials. It’ll reduce development times. That will be AI’s biggest contribution,” he said.

According to him, turnaround time for developing a ground-up vehicle — which traditionally took around 4.5 years — has been compressed to just over two years. “There’s less need for prototypes because of software leaps, turnaround time for ground-up vehicles which used to be 4.5 years is now down to 26 months,” he added.

EV Platforms Bringing Structural Advantages

Johnston also underlined how EV platforms are offering new efficiencies across design and manufacturing. “Platform itself for EVs is key to making a business case for a vehicle. It improves the time to market. EVs allow you to package the materials better. It gives a wide saving. One of the clear trends is that what was the front wheel drive has gone to the back. This gives a very good crash performance. This allows you to put structure in places where you couldn’t before because of the engine. That’s a major advantage of EVs over ICE engines,” he explained.

With EVs typically being more expensive to produce than their ICE counterparts, material efficiency has become a focal point. “The idea is to reduce both cost and the weight as EVs become more expensive. The type of materials used is highly defined by the type of materials and manufacturing processes. If it’s a niche product then more exotic materials are used. Volume vehicles require high investments and more mainstream materials,” Johnston said.

He emphasized the growing role of advanced steels in achieving this balance. “Key to the volume segment is the proliferation of modern steel, advanced high strength steel. They reduce weight and can take a lot more strength,” he said.

As Tata Technologies continues to partner with global OEMs on next-gen mobility solutions, the interplay of AI, smart materials, and EV-specific platforms is clearly reshaping how vehicles are conceived, engineered, and brought to market, he added.

Source: https://www.tatatechnologies.com/media-center/ais-biggest-contribution-to-automotive-development-will-be-reducing-turnaround-times-tata-technologies/

#ai #tata technologies

How Tata Technologies Accelerates Innovation To Power Future Of Mobility

For next-generation vehicle manufacturers, speed and innovation are paramount. As the demand for cutting-edge technology grows, engineering service providers must evolve to meet the fast-changing expectations of modern OEMs. Tata Technologies has been at the forefront of this transformation, expanding its capabilities across multiple segments to help new-age automakers accelerate development cycles and seamlessly integrate software-defined vehicle (SDV) solutions.

As Marc Manns, Vehicle Line Director — EE at Tata Technologies, explains, over-the-air (OTA) updates are becoming essential, enabling manufacturers to introduce bug fixes, cybersecurity patches, and new features iteratively — enhancing vehicle performance post-production.

In a recent project, the company played a crucial role in rescuing a struggling OEM, stepping in just three months before launch to conduct a gap analysis and develop an OTA solution within six months. By deploying the right expertise and ensuring on-ground presence, the company helped accelerate the project timeline to under two years, significantly faster than conventional development cycles.

For emerging automotive players, agility is key, and Tata Technologies continues to redefine collaboration, providing tailored solutions that enable next-gen manufacturers to bring vehicles to market faster, smarter, and more efficiently, he stated.

Bridging Knowledge Gaps

As emerging technologies such as satellite communications, V2X, AI, and Machine Learning continue to reshape mobility, engineering service providers must bridge interdisciplinary knowledge gaps without slowing down development. Tata Technologies addresses this challenge through targeted training, cross-industry collaboration, and knowledge-sharing initiatives.

The company has established Tech Varsity, an internal training programme, along with platforms like LinkedIn Leap to upskill employees and onboard new talent. Additionally, cross-project learning ensures that expertise gained from one engagement is quickly disseminated across teams and regions, enhancing agility and accelerating development.

Advancing Connectivity With Satellite Solutions

In the world of SDVs, seamless connectivity is critical. Tata Technologies is exploring satellite-based solutions to complement 5G networks, ensuring uninterrupted connectivity even in areas prone to signal dropouts. Collaborating with partners like CesiumAstro, the company is working on intelligent network switching — leveraging AI and digital twins to predict dropouts and seamlessly transition between networks, maintaining continuous communication with cloud-based vehicle systems, he said.

AI-driven predictive analytics plays a crucial role in optimising connectivity, enhancing user experience, and improving safety. The company is harnessing automation, AI, and ML to anticipate network disruptions and make real-time decisions, ensuring that next-generation vehicles stay smarter, safer, and always connected.

Overcoming Edge Computing Challenges

Scaling ML to embedded edge devices presents several challenges in the automotive industry, particularly regarding latency, hardware constraints, power efficiency, and storage limitations. These factors are critical in ensuring that safety systems function reliably without delays, especially in real-time applications.

According to Manns, Tata Technologies is actively addressing these challenges by implementing a hybrid edge-cloud approach. This strategy involves offloading complex, ML-intensive tasks to the cloud, while ensuring that critical real-time processing remains at the edge. Selecting the right hardware is also essential. The company collaborates with OEMs to integrate specialized AI acceleration chips, such as Qualcomm Snapdragon, which optimize latency, performance, and power efficiency.

Each OEM’s journey towards SDVs is unique, and Tata Technologies works closely with them to tailor the right balance between edge and cloud computing. By leveraging cutting-edge hardware and intelligent workload distribution, the company ensures that vehicles remain safe, efficient, and compliant with regulatory standards — pushing the boundaries of next-generation automotive technology, he pointed out.

Digital Passports

As sustainability and regulatory compliance take centre stage in the electric vehicle industry, battery passports are emerging as a critical solution for tracking battery lifecycle from raw material sourcing to recycling. Tata Technologies is actively developing its own battery passport solution, collaborating with OEMs and battery manufacturers to ensure traceability, compliance, and sustainability, he mentioned.

According to Manns, the battery passport will provide end-to-end visibility, enabling the tracking of battery characteristics from mining, suppliers of raw materials to OEMs, aftermarket services, and eventual recycling. The solution integrates static data from manufacturing with real-time vehicle data via cloud connectivity, ensuring compliance with evolving global regulations in the EU, California, India, and China. The company is also incorporating blockchain technology to enhance security and traceability, reinforcing trust and accountability across the battery supply chain, he said.

Shaping The Future Of Vehicle Lifecycle Management

As vehicles become increasingly software-driven, digital passports are gaining prominence in managing vehicle history, maintenance, and compliance. While digital passports are already in use for commercial vehicles in Europe and the US, the concept is expected to expand into passenger cars, including internal combustion engine (ICE) vehicles.

Prognostic solutions, initially developed for EVs, are now being explored for ICE vehicles, particularly as their lifespan extends in certain markets. A digital ID can provide a transparent and tamper-proof record of a vehicle’s history, helping to prevent fraud, improve resale value, and enhance regulatory compliance.

Manns emphasizes that digital passports will play a crucial role in building consumer trust, facilitating better maintenance tracking, ensuring compliance, and streamlining enforcement mechanisms. As the automotive industry shifts toward connected and intelligent mobility solutions, battery and digital passports will redefine lifecycle management, driving transparency, efficiency, and sustainability, he summed up.

Author: Marc Manns, Vehicle Line Director — EE at Tata Technologies.

Source: https://www.tatatechnologies.com/media-center/how-tata-technologies-accelerates-innovation-to-power-future-of-mobility/

#mobility #automotive #tata technologies

Generative AI empowers to develop environmentally responsible vehicles

Beyond sustainability, Generative AI is also enhancing user experience by enabling more natural and intuitive interactions with vehicles. Through voice commands and contextual understanding, advanced AI algorithms allow seamless communication between driver and vehicle. By analysing driving patterns and personal preferences, it can anticipate user needs and proactively deliver relevant features — such as suggesting optimal routes, adjusting cabin temperature, or managing in-vehicle infotainment — creating a smarter, and more personalised driving experience.

Says, Jeffry Jacob, Partner and National Sector Leader for Automotive, KPMG in India, “Generative AI is helping bring in innovation and efficiency by assisting companies to create more advanced, lightweight and optimised designs.” Some of the major areas where Generative AI is already used widely include rapid prototyping, optimizing performance to meet the required design/safety standards, as well as lightweighting (especially for electric vehicles).

“It helps reduce the time needed from design to prototype through rapid iterations. It also helps improve safety and performance by evaluating designs for crash optimisation systems, more effective thermal management for EVs and improving battery performance, as well as better integrating smart features in connected vehicles,” shares Jacob.

Highlighting that Generative AI can help drive material optimisation through improved prototypes, Jacob expresses, “At the initial stage itself, it can help design components with minimal materials thereby reducing raw material costs and resource utilisation. It can help reduce waste via design of products considering end-of-life recyclability, as well as optimised production processes which reduce scrap during cutting and machining. Dynamic resource and supply chain management can help reduce energy consumption and optimise inventory.”

Yogesh Deo, EVP & Global Head — DES Delivery, Tata Technologies, states, “Generative AI can contribute in a big way to create digital prototypes that can be virtually tested, reducing the need for physical prototypes and accelerating the design process. It can generate personalised designs based on individual preferences, enabling mass customisation without compromising efficiency. AI-powered tools can create modular designs, allowing for greater flexibility and customisation in product configurations.”

