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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
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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
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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
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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
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Leveraging plastics and composites for a sustainable automotive future
In the last decade, the automotive industry has witnessed a remarkable shift towards using plastics and composites. This transformative journey has been revolutionary, as we’ve observed an ever-increasing reliance on these materials in vehicle manufacturing. This shift’s implications are far-reaching and set to redefine the future of the automotive landscape. Global Electric Vehicle Plastics market is expected to grow from $3.7 billion in 2022 and is projected to reach $12.6 billion in 2027, at a CAGR of 27.9% during the forecast period.
Application & challenges of plastics and composites in automotive
The data paints a compelling picture. The use of plastics in automotive applications is on the rise, with predictions suggesting that by 2030, there could be up to 17% more plastic used per vehicle. Today, plastics account for up to 50% of a vehicle’s volume, indicating a substantial shift away from traditional metal components.
This transformative journey not only tackles challenges in e-mobility adoption but also shapes a future where these materials redefine automotive efficiency, safety, and sustainability. Embracing the shift toward electric mobility, the automotive industry encounters several challenges, each met with innovative solutions through the application of plastics and composites.
Weight constraints — It is a crucial concern for electric vehicles (EVs), which are effectively addressed as these materials offer a substantial reduction compared to traditional metals, enhancing overall efficiency and extending the range of EVs. For electric vehicles, a 10% weight reduction typically equals a 13.7% increase in range.
Battery Weight and Range Anxiety — The weight of batteries, a major contributor to range anxiety, is mitigated by incorporating plastics and composites in battery enclosures.
Safety Standards and Flame Retardancy — Safety standards, particularly flame retardancy, are diligently met through the inherent properties of these materials, reducing the risk of thermal incidents in EVs.
Electrical Components and Heat Management — The need for efficient heat management in electrical components finds a solution in the superior thermal insulation properties of plastics and composites, ensuring optimal performance.
Environmental Sustainability and Recycling — As environmental consciousness grows, the industry grapples with concerns regarding the ecological impact and recycling of materials. Plastics and composites contribute to a more sustainable approach, with ongoing advancements in recycling technologies addressing the issue of plastic waste.
Design Freedom and Aesthetics — Yet the benefits of plastics and composites extend far beyond lightweight, electrical applications and batteries. These materials offer a world of design possibilities, enabling manufacturers to craft intricate forms for exteriors, interiors, and even powertrain components. They have also revolutionized lighting design and offer exceptional electrical and thermal insulation properties, along with corrosion resistance.
The Adoption Process
Core competence with engineers and designers at automotive OEMs, engineering service providers, and raw material manufacturers have developed methods to create applications using materials based on key engineering performance requirements, manufacturing process requirements, design attributes, and cost considerations.
Identifying Needs and Desires — For adoption of polymer composites follow a structured approach, beginning with the identification of needs and desires, driven by the desire to enhance product performance, productivity, or meet regulatory requirements.
Feasibility Studies — Conduct feasibility studies to determine the technical and economic viability of adopting plastics and composites for a specific application. This study should include an assessment of performance, cost, and the readiness of technology (TRL — Technology Readiness Level).
Developing a Comprehensive Plan — Based on the results of the feasibility study, a technology adoption plan is developed. This plan includes a timeline for implementation, resource requirements, and a budget.
Collaborative Efforts — Suppliers and collaborators are identified based on their capabilities, technical expertise, and experience in research and development.
Employee Training and Integration — Training and development of employees for the new technology, including design, manufacturing processes, and testing.
Ensuring Compliance and Safety — Integrating the technology with existing automotive systems and testing the integrated system for safety and performance compliance with regulations.
New Materials and Applications — Launching materials and their applications for systems while promoting their benefits to customers and stakeholders.
The automotive industry’s embrace of plastics and composites marks a pivotal moment in its evolution. These materials drive innovation, offer design flexibility, durability, and eco-friendly properties, promoting sustainability. As we navigate towards a cleaner and more efficient automotive future, companies are moving ahead with 5R right weighting approach, stand at the forefront. This approach focuses on the Right Material, aligning with the industry’s shift towards lightweighting and sustainable solutions. This will drive a positive change, with innovative solutions in the automotive industry benefiting both society and the environment.
Original Source: https://www.tatatechnologies.com/media-center/leveraging-plastics-and-composites-for-a-sustainable-automotive-future/
Abhay Deshpande, Technical Specialist — Materials, ER&D at Tata Technologies
#ai#generative ai#engineering#tata technologies#electric vehicle engineering#electric vehicles#automotive
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For Manufacturing Competitive Products
Product Lifecycle Management (PLM) has come a long way from being utilized as a repository for the CAD data authored in Mechanical CAD tools to managing the critical process areas for new product development (NPD).
New Product Development (NPD) process typically captures the journey of a product from ideation to retirement with multiple milestones along the journey, providing input and feedback to the corresponding milestones and business gates. The NPD journey starts with ideation, concept, design, development, prototype, validation, production, sales, service & warranty, and product retirement.
The present-day PLM
The present-day PLM predominantly addresses engineering aspects like product definition, product configurations, classification, Bill of Material (BOM) management, and change management of NPD process. It extends its reach to upstream and downstream areas, including requirements, manufacturing, quality, project management, and supplier management.
The critical elements of product data from Mechanical Design perspective and PLM’s ability to incorporate Mechanical CAD authoring tools have undergone significant evolution and maturation.
The transfer of CADBOM from CAD authoring tools to PLM, coupled with the concurrent generation of the corresponding Engineering BOM (EBOM), establishes EBOM as the primary BOM within PLM. Other BOMs like Purchase BOM (PBOM), Manufacturing BOM (MBOM), Plant specific BOM, Service BOM (SBOM) can be derivatives of EBOM.
The evolution for Electrical and Electronics (E&E) data that needs to be managed in PLM has begun, wherein the E&E data related to EBOM is transferred to PLM and utilized for collaboration with other streams and stakeholders (for example, design reviews, clash detection, etc.).
The present-day PLM enables concurrent engineering by providing a platform for collaboration of stakeholders, integration of systems among disciplines like design, engineering, manufacturing suppliers, etc. with an objective to enhance product quality and streamline the business process. This enables processes to run in parallel, thereby improving efficiency, productivity, and reducing time-to-market for products.
As the consumer gravitates more towards software-defined connected products, the businesses that cater to these needs have to naturally manufacture these products efficiently with mandated quality and higher margins. The products should be better in terms of value (real and perceived) in the segment they compete and be sustainable environmentally.
The future of PLM
With a radical shift in consumer behavior, products too have evolved into ‘software-defined connected products’. They are complex (a combination of mechanical, electrical & electronics, and software) and connected (through Internet of Things [IoT]). They transmit product and user data to the cloud through 5G technologies and have a high volume of software, enabling updates over-the-air (OTA). For example, premium vehicles contain 150 million lines of software code, distributed among as many as 100 electronic control units (ECUs) and an array of sensors, cameras, radar, and light detection and ranging (lidar) devices.
When businesses embark on a journey to create high-performing, software-defined connected products, they need to make sure that they have the pre-requisite foundation and framework that can bring process, systems, and data together and leverage Artificial intelligence (AI) for trends and analysis, predictive maintenance, etc., hosting enterprise-wide applications on cloud and connecting products through IoT, Augmented Reality and Virtual Reality for enhanced customer experience.
Businesses that aspire to manufacture competitive products now and also in the future with shorter time-to-market, thereby increasing margins, should consider the following aspects for next generation PLM solutions:
Manufacturing ‘software-defined connected products’ that are sustainable should have an enterprise wide framework, whereby connected products are a natural outcome of efficient processes, integrated application(s) with which, data is seamlessly traced and consumed, and collaborations among people on topics like design reviews, quality improvements, cost optimizations happen with zero or limited effort.
The ultimate objective for companies is to create a seamless NPD process with enterprise wide foundation or framework, which would have an integration between ALM-PLM-ERP-MES-CRM. This ecosystem will enable single source of truth for the product & traceability from Requirements Bill of Material (Requirements BOM) to CADBOM to Engineering BOM (EBOM) to Purchase BOM (PBOM) to Manufacturing BOM (MBOM) to Plant Specific BOM to Service BOM (SBOM) etc (Requirements BOM-CADBOM-EBOM-PBOM-MBOM-Plant BOM-SBOM)
With global economies aligning towards achieving climate neutrality by 2050, businesses worldwide are compelled to shift towards sustainable products. This underscores the imperative for adopting next-generation Product Lifecycle Management (PLM) as a crucial strategy for businesses to thrive and endure. The integration of carbon calculators with PLM equips designers in the early stages of New Product Development (NPD) to make informed choices regarding materials, manufacturing processes, and more, facilitating the creation of sustainable products with reduced carbon emissions.
