This assignment analyzes semiconductor strategies essential for software-defined vehicles, covering supply chain resilience, tech innovation, geopolitical impacts, and collaboration between automotive OEMs and chip manufacturers.
The vehicle of the future has undergone a transformation change since it was invented in metal form. In the later part of the 19th century, electronics began to creep into the vehicle and create drive-by-wire concepts to create end-to-end processing via sensors to actuators where electronic control of the vehicle was the trends. Software-defined vehicles (SDVs) are revolutionizing the automotive industry, which is unpinned by megatrends in connectivity, autonomous mobility, and shared (CAMS). For students seeking assignment writing help, understanding these complex trends is essential.
The vehicle of the future is defined by software-driven, the decision-making of how the vehicle behaves and is controlled is determined by a software-first approach!
The value of automotive software will become increasingly prominent. According to McKinsey's research, the software codes on each vehicle have exceeded 200 million lines, and the market scale of automotive software and related services has exceeded 24 billion US dollars, which will continue to increase at high speed in the next 5–10 years (Kuiken, 2022).
For traditional vehicles, hardware is both necessary and sufficient condition. At the same time, software is only an auxiliary, while for future vehicles, hardware is only the basic necessary condition, and software is the sufficient condition to determine user experience. Compared with data-defined vehicles, software-defined vehicles (SDVs) can better reflect the change of the relationship between hardware and software and the evolution direction of automotive products. It is the vehicle whose architectural design is determined by software and whose function, performance, service, and experience mainly depend on software (Liu, Zhang and Zhao, 2022).
To bring the SDV concept to street near our us and to realize the grand vision it’s heavily reliant on the availability of computing in the form of semiconductors, the "brains" of modern vehicles. Given the complex global supply chains and increasing geopolitical tensions, ensuring the strategy to build and to have the resilience of the semiconductor supply chain is paramount for the successful deployment of SDV (Liu, Zhang and Zhao, 2022).
Semiconductor devices are crucial in modern technology, enabling various functions like information creation, storage, processing, and communication (Ho, 2016). The manufacturing process is key to the production of these devices, transforming designs into physical products. However, the industry faces challenges due to semiconductor technologies' rapid advancements and competitive nature. Organizations must invest heavily in capital and stay adaptable to technological changes (Santos et al., 2022). Strategic assessments of emerging technologies help businesses navigate these challenges by understanding the impact on their strategies and operations
On the hand, the world is experiencing an ongoing global chip shortage that is affecting more than 169 industries, according to (Goldmansachs.com, 2021), determining a disruption of the GVCs of these industries, and thus their slowdown. Such a high number of industries affected by this shortage stems from the spread of semiconductor chips in many products and devices of our everyday life, from PCs and smartphones to toothbrushes and air conditioners.
This is not a traditional shortage, caused by the fact that companies cannot make chips. The problem is that the demand of chips has exceeded supply. The semiconductor industry has always been characterized by a sort of cyclicality, but the COVID-19 pandemic exacerbated the situation, influencing the critical point of the value chain [2]. Hence, we can say that the crisis seems less cyclical and more structural.
Delimitation:
The paper seeks to offer a strategic analysis of the Semiconductor and Automotive ecosystem players targeting the delivery of the automotive industry objectives of delivering a SDV to every vehicle in the pipeline
The automotive industry is standing at the edge of the most incisive transformation since its origin in the late nineteenth century. The degree of private mobility and autonomy that the global diffusion of passenger cars has created is beyond comparison. Value along the automotive value chain is shifting away from traditional sources toward emerging technology value pools of EV, Autonomous drive infotainment and connectivity, which loosely attributed to the functions of the SDV.
The literature search was conducted to investigate the prevailing knowledge regarding the role of technology in competitiveness and to identify the gaps in the literature. Due to the increasing complexity of the business context and technological systems, literature regarding this topic is rapidly growing
Although many factors determine overall competitiveness, technology has been identified as the most important one that determines competition rules [8]. Technology alters industry structure through new product launches and manufacturing cost reduction and eventually creates competitive advantages [10]. Understanding the relative importance of various technologies is essential before those technologies can be adopted. The literature provides a landscape of the technological changes in SDVs and their impact on business and market scenarios across geographies. It also analyzes the push and pull factors for semiconductor companies.
This literature aims to provide the state of the industry view and strategy frameworks analysis view of it. The flow of content would be as conceptualized in the below figure[REF].