Indicating that Generative AI enables dynamic, adaptive production systems by optimising manufacturing workflows in real time, Deo adds, “Generative AI is evolving the maintenance and diagnostics systems by analysing vast amounts of data to identify trends, anomalies, opportunities for improvement, by optimising maintenance schedules and resource allocation, thereby reducing maintenance costs and improve overall equipment reliability.”He also highlights that Generative AI can accelerate research and development in sustainable materials, enabling businesses to create and deploy sustainable solutions faster.

Generative AI has been very effective in terms of providing prescriptive analytics to prevent plant equipment failures and breakdowns, thereby reducing the need for an unplanned repairs and replacements. Moreover, it can also identify potential disruptions in the supply chain, such as natural disasters or geopolitical events, and develop contingency plans to mitigate their impact.

Reflecting on the role of open car operating systems in advanced software-driven vehicles, Deo explains, “Generative AI can enhance the flexibility of open car operating systems through rapid application development, seamless integration of diverse software components and services, and by ensuring a smooth, cohesive user experience.”

Generative AI is poised to play a pivotal role in the adoption of software-defined vehicles (SDVs), where millions of lines of code enable greater flexibility and responsive solutions for consumers. It can accelerate the creation and optimisation of software and control systems while also improving the performance and efficiency of a vehicle’s hardware. This integration of AI-driven software and intelligent hardware is expected to drive innovation, offering smarter, more adaptive automotive experiences.

However, there are also concerns about ensuring the privacy and security of user data, ethical considerations where AI algorithms must be designed and trained responsibly to avoid biases and discrimination. AI-powered systems must be highly reliable and robust to ensure safety and prevent accidents. “AI models can sometimes exhibit unexpected behaviour, especially in cases with unfamiliar situations. They often require large amounts of data, including personal information, which could be vulnerable to breaches. The use of AI for surveillance and data collection raises concerns about privacy and surveillance,” explains Deo.

Original source: https://www.tatatechnologies.com/media-center/generative-ai-empowers-to-develop-environmentally-responsible-vehicles/

Author: Yogesh Deo, EVP & Global Head — DES Delivery, Tata Technologies

#tata technologies #generative ai #genai #electric vehicles #engineering

How OEMs can leverage SDVs to stay ahead of the curve

The USD 3.6 trillion global automotive industry is undergoing a transformative shift towards Software-Defined Vehicles (SDVs), signalling a new era where software takes precedence over hardware. This evolution demands massive investments in infrastructure, software ecosystems, and a rethinking of traditional hardware integration approaches. As both legacy and greenfield OEMs embrace SDVs, their strategies diverge: traditional OEMs take a phased approach, focusing on stability with zonal compute systems, while newer OEMs are accelerating towards centralized compute architectures, driven by agility and innovation.

OEMs today are not simply choosing between the latest technology and established best practices. Instead, they are striving for a balance — one that supports dependability, high-performance computing, and software adaptability. This future-focused approach is redefining the automotive landscape, helping manufacturers stay competitive in the software-first world.

Engineering Dependability for Future

The evolution from microcontroller-based architectures to software-dominated systems has been driven by the growing demands for safety, security, and performance. To manage this complexity, OEMs require solutions that combine functional safety with the flexibility needed for next-gen infotainment, connectivity, and Advanced Driver Assistance Systems (ADAS). The goal is to empower OEMs with the tools to deliver software-driven products that are faster, safer, and more efficient.

Building SDV ecosystem — the five key measures — To support this paradigm shift towards SDVs, five critical elements need to be integrated:

Lingua Franca for Predictability — The Lingua Franca deterministic framework ensures real-time, safety-critical automotive applications operate predictably across platforms, offering consistency in performance and safety.

Automotive Grade Linux — With Linux becoming the standard for automotive operating systems, Tata Technologies pioneers efforts to qualify Linux kernels with real-time patches. This enables high-performance computing in vehicles while meeting the stringent demands for safety and updatability.

Containerisation for Flexibility — Containerization solutions like WebAssembly 3.0 and Docker simplify SDV architectures, enabling isolated process execution with cross-platform compatibility, reducing the complexity of traditional systems.

Embracing Rust for Safety-Critical Systems — As the industry moves away from C++ for safety-critical environments, the adoption of Rust, a memory-safe programming language, is transforming ADAS and autonomous systems, ensuring future-ready software frameworks.

Hardware Integration for Functional Safety — Functional safety in automotive hardware presents unique challenges, and the need of the hour is an approach that integrates hardware IP compliance and software frameworks to create a seamless ecosystem, empowering OEMs to develop SDVs that exceed safety and performance standards.

Original source: https://www.tatatechnologies.com/media-center/how-oems-can-leverage-sdvs-to-stay-ahead-of-the-curve/

Rinat Asmus, Vice President, Business Development, SDV, Tata Technologies

#automotive #sdv #software defined vehicle

Collaboration with ITIs for advanced vocational training can help bridge the skill gap in Indian workforce

In today’s rapidly evolving technological landscape, there is a need for a skilled workforce. Bridging the skills gap and empowering the next generation of engineers can be achieved through strategic upskilling initiatives such as Industrial Training Institutes (ITIs) and collaboration with Original Equipment Manufacturers (OEMs).

The OEM sector, which plays a crucial role in India’s economy and industrial landscape, struggles with a shortage of industry-ready, skilled workforce. On one hand, we have a large pool of young people seeking employment, and on the other hand, manufacturing companies are struggling to find job-ready youth to hire. A strategic planning and upskilling initiative with the support of ITIs can address the issue of workforce shortage in the manufacturing sector.

ITIs not only meet the advanced skill requirements of students and prospective employers, but also act as technology hubs, including skill centres for large industries and Micro, Small, and Medium Enterprises (MSMEs). The centres help in qualitative improvements in the field of industrial training and education, creating an enhanced learning environment to train youth to meet industry requirements.

At Tata Technologies, an initiative that collaborates with ITIs and OEMs helps enhance the availability of advanced skills necessary for smart manufacturing, which helps industries seamlessly embrace technologies of the Fourth Industrial Revolution.

AI-enabled vocational education

As we stand at the threshold of a transformative era in vocational education, it is crucial to recognise the role that advanced machinery and technology play in shaping the future of our workforce.

Vocational education contributes to skills development and prepares the younger generation for the challenges and opportunities of future requirements.

However, the industry landscape is rapidly evolving, driven by technological advancements. To ensure that our vocational schools remain relevant and effective, embracing technological upgradation is imperative. Integrating advanced technologies such as Artificial Intelligence (AI), Augmented Reality (AR), and robotics into vocational training can significantly enhance learning outcomes.

These technologies provide immersive learning experiences, allowing students to simulate real-world scenarios and develop practical skills in a safe environment. The technologies enable students to learn at their own pace and access resources beyond traditional classroom settings.

Moreover, technology facilitates collaboration and connectivity, breaking down geographical barriers and connecting vocational schools with industry experts and employers. This ensures that graduates are equipped with industry-relevant skills that meet current market demands.

Investing in technology upgradation in vocational training is not just about preparing students for today’s jobs but empowering them to thrive in the jobs of the future. By equipping students with technological literacy and adaptability, we nurture a workforce that is agile, innovative, and capable of driving economic growth.

Need for strategic partnerships

The journey towards establishing modern skilling centres for today’s workforce requires collective effort and commitment from both government and private players.

It necessitates partnerships between government institutions, industry leaders, policymakers, and communities to establish skilling hubs that will facilitate upskilling in sectors related to Industry 4.0, including product design & development, product verification and virtual analysis, design for artisans and handicrafts, additive manufacturing (3D printing), modern automotive maintenance, battery electric vehicle training, IoT and digital instrumentation, advanced manufacturing and prototyping, industrial robotics with arc welding, AI-based virtual welding & painting, advanced plumbing, and digital meters, among others.

Such partnerships can create an ecosystem where ITI skilling centres will help in the inclusive and sustainable development of the Indian workforce.

Original source: https://www.tatatechnologies.com/media-center/collaboration-with-itis-for-advanced-vocational-training-can-help-bridge-the-skill-gap-in-indian-workforce/

Sushil Kumar, vice president and head, Education Services, Tata Technologies

#education #automotive #engineering

Tata Technologies Builds First-of-its-Kind Design Studio Using Llama 2 and Stable Diffusion 3

Tata Technologies has cracked the code on generative AI. Recently, the company told AIM that it has built a solution (design studio for automotive selling) using Llama 2 and Stable Diffusion 3 which will revolutionise the design process for automotive companies. This new solution is expected to enable rapid prototyping and visualisation of design changes, reducing the time for design iterations.