Deploying enterprise-wide applications on the cloud facilitates seamless access for both internal and external stakeholders, partners, and vendors through web browsers. This reduces costs associated with maintaining on-premise hosted applications.
Creating a strong foundation, which will enable digital twin (a digital representation in sync with the physical product) in its truest sense. This makes concepts such as enterprise change management and traceability across the entire new product development etc., become a reality.
Establishing a single source of truth for the product(s) so that the product data is authored by a business unit within one application and utilized by other stakeholders, as well as other applications positioned either upstream or downstream in the process.
Conclusion
Businesses need the framework and foundation to be architecturally strong, yet be flexible, scalable, and secure. Hence, PLM is mandatory for businesses not just to succeed but also to stay afloat and relevant in these ever-growing and competitive markets where the customer has to see the value in the product(s) to buy them.
Original Source: https://www.tatatechnologies.com/media-center/for-manufacturing-competitive-products/
Sundaram Shanmuga, PLM COE Lead, Tata Technologies
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Tata Technologies Steps On The Gas In New Skills
The company believes that this is essential in understanding customer requirements better when it comes to new technologies.
Nachiketa Paranjpe, President — Automotive Sales, Tata Technologies, believes that the changes underway in the automotive industry will translate into huge opportunities going forward.
This is because every new technology or feature that needs to be integrated into the vehicle requires specialised skills, he told Mobility Outlook. For vehicle manufacturers, maintaining various software and vehicle configurations, hardware and software combination or the entire database of updated software is a challenge.
Tata Technologies can help OEMs and Tier 1 suppliers in adapting to new age technologies but this means it needs to be ahead of the curve and at the cusp of tech changes. According to Paranjpe, updating workforce skills becomes critical and this is where a nuanced approach is essential. Here is where Tata TechVarsity offers upskilling and cross-skilling learning paths with its e-learning modules, accreditation support and workshops on latest technologies.
The internal tech university uses a proprietary tool called ‘IGetIt’ to maintain the content database for education and keep the database updated. The idea is to help students focus on self-learning and avoid unnecessary pitfalls. While the ‘TechVarsity’ initiative is limited to India, the company has collaborated with universities for joint courses overseas. Beyond this, it ensures that employees have the right mental attitude to learn new things.
Tata Technologies also works closely with leading customers to stay abreast of technology changes. Paranjpe said this was essential especially when today’s modern age customers are technically proficient.
Global Inputs
It is in this backdrop that a global buyer base has also come in handy when it comes to understanding the pace of change. The company has 70 R&D centres across North America, Europe and Asia Pacific which have specific approaches in meeting technological challenges. For instance, distances are more in North America which means trucks and SUVs rule the roost.
This, in turn, has led to greater use of hydrogen and this becomes a useful marketing input to Tata Technologies. Europe, on the other hand, is focused on electrification while Asia-Pacific is more about newer and exciting technologies for the vehicle’s cockpit. Paranjpe said all three regions have different emerging trends which will overlap at some point in time which is “when we will benefit from working with these varied customers across the globe”.
Tata Technologies has also become part of the AUTOSAR (AUTomotive Open System ARchitecture) consortium created in 2003 and intended to develop an open and standardised software architecture for automotive electronic control units.
Scalability of vehicle and platform variants, software transferability, consideration of availability and safety needs, collaboration among various partners, sustainable use of natural resources, and maintainability throughout the product lifecycle are among the goals of the consortium.
Paranjpe said by being a premium member, Tata Technologies is able to have a better perspective of the roadmap articulated by AUTOSAR. Engineers in the consortium try out new proof of concepts and understand various upcoming trends like cybersecurity in terms of basic software stack and diagnostics. This helps the company understand how the stack will evolve.
“With this, Tata Technologies has a sort of a look-into-the-future of what skills are needed to improve stack configuration and integration. This way, we are always aware of the upcoming latest trends in AUTOSAR versions,” said Paranjpe.
Original Source: https://www.tatatechnologies.com/media-center/tata-technologies-steps-on-the-gas-in-new-skills/
Nachiketa Paranjpe, President — Automotive Sales, Tata Technologies
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Journey through time: A comprehensive look at connected vehicles
This article is authored by Nitin Kamble, head CoE, digital technologies, Tata Technologies.
The term “connected vehicles” refers to software, services, and technological developments that connect a vehicle to its surroundings. Now that connected vehicles have become the industry norm, we should expect them to keep getting better. To put it simply, a connected vehicle is one that has wireless networks on board that can communicate with nearby electronic devices. A connected vehicle is frequently described as a highly advanced Internet of Things (IoT) technology.
The technology of today is a marvelous feat of engineering. The concept of connected automobiles has seen tremendous transformation over the past few decades, particularly in recent years. The connected vehicle technology of the past was quite rudimentary in comparison to what we have now. It was first proposed to connect autos to external networks and systems in the 20th century. Early systems focused on basic telemetry and tracking, which were widely used for fleet management. These gadgets conveyed fundamental data via radio or cellular connectivity, such as position and speed.
A turning point in the development of connected car technology was reached when General Motors unveiled their connected vehicle technology platform, “OnStar”. In the early 2000s, telematics systems for cars started to evolve. Telematics combined telecommunications and informatics to enable two-way communication between auto and external systems. These systems offered remote diagnostics, GPS navigation, roadside assistance, and automobile tracking for stolen vehicles.
In-car networking and infotainment capabilities saw a significant change in the 2010s. Manufacturers began adding smartphones, advanced multimedia systems, and touchscreens. It became feasible to use programs while driving, make hands-free calls, and stream media. Vehicle-to-vehicle (V2V) enables cars to communicate with one another and share information like position, speed, and direction to improve safety and traffic flow. V2I communication requires cars to interact with infrastructure, such as traffic lights and road sensors, in order to improve traffic management. In the second half of the 2010s, connected automobiles began to incorporate advanced driver assistance systems (ADAS) such as adaptive cruise control, lane departure warnings, and automatic emergency braking.
There have been numerous advancements in connected vehicle technology since then. Connected vehicles now include advanced ADAS technologies that go beyond standard alerts and warnings. Advanced collision avoidance systems, automatic parking, adaptive cruise control, and lane-keeping assistance are examples of such systems. Manufacturers’ related services include remote vehicle monitoring, diagnostics, and maintenance notifications. These services allow owners to monitor the status of their vehicles and receive alerts when repairs are required.
Today, several automakers use over-the-air (OTA) updates to remotely update vehicles with software upgrades and bug fixes. This capability reduces the number of times customers must visit dealerships for routine maintenance and allows automakers to constantly improve car features and performance.
Infotainment systems in modern cars smoothly interact with cellphones and other devices. Both drivers and passengers have access to a range of apps, music, navigation, and speech recognition services. The implementation of 5G networks will play a pivotal role in shaping the future of connected vehicles. With ultra-low latency and high data transfer rates, 5G will enable real-time communication between vehicles, infrastructure, and the cloud. By 2026, it’s estimated that 5G-enabled vehicles will account for 70% of total connected vehicles, according to Frost & Sullivan.
Vehicle-to-everything (V2X) communication allows vehicles to exchange real-time data with other vehicles and infrastructure components such as traffic signals and road sensors. This communication increases safety by providing information about potential hazards and driving conditions. Even though completely driverless vehicles are not yet common, connected vehicle technology is essential for making autonomous driving possible. In autonomous or semi-autonomous modes, vehicles communicate with one another and the local infrastructure to improve navigation and safety. The significance of cybersecurity has expanded with increased connectivity. To safeguard connected vehicles from hacking and illegal access, automakers are putting a lot of effort into putting strong cybersecurity safeguards in place.