Literature review:
The following literature review contextualizes this dissertation at the convergence of semiconductor strategies and Automotive industries CASE strategy with global consumer demand and aspirations.
Software-defined vehicles (SDVs) lack a precise and universally accepted definition. However, the next generation of vehicles relies heavily on software functionalities, which enhance user experience by providing advanced features such as autonomy and connectivity (Alberti et al., 2024). These functionalities dictate the computational hardware requirements. Furthermore, the new generation of Electrical/Electronic (E/E) architecture facilitates improved in-vehicle communication and enables strategies for cloud-to-vehicle interactions, including Over-The-Air (OTA) software updates and digital twin monitoring of the vehicle.
Electrification:
The world is being electrified, as EVs, PHEVs and HEVs, the EV Powertrain, Battery management units, and charging infrastructure are foundational to steering a successful electric vehicle (EV) transition. After a strong start, driven by EV enthusiasts, EV growth has moderated due to the impact of high ownership costs, reduced subsidies, range anxiety, slower-than-expected rollout of charging infrastructure and the increased interest in hybrids.
Market:
By 2035, BEVs will comprise nearly 60% of worldwide new car sales. At the same time, the shift to BEVs will lead to an increase in industry profitability from new car sales (NCS), from $122 billion in 2021 to approximately $135 billion in 2035 (IEA, 2024). The below snippet from (Counterpoint, 2024) shows the growth trajectory of EVs
Autonomous (AD) and On-demand Mobility::
The market for on-demand mobility services is projected to expand significantly, reducing the reliance on personal vehicles, taxis, and public transportation. As per (BCG, 2023), By 2035, it is anticipated that 23% of trips in major cities will utilize shared, on-demand mobility services, up from 8% in 2021. This sector is expected to become the second-largest profit pool in the automotive industry, with total profits projected to reach $82 billion. Within this segment, robo-taxis are predicted to generate the majority of profits, amounting to $61 billion by 2035 (BCG, 2023).
Autonomous vehicles (AVs) are expected to be a major factor in future profitability within the industry, affecting both private vehicle sales and on-demand mobility providers (Wadud and Mattioli, 2021). Although the initial years of robo-taxi operations will not be profitable due to the high initial investments required, sales of AVs and AV components are expected to become profitable earlier. By 2035, these two categories are forecasted to generate $13.4 billion and $5.8 billion in profits (BCG, 2023), respectively. As traditional profit sources in the auto industry decline due to technological advancements, companies that adapt to these new opportunities will benefit. A quarter of every vehicle in the US and China in the next decade would be a self-driving robotaxi, attributed by Gen-Z demographics, better utilization of resources and more of subscription-based model (Wadud and Mattioli, 2021).
Infotainment & Connectivity:
Connected cars also generate a wealth of data on driver behaviour, vehicle use, location and customer preferences, providing various data monetization opportunities. Moreover, advances in artificial intelligence (AI), machine learning (ML) and vehicle-to-everything (V2X) technologies are transforming the collection and analysis of connected car data, providing customer insights to enable value-added services and enhance operational efficiencies (Christopoulou et al., 2023).
A software-defined car must also be updated frequently (via over-the-air updates) to ensure optimal functionality. With increased sensors, ADAS recalibration will become critical as minor malfunctions or misalignment can lead to inaccurate readings, causing safety and vehicle performance issues. Connected cars will also enable predictive maintenance using advanced analytics to reduce downtime and improve vehicle performance.
Impact on the Semiconductor industry:
All of the megatrends lead to a change in the fundamental re-wiring of the vehicle to provide a scalable solution. One of the key technical characteristics of SDV is the decoupling of software and hardware. Software in the component subsystems of traditional vehicles is embedded in hardware. The high binding of software and hardware results in the solidification of vehicles' functions and performance (Xiang et al., 2024). To improve the flexibility of automotive software for better. user experience, the component subsystems of traditional vehicles will be gradually layered, and the coupling between technical elements at different levels will be reduced [5].