During the design process of an automobile, it’s common to undergo multiple changes before finalising one. “With this solution, engineers won’t need to use design softwares like Autodesk Maya, which can be quite expensive and cumbersome,” said Santosh Singh, executive vice president at Tata Technologies, adding that their solution is much more cost-effective and simple to use.

“The team uses generative AI to develop multiple design options on the fly. It helps reduce design time, engineering time, and product development time,” he added.

Singh said that car manufacturers can effortlessly introduce new models by modifying the existing design, like altering the front section, using generative AI. They simply need to mask the desired area of the vehicle for changes and input the prompt describing the new design.

He further said that Tata Technologies generative AI solutions are compatible with Azure and AWS as well as opensource models like Meta’s Llama 2 and Llama 3. “We are using open-source models because we can fine-tune them based on the requirement. With Llama 2, we have the base model ready; we just need to fine-tune and connect it with our internal data,” he added.

“We don’t run a model where we have to expose customer data to the cloud. The way we have designed our model is simple. It’s on the cloud only for LLM capabilities, the rest is within the premises, and we have a connector to train the data,” he explained.

Better than Autodesk Maya?

Industry-standard software like Autodesk Maya, CATIA, or Siemens NX are highly sophisticated. These programs offer a vast array of features for 3D modelling, simulation, and rendering, requiring significant training and practice to master effectively. Moreover, they can be expensive, making them less accessible to hobbyists or beginners.

Last year, Autodesk announced its plans to add generative AI capabilities across its suite of products. Its acquisition of Blank. AI’s generative AI capabilities enables rapid conceptual design exploration in the automotive sector. This allows for real-time creation, exploration, and editing of 3D models using natural language and semantic controls, eliminating the need for advanced technical skills.

Singh said that a major challenge Tata Technologies is facing today is to not be able to integrate its generative AI solutions to existing software like Siemens, Dassault, and Autodesk, which are used for designing vehicles. “These are all closed proprietary software systems, so they don’t allow external software to penetrate inside and access the designs,” he explained, saying this is where its Design Studio platform is quite flexible to use for companies.

What’s Next?

Tata Technologies has also built a Virtual Sales Assistant which helps people in sales to increase productivity by 15–20%. This AI-powered tool streamlines the sales process and empowers the front line sales team by providing them relevant product information on the go thereby optimises enhancing customer engagement and sales.

Moreover, the company has also developed the Warranty Analysis solution using generative AI which is very useful for identify the root causes of warranty claims and can empower the quality departments to identify and fix root cause of failure. The company is also currently working on two projects –a Factory Copilot solution. and Warranty Analysis using generative AI.

Factory Copilot aims to enhance productivity and quality in manufacturing plants by providing real-time support to workers through phone-based assistance, digital displays, and multilingual support.

“We are working with one of the biggest manufacturers in India to develop this. It’s currently in the R&D stage. We hope that in the next three months, we will have some clarity on how to make this happen,” said Singh.

On the other hand, Warranty Analysis and Repair Solutions leverage AI to optimise after-sales services, improving efficiency and customer satisfaction in warranty-related processes. “Through this solution, we are trying to reduce the analysis time and make it more accurate so that the team on the ground can get clear and correct insights on the problem,” said Singh.

Original source: https://www.tatatechnologies.com/media-center/tata-technologies-builds-first-of-its-kind-design-studio-using-llama-2-and-stable-diffusion-3/

Santosh Singh, EVP and Global Head — Marketing and Business Excellence

#generative ai #engineering

Computer Aided Engineering Simulation Solutions for SDVs on Cloud

Mechanical Domain Architecture

The significant reduction in the number of parts in an electric vehicle, has led to modular architecture, which gives multiple advantages to an OEM to design and manufacture various types of vehicles in different lengths, sizes, and shapes in a short duration.

Electrical and Electronic Domain Architecture

To achieve the simplicity of a smartphone in the automobile domain, zonal and distributed types of EE architecture have evolved related to domains like ADAS, Powertrain, and Passive Safety, which are centred around the domains. Depending on which type of architecture is used by an OEM, the usage of ECUs varies.

Electrical Vehicle Challenges

Even though the EV market is increasing for automobiles, the biggest challenge of high-voltage batteries (HVB) still exists. A simple case of HVB during high or low outside temperatures makes the battery inefficient due to a reduction in range or increased charging times. In short, the challenges of SDVs and their extensive use in EVs require very efficient thermal management, and virtual validation is a well-known domain that is significantly used by OEMs to keep automobiles cool and smart by making Software-Driven Vehicles work efficiently.

Importance Of CAE Simulations In Engineering

In the engineering world, it is a well-known fact and an established process to use Virtual Validation, also called Computer-Aided Engineering (CAE), in the development cycle of a new product. The product can range from a small plastic clip to an automobile, industrial heavy machinery, or an aeroplane, which needs to be developed from a concept phase to production. CAE is a discipline that guides a design team in a product’s journey, ensuring it is competitive in terms of weight and manufacturing cost by analyzing techniques in selecting the right material and manufacturability to meet its ultimate function.

With the advent of high-end computing, CAE has become a principal domain with multiple sub-domains evolving to cater to manufacturing simulations, Crash & Safety, Computational Fluid Dynamics (CFD), Noise Vibration & Harshness (NVH), Durability, and Multi-body dynamics (MBD). The various disciplines need various mathematical models leading to software like LS-DYNA, ABAQUS, NASTRAN, MSC ADAMS, ALTAIR’S OPTISTRUCT, and Star CCM+, to name a few. Virtual validation plays a significant role in left shifting the development cycle, saving millions of dollars by replacing physical prototypes during the initial stages of product development.

A decade ago, there used to be high-performance computers (HPCs) that would take a good amount of time to simulate a test case scenario. However, due to the advent of fast semiconductor chips, this situation has improved significantly. Earlier, engineering companies, whether OEMs or Engineering Service Providers (ESPs), were either purchasing or leasing HPCs and spending millions of dollars for years to provide speedy products at competitive pricing. The in-house on-premises HPC would incur IT costs to maintain the server running 24/7 and electricity costs to keep the HPC machine cool to avoid overheating, leading to downtime.

Cloud computing is the new trend where large software companies are ready to provide services to OEMs and ESPs alike. This changes the entire landscape of HPC and CAE offerings. Cloud services reduce significant investment and logistics costs, giving OEMs a competitive advantage.

Future Of Virtual Validation and Software Driven Vehicles

Both virtual validation and SDVs are going to take advantage of Artificial Intelligence and Machine Learning (AI/ML) tools, which will help OEMs left shift the development cycle. In the coming decade, the usage of these tools will be as high as 60%, making automobiles safer and smarter. Thus, most of the passive safety features today will be part of active safety services due to smarter automobiles, be they passenger vehicles or commercial vehicles.

OEMs are also finding multiverse computer platforms with many advantages, like digital twinning and Universal Scene Description (USD), which will allow IT, CAD, CAM, and CAE engineers to access various design models simultaneously, leading to fast solutions. This provides a huge advantage to be competitive as a product developer, as logistics are simplified. Exciting times are yet to come with SDVs involving SOTA and FOTA, making the automobile not just a means of transportation but a more adaptive entity that will evolve and adapt to the users’ needs, providing a delightful experience to the customer.

Original source: https://www.tatatechnologies.com/media-center/computer-aided-engineering-simulation-solutions-for-sdvs-on-cloud/

| Gopal Musale, Vice President and Global Head, Virtual Validation Centre Of Excellence, ER&D at Tata Technologies.

#sdv #software defined vehicles #tata technologies #engineering

Unlike IT services’ piecemeal approach, we focus on full-spectrum vehicle engineering: Tata Technologies’ CTO

The automotive industry is rapidly embracing the concept of software-defined vehicles (SDV). Today, vehicles are evolving into high-performance computing platforms, and the overall industry is moving towards creating what could be described as “mobile phones on wheels.”

SDV is an umbrella term that encompasses a variety of components. These include infotainment software systems, Advanced Driver Assistance Systems (ADAS), cybersecurity, vehicle testing, and more. Pune-based Tata Technologies counts SDV and by extension full vehicle engineering as one of its core propositions. “Vehicles are now evolving into high-performance computing platforms, and we’re at the forefront of this transformation. We’re engaged in cutting-edge work, developing multiple proof-of-concepts (POCs) and minimum viable products (MVPs) that explore how this evolution will unfold,” Sriram Lakshminarayanan, President and chief technical officer told TechCircle in an interview.

Comprehensive approach to vehicle engineering

Lakshminarayanan says that while IT companies have traditionally supported this area in parts, Tata Technologies takes a more comprehensive approach. “Often, IT companies have supported automotive projects in a piecemeal fashion, focusing primarily on areas like mobile app development or cloud hosting. We position ourselves as a full-spectrum vehicle engineering company. We combine our expertise in Full Vehicle Programs with cutting-edge technology, offering a cohesive, end-to-end solution.”