According to a recent report by Draup, the global connected car market size was USD 73.16 Billion in 2022 and is expected to grow to USD 156.6 Billion by 2027 with a CAGR of 16.44% from 2022 to 2027. Communication from V2X will spread more widely. This includes communication between vehicles, including V2V, vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and other forms. By enabling vehicles to exchange real-time data with each other and their surroundings, V2X will improve safety and traffic management. To reduce latency and enhance real-time decision-making, vehicles will process and analyse data more and more locally (edge computing). For applications like autonomous driving and V2X communication that require safety, this is essential. Through real-time route, speed, and driving behavior optimisation, connected car technology will help make driving more energy-efficient. This may result in less fuel use and pollution.
The usage of completely driverless vehicles will probably increase as autonomous driving technology continues to progress. The autonomous vehicle market is projected to reach $556.67 billion by 2026. To securely navigate complex urban landscapes and highways, these vehicles will interact with one another and the supporting infrastructure. The connectivity between vehicles and the larger network will keep becoming better as 5G networks develop and go beyond. High-bandwidth applications like augmented reality navigation, in-car entertainment, and improved remote vehicle monitoring will be supported by this. Biometric sensors could be included in connected cars to track factors like driver distraction and weariness. Through their intervention when a driver’s attention begins to wander, these sensors may improve safety. Advanced predictive maintenance capabilities will be built into vehicles, using real-time data to identify and fix potential mechanical flaws before they become major difficulties. Passengers will enjoy immersive experiences thanks to reinvented interior design and infotainment systems. Displays for augmented reality, adaptable cabin settings, and entertainment choices all might become commonplace.
Subscription-based business models could overtake traditional car ownership as the preferred mode of transportation. Users had access to various vehicles based on their needs, all of which were connected to their individual accounts for a smooth experience. Smart city initiatives will be greatly aided by connected automobiles, which will provide data for dynamic traffic control, congestion reduction, and better urban design.
The protection of data privacy and cybersecurity will be of utmost importance as vehicles collect and transmit more data. To preserve personal information and stop cyberattacks, more stringent laws and improved security measures will certainly be put in place. By facilitating shared mobility services, enhancing traffic flow, and encouraging eco-friendly driving practices, connected car technology can help to lessen the environmental effect of transportation.
While these options reflect potential pathways for connected vehicle technology, it’s crucial to keep in mind that the precise course will rely on a number of variables, including technological advancements, governmental decisions, consumer preferences, and societal changes. These elements will likely combine to create a dynamic future for connected automobiles.
Original Source: https://www.tatatechnologies.com/media-center/journey-through-time-a-comprehensive-look-at-connected-vehicles/
Nitin Kamble, head CoE, digital technologies, Tata Technologies.
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Emerging trends and technologies in CAD/CAM simulation and testing
Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM), and Simulation have been integral parts of global manufacturing industries for several decades. The evolution of computer-aided design (CAD), computer-aided manufacturing (CAM), and industrial simulation is a testament to the transformative power of technology.
The evolution of CAD/CAM and simulation
For several decades, Computer-Aided Design (CAD) has revolutionized design processes, with its inception in the 1960s and significant advancements in the 1980s and 1990s, introducing 3D modelling and parametric design capabilities. In parallel, Computer-Aided Manufacturing (CAM) emerged in the 1950s and 1960s with the utilization of computer-controlled machinery, gaining recognition in the 1970s. The integration of CAD and CAM systems further streamlined the transition from design to manufacturing. Alongside CAD and CAM, industrial simulation, including techniques like discrete event simulation and finite element analysis, has been employed in manufacturing since the mid-20th century. Early simulation efforts focused on process efficiency and production schedule optimization. As computing power grew, sophisticated simulation software emerged, enabling the modelling and simulation of complex manufacturing processes.
CAD/CAM & simulation technologies globally have reached a level wherein many manufacturing industries’ technical decisions are made at a digital level. These technologies have evolved over a period of five decades where drawing boards, which were earlier used to design the product, are being completely eliminated & made imagination of product designs much simpler. CAM played a key role in minimizing the laborious work of handling machines with precision to make the physical product. The integration of CAM software with Computer Numerical Control (CNC) machines has become more common in manufacturing industries.
This integration allows for the automatic generation of toolpaths, reducing manual programming and improving machining accuracy to create the right quality shape of the part, tools & fixtures. CAD evolved around generating shape options either through solid modelling or surface modelling to build complex shapes for products in Automotive, Aerospace & other manufacturing industries. Advanced Simulation technologies like Finite Element Method (FEM), Computer-Aided Engineering (CAE), formability analysis, and mould flow analysis played a significant role in analyzing the actual formation of the parts or evaluating the structural strength & validation to a highly accurate level.
CFD (Computational Fluid Dynamics) played a role in designing optimum airflow & fluid in aerospace, automotive & other industries. This helps reduce production errors, test various scenarios, and improve production lines’ efficiency.
Emerging trends
Today, CAD and CAM are integral parts of various industries, including aerospace, automotive, construction equipment, and manufacturing. These technologies have evolved significantly, enabling engineers, designers, and manufacturers to create and produce complex products with greater precision and efficiency. The adoption of advanced software, integration with other technologies, workforce training, and focus on efficiency, sustainability, and customization have all contributed to this evolution. While these technologies are maturing, there are emerging CAD/CAM simulation technologies trends, which have expanded in the following areas.
Digital Twins: Digital twins are virtual representations of physical products or systems. They enable real-time monitoring and simulation, providing insights into the performance of products in the field and helping with predictive maintenance.
Primarily in manufacturing industries, digital twins are used in two areas: product & process. Product twins represent physical products like machinery, equipment, or consumer goods. They include detailed 3D models, specifications, and product performance and usage data. Product twins are used for product design, testing, and ongoing maintenance. Process twins model and simulate real-world processes, like manufacturing processes, supply chain operations, and industrial workflows. They help optimize processes, improve efficiency, and identify potential bottlenecks or inefficiencies.
Robotic Process Automation (RPA): After Covid, Robots have played a vital role on the shop floor. Automation in manufacturing was expanding beyond the shop floor to administrative tasks. Manufacturers used simulations to evaluate and optimize RPA implementations. The integration of robots alongside human workers was increasing. Simulations were used to ensure safe and efficient interactions between humans and robots in a shared workspace.
3. Virtual Reality (VR) and Augmented Reality (AR): VR and AR technologies are being integrated into CAD/CAM systems to provide immersive design experiences and facilitate real-time collaboration between teams spread across distinct locations. Virtual reality (VR) and augmented reality (AR) simulations were used for worker training, enabling them to learn and practice tasks in a safe and controlled environment. Manufacturers were increasingly looking to offer customized and personalized products. Simulations helped in configuring production processes to accommodate products efficiently.
4. Virtual commissioning: — Virtual commissioning is a process used in manufacturing and automation industries to simulate and evaluate automated systems in a digital environment before they are physically built or implemented. This helps identify and resolve issues, optimize performance, and reduce the risk of errors in the real-world commissioning process.
This approach helps identify and address issues, optimize performance, and reduce the risk of errors in the real-world commissioning process. Here are the typical steps involved in virtual commissioning. The control logic for the automation system, which includes the program that governs the behaviour of the machinery and robots, is developed or imported into the virtual commissioning software. This logic typically represents how the system should operate and respond to various inputs and conditions.
Conclusion
Manufacturing industries should consider adopting technologies like digital twins, augmented reality/virtual reality (AR/VR), robotic automation, and virtual commissioning to improve operational efficiency, reduce downtime, and better resource utilization. Digital twins create virtual replicas of physical assets, allowing manufacturers to monitor and optimize processes in real-time. Digital twins can help manufacturers test and optimize new production processes or equipment in a virtual environment before physical implementation. Robotic Automation can be easily scaled to accommodate changing production demands. Robotic Automation is where Robots can work tirelessly and with great precision, enhancing production speed and consistency, leading to increased productivity.
AR/VR technologies enable immersive training experiences for workers, reducing the learning curve and improving their skills. Virtual commissioning allows operators to become familiar with new systems before they are installed. Embracing advanced technologies can give manufacturers a competitive edge by delivering products more efficiently, at higher quality, and with greater flexibility in adapting to market demands. To conclude, adopting digital twins, AR/VR, robotic automation, and virtual commissioning can significantly enhance the capabilities of manufacturing industries by improving efficiency, quality, safety, and cost-effectiveness while promoting sustainability and innovation.