To deliver the systems primarily rely on onboard hardware, including functional hardware computing platform, which is the competency source of data generation, processing and interaction [6]. Functional hardware includes sensors and actuators responsible for generating data and receiving instructions for corresponding functions. EEA connects all functional hardware and computing platforms through the in-vehicle network to promote data interaction and gathers data on computing platforms for processing [7]. The computing ability of the platform determines the ability of data processing inside the vehicle
Political :
chips are fundamental for the production of all electronic products, for digital transformation and for many industries like automotive, and consumer electronics but also medical devices, space, defence and national security. Many governments around the world are implementing initiatives to face potential future disruptions of the value chain in semiconductors and are actively ensuring the supply chains are well served for their interests
The recent causes of this chip shortage are multiple, and all contributed to different degrees of political situation. Some of them are related to past actions made by governments and the consequences occur now, other are related to casual and natural events (Fang et al., 2024). Below are mentioned the four main causes of this shortage according to experts
Geo-political impact of Semiconductors:
The value chain of the semiconductor industry can be considered genuinely global because the six major regions (US, China, Europe, South Korea, Japan and Taiwan) each account 8% or more to the total value-added [REF]. The US is the global leader in the design of electronic devices, while China is the biggest manufacturing hub for electronic devices. Europe is the global leader in automotive and industrial automation equipment. US, Europe, Japan and South Korea are powerful in the R&D activities, while China and Taiwan are leaders in the manufacturing and assembly and testing activities [REF]. Regarding the fabrication of chips, Europe is one of the leaders in the production of Discrete, Analog, Optoelectronics and Sensors (DAO) and accounts for the 12% of the logic capacity of 10-22 nm chips [1].
Environmental:
Technology:
The industry's focus on connected cars, autonomous driving, shared mobility and electrification (CASE) is causing a shift in business models and product mix. As the automotive industry evolves from ICEs to hybrid, plug-in, battery electric vehicles (BEV) and fuel cell vehicles (FCEV), industry suppliers are reshaping their portfolios to emphasise electric motor technologies, advanced driver assistance systems (ADAS) and battery-related innovations, while reducing the importance of external body systems, tyres and combustion engines. As a result, most of the technical content of the car of the future is expected to come from software, a change accelerated by increasing customer demand for 'experiences' while driving (Singhal et al., 2024).
The increasing use of electronics in cars, driven by electrification (from hybrid to fully electric vehicles) and enhanced connectivity, means that electronics will soon account for over 60% of a car's added value. This innovation and value growth is due primarily to semiconductor components, which are essential for various systems, from safety features like airbags to navigation and communication technologies like automotive Ethernet. Microprocessors, microchips, and analogue components are the fundamental building blocks for these automotive electronic systems.
6 Years | PhD
Levels of vehicle autonomy are already rising beyond lane departure warning and blind spot detection, requiring sophisticated radars, light detection and ranging systems (LiDARs) and cameras (König et al., 2024). Legacy infrastructure must also evolve to seamlessly integrate hardware and software (via OS), facilitate data exchange between OS and applications (via middleware) and accelerate agile development (via standard processes or toolchains).
Both the Automotive and semiconductor industries have high capital and investments, and with significant barriers for entry and for the top technology nodes of semiconductors, we do have an oligopoly with TSMC, Samsung and Intel foundries.
Societal factors:
Beyond advancements in technological capacities, the introduction and commercialization of alternative mobility solutions have gained momentum by evolving population demography, ethical consumerism, and a diversification of product and market core areas.
The demographic change in Germany is incrementally causing a reduction of the most critical customer segments as measured by the average purchasing power. The baby-boomer generation, representing the most profitable consumer group in terms of quantity of sales and average profit margin per purchase, will reach retirement age in the mid-term (Santosa et al., 2021).
The continuous urbanization and value shifts of younger target groups also affect mass-market producers' profit structures and rate of returns. As a result of urban growth, the demand for small, cost-efficient, and thus low-profit vehicles is increasing Public transport and emerging mobility ecosystems are serving the demand for personal locomotion, diminishing the necessity of privately owned vehicles (Santosa et al., 2021). In saturated markets, ecological sustainability and de-motorization trends have replaced traditional values associated with private vehicle ownership.
Semiconductor growth in automotive
The growth in market is highly dependent on the market
The semiconductor industry has a direct impact on the EV growth story with increased silicon content in battery power management, Motor control units, and Onboard chargers these systems require semiconductors such as Microcontroller units, silicon carbide (SiC) and gallium nitride (GaN) power electronics, which offer higher efficiency, thermal performance.
According to various industry reports[REF], the semiconductor content in electric vehicles is expected to continue increasing as EVs become more advanced. In 2020, the average semiconductor content in an EV was estimated at around $500–$600 per vehicle. By 2030, this is expected to increase to $1,000–$1,500 per vehicle, driven by the increasing complexity and adoption of advanced technologies.