This comprehensive approach also involves integrating a robust technology layer. For instance, partnerships with chip manufacturers and vehicle OS developers are crucial, he says. To this end, Tata Technologies has collaborated with companies like ARM and NXP Semiconductors. The memorandum of understanding with Arm was signed in July, enabling the integration of Tata Technologies’ software with Arm’s Automotive Enhanced (AE) portfolio to speed up the development of high-performance vehicle computing systems.

In one of the major deals in this area, Tata Technologies in April partnered with German automobile company BMW Group to form a joint venture (JV) to deliver automotive software, including SDV solutions for BMW Group’s vehicles and digital transformation solutions for its business IT. Under the agreement, the two firms are establishing automotive software and IT development hubs in Pune, Bengaluru, and Chennai.

“Going forward, We are making significant investments in our Global Practice function that I lead. We’re doubling down on expanding our pool of global small and medium enterprises (SMEs) and enhancing our presence in key markets with market-facing SMEs. Additionally, we’re heavily investing in R&D projects focused on developing MVPs, POCs, and other innovative solutions,” said Lakshminarayan.

Acquisition-led growth in ER&D

Beyond, SDV, engineering research and development (ER&D) is a huge growth opportunity area for Tata Technologies, Lakshminarayan said. In November 2023, Nasscom and BCG released a report which that India will likely contribute 22% to the Global ER&D sourcing market by FY30. Software, Automotive, and Semiconductor sectors are expected to contribute 60%+ of India’s share of ER&D sourcing by FY30.

Amid limited organic growth opportunities, IT companies are increasingly using mergers and acquisitions (M&A) to enter this specialized field. Recent high-profile deals include Cognizant’s $1.3 billion acquisition of Belcan and Infosys’ purchases of In-Tech and InSemi. Midcap firms also actively engaging in acquisitions to enhance their expertise in sectors. Case in point is Coforge’s acquisition of Cigniti Technologies and Happiest Minds’ acquisition of Noida-based digital engineering firm PureSoftware Technologies.

“Acquisitions-led growth for IT services company in the ER&D sector is bound to happen across the industry, and we will also need to carefully consider our options. Our M&A strategy team continuously evaluates the pros and cons to determine what aligns with our goals. When the time is right, and there’s a need for complementary skill sets that fit our objectives, acquisitions will certainly be on the table. However, I would say that while we may explore these opportunities in the three to five-year horizon, it’s unlikely to happen in the short term.”

To be sure, in November 2023, Tata Technologies became the latest and the first initial public offering (IPO) for a group entity in two decades since Tata Consultancy Services’ IPO in 2004. In quarterly earnings announcement for Q1FY25, the engineering and digital services firm reported a slump in the net profit of 15% to ₹162 crore owing to declining revenue from its services segment and higher expenses. The company said that its total operating revenue rose 0.9% to ₹1,269 crore year-on-year (YoY) and fell 2.5% sequentially.

Original source: https://www.tatatechnologies.com/media-center/unlike-it-services-piecemeal-approach-we-focus-on-full-spectrum-vehicle-engineering-tata-technologies-cto/

Sriram Lakshminarayanan, President and chief technical officer at Tata Technologies.

#sdv #software-defined vehicles #tata technologies #automotive

Transformative Strategies in Construction Equipment Design

The heavy machinery that builds our world mirrors human ingenuity and the collective power of our consciousness. From the majestic skyscrapers that touch the sky to the expansive highways that connect our lives, the design and evolution of construction equipment have been fundamental in shaping our reality. As global infrastructure projects expand in scale and intricacy, OEMs are under mounting pressure to produce machinery that is powerful, efficient, environmentally friendly, cost-effective, and safe.

Optimizing Product Costs

The high cost of machinery stems from various factors such as suboptimal design, expensive materials, and high engineering and manufacturing expenses. These costs accumulate, inflating the final price of the equipment. OEMs are adopting a platform strategy for new product ranges, focusing on modular design. This involves creating multiple variants on a single platform, enabling customization for specific markets or applications without the need to design entirely new machines. Beyond traditional value engineering practices, OEMs are leveraging product and cost benchmarking to critically evaluate their cost structures. Engineering outsourcing has become a prominent strategy to reduce development costs, with OEMs increasingly relying on ESPs for core engineering tasks and new technology areas like electrification, AI-ML, and automation.

The rise of GCCs in India reflects this trend, providing in-house or outsourced support. Most North American and European construction OEMs have established GCCs in India. A leading North American OEM operates multiple R&D centres worldwide, including in India. Similarly, a prominent Japanese OEM is deploying digital twin technology, which helps build virtual prototypes to simulate and test equipment performance in various scenarios, thereby improving design and reliability before physical production. Investments in smart manufacturing and Industry 4.0 are also helping optimize manufacturing costs, with technologies like digital twins enabling virtual prototypes to simulate and test equipment performance before physical production.

Enhancing Operating Efficiency and Machine Performance

Customers demand lower operating costs, enhanced efficiency, real-time performance data, minimal breakdowns, and ease of operation, while operators prioritize safety, comfort, and intuitive interfaces. Rapid advancements in technologies such as IoT, telematics, connected and autonomous systems, analytics, AI-ML, and digital twins have positioned CE OEMs to better meet these demands. North American CE OEMs have developed their own telematics and IoT platforms, providing real-time data on equipment performance, fuel consumption, and maintenance needs. Remote monitoring and diagnostics are becoming standard, allowing for proactive maintenance and reduced downtime. AI-ML technologies are being used for predictive analytics, optimizing service schedules and reducing downtime. A leading European OEM uses machine learning to optimize machine performance, enhance precision in construction tasks, and provide predictive insights based on operational data. These innovations help operators and fleet managers optimize machine use and plan preventive maintenance, ultimately improving efficiency and performance.

Autonomous Operations and Safety

Autonomous machines that operate without human intervention are revolutionizing safety and efficiency in the industry, particularly in hazardous environments. Semi-autonomous construction equipment, operator assist technologies, and advanced camera systems enhance visibility, collision avoidance, and operator awareness. A leading Japanese CE OEM is in the advanced stages of introducing a semi-autonomous excavator and an autonomous haulage system. OEMs are enhancing visibility and monitoring features alongside geofencing and operator fatigue monitoring that prevent accidents and improve operator safety.

Addressing Workforce Challenges

To combat the shortage of skilled labour, manufacturers are investing in advanced training programs and simulators, providing realistic, hands-on experience in a controlled environment. AR and VR guide technicians through maintenance and repair tasks, ensuring accuracy and efficiency. User-friendly interfaces with intuitive controls are being designed to reduce the learning curve for operators and make advanced features more accessible. Partnerships with technology companies are bridging skill gaps, with India-based ESPs supporting OEMs in integrating cutting-edge innovations.

Sustainability

With increasing awareness of climate change, there is growing pressure on manufacturers to develop environmentally sustainable equipment. This includes reducing emissions, optimizing fuel efficiency, and using recyclable materials. OEMs are adopting sustainable practices such as using high-strength, lightweight materials, integrating eco-friendly technologies like hybrid systems, and implementing recycling programs. A leading Swedish CE OEM is developing excavators and loaders with the same power as their diesel counterparts. A prominent North American OEM is remanufacturing items with special incentives to promote recycling while another leading North American OEM is providing alternative fuels such as biodiesel, biogas, hydrogen, HVO, and methanol to reduce emissions. The development of electric and hybrid powertrains significantly reduces emissions and noise levels, making them ideal for urban environments and projects with stringent environmental regulations.

Supply Chain Disruptions and Market Competitiveness

By focusing on localization and engineering products customized for specific markets, OEMs can offer competitive solutions. This strategy helps multinational CE OEMs outperform local competition and meet market-specific demands.

The construction equipment industry is navigating a transformative era, driven by the need to address complex challenges with innovative solutions. Through electrification, automation, digital integration, and sustainable practices, OEMs are overcoming these challenges and setting new standards for efficiency, safety, and environmental stewardship. As these innovations continue to evolve, they promise to shape the future of construction, making it more sustainable, efficient, and safe for all stakeholders.

Original source: https://www.tatatechnologies.com/media-center/transformative-strategies-in-construction-equipment-design/

Abhay Kulkarni, VP & Global CoE Head, ER&D at Tata Technologies.

#ai #ml #tata technologies

Accelerating vehicle engineering from concept to road in 24 months

New Delhi: Two years, 24 months. The ambition is clear: engineer a new vehicle from sketch to job 1 within 24 months. You might be thinking, “That’s absurd,” or perhaps, “Why not?” Your reaction likely stems from your background — whether you work for an established OEM, have a technology background, or are involved with a Southeast Asian startup.