Original Source: https://www.tatatechnologies.com/media-center/emerging-trends-and-technologies-in-cad-cam-simulation-and-testing/
Naren Brahme, VP — ER&D at Tata Technologies
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EV Engineering (Technological Outlook)
Researchers and Automotive Industry are continuously enhancing the existing technologies and evolving new ways of engineering the EVs.
The electric vehicle (EV) sector is experiencing an extraordinary technological transformation, propelling the world toward sustainable and efficient transportation solutions. With the imperative to combat climate change and reduce greenhouse gas emissions, manufacturing and engineering companies have stepped up to the challenge, ushering in an era of electrifying mobility solutions. As engineers play a pivotal role in shaping the future of EVs, it is essential to delve into the technical aspects and emerging trends that are driving this electrifying revolution. The article explains challenges, solutions and cutting-edge engineering trends in the EV domain, highlighting key technical aspects that are reshaping the automotive landscape.
Electric Vehicle Engineering: Challenges
While there are new technological developments happening in the EV space, the EV designer still needs to address some fundamental problems while designing the best electrical vehicle.
Range Anxiety: Concerns about limited driving range and the availability of charging infrastructure contribute to range anxiety among potential EV buyers. Developing and sourcing advanced battery technology that offers sufficient energy density and driving range while keeping costs under control is one of the most significant challenges.
Charging Infrastructure & Charging Time: The availability and accessibility of charging stations are critical for widespread EV adoption. While charging technology is improving, long charging times compared to refuelling ICE vehicles remain a challenge.
High Initial Cost: The upfront cost of purchasing an EV is often higher than that of a comparable ICE vehicle due to the cost of batteries and electric drivetrain components. While there is a significant work done in the optimization of battery and other electrification components, still it is not enough to close the gap between ICE vehicle and EVs.
Vehicle Weight and Efficiency: The weight of battery packs impacts the overall vehicle weight, affecting energy efficiency and handling.
Performance and Customer Expectations: Consumers expect electric vehicles to match or exceed the performance of ICE vehicles.
Safety and Thermal Management: Ensuring the safety of high-voltage systems, battery packs, and thermal management of batteries during charging and operation is a critical engineering challenge.
Regulatory Landscape: Evolving regulations related to emissions, vehicle safety standards, and incentives can impact the development and adoption of electric vehicles.
Apart from the challenges related to design and development of EV, OEMs also need to address issues like huge capital expenditure to develop diverse range of EV models for different body types, price points, and performance options. Supply Chain challenges include ensuring a stable supply chain for lithium, cobalt and nickel while addressing environmental, ethical and sustainability concerns.
How are OEMs overcoming these challenges?
Design & Development of Electrical Vehicle encompasses a wide range of disciplines and activities related to the design, development, and manufacturing of electric vehicles. Researchers and Automotive Industry are continuously enhancing the existing technologies and evolving new ways of engineering the EVs. Here are some strategies to address these challenges:
Advancements in the Battery Technology: Improving battery technology to enhance energy density, charging speed, and overall performance while reducing production costs is crucial to make EVs more affordable and competitive. Lithium-ion batteries have been the go-to choice for EVs, but researchers are exploring alternate chemistries like Lithium-Sulphur (Li-S), Lithium-Silicon (Li-Si) etc. In the recent times, Solid-state batteries are gaining significant attention due to their higher energy density and improved safety characteristics.
Power Electronics stream has gained significance in EV Engineering which is required for Designing and optimizing electronic systems that control the flow of electric power between the battery, motor, and other vehicle components. Engineers are also working on developing sophisticated Software and Control algorithms to manage the vehicle’s electric power distribution, battery charging, and vehicle performance.
Developing high-performance and energy-efficient electric motors that drive the wheels of the vehicle is another important aspect of optimizing the power and improving the performance. Engineers are focusing on improving the Energy Efficiency by optimizing the vehicle’s energy consumption. Regenerative braking systems plays very significant role in enhancing the driving range. It also reduces wear on traditional friction braking systems, leading to decreased maintenance costs and increased longevity.
Charging Infrastructure: Designing and developing charging stations and infrastructure to support the widespread adoption of electric vehicles is very crucial. Engineers are working on developing faster and more reliable charging solutions. High-power DC fast-charging stations are becoming increasingly common, drastically reducing charging times and addressing range anxiety. Smart Grid integration is a crucial aspect of managing the increasing demand for electricity from EVs. Intelligent charging solutions that optimize charging times based on grid demand, renewable energy availability, and user preferences. Few OEMs like NIO are addressing the charging time challenge by deploying Battery Swapping technology. They have built a network of battery swapping stations across major cities in China where they can change the battery in 3 to 4 minutes equalling the time required to fill the gasoline for ICE vehicles.
Thermal Management: Ensuring proper cooling and temperature control of electric vehicle components, especially the battery, to maximize performance and extend their lifespan. This is importance from the safety perspective as well.
Efficiency is a fundamental aspect of EV engineering, and weight reduction without compromising the safety and performance is very crucial. Lightweight materials such as carbon-fiber composites, aluminium alloys, and high-strength steels are becoming more prevalent in EV design, thanks to advancements in manufacturing techniques.
All these factors including advancement in the battery technologies, energy efficient drivetrains, lightweight material and improving charging infrastructure will significantly help to reduce the Range Anxiety among the customers.
To address the challenges related to vehicle weight, efficiency, performance & flexibility OEMs are developing Electric Vehicle Platforms. It serves as the foundation for various types of electric vehicles, including cars, trucks, SUVs & Vans. Skateboard is one of the most popular types of platform used by many leading OEMs including Tesla. Many OEMs are also adopting Modular Platforms making it easier for them to create different vehicle models using the same underlying platform, leading to cost savings in production. Drive- by-wire technology is also gaining popularity in the Electrical vehicle as it brings latest technology along with the compactness which leaves more space for the passenger use or cargo volume. Skateboard and modular platforms provide the benefit of modular design, uniform weight distribution, design flexibility, Safety and improved performance.
OEMs are balancing factors like acceleration, range, and affordability while meeting customer expectations by offering different combinations of drivetrain configurations like Front wheel drive (FWD), Rear Wheel Drive (RWD) and All wheel drive (AWD). FWD is a common configuration for electric vehicles. Being most cost effective it is also prevalent in many mass-market EV models. RWD is often used in high-performance electric vehicles, luxury cars, and some larger electric SUVs, it offers a sportier driving experience. Twin motors rear drive is also getting traction in high performance cars. AWD is popular particularly in SUVs and high-performance models, it offers enhanced traction and stability, making it suitable for various road conditions and driving scenarios.
The collaborative efforts of engineers, researchers, and industry stakeholders will play a pivotal role in making electric vehicles more accessible, sustainable, and efficient for consumers worldwide. With the constant drive for improvement and innovation, the future of EV engineering is electrifyingly bright, heralding a new era of clean and green transportation.
Original Source: https://www.tatatechnologies.com/media-center/ev-engineering-technological-outlook/
Abhay Kulkarni, VP & ER&D Global COE Head, Tata Technologies
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Paradigm shift in ‘Virtual Validation’ with the emergence of EVs
The automotive industry has undergone a remarkable transformation over the past decade. In an era where global environmental consciousness has become a paramount concern for the well-being of future generations, the electric vehicle (EV) sector has surged in prominence across the globe. This growth isn’t confined solely to two and three-wheelers but extends to encompass commercial vehicles as well.
Traditional original equipment manufacturers (OEMs) have been compelled to reassess their vehicle development strategies, pushing for rapid turnarounds, particularly within the EV sector. Moreover, startup companies worldwide, whether situated in Western North America or Eastern ASEAN nations, have demonstrated remarkable audacity by challenging established engineering norms and achieving the development of EVs in as little as 24 months, from the initial design phase to the showroom floor.
EV landscape
While EVs may boast a simpler mechanical structure when compared to their internal combustion engine (ICE) counterparts, it’s crucial to note that safety requirements, compliance standards, and validation criteria remain as robust as ever. In fact, EVs introduce a set of unique challenges due to their distinctive architecture, featuring high-capacity electric batteries (HEV) and all-wheel-drive systems equipped with two motors, one in the front and another in the rear. These intricacies compound the difficulties in meeting the ever-escalating safety standards such as US NCAP, ENCAP, and ASEAN NCAP, which are well-recognised by consumers worldwide.