The semiconductor market for EVs is estimated to grow at a compound annual growth rate (CAGR) of around 20-25% over the next decade.[ref]
[tesla]According to a recent market analysis by Statista, the EV sector's global revenue is expected to reach a remarkable $906,7 billion by 2028 (Statista, 2024). Furthermore, a Bloomberg report forecasts a significant surge in EV sales, projecting an increase from 10.5 million units in 2022 to nearly 27 million by 2026, with the number of passengers EVs on the road expected to reach 730 million by 2040 (Bloomberg, 2023).
Marco economic impact:
As indicated in the problem definition, the economic and social significance of vehicle-induced mobility is indisputable. Particularly in Germany, the automotive industry can look retrospectively at a success a story steeped in tradition. The direct and indirect proportions of the annual gross value of this economic sector account for 4.5% of the gross domestic product generated in Germany in 2017. Sixty-four percent of the total revenues of 426 billion.6 Euros were generated in foreign markets, making this industry one of the most relevant in terms of global trade and export strength. Beyond the economic importance, the international preeminence of the domestic automotive sector has substantially contributed to the superior global perception of the German industry as a benchmark for high-quality engineered products.
This research work aims to evaluate to what extent the key drivers of the business transformation will impact the existent German automotive industry and how Original Equipment Manufacturers (OEM) can (re-)position themselves to leverage novel business models and gain access to new revenue sources (Berg and Liljedal, 2022). In the framework of this evaluation, new phenotypes of mobility scenarios are presented that serve as initial points and guidelines for the creation of the business model 2030.
Ultimately, the most decisive question is: Will the German automotive industry be able to defend its pre-eminence and hold its ground in the competitive environment?
The use of semiconductors in vehicles will continue to increase due to electromobility and autonomous driving. Therefore, the stability of the supply chain is of high strategic importance for both the German and the European automotive industries.
Markets and revenue pools are shifting to new business models and new technologies, such as data-enabled services, advanced driver assistance systems (ADAS) technologies, and alternative powertrains (Ritala et al., 2024). This development results in the emergence of new competitors as tech players, start-ups, and digital/e-commerce companies can be expected to grow rapidly. However, for established players, this presents new threats and opportunities. To successfully shape these disruptive technologies, the industry will have to manage a significant employment transition, with accelerated importance of software and electronics engineering skills. Furthermore, industry collaborations are becoming increasingly relevant not only to gaining critical market shares, e.g., in cloud-based mobility services but also to shaping the necessary infrastructure, e.g., with telecommunications or energy providers. Finally, the four megatrends influence customers’ mobility habits. Customers are beginning to demand innovative and individualized products, such as pay-per-use mobility packages or mobility as a service (MaaS), that integrate different modes of mobility according to individual needs (Ritala et al., 2024). Furthermore, customer demand for sustainable mobility products is rapidly increasing. Thus, while China is clearly leading the market in recent years, there has been a 145% increase in new battery electric vehicle (BEV) registrations.
The shift from hardware to software-defined vehicle (SDV) architectures will not only unlock new revenues in technology and data-based services but also drive cost efficiencies, enhance faster software delivery and improve the quality of fleets (Fürstenau et al., 2021). This megapool comprises SDV-enabling technologies, advanced driver-assistance system/autonomous vehicle (ADAS/AV) components, data monetization, and software-based repair and maintenance. The combined value of this group of adjacent opportunities is expected to reach about US$169 billion by 2030 at a CAGR of 18.8% between 2023 and 2030.
Currently, there is a trend towards greater concentration of automotive production in Asia Pacific and a slight weakening of the positions of North America and Europe in percentage terms. The figure below (Figure 4) shows the distribution of global automotive production across regions in 2006, 2011, 2016 and 2021
As we can see from the data in the table, the last twenty years have seen a steady growth in car production. However, this increase is very uneven between countries: while the USA, Japan and Germany (leaders in 2000) have experienced a slight to moderate decline, developing countries have experienced exponential growth. In particular, China has seen a very substantial surge from 2 million units produced in 2000 to almost 28 million in 2018.
Over the last decade, China has emerged as one of the most important growth markets for all players in the world automotive industry to establish itself in recent years as the world leader in the production of cars.