We were recently challenged by one of our customers to deliver two electric vehicles in just 22 months. While we didn’t quite hit the mark, we came close. The lessons learned from this experience have reshaped our approach to accelerating vehicle design and development.

Understanding the motivation for faster delivery and the business constraints at play is crucial. Startups, for instance, are driven by the need to bring their vision to market swiftly and start generating revenue to satisfy shareholders and secure their survival. They often have new factories and a flexible approach to engineering and validation, making them risk-tolerant and highly motivated to expedite development.

Established OEMs, on the other hand, operate with mature business plans and entrenched production facilities that are difficult to modify. Their processes, honed over a century of developing internal combustion engine (ICE) vehicles, cater to a loyal customer base with high expectations for quality and reliability. These companies are naturally risk-averse, striving to keep pace with competition from the likes of Tesla and emerging Chinese manufacturers, yet constrained by their size and structure. The bigger the ship, the harder it is to turn.

This is where we come in. OEMs often outsource projects that don’t fit neatly into their existing frameworks, such as supercars or groundbreaking innovations. By doing so, they can prove the “art of the possible” within their organizations and drive change. Some, like Renault and JLR, have taken more drastic measures, restructuring into smaller, independent teams to enhance agility.

Our 24-month customer is a startup. During the planning phase, we scrutinized our new product introduction (NPI) process, which initially described a 32-month timeline — far too slow. We interrogated every activity, asking, “Why do we do this? How can it be faster?” We made tough decisions, such as foregoing new cell technology, and continued learning throughout the project, which didn’t always go as planned. Yet, this journey yielded a scalable NPI process forged from real-world vehicle delivery.

So, what does it take to deliver a vehicle program in 24 months? Here are the key enablers:

Step 1 — EV Platform: The inherent simplicity of the EV platform streamlines development. Most OEMs now have one, and startups can invest in proven platforms or form joint ventures with OEMs to accelerate development.

Step 2 — Use What You Know: Leveraging existing designs and solutions saves time. Instead of ground-up testing, consider delta testing or virtual validation. Use rigs and bucks rather than waiting for full vehicles, and test complete vehicles only when absolutely necessary.

Step 3 — Make Decisions Quickly: Early decisions are better, even if slightly off, than delayed or poor decisions made late. Empower leadership to make timely choices and avoid system-based bottlenecks, such as the 80–20 rule.

Step 4 — Define Your Electrical Architecture: As software-defined vehicles become more prevalent, defining the electrical architecture early is crucial. This system should be among the first to be developed and validated.

Step 5 — Understand Your Critical Path: The critical path now often lies in software validation, electrical engineering, and battery implementation. Focus on these areas and ensure software and electrical integration are top priorities.

Step 6 — Work in an Agile Way: While true Agile methodology is still debated in auto manufacturing, the principles of sprints, regular communication, and short-term goals are essential. A racing team’s adaptable approach, combined with immediate escalation and decisive leadership, can be incredibly powerful.

Step 7 — Think About Tooling: Rapid tooling allows for extended virtual validation and quicker production. Though this approach may require post-job 1 revalidation and higher overall costs, it offers significant time savings.

AI is revolutionizing the industry, promising increased efficiency and less mundane work. While AI is not yet capable of designing a car with the push of a button, its rapid advancement is reshaping how we learn and work. The automotive industry is undergoing massive change, ignited by the EV revolution and fueled by software-defined vehicles and autonomy.

Ten years ago, a 24-month program seemed unthinkable. Today, it’s challenging but achievable, at least for high-volume cars. We aim to move even faster, but not at the expense of quality. That’s the delicate balance we strive to maintain.

Original source: https://www.tatatechnologies.com/media-center/the-future-of-smart-manufacturing/

Steve Brown, Global Head-Body Engineering CoE, ER&D at Tata Technologies.

#electric vehicles #electric car #engineering #tata technologies

Tata Technologies InnoVent 2024

Tata Technologies InnoVent solutions for a better world - We offer young engineering students a platform to develop innovative solutions that address the most significant challenges in the manufacturing industry. We will train, mentor, and recognize the best project teams, assisting them in realizing their full potential. If you’re a bold, creative thinker with a passion for innovation, our hackathon offers the ideal environment for you to flourish.

Through the Tata Technologies InnoVent program, engineering students have a unique opportunity to tackle real-world challenges across the product value chain using Generative AI. This initiative, in collaboration with Microsoft and Tata Motors, equips shortlisted project teams with cutting-edge innovation tools and access to Generative AI use cases and technologies. We aim to empower young minds to innovate, design, and engineer next-generation product engineering, product manufacturing, and digital transformation solutions. Top-performing teams will receive mentorship and guidance from Subject Matter Experts (SMEs) from Tata Technologies, Tata Motors, and the Microsoft Azure community. These experts will help shape the projects for scalability and impact, ensuring that the innovations are practical and transformative.

Why InnoVent? / Cash prizes worth INR 4.5 Lakh for winning teams and paid internship opportunities for the top teams / Learning and mentorship opportunities with Subject Matter Experts (SMEs) from Tata Motors, Microsoft and Tata Technologies / Exclusive access to the Innovation tools and technologies developed by Tata Technologies engineers / Complimentary access to Microsoft Azure Sandbox for testing and scaling Generative AI projects

Get more details at https://www.tatatechnologies.com/innovent/

#hackathon #innovent #tata technologies #engineering #generative ai #engineering services

The future of smart manufacturing

The manufacturing landscape has undergone a transformative shift over the past two decades, evolving from manual labour-intensive processes to highly automated and intelligent operations. Let us explore the progression of manufacturing from traditional methods to the advent of smart manufacturing, projecting forward to a vision of the industry in 2030.

Historical perspective

In the late ’90s, as a Graduate Engineer Trainee at a paper manufacturing plant, I observed a heavy reliance on manual operations. The plant teemed with workers; automation was minimal and used sporadically due to the prohibitive cost of sensors and basic control systems composed of relays, switch gears, and mechanical gauges. This era, while still nascent in automation, saw the emergence of ERP systems like SAP, albeit constrained by significant capital and operational expenditures.

During this period, equipment control was largely managed by AC motors and variable frequency drives, with SCADA/DCS systems playing a crucial role within a predominantly PC-controlled environment. Challenges in supply chain management often led to overstocking or stock-outs, causing production delays. Decentralised operations necessitated extensive manpower, escalating infrastructural and operational costs, and manufacturing plants typically struggled to achieve an Overall Equipment Effectiveness (OEE) of over 60 per cent.

The Rise of smart manufacturing

The turn of the century heralded the beginning of what we now term smart manufacturing. This phase was marked by the rapid embrace of automation, robotics, and enhanced connectivity. Technologies such as Programmable Logic Controllers (PLCs), Human-Machine Interfaces (HMIs), and robotics transformed traditional manufacturing workflows, supported by advancements in communication technologies like Ethernet and Wi-Fi that enabled real-time data transfer and decision-making. Sensors, IoT platforms, and AI-based predictive systems have become fundamental in enhancing manufacturing efficiency.

Technological revolution and enhanced efficiency

Advancements in technology have drastically reduced the need for manual intervention in manufacturing processes. Condition monitoring systems and sophisticated algorithms now enhance asset uptime and optimise OEE. Today, operational data from the shop floor is transmitted in real-time, allowing for immediate responses and adjustments to production metrics. The introduction of automated material handling systems and improved logistics planning has transformed the supply chain and material management, enhancing operational excellence.

Operational excellence and safety improvements

Modern manufacturing operations emphasise stringent safety and compliance measures, with sensor-equipped personal protective equipment and digital incident logging systems ensuring adherence to safety standards. The integration of real-time data analytics platforms has streamlined data management, enabling manufacturers to quickly address quality issues, minimise downtime, and improve product quality.

Unlocking the potential of smart manufacturing: A vision for 2030

Looking ahead to 2030, manufacturing is poised for further groundbreaking advancements, driven by technologies in automation, artificial intelligence (AI), and the Internet of Things (IoT). IoT serves as the backbone of data produced and is expected to witness remarkable growth driven by the need for increased automation and connectivity within factory settings, highlighting the critical role of technological integration in modern manufacturing practices.

The concept of discrete manufacturing will evolve significantly, incorporating software-defined products and traceable, interconnected platforms. Paperless operations, wireless factory networks, and cloud-based enterprise platforms are expected to dominate, reducing the environmental impact and fostering a more dynamic production environment.

Sustainable manufacturing and future

Sustainability is set to become a cornerstone of manufacturing, with an increasing reliance on renewable energy sources like solar and wind power factories and integrated sustainability features in tools ranging from Mechanical Computer-Aided Design (MCAD) to ERP and IoT platforms. The traditional roles of managers and supervisors are likely to be replaced by AI-powered manufacturing co-pilots, reducing managerial overhead and enhancing efficiency.