In this dynamic landscape characterised by swift turnarounds and the unwavering commitment to stringent safety norms, the utilisation of Virtual Validation tools, commonly referred to as Computer-Aided Engineering (CAE), assumes an even more pivotal role. While this necessity undeniably benefits the Virtual Validation industry, it simultaneously exerts pressure on the established knowledge base and standard operating procedures that have endured for over three decades. The wealth of expertise accrued by OEMs and service providers underscores the growing importance of effectively addressing the unique challenges presented by EVs.
The reliability and effectiveness of Virtual Validation tools and methodologies have been consistently demonstrated, owing to the advancement of high computing power. Engineers have steadily built confidence over the years by methodically bridging the gap between virtual and physical testing. This ongoing progress provides a sturdy foundation for navigating the ever-evolving EV landscape, marked by accelerated development cycles, exacting safety regulations, and distinctive architectural demands.
Virtual Validation in the past and recently
In recent years, a notable shift has been observed in the practices of testing agencies, exemplified by ARAI (Automotive Research Administration of India, Pune), responsible for certifying vehicles as roadworthy. These agencies have increasingly shown receptivity to Virtual Validation reports submitted by OEMs for various test cases. While this acceptance is not yet ubiquitous among testing bodies, it serves as compelling evidence of the pivotal role that Virtual Validation plays in the development of both ICE and EV vehicles. This process not only expedites development timelines but also yields substantial cost savings for new vehicle programs and facelifts.
Virtual Validation finds application across a diverse spectrum of domains, encompassing Noise, Vibration, and Harshness (NVH), Durability, Structural Crash and Occupant Safety, Multi-body Dynamics (MBD), Computational Fluid Dynamics (CFD), and Mold Flow, among other major areas. Numerous companies have made substantial investments over the decades in the development of Virtual Validation tools, with the singular objective of enhancing accuracy and streamlining development timelines while curbing testing expenditures — a pressing need within the automotive industry. These tools have undergone a remarkable evolution, progressing from 1D (one-dimensional) and 2D methodologies to sophisticated 3D models that closely mirror the actual design and functionality of vehicles. Following the generation of Computer-Aided Design (CAD) data during the styling phase, Virtual Validation can be applied in the early stages of a program to evaluate various domain-specific feasibility requirements and provide invaluable feedback to design engineers, ultimately fostering a ‘first-time-right’ approach.
The strength of a Virtual Validation team, whether it resides within an OEM’s CAE department or is provided by a specialised service supplier with expertise in meeting the demands of the EV industry, directly correlates with the competitive advantage enjoyed by the car manufacturer. The significance of Virtual Validation in conferring a competitive edge becomes even more pronounced for EV startups. These startups are at the forefront of innovation, relentlessly pushing the boundaries of rapid turnaround times.
Unlike well-established OEMs burdened by decades-old legacy systems and development procedures, EV startups are agile in their adoption of Virtual Validation tools and processes. They not only embrace these tools but also actively seek ways to minimise their reliance on prototype testing. Building prototype vehicles is a costly endeavor, often running into the millions of dollars. Consequently, Virtual Validation tools play a pivotal role in significantly reducing the number of required test vehicles, with some OEMs achieving remarkable reductions ranging from 40 percent to 60 percent. Their ultimate vision is to eliminate the need for prototypes in the years to come.
Future of Virtual Validation
Virtual Validation has a track record of utilising intelligent tools like Design of Experiments (DOE) and optimisation techniques, which have consistently proven their worth in uncovering optimal design solutions amid the intricate web of permutations and combinations. However, with the advent of Artificial Intelligence (AI) and Machine Learning (ML), the Virtual Validation industry is poised for a transformative revolution in the field of automotive engineering. AI/ML holds the potential to significantly expedite the advancement of EV programs, especially when coupled with adaptable modular approaches.
Beyond AI/ML, the proliferation of versatile computer platforms is set to further propel the acceleration of EV development in the years to come. These platforms offer an array of advantages, including digital twinning, Universal Scene Description (USD), and the availability of in-house Virtual Validation tools catering to various domains. These advancements will empower engineers from diverse domains, encompassing CAD, CAM, and CAE, to seamlessly collaborate, leading to swift problem-solving.
Furthermore, harnessing the capabilities of cloud computing within this multiverse will facilitate rapid data sharing, computing, and analysis, ultimately amplifying the momentum of the leftward shift in EV development. This transformation promises to confer a substantial competitive edge and facilitate the creation of exceptional products that cater to the ever-discerning demands of consumers.
Original Source: https://tatatechnologies.com/media-center/paradigm-shift-in-virtual-validation-with-the-emergence-of-evs/
Gopal Musale, Regional Manager, Virtual Validation Centre of Excellence, Tata Technologies
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Proactive maintenance using new-age technology
Predictive Maintenance (PdM) and Preventive Maintenance (PM) are twin pillars that elevate manufacturing operations to an unprecedented level of productivity. While PM entails scheduled inspections and interventions to pre-empt equipment failures, PdM harnesses a sophisticated blend of sensors, data analytics and Machine Learning (ML) to forecast potential issues before they cascade into operational disruptions.
Maintenance maturity
Reactive maintenance
After machine failure
High downtime impact
Preventive Maintenance
Planned maintenance at scheduled intervals
Less unplanned downtime
Condition Based Monitoring
Condition/ based maintenance for critical machines
Lesser unplanned downtime
Predictive Maintenace
Predictions for machine failure well in advance
Least unplanned downtime
Embracing the technological ecosystem
Central to the success of predictive and preventive maintenance in manufacturing is the intricate web of technologies driving this transformation:
• Industrial IoT and sensor fusion for condition monitoring: The convergence of IoT and sensors equips machines with the ability to communicate and share real-time data. Sensors embedded in machinery monitor temperature fluctuations, vibration patterns and energy consumption, offering real-time insights into equipment health. Real-time facility monitoring solutions are proven to play critical role in such scenarios.
• Data analytics and Big Data over Cloud: The proliferation of data from sensors and other sources demands expandable storage and robust analytics solutions. Cutting-edge cloud-based data analytics platforms process massive datasets to unveil patterns, anomalies and trends that hint at impending issues.
• Advanced ML and Artificial Intelligence (AI): Machine learning algorithms dissect historical data to discern patterns and anomalies, enabling precise predictions. AI-driven systems scrutinise vast datasets, transforming raw information into actionable insights that augment decision-making prowess.
• Digital Twins for simulated brilliance: The advent of digital twins creates virtual replicas of physical assets, enabling real-time monitoring and experimentation in a digital realm. These twins simulate various scenarios, aiding in the prediction of equipment failures and in devising optimal maintenance strategies. This approach can also help in virtual commissioning.
• Augmented Reality (AR): AR can be used effectively for maintenance training and assistance. Technicians can use AR glasses or devices to receive step-by-step instructions for complex maintenance tasks. • Blockchain: Blockchain can be used for equipment maintenance records and service history, ensuring data integrity and transparency.
Industry specifics
Across various industries, predictive and preventive maintenance solutions are proving to be transformative, optimising operations and bolstering efficiency. Incorporating these strategies across sectors leads to minimising downtime and costs to enhancing product quality.
• Manufacturing: Within the manufacturing domain, machinery plays a pivotal role in achieving production targets, with predictive maintenance emerging as a foundational approach to ensure uninterrupted machine functionality. Take the example of automotive assembly lines where sensors discern motor vibrations and deviations, signalling misalignments. Addressing such concerns proactively not only averts defects but also prevents disruptions in production.
• Aviation: The aviation industry’s paramount concern is aircraft safety, where predictive maintenance shines. Embedded sensors monitor engine performance, airframe health and avionics functionality, providing a comprehensive view of an aircraft’s condition. MRO solutions empowered with PdM can help in anticipating such scenarios and maintaining overall LLP traceability.
• Automotive manufacturing: In the fiercely competitive automotive manufacturing landscape, even minor disruptions can have far-reaching consequences. By employing sensors to detect motor vibration fluctuations, misalignments are identified before they escalate into production standstills. This proactive strategy not only ensures product quality but also curtails costly delays.