As value migrates toward new disruptive arenas, auto industry players must quickly adapt or risk becoming irrelevant. They must assess where the greatest revenue opportunities will lie, how quickly they will grow and what specific expertise, capabilities or strategies will be required to unlock their potential.
There are fundamental changes in the industry that threaten incumbents' positions, the completive positions are changing, realtering global automotive auto hotspots of the US and Europe to Asia, which China and Vietnam and are merging as new producers. [REF]
Consumer behaviour. Consumer behaviour and awareness are changing in light of the ‘software-defined vehicle’ and the fact that more and more people are accepting alternative and sustainable forms of mobility. In the past, the customer experience with a car was mainly defined by hardware, but software is taking on a much more important role (Dieck and Han, 2021). The sharing economy is disrupting conventional notions of car ownership, moving away from traditional individual transport towards completely new forms of mobility, such as self-driving taxi robots (with an automation level of SAE 4 or 5). This affects car suppliers, as the next generation of cars will need different components and entirely different chassis, systems or interiors (Dieck and Han, 2021). This evolution not only affects the development and operations of suppliers but also makes possible new business models and types of collaboration, e.g. using model-based systems engineering (MBSE), virtual simulation and digital twins
since 1970 the “economic centre of gravity” has moved from the westernmost parts of Europe to Turkey and is further shifting towards Asia (Exhibit 3). This is primarily due to the increasing importance of the Chinese automotive market, which has grown from an annual production of 87,000 vehicles in 1970 to 28 million in 2018.
This analysis reveals how the great value shift is shaking long-held assumptions about the sources of value as the revenue potential of once-lucrative income streams rooted in the manufacture of internal combustion engine (ICE) vehicles and related components wanes. Value is shifting from traditional revenue streams to adjacent new opportunities, which we have categorized as transitional and growth.
Transition from Hardware as a commodity to a full-stack solution
From hardware to software: How semiconductor companies can lead a successful transformation
It’s a familiar scenario: a semiconductor company sees profits drop as core hardware products become commoditized. In response, it tries to move into embedded software and associated application software. The transformation begins optimistically, with the company projecting strong software sales, but difficulties quickly emerge. Timelines increase, the project hits snags, and software revenues fall below expectations. Instead of improving margins, the new business creates even more financial stress. strategy development and execution (Exhibit 1). Although the framework aims to create a thriving software business, the recommendations will also help companies enhance their core hardware business, which will always provide some of their revenues (Awan, Sroufe and Shahbaz, 2021).
Software strategy: Keeping the focus on value Traditional hardware players will be on unfamiliar ground when creating a software strategy. With a limited knowledge of the competitive landscape, customer needs, and effective pricing models, they may have difficulty developing a targeted approach. The following steps can help.
Creating a detailed transformation plan and incorporating it into the existing corporate strategy Many semiconductor companies assume that their existing corporate strategy will serve them well for software. But software customers are fundamentally different from their hardware counterparts, requiring more frequent product upgrades and greater ongoing support. To reach them, companies will need a specific plan.
Finally, the software strategy should support the existing corporate strategy. That means executives need to consider goals for the core hardware business. This segment will always contribute to a company’s bottom line, especially in the early days of a transformation, when it may be difficult to take market share from digital natives with strong customer ties. For instance, NVIDIA created deep learning software based on its latest-generation graphic-processing unit, hoping the new product would encourage existing device sales (Garon, 2023). It’s also important to support the brand image articulated in the corporate strategy. Consider the auto manufacturer Daimler, which has a reputation for producing leading-edge hardware. To maintain its image as a technology leader, the company recently built the digital capabilities needed to create sophisticated software offerings.
Since semiconductor companies have traditionally focused on hardware, board members must gather extensive information on the software value chain before creating a strategy. They may be able to gain customer insights by analyzing how their competitors moved into software since this could help them identify popular products and services. As with any strategy, many board members will have firm opinions about the best direction (Rella and Campbell-Verduyn, 2024). Some, for example, may want to focus on becoming the top software provider in the semiconductor industry, while others view software as a lever for increasing hardware sales (Rella and Campbell-Verduyn, 2024). Boards can avoid these differences by closely involving all members in strategy development from the earliest stages. Sometimes, it may help if the board creates a fact base that members can consult when making decisions, mainly if leaders have limited software experience.