Digital twins and cybersecurity

The adoption of digital twins will enable manufacturers to simulate and optimise production processes more effectively, while augmented and virtual reality technologies will enhance training and maintenance protocols. As manufacturing becomes increasingly interconnected, robust cybersecurity measures will be essential to protect sensitive data and maintain trust across the supply chain.

Market potential

The Asia Pacific region claims the largest share (37 per cent) of the global smart manufacturing market, propelled by the dynamic ecosystem across Japan, India, and Australia. Small and Medium-sized Enterprises (SMEs) are anticipated to be the primary adopters of smart manufacturing solutions. The escalating adoption of disruptive technologies such as Industry 4.0, artificial intelligence, augmented reality, and IoT, underlines the region’s stride towards cloud adoption, thus boosting demand for cloud-based smart manufacturing solutions across these countries.

The global smart manufacturing market is projected to experience robust growth, anticipated to reach US$ 985.5 billion by 2032 at a compound annual growth rate of 16 per cent. This expansion is fueled by emerging technologies such as artificial intelligence, cloud computing, Big Data, and machine learning, which are pivotal in propelling the smart manufacturing market forward.

As the industry evolves, it will offer vast opportunities for service providers and manufacturers alike to capitalise on the enhanced capabilities of smart technologies, promising a future of heightened agility, efficiency, and sustainability in manufacturing.

Original source: https://www.tatatechnologies.com/media-center/the-future-of-smart-manufacturing/

Gowthaman Swarnam, Global Practice Head — Digital at Tata Technologies.

#tata technologies #smart manufacturing solutions #cybersecurity #sustainability #digital twin

‘OEMs are transforming into Software Organisations’: Tata Technologies’ Sandeep Terwad

OEMs are realising that the Vehicle is now an ‘edge device’ of the IoT world, with software playing a crucial role. OEMs are transforming themselves into Software Organisations making the suppliers also change likewise, says Sandeep Terwad, Associate Vice President, Tata Technologies.

He also added that ADAS will evolve into a more affordable and indispensable component of vehicles across the spectrum. Level 3 shall become the minimum preferred level by the end-users, says Sandeep Terwad, Associate Vice President, Tata Technologies. Edited excerpts.

Can you take us through the evolving ADAS technologies and their impact on automotive safety and innovation?

ADAS has evolved from rudimentary safety features like rear parking sensors to sophisticated, AI-powered systems that play a central role in improving road safety, enhancing driving experience to paving the way for having true autonomous mobility in almost all cars soon.

On the Sensor side, Radars have become increasingly sophisticated offering higher resolution and improved performance in detecting objects, obstacles in adverse weather conditions. Lidars have become smaller and affordable providing detailed 3D mapping and precise object detection.

These sensors, in combination with AI algorithms and ML techniques make the ADAS system more perceptive, get better at decision making and predictive capabilities.

What are the most prominent changes you have seen in the industry in the past few years?

There has been a paradigm shift in the Automotive Industry in the past few years, mainly in the Connected, Autonomous and Sustainable areas. This shift has been driven by the technology advancements, evolving user preferences, integration of Cloud computing, SDV, connectivity, and mobile apps. The regulatory bodies world over has also contributed to the change in the way of policies that promote electric vehicles (EVs) and\or alternative Powertrains, including subsidies, infrastructure support, tax incentives and even to the extent of funding programs to encourage EV adoption.

In the case of EV, there have been advancements in the past few years in battery technology and in general the ePT components, charging infrastructure are becoming better day by day. In the case of Autonomous and Connectivity, there has been a significant advancement in ADAS, Sensor Fusion, integrating AI, ML to make better predictions and prognostics, having Over the Air Updates, remote diagnostics etc. These are some of the changes brought in by technology. The end-users are demanding personalised experiences in their vehicles, leading to the integration of voice assistants, customisable interfaces, and adaptive driving modes tailored to individual preferences.

Vehicles are becoming increasingly connected to smartphones, smart devices, and IoT ecosystems, enabling seamless integration with mobile apps for remote control, vehicle tracking, and digital key access. OEMs are realising that the Vehicle is now an edge device of the IoT world, with software playing a crucial role from now on. OEMs are transforming themselves into Software Organisations making the suppliers also change likewise. A slew of standards, bodies, alliances have formed to aid these changes in the last few years.

How have you seen the auto landscape evolve in your 26 years of experience? What would you consider your biggest challenges today?

Globalisation has led to increase competition and collaboration among Automotive manufactures, suppliers and ESP like Tata Technologies. India with its skilled technical base has become a significant player in the Automotive Industry in the past several years, driving growth and innovation. The regulatory requirements, especially related to emissions, safety standards, data privacy have become more and more stringent influencing vehicle design, manufacturing processes and business strategies. The rise of mobility-as-a-service, like Shared mobility, subscription-based models has disrupted the traditional business model forcing the OEMs to adapt to challenging market dynamics and customer expectations.

On the biggest challenges, my view is there is still room for improvement in bringing in efficiency in battery technologies and ePTs (electronic precision technology) to bring out a better balance in range, cost. Integrating complex software systems for ADAS and infotainment seamlessly without incidents is a challenge looking at the shrinking vehicle timelines.

On the business challenges, there is increased and intense competition from traditional OEMs, new entrants to differentiate their products and services. Adapting to the shift towards mobility services and shared mobility models requires OEMs to diversify their revenue streams, develop new business models, and forge partnerships with mobility service providers.

Can you take us through some regulatory challenges and compliance considerations in the development of ADAS solutions?

Compliance with safety standards established by the NHTSA in the US and similar bodies in other regions is essential to ensure that ADAS features meet minimum safety requirements and do not pose undue risks to drivers, passengers, or other road users. The ADAS technology may require regulatory approval and certification before they can be deployed in production vehicles. The process to get the certification involves submitting technical documentation, test results and safety assessments to authorities for review. This process is a bit time consuming and expensive and requires close collaboration with OEMs, Suppliers and regulatory agencies.

The Automotive OEMs and suppliers are also now needed to carefully consider liability issues, product liability laws, and contractual obligations to mitigate legal risks and ensure compliance with applicable laws and regulations in case the ADAS features are involved in accidents or do not function as intended.

What are the future trends in ADAS, including convergence of AI, technology and automation?

My view is that ADAS will evolve into a more affordable and indispensable component of vehicles across the spectrum. Level 3 shall become the minimum preferred level by the end-users. Advancements in sensor fusion, AI, and machine learning will enhance the perception and decision-making of ADAS systems making more accurate decisions and predictive responses possible. Companies are exploring the integration of AR into ADAS solutions to enhance situational awareness and improve driver decision-making processes. This could revolutionise how drivers interact with their vehicles and the road.

ADAS will become a key differentiator for OEMs in their product offerings and the USP shall be on Safety, Comfort, Convenience.

Strategic partnerships and collaborations between OEMs, suppliers, and tech companies like TTL will become more prevalent to leverage synergies and accelerate innovation in ADAS development. An OEM ecosystem that has better AI models will stay ahead of the game.

Can you take us through some aspects of industrial automation?

AI, ML has already started impacting Industrial Automation and Manufacturing processes, for eg, in Predictive Maintenance by analysing data from sensors, a particular machine logs, maintenance records and based on these can schedule the predictive maintenance for that machine or plant. Computer vision systems nowadays, inspect products on the production line for any anomalies, surface, dimensional variations and other imperfections in real time to ensure high-quality standards. AI and ML algorithms optimise supply chain operations by forecasting demand, optimising inventory levels, and streamlining logistics processes.

Tata Technologies is innovating with cobots that work alongside human operators to increase production efficiency without compromising safety. These cobots are equipped with sensors and AI capabilities to adapt their movements and responses to their human counterparts’ actions. This synergy can lead to more efficient assembly lines, where precision and safety are enhanced by robotic assistance.

How can AI and IoT be breakthroughs now? Most features have been achieved now it will be only incremental increases. What are your thoughts?

There is still quite some way to go in the AI & IOT space. I do not think, it will be incremental innovation, nor can it afford to be that. There is still space for including Contextual Intelligence in a system so that it can adapt and personalise its behaviour based on real-time context, user preferences, and environmental factors could revolutionise user experiences.

We need a breakthrough in edge computing technologies that could enable more efficient, real-time AI inference and decision-making at the device level. There is also a need for standardisation, interoperability across different devices, platforms, and protocols, and seamless integration frameworks to unlock new possibilities for AI and IoT applications.

Orignal source: https://www.tatatechnologies.com/media-center/oems-are-transforming-themselves-into-software-organisations-tata-technologies-sandeep-terwad/

Sandeep Terwad, Associate Vice President, Tata Technologies

#automotive #tata technologies #engineering services

How AI can be leveraged in Manufacturing value chain?