• Heavy equipment manufacturing: Industries reliant on heavy machinery, like construction and mining, are acutely aware of the financial implications of downtime. Sensors embedded in equipment can pinpoint anomalies in components, pre-emptively addressing wear and tear. This approach streamlines maintenance schedules and resource allocation, resulting in reduced operational expenses and unplanned downtimes.
• Electronics manufacturing: Precision and reliability are paramount in electronics manufacturing. Utilising data analytics and AI, manufacturers scrutinise intricate machinery involved in circuit board assembly. This meticulous analysis identifies potential issues early on, guaranteeing top-notch output and minimising defects.
• Healthcare: Predictive maintenance is employed in healthcare facilities to ensure the reliability of medical equipment, such as MRI machines and X-ray equipment, which are crucial for patient care. Routine check of these equipment helps healthcare institutions as well as patients receive effective treatment.
• Energy: According to a report titled Predictive Maintenance in the Energy Market, PdM is expected to grow at a CAGR of 25.77% between 2023–2028. In the energy sector, power plants and wind farms use PdM to maximise the uptime of turbines and generators, ultimately increasing energy production and reducing operational costs.
Challenges
• Initial Investment: Implementing PdM can be costly due to the need for sensors, data infrastructure and analytics tools. Preventive maintenance also requires an initial investment in setting up schedules and processes.
• Data quality and management: High-quality predictions hinge on impeccable data quality. Robust data collection and management protocols are very important to ensure the integrity and reliability of collected data.
• Integration complexity: Introducing advanced technologies into existing manufacturing systems requires meticulous planning and integration to avoid disruptions and ensure seamless operations.
• Skill enhancement: The operation and maintenance of these advanced systems necessitate a skilled workforce proficient in both machinery mechanics and data analytics.
• Cybersecurity: With the increased use of IoT devices and cloud platforms, there is a greater risk of cybersecurity breaches. Protecting sensitive maintenance data is crucial.
• Scalability: As businesses grow, the maintenance needs also expand. Ensuring that predictive and preventive maintenance systems can scale to meet these growing demands is essential.
Blueprint for progress
• Investment in technology: Manufacturing enterprises must allocate resources to acquire and implement state-of-the-art technologies like sensors, IoT platforms and analytics tools.
• Robust data governance: Establishing data collection and management protocols is crucial to ensuring data accuracy and consistency.
• Skills empowerment: Manufacturing leaders must invest in training programmes to up-skill the workforce, equipping them to operate and decipher data from these advanced systems.
The realm of manufacturing stands poised on the cusp of a technological revolution. Predictive and preventive maintenance, fuelled by the convergence of sensors, IoT, data analytics and AI, has metamorphosed maintenance from a reactive chore to a proactive art. As technology advances further, the synergy of human expertise and cutting-edge tools promises to reshape the landscape of manufacturing operations, forging a future where disruptions are minimised, assets are optimised and precision prevails.
Original Source: https://www.tatatechnologies.com/media-center/proactive-maintenance-using-new-age-technology/
Ranjit Patil, CoE Head, Mfg Ops Management, Tata Technologies
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Tata Technologies Hones Focus On Hiring More Women
Tata Technologies has launched a gender diversity hiring campaign focusing on a structured and comprehensive approach to diversity, equity and inclusion (DEI).
Naaz Mushrif, AVP & Global Head-Talent Management and L&OD, told Mobility Outlook that the company’s DEI initiatives could be seen through its EVE (Empower via Education) programme for underprivileged girls and the ready engineer drive. These aim to provide STEM (science, technology, engineering, and math) education and employment opportunities to young individuals.
Consequently, Tata Technologies has expanded its global presence and is now catering to a diverse clientele across cultures and age groups over the past three years. This growth has also fuelled the demand for skilled engineers and spawned this gender diversity hiring initiative in the process.
Targeted Hiring
Last May, the company set itself a target to appoint 1,000 women and Mushrif said it was on track with this drive. The focus for the second half of this fiscal year would be on strengthening and boosting lateral hiring efforts.
According to Mushrif, the DEI programme is built on four key pillars: communication, growth, enabling and support. The company conducts workshops for managers to address biases, provides targeted leadership development programmes for aspiring women, offers mentoring opportunities with business leaders, and facilitates insightful conversations with women leaders.
Additionally, it prioritises state-of-the-art infrastructure, flexible policies, childcare benefits and wellness services like gym, yoga and counselling to enhance support for women employees.
Tata Technologies caters to the automotive, industrial heavy machinery and aerospace sectors. Its focus areas are determined based on talent availability and upcoming ramp-up plans for the next six months, with active collaboration from the talent acquisition team. Its diversity hiring champions are working within their respective areas to enhance representation of women in the workforce.
“While our historical expertise may lie in a few sectors, which has allowed us to tap into existing talent pools, we are exploring opportunities to tap talent by upskilling and cross-skilling women on career breaks through our skill-building hub, TechVarsity. This strategy broadens our focus and optimises talent, supporting growth and innovation in emerging sectors,” said Mushrif.
Unique Value Addition
Women, she continued, bring diverse strengths to the workplace and excel in multitasking, a critical skill in today’s dynamic environment. Their adeptness in managing priorities is invaluable in the company’s 27-country virtual team setting. Celebrating women’s innovation, “we initially sought 1–2 standout individuals but received numerous nominations”.
Women leaders effectively navigate collaboration challenges due to their nurturing and empathetic approach. They excel in engaging and retaining a multigenerational workforce, enhancing work standards and ethics. Talented women engineers drive innovation and uphold quality.
Mushrif said TechVarsity and LeaderBridge Academy provide diverse learning opportunities through various mediums and thereby integrate learning into key performance indicators. Individuals can delve into a broad range of technologies and domains which is in sync with the organization’s emphasis on continuous learning and skill enhancement.
“We have seen women leaders who initially joined as campus recruits progressing to become integral members of the executive leadership team. The fact that we have three women at this level is a testament to the growth opportunities that Tata Technologies offers its women talent,” she added.
The company also offers various flexible options including accommodation of project assignments, childcare needs, career breaks and promoting the DEI culture.
Unlike some organisations which have compensation disparity, Tata Technologies’ prioritises equity, reiterated Mushrif. Its approach centres on fair performance assessments using common measurement parameters and skill/role-based reward systems, promoting a level playing field for everyone.
The company reviews promotions, assessments, and rewards, focusing on DEI perspectives. The findings are consolidated into a formal report submitted to the Chief Human Resources Officer to prevent conscious/subconscious biases.
Career Path
Mushrif said Tata Technologies is implementing an organisation-wide career and competency path framework, which empowers individuals to define their career paths. It encompasses competency-based training and role-specific learning pathways, enabling individuals to pursue their goals. Proactively identifying potential leaders involves regular talent review forums and formal assessments of leadership competencies.
“We facilitate opportunities for individuals to express their interest in various roles aligned with their career aspirations through internal job postings, job rotations, and the GROW Policy,” she explained.
Additionally, there is a deliberate effort to tap into the top diversity talent pool for upcoming positions by leveraging talent review forums as a platform for talent identification and development. “This approach ensures a clear and supported career progression for women within the organisation,” signed off Mushrif.
Original Source: https://www.tatatechnologies.com/media-center/tata-technologies-hones-focus-on-hiring-more-women/
Naaz Mushrif, AVP & Global Head-Talent Management and L&OD at Tata Technologies
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Tata Technologies’ use of AI/ML reduced power consumption by 18% on shopfloor
The integration of Artificial Intelligence (AI) and Machine Learning (ML) models in vehicle shop floors has revolutionised manufacturing processes.
Tata Technologies is using artificial intelligence and machine learning to make its manufacturing processes more robust for its clients. The company is consistently using new technologies to reduce downtime and curtail pollution as well. Yogesh Deo, Senior VP and Delivery Head, ER&D (Engineering Research and Development), Tata Technologies explains how this has been achieved.
“Essentially, we studied the entire related process data for a year, and with the use of AI/ML models, we were able to reduce power consumption by 18 percent, there was an improvement in the process quality and a reduction in fuel consumption by another 10 percent and chemical consumption reduction by another 8 percent,” Deo told Autocar Professional.
Further assessing the importance of using AI, Deo explained how they have been able to identify anomalies to reduce downtime on the shopfloor. Taking the case of the paint shop blower, he said that if one of them goes down, it results in the entire process getting halted, and the repairs take anywhere from 7–8 hours to get it running at an optimal state.