In recent years, the manufacturing industry has undergone remarkable transformations, driven by the adoption of Industry 4.0 and the integration of Artificial Intelligence (AI) as pivotal game-changers. Just half a decade ago, we were still heavily reliant on manual or semi-automated product quality inspection processes. However, today, these tasks can be effortlessly automated, thanks to cutting-edge computer vision systems powered by AI.

Presently, AI holds the potential to revolutionise the entire manufacturing value chain, encompassing product design, production, supply chain management, and customer service. Its momentum has been significantly accelerated by the recent evolution of generative AI technology, marking a profound shift in how we approach manufacturing processes and innovation.

DESIGN AND PROTOTYPING

AI-powered design tools have revolutionised product development in the manufacturing industry. These cutting-edge tools leverage generative design algorithms to generate multiple design iterations based on specified parameters. This not only accelerates the design phase but also optimises products for enhanced performance, reduced costs, and improved manufacturability. As a result, manufacturers not only save time but also minimise material waste, leading to more cost-effective production processes.

PREDICTIVE MAINTENANCE

The adoption of AI-driven predictive maintenance has brought a paradigm shift in equipment management for manufacturers. By meticulously analysing data from sensors and IoT devices, AI algorithms can accurately forecast — when machinery is likely to experience failures and schedule maintenance proactively. This proactive approach significantly reduces downtime, extends the lifespan of equipment, and slashes overall maintenance costs. Manufacturers can now operate with heightened efficiency and reliability.

QUALITY CONTROL

In the domain of quality control, AI based computer vision systems have emerged as an indispensable tool. These systems meticulously inspect products for defects, ensuring not only higher quality but also unmatched consistency throughout the manufacturing process. By harnessing AI’s capabilities, manufacturers can maintain rigorous quality standards and meet customer expectations with precision.

SUPPLY CHAIN MANAGEMENT

AI plays a crucial role in the optimisation of supply chain operations. AI-driven inventory management systems employ demand forecasting and real-time data analysis to finely tune stock levels. This ensures that manufacturers always maintain the ideal balance of raw materials and finished goods, effectively minimising storage costs, while sidestepping the pitfalls of stockouts or overstock situations. Furthermore, AI contributes significantly to route optimisation, resulting in reduced transportation expenses and a decreased environmental footprint. This dual benefit not only improves cost-efficiency, but also aligns with sustainability goals.

CUSTOMISATION EXPERIENCE AND PERSONALISATION

AI’s transformative impact extends to enabling mass customisation, enabling efficient production of highly personalised products. The customer experience receives a substantial boost from AI by facilitating product customisation and personalisation. Chatbots and virtual assistants, powered by generative AI, deliver instant and responsive customer support, while recommendation engines draw on individual preferences to suggest tailored products.

ENERGY EFFICIENCY

The manufacturing industry’s growing commitment to sustainability finds a reliable ally in AI. AI-driven systems excel in monitoring and optimising energy consumption by analysing data gleaned from sensors and smart meters. This dual-pronged approach not only trims energy costs but also significantly reduces environmental footprint of manufacturing operations. Artificial Intelligence is unquestionably reshaping the manufacturing landscape, driving process optimisation, elevating product quality, curbing expenses, and bolstering overall competitiveness. Manufacturers that harness AI across their value chain gain a substantial edge in today’s dynamic marketplace. As AI technologies continue to evolve, it remains imperative for manufacturers to adapt and wholeheartedly embrace these innovations to maintain their leadership position in the industry.

Original source: https://www.tatatechnologies.com/media-center/how-ai-can-be-leveraged-in-manufacturing-value-chain/

Jay Shah, Global COE Head — Data Science at Tata Technologies

#generative ai #ai #tata technologies

Software Defined Vehicles (SDV) — Shift in Vehicle Cybersecurity

This evolution enhances safety, comfort, and connectivity features, providing a richer and more personalized driving experience. Unlike hardware functionally defined traditional vehicles, SDVs can be updated over the air (OTA), enabling continuous enhancements and the addition of new functionalities and security measures without needing physical changes. The market for SDVs is expected to reach an impressive USD 210.88 billion by 2032, highlighting the opportunity for OEMs to transform the automotive industry.

The transition to SDVs offers multiple benefits, including the simplification of vehicle architectures and a reduction in manufacturing costs. These improvements are made possible by optimizing electrical and electronic layouts and adopting High-Performance Computing (HPC) to eliminate outdated wiring and Electronic Control Units (ECUs). This technological leap forward is set to fuel innovation in creating connected, intelligent, self-driving electric vehicles. Furthermore, the introduction of vehicle operating systems and open API interfaces allows manufacturers to unlock new revenue streams through the provision of additional services. SDVs also promise to enhance the driving experience through regular software updates and the use of cloud-based virtual target units (ECU~HPC) for software (function/features) developments and validation tools to speed up the time to market for new vehicle features.

The shift towards SDVs transforms the cybersecurity landscape from focusing solely on physical components and basic electronic interfaces to addressing a broader spectrum of digital threats. The complexity of SDVs, potentially comprising over 100 million lines of code, and their integration into the Internet of Things (IoT), exposes them to various cyber threats. These include data breaches, remote hijacking, and vulnerabilities affecting software updates, and vehicle sensors. Open APIs, backend systems as well as customer privacy and connected devices in SDVs also need robust cybersecurity frameworks that cover both their hardware and software aspects.

The balancing act between ensuring robust security measures and maintaining user privacy becomes a critical issue that demands thoughtful consideration. This shift necessitates a comprehensive approach to security, focusing on the entire ecosystem, including the vehicle’s software, data privacy, and secure communication channels. The scarcity of skilled professionals who can effectively implement and manage cybersecurity measures for SDVs further aggravates these critical challenges.

In response, automakers are adopting best practices from the software industry, like DevSecOps and closed-loop security processes, which integrate security at the initial stages of development. The vision of transforming vehicles into “computers on wheels” involves deploying real-time software updates to address vulnerabilities swiftly. Advanced artificial intelligence (AI) and machine learning (ML) algorithms are being utilized to predict and prevent security breaches effectively. Continuous monitoring and quick response to security incidents are crucial in protecting both the vehicle and user data from unauthorized access and misuse, ensuring privacy and compliance with data protection regulations.

Looking ahead, Accenture’s estimate that revenue from digitally-enabled services in the automotive sector will rise to US$3.5 trillion by 2040 brings to the fore the importance of SDVs. The journey towards fully realizing the potential of Software Defined Vehicles hinges on successfully navigating the complex landscape of vehicle cybersecurity. It requires a multifaceted strategy that incorporates security within the design, in-depth defence protocols, continuous risk management processes, and a comprehensive cybersecurity management system at the business level.

The automotive industry is being redefined by vehicle cybersecurity as it progresses rapidly towards software-defined vehicles that enhance user security and data privacy. This shift will ultimately usher in a new era of automotive excellence and digital intelligence.

Original source: https://www.tatatechnologies.com/media-center/software-defined-vehicles-sdv-shift-in-vehicle-cybersecurity/

Jhenu Subramaniam, Cybersecurity Solutions Architect at Tata Technologies

#automotive #sdv #cybersecurity

In today’s fast-evolving automotive landscape, both automotive enthusiasts and automakers share a common goal: delivering exceptional customer experiences across every touchpoint of the customer journey.

At Tata Technologies’ Experience Centre, we are leveraging cutting-edge tools and technologies to design customer-centric solutions that enable a virtual, end-to-end customer journey.

Augmented Reality (AR), Virtual Reality (VR), Artificial Intelligence (AI), and Machine Learning (ML) are the cornerstones of our approach, enabling us to understand customer emotions, fine-tune product configurations through AI-aided lead scoring, execute real-time campaigns, and offer recommendations for post-sales needs. These solutions empower automakers to maintain a competitive edge in the dynamic business world.

As we traverse this era of unprecedented transformation, customer expectations have undergone a significant metamorphosis. Today’s customers demand real-time or near-real-time interactions with OEMs, retailers, or product vendors. This change in dynamics has prompted OEMs to proactively engage with customers, understand their sentiments, and adapt rapidly, especially in the face of unforeseen events like the pandemic.

We now exist in a world that revolves around the customer, where the shape of the customer journey remains unaltered but the reach and influence have expanded exponentially. While the dynamics of purchasing power and service channels have morphed, OEMs are at the forefront of offering the right solutions for customer-centricity. Recognising the shifts in consumer behavior is the key to success, allowing OEMs to engage customers in meaningful conversations and arm them with digital tools and solutions.

Customers today have a multitude of channels at their disposal, and the advent of social media has empowered them with information about the services and products they desire. Hence, a digitally enabled and integrated IT landscape has become imperative, facilitating the analysis of data-driven insights, ensuring customer interest, supporting purchases, and offering after-sales services that stay ahead of the curve.