“You can call it in like an anomaly detection just before the failure or actual shutdown. So that means we are not even waiting for the actual shutdown or failure to happen, but the moment when we are able to identify the anomaly. Then we are able to understand it and then we can take the corrective action there and there itself,” he added.
He also explained how for tooling, artificial intelligence was used to predict tooling failure in the case of dye design. “Here what we have done in the case of dye design is that we have gone through the historical operational data and prepared a model, which you can compare with the current operational data and then you can predict whether the dye will perform to its optimal position or it may fail. So we were able to correlate the operational data and failure cases and thereby we are able to determine the remaining useful life of the dye.
The company is “putting a lot of effort and investment into building a Centre of Excellence for AI technology,” he said without divulging more information at the moment.
Original Source: https://www.tatatechnologies.com/media-center/tata-technologies-use-of-ai-ml-reduced-power-consumption-by-18-on-shopfloor/
Yogesh Deo, Senior VP and Delivery Head, ER&D (Engineering Research and Development) at Tata Technologies
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Powering the Future: The Silent Revolution Behind Electric Vehicles
The evolution of electrical and electronic architectures in electric vehicles (EVs) is at the heart of the transformation from internal combustion engines (ICEs) to EVs. The EV market is growing rapidly, with predictions of sales increasing up to six times by 2030. The intricate network of electronic control units, power electronics, and electrical and electronic architectures is the game changer in EV technology. Different types of architectures, including traditional, domain-based, and zonal, are being used, each with its own challenges. Strong partnerships with component and software providers, virtual testing, and systematic engineering practices are crucial for success in the EV industry. Vertical integration allows companies to have control over key components and offer a superior customer experience.
In many cities today, the noise of internal combustion engines (ICEs) is transforming into the low hum of electric vehicles (EVs). While the ripples of this transformation touch everything from urban planning to geopolitics, at the heart of it is a quiet revolution — the evolution of the electrical and electronic architectures that power these vehicles.
Surging Towards an Electric Future
The EV market, once considered a niche, is speeding up at an unprecedented pace. Global sales may still be modest as compared to traditional vehicles, but the graph is changing rapidly. By 2030, EV sales are predicted to grow by up to six times their current volume. This growth isn’t just in numbers. Since 2010, there’s been a huge investment of over $280 billion into automotive innovation, with approximately $120 billion dedicated to electric vehicles alone.
The Tech Under the Hood
Delve under the hood of any EV, and you’ll find an intricate convergence of the best of modern technology. It’s not just about batteries and motors. The real game changer is the intricate network of electronic control units (ECUs), power electronics, and the increasingly complex Electrical & Electronics architecture (EEA). This architecture, combined with embedded software, ensures that every component of an EV — from its battery management system to its cooling mechanisms — communicates, integrates, and operates seamlessly and harmoniously.
Trends & Challenges in Electrical & Electronics architecture (EEA)
The Electrical & Electronics architecture (EEA) landscape has been evolving based on the demands and challenges of the market:
Traditional EEA: Largely used in vehicles transitioning from ICE to EV, low-volume niche vehicles, emerging markets, and start-ups. These are usually products with a short time to market, lower costs, and fewer features.
Domain-based architectures: High-end products in the mass market usually use domain-based architectures. However, these systems have challenges, especially in making different hardware and software components work together. Often, these components are custom-designed for a specific manufacturer.
Zonal architectures: The future belongs here. Next-generation zonal architectures are tailored for self-driving vehicles that need extreme computing power and agility. These systems are custom-made and often a unique feature of the manufacturer that creates them. These next-generation units are unique, bespoke creations, often closely guarded by their OEMs.
However, developing these systems is not without its challenges. EEA and EV developments are seldom built entirely from scratch. Often, they’re built upon or tweaked from existing models. Even when something is newly developed, by its completion, there are challenges like updates to standards, changing customer needs, and supply chain issues. These challenges, known as ‘late changes,’ can affect the timing, cost, and quality of the product.
The solution? A shift in vehicle development. Increasingly, the electrical and software components are becoming the critical path in the production timeline, frequently causing production delays. To address this, strong partnerships with component and software providers are vital. It’s also important to embrace virtual testing, using techniques like hardware-in-loop (HIL), software-in-loop (SIL), and model-in-loop (MIL) to enhance quality before testing actual vehicles.
System Engineering: The Backbone of Success
To make complex systems work well, it’s essential to use thorough and systematic engineering practices. Drawing parallels from the defence sector, a logical, end-to-end system engineering practice is imperative. This approach referred to as the V-Cycle, ensures the traceability of systems from conception to integration. Standards like Functional Safety (FUSA ISO 26262) and Safety of the Intended Function (SOTIF ISO 21448) are crucial for ensuring system safety and reliability.
With the rise of connected vehicles, there’s a bigger risk of cyberattacks. Therefore, strong cybersecurity measures adhering to ISO 21434 standards are a must. These set the bar for safety, reliability, and resilience and are now mandated by European Legislation under UN Regulation R155 for cybersecurity and R156 for software updates and software update management systems.
The Power of Vertical Integration
Peek into the operations of the most successful EV passenger car makers, and you’ll spot a common thread — vertical integration. By having control over key components like electrical and electronics systems and propulsion mechanisms, these companies benefit from cost savings, agility, and reduced supply chain dependencies. Moreover, this holistic ownership ensures a superior customer experience, offering them unparalleled user interfaces and unique features such as a seat warmer subscription from a luxury automaker!
Driving Forward
In essence, the electric vehicle wave is more than just about eco-friendly transport. It’s a technological marvel, driven by intricate systems, innovation, and a vision for the future. To truly thrive in this domain, companies must commit to robust system engineering, forge strategic partnerships, and be agile enough to adapt to the ever-evolving landscape of automotive tech.
Fasten your seat belts, the journey to an electric future is accelerating!
Disclaimer: Views and opinions expressed in this article are solely those of the original author and do not represent any of The Times Group or its employees.
Original Source: https://www.tatatechnologies.com/media-center/powering-the-future-the-silent-revolution-behind-electric-vehicles/
Kiran Devlukia, Global Head Electrical & Electronic Integration CoE at Tata Technologies
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How AI/ML applications are transforming the automotive value chain
AI driven ADAS (Advanced Driver Assistance Systems) technologies are definitely going to be a ‘standard feature’ in each vehicle that would come out from production line very soon.
Five to six years ago, we used to find AI/ML applications mostly in web and e-retailer domain from the tech powerhouses like Google or Amazon. Then slowly people started thinking about the of possibilities of taking it to the engineering and manufacturing domain and leveraging the power of AI. This became a reality when businesses got a sense of the power of data insights and decided to capture critical information in a structured and disciplined manner. Today we find most of the research and innovation projects are powered by AI/ML, not just because the complex algorithm libraries became the open-source but also because of sheer computation ability of GPU/TPU (graphical processing unit and tensor processing unit) powered infrastructure quickly churning huge amount of data, giving multiple possibilities of configuration and optimisation.
Autonomous Vehicles and ADAS
Today, car manufacturers are racing to develop the technology to make fully self-driving cars a reality. Advances in processors, camera technology and AI have brought us closer than ever before. Developers of self-driving cars use vast amounts of data from image recognition systems, along with machine learning and neural networks, to build systems that can drive autonomously. Though the ‘full driving automation’ where the driver does not exist anymore and the human being becomes a passenger, may not be realistic or very popular in countries with huge traffic congestion, but the AI driven ADAS (Advanced Driver Assistance Systems) technologies are definitely going to be a ‘standard feature’ in each vehicle that would come out from production line very soon.
Automotive Safety
A lot of work has already been done in automotive safety, starting from automatic emergency braking, pedestrian detection, surround view, parking assist, lane departure warning, and so on, and it seems that the sky is the limit. AI algorithms can analyse sensor data to identify potential dangers in real-time, which mitigates the risk of accidents. AI-based safety features, such as blind-spot detection and adaptive cruise control, use sensors and cameras to detect vehicles in the driver's blind spot and monitor the distance between the car and the vehicle in front. Various safety features starting from “driver distraction/drowsiness/rash driving alerts” to “advance road irregularities notification” (potholes, speed breaker, etc.) are also part of AI-powered safety systems.