In the past, the automotive customer experience primarily revolved around showroom visits, allowing customers to explore products and understand the associated offers and features. However, the present and future states of this journey are undergoing a fundamental transformation, evolving from a predominantly physical journey to a “Phygital” one, characterized by seamless integration of virtual and dynamic touchpoints.

The automotive industry is undergoing a seismic shift, driven by advancements in Connectivity, Electrification, Autonomous vehicles, and Sustainability, largely in response to environmental concerns. With the advent of technologies like connected vehicles and the progress made by automakers in autonomous vehicles, customer expectations are crystal clear: they want to interact with their cars from anywhere, at any time.

To provide an immersive customer experience and to remain competitive, a digital ecosystem that offers a seamless omnichannel experience across the entire customer lifecycle journey is essential. This ecosystem spans research, purchase, ownership, and post-sales services. While some automakers may choose to stick with traditional approaches due to the rigidity of their IT systems, the successful ones will be those that embrace next-generation experiences, underpinned by technological advancements and innovative solutions, enabled by AI and ML.

Various solutions have been implemented by automakers to remain competitive. Some of these include:

• Personalization: Personalization is not merely a buzzword but a pivotal element in today’s automotive landscape. With the assistance of Artificial Intelligence (AI) and Machine Learning (ML) algorithms, the depth of personalization extends far beyond traditional approaches. These advanced technologies empower automakers to delve into user data, comprehending usage patterns, customer preferences, and even intricate demographic information.

Through this in-depth analysis, automakers can offer personalized campaigns that truly resonate with each individual customer. Recommendations during both the sales and post-sales journeys are fine-tuned to match specific needs and preferences. AI-powered Chatbots further enhance the customer experience by providing real-time, customer-centric responses. This level of real-time assistance results in seamless communication across all customer touchpoints, leaving a lasting, positive impression.

Vehicle 360 and Customer 360 are the cornerstones for offering a truly personalized experience. AI and ML plugins, coupled with Generative AI algorithms, shed light on the KPIs of stakeholders, making their objectives clearer and the actions to achieve them more transparent. This has the added benefit of not only enhancing customer experiences but also improving Employee Experience (EX) and Dealer Experience (DX). The result is a more productive team with increased focus on business, ensuring better conversations and outcomes.

• Mobile Ready & NextGen platform: The modern automotive landscape necessitates an agile, mobile-ready IT infrastructure that can seamlessly integrate a multitude of customer touchpoints. As we step into the future, customer reach and interaction have evolved significantly. This evolution has given rise to the concept of “Phygital Channels.” Augmented Reality (AR) and Virtual Reality (VR) are now central to the customer experience, allowing them to explore and interact with vehicles in a virtual realm. Customers can engage in digital consultations with experts, creating a transformative experience.

This shift has transformed traditional showrooms into what can be described as “Digital Dealerships.” The customers’ journey from online to offline is smooth, ensuring no loss of information and a consistently unique experience. Furthermore, this approach has empowered customers to engage in ‘Direct to Customer’ interactions, establishing a direct connection between automakers and customers. This direct communication channel facilitates the exchange of vital information, promoting a more personalized and tailored experience.

With these technological advancements and the seamless integration of mobile-ready platforms, customers have more control over their purchase journey. They can configure products with ease, handle financial transactions, reimbursements, online payments, and renew annual contracts from the comfort of their chosen online channel. As a result, customers have become more empowered and influential throughout the purchase journey.

• 2C (Convenient and Connected): Automakers offer flexible engagement options, such as subscription-based services, online retail channels, and shared mobility. Vehicles have evolved from mere products to data-driven assets connected to a robust IT ecosystem, which tracks, monitors, and provides valuable insights. This continuous connection enhances the customer experience, fostering loyalty through ongoing value.

Every facet of the customer experience is increasingly interconnected with the digital ecosystem. By integrating various channels and securing customer consent, every customer need can be automated, creating a seamless experience.

Automakers that embrace a “N-E-X-T Gen” approach — focusing on Customer Centricity, Connectedness, Digital Supply Chain & Smart Manufacturing, and Engaging Workforce with Mobility Services — will earn the loyalty of their customers. As Steve Jobs famously said, “The customer is always the hero of the story,” and customer centricity is the linchpin for fostering strong emotional bonds between customers and their products. It offers curated, hyper-personalized experiences throughout the customer journey, making customers not just loyal but also influencers and ambassadors for your brand.

Original Source: https://www.tatatechnologies.com/media-center/how-automakers-can-use-ai-ml-to-gauge-customer-sentiment/

Vimal Limbad is Head, Global Centre of Excellence, Customer Experience at Tata Technologies

#ai #ml #automotive

Opinion: Will Gen AI spark the next major disruption in automotive ER&D?

Gen AI could be the next EV moment for automotive engineering and development (ER&D) industry, as it could help companies reimagine the entire product development and realization process and reduce product development time and cost significantly to disrupt the market. This article explores the possibilities across three key areas of Technology, Data and People.

New Delhi: Generative Artificial Intelligence (Gen AI), unveiled by Open AI in late 2022, has captivated digital consumers and Chief Experience Officers (CXOs) alike and drawn widespread attention. The State of AI 2023 Report by CB Insights reveals that Gen AI dominated 2023, attracting 48% of all AI investments with startups securing USD 42.5 billion across 2,500 equity rounds. This investment boom marks a new era in Artificial Intelligence, with companies rushing to adopt Gen AI to drive innovation and improve operational efficiency.

Gen AI’s capabilities in image design, content creation, summarization, and conversational agents have led to its adoption across various industries, including retail and advertising. Companies like Adobe have introduced their own Gen AI tools as a supplement to their existing design software, while others have integrated enterprise AI solutions to boost internal productivity. Despite this, the manufacturing sector, and specifically product engineering and development (ER&D), has witnessed a more cautious approach to Gen AI adoption, primarily limited to proofs of concept in customer service and training.

However, Gen AI could be the next EV moment for the automotive ER&D industry, as it could help companies reimagine the entire product development and realization process and reduce product development time and cost significantly to disrupt the market. Let us explore the possibilities across three key areas of Technology, Data and People.

Technology: Driving Innovation, Large Language Models (LLM) synthesize and innovate from extensive datasets, including product manuals and existing knowledge which is indexed properly. However, the product development process is fragmented across stages and spread across team/s, often using different software at various stages. A Gen AI application, whether based on an open-source LLM or a custom Small Language Model (SLM), that indexes internal design data could transform automotive design, testing, development, and realization process.

It would enable the creation of innovative designs and engineering solutions through simple commands, leveraging existing databases. This approach could produce multiple design variants and geometric engineering designs at unprecedented speeds, enhancing efficiency and innovation in automotive design like never seen before.

Imagine OEMs using Gen AI to analyze design data, performance metrics, and consumer insights, producing unique design blueprints rapidly. This method drafts new concepts and engineers design visions that align with market trends and exceed customer expectations, all at lower costs and higher speeds. With Gen AI, testing could leverage historical data for validation, testing outcomes, and synthetic data generation to deliver outcomes rapidly. Predictive and curative maintenance, powered by digital twins and Gen AI, could become the new norm, with Gen AI creating digital twins that predict breakdowns and offer solutions. Furthermore, Gen AI-powered vehicles could enhance customer experience by having intelligent conversations with drivers, assisting with travel plans, service visits, and support technicians easily in solving issues.

Data: Gen AI transforms historical data into an asset, creating design solutions that meet performance, safety, and consumer expectations. Automotive OEMs need to invest in data maturity to build an ecosystem that supports this transformation and creates consumable indexable reliable data. For Gen AI to succeed, it must learn from well-organized, high-quality data sets, requiring companies to invest in data collection, organization, and sanitization. Integrating AI with CAD and PLM systems requires technical innovation for seamless interoperability, while organizational changes, including AI adoption training and strict data ethics, are crucial for maintaining trust.

People: The shortage of AI talent poses a challenge, but Gen AI aims to democratize innovation, freeing creative minds from routine tasks and redirecting their focus to innovation. Gen AI enables non-coders to develop applications through simple interactions, unlocking productivity and cost efficiencies. As the automotive industry adopts Gen AI for electric vehicle development, challenges such as data maturity readiness arise.

Automotive OEMs that effectively utilize Gen AI can significantly shorten product development timelines, reduce costs, and surpass competitors. This new frontier offers traditional OEMs an unexpected advantage, allowing them to use their extensive data reserves to power SLMs and fully harness Gen AI’s potential. The future belongs to those who embrace Gen AI. The opportunity to redefine market leadership waits.

Original Source: https://www.tatatechnologies.com/media-center/opinion-will-gen-ai-spark-the-next-major-disruption-in-automotive-erd/

Santosh Singh, EVP and Global Head, Marketing and Business Excellence, Tata Technologies

#generative ai #engineering #electric vehicles #automotive #engineering services #tata technologies

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