Personalisation
Driver and passenger comfort through personalised experience is also an emerging area of application of AI where successful use cases range from interactive voice command to configuring a seat relaxation function, altering the interior lighting, music, air-conditioning, illumination intensity etc.
Product Design and Development
Having talked about product (vehicle) specific and customer (driver, passenger) experience centric applications, AI is also revolutionising the way product design, development and production are carried out. The Auto OEMs are extremely concerned about the cost optimisation and productivity improvement to stay ahead in the market and hence, using AI extensively to improve underlying processes, saving on material resources and improving on time to market.
And no regret, AI has not disappointed them. It is going to be a reality soon when Generative Adversarial Networks (GANs) will start throwing multiple vehicle concept sketches basis a few input requirements for quick evaluation. Various cognitive models are already in action to bring down NVH (Noise, Vibration & Harshness) in the vehicle, thus improving overall vehicle quality. A driver assistance system scales Real Driving Emission (RDE) trip validity from existing 35% to 80% and thereby reducing RDE testing expenses by 44%. Predictive failure or underperformance of components has led to consumers to stay alert and helped in informed decision making to reduce overall turnaround time, particularly in commercial vehicle space! For example, Tyre wear and engine nozzle failure issues during the warranty period are reduced by 20% and 35% respectively, thanks to real-time field data made available through ‘Connected Vehicle Platform’.
Manufacturing
AI is redefining manufacturing practices with implementation of Industry 4.0 that are otherwise impossible to achieve. IoT and AI are truly revolutionary technologies that use real-world data to improve business practices and help companies make better decisions. Below are the ways that AI help automotive companies reduce costs and optimize manufacturing processes:
a. Quality Control
b. Predictive maintenance
c. Production optimization and integration
d. Digital twin
AI analyses historical operational data of an equipment and compares with its current state of operation to forecast whether the equipment performance is optimal or needs maintenance. It can even establish relationship between equipment operational data and failure instances to predict Remaining Useful Life (RUL) of the equipment or its components. Avoiding unplanned downtime, reducing maintenance cost and improved asset utilisation are the key business benefits here.
Total Cost of Ownership
TCO has always been a concern for the fleet owners and fuel economy remains a universal expectation. To address that several models are being worked upon incorporating manufacturing parameters, powertrain calibration as well as consultancy on driving patterns and behaviours.
Sales and Service: AI is being imbibed into sales and marketing, giving a boost to retail prognosis, customer propensity, and likelihood of deal conversion. Service and warranty are the two major areas where there are lots of scope for AI driven decision-making, automation in claim validation and brining out actionable insights for all stakeholders like suppliers and engineering and plant quality teams.
NextGen Generative AI and Foundation Models
Traditionally, people used to build ML models using historical labelled data and used it for prediction purposes. Now with the introduction of Generative AI, the new trend has become working on huge unlabelled data on a self-learning mode and let thus created Foundation Models adapt to prediction and other decision-making tasks. Clearly, there is a shift from traditional ML models to Foundation models.
Advancements in AI, such as reinforcement learning and deep neural networks, will continue to shape the evolution of autonomous vehicles and connected cars. Further integration of AI into the automotive ecosystem will result in enhanced safety, improved energy efficiency, and a seamless driving experience.
Original Source: https://www.tatatechnologies.com/media-center/how-ai-ml-applications-are-transforming-the-automotive-value-chain/
Avijit Santra, Program Manager at Tata Technologies.
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Connected Car Based Data Monetization — An Untapped Opportunity for OEMs
“Most opportunities are disguised as problems”
Connected-car features are trickling down to mass market passenger car segments at a rapid pace. The vailability of connected car features currently influences the purchase decision of a particular car brand or variant. A global consumer study by McKinsey conducted in 2020, on Connected, Autonomous, Shared, Electric Mobility technologies, revealed that 37% percent respondents were willing to switch car brands to avail connectivity features.
Quite interestingly, this trend also applies to the fastest growing automotive markets in the world. For instance, in India certain OEMs are seeing a 30% penetration of connected cars in mid and high-end segment SUVs wherein the infotainment units come with a built-in TCU (telematics control unit). The percentage penetration of connected features using the driver’s mobile phone data has seen an even higher proliferation with even mid-variants of sedans, hatchbacks being offered with connected features.
Moreover, connected features such as Over-The-Air (OTA) updates, are becoming enabling technologies for EVs charging and control, and frontier technologies related to software defined vehicles (SDVs). Connected features are on the path to becoming an industry standard, and their convergence with the control system of the vehicle, will blur the lines of them being seen as a separate feature set on the infotainment screen. However, a key challenge for OEMs is to monetize connectivity features provided onboard their products where there is a huge untapped potential.
Data strategies are top of mind in the automotive industry, where connected vehicles are becoming data-gathering hotspots. As vehicles become increasingly connected, leveraging their collected data through novel business models would be a promising value proposition for the automotive industry. This valuable car data eventually paves the way for novel types of data-driven business models (DDBMs), forcing original equipment manufacturers (OEMs) to wade more deeply into connectivity. Consequently, the entire global automotive industry is facing the question of how to monetize this valuable data.
Although connected car capabilities are advantageous to customers, they are not thought to be important enough to be worth a recurring financial commitment.
Car makers are strongly stuck in a product-centric approach, and they want to move away from it.
Problem Definition 2 — Accumulation of Connected Car Data
Considering an average payload size of telemetry data of 4KB per 15 seconds, one vehicle will produce 1GB Data by taking average 3Hrs car usage per day.
Imagine 20,000 connected vehicles are produced every month for an OEM and it will add 250 TB data to server every year.
According to a recent Intel Corporation report, an autonomous car generates approximately 4TB of data per day.
What will we do with this enormous amount of data? How can OEMs make the most of this? Most OEMs are uncertain of the answer to this, and they are having trouble doing so.
Utilization of Data
The monetization of Connected Car data requires a set of enablers in the ecosystem.
On-board the car: Internet connectivity, navigation system, HMI, high performance controller, sensors etc.
Infrastructure: 4G/5G Mobile towers, big-data analytics, artificial intelligence, cloud computing, smart road infrastructure, software platforms etc.
Potential Revenue Streams
1. Data Driven Recommendation for Tyre Change
Since tyres are subjected to significantly harsh operating conditions, it is important to continuously assess and forecast their health. Multiple ML/AI algorithms utilize cloud-based data for continuous learning, which aids in anticipating tyre wear and/or failure and alerting end-users to make right decisions when they are needed. OEMs could potentially recommend the product at the greatest discount through their partnerships with tyre manufacturers. This will enable the buyer to purchase the item for a lower cost, and OEM will also benefit from such a business model.
2. Tailored Advertisement
How can we take advantage of this opportunity using connected car features, where movie production firms are spending millions on marketing? Based on consumer preferences like fan-base and language, OEM can use the vehicle data to push movie trailers or any other promotional items to car infotainment systems.
3. Restaurant Suggestion Based on Customer Food Habits
4. Usage Based Insurance (UBI)
Monetization Model
The two main categories of revenue generation for OEM are subscription and third-party services. Customers have the option of choosing a premium account or a free account, subject to watching any advertisements the OEM may promote. Another approach allows customers to enrich OEM data models by contributing their personal information for a much lower premium fee, creating a win-win scenario for both parties.
OEMs have a lot of work to do on increasing the amount of data generated that can be useful for monetization. There are several low-hanging fruits that OEM can monetize such as prognostics services, and security applications such as remote ignition disablement or slowdown for stolen vehicle protection and assistance. Also, exciting applications are possible in areas such as parking as a service, productivity enhancement services and integration into multimodal transport networks.
Conclusion
Data-driven services are developed to generate new value streams, which eventually result in new market configurations and the appearance of new business models. The data produced by operating automobiles opens the door for new categories of data-driven services in the automotive sector. Although still in its infancy, the monetization potential of automobile data is enormous. The business models that will support these use cases are already developing and car data-enabled business models have the potential to transform transportation into a service. A clear strategic shift towards developing digitalization as a core revenue stream is required. This would lead to the allocation of significant investments to enhance product development capabilities, by successful collaborations with key players in the digital ecosystem.
Original Source: https://www.tatatechnologies.com/media-center/connected-car-based-data-monetization-an-untapped-opportunity-for-oems/
Haseel Veluthethodi, FOTA — Senior Systems Engineer, Tata Technologies
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