The automotive industry is characterized by a large network contributing a large share of the added value. The network consists of suppliers on a tier 1 and tier 2 level, where the semiconductor industry, most often, acts on a tier 2 level, contributing to a small but critical share of the added value. There are multiple personas of OEMs engaging the supply chain quite differently.
Legacy OEMs:
With Incumbent Legacy Automotive, OEMs primarily rely on tier 1 like Bosch, Continental, and Denso to achieve their silicon selection strategy. Traditionally, this is primarily addressed by the combination of the research and procurement teams to ensure the right supplier is selected and, in many instances, have no say in the silicon as part of the RFQ. The Tier-1s and Tier2 selected either based on broad-based product fitment strategy across OEMs and Tier-1 solutions or based on pricing. The strategic sourcing with silicon suppliers was missing in many instances and on a project basis. The Tier-1s are a binding force in old construct and were key in linking silicon, software and OEM requirements to meet
This trend has primarily changed post-chip crises; there is active engagement and dialogue across the automotive supply chain, OEMs, Silicon, and Tier-1s, as well as Silicon players. Software vertical stack teams are aligning with Silicon providers like QUALCOMM, Mobileye and others to build their solutions. Nvidia, on the other hand, as a silicon and software provider, directly engages OEMs and a similar playbook is replicated by other OEMs to either have specific RFQ or strategic sourcing agreements. As (Porsche-consulting.com, 2022) highlights, the OEMs would lack visibility on the capability of the supply chain, and hence, their product timing and supply chain portfolio may not be well aligned.
Next-Gen Auto OEMs:
With new-age companies which are EV first, with likes of Tesla, NIO, BYD, Rivian, etc, these are greenfield E/E architectures implemented on the vehicles, the silicon strategy initially sourced directly from Silicon vendors bypassing the tier-1s, the next generation of their products have in house silicon design team to build silicon, directly engage with Fabs and build the right silicon for their vehicle and their users to provide the fulfil their SDV strategy (Pennisi, 2022). Technologically, they are entering the space of Silicon integrators and vertically integrating the software solutions.
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With the change in E/E architecture the Legacy OEMs would need to move from passive engagement to active engagement strategy to fulfil their SDV strategy:
(Porsche-consulting.com, 2022) provides key insights into multiple strategic approaches that can take in short, medium and long terms based on their appetite for product portfolio to align on the Semiconductor strategies for SDVs.
Gearing Up the OEM–Supplier Interface
With the increasing shift from product-centric to service-centric mobility,[REF] automotive manufacturers are forced to integrate alternative powertrains, interconnected applications, and advanced technological modules into their supply portfolio. The complexity of supplier networks is thus intensified by the demand for software-based components beyond the mere acquisition of hardware parts.
This traditional approach of supply chain management it would lead to procurement and transactional management of suppliers.
Semiconductor devices have become one of the driving forces in the information age (Wang, Liu and Zhou, 2022). These devices are embedded in a wide variety of products in order to enable the functions of creating, storing, processing and communicating information.
Semiconductors, or chips, are used to produce several modern technologies, such as smartphones, cars, and televisions. As a result, a global shortage of chips has affected several sectors beyond just the automotive. Unfortunately, the semiconductor industry has long struggled to manage its limited number of suppliers and volatile demand schedules (Wang, Liu and Zhou, 2022).
These issues were further exacerbated by the pandemic, which dramatically altered demand expectations and, eventually, production. Accordingly, semiconductor shortages have been at the forefront of the discussion surrounding supply chain disruptions since the start of the pandemic.
Semiconductor device manufacturing is critical to the supply chain of information products. It is the manufacturing process that turns designs of devices into physical products. Due to the advancement of semiconductor manufacturing technologies and fierce competition, technology evaluation and selection in order to be competitive in the semiconductor foundry industry is never an easy task. It involves high capital investment as well as technological uncertainty. High capital investment may constrain an organization to certain strategies and make the organization less flexible (Kumar, Anirudh Thorbole and Gupta, 2024). On the other hand, technological uncertainty requires an organization to be able to adapt to the changes brought about by emerging technologies. In this situation, strategic assessment of emerging technologies helps organizations understand the influences of new technologies on their strategies and businesses
Automotive trends: Make vs Buy for Semiconductor designs.
In recent years, organizational economics has made considerable progress in explicating make vs. buy and vertical integration decisions in many industrial contexts. The virtues of vertical integration are commonly expressed in terms of the ability of administrative systems to attenuate contractual hazards stemming from small numbers and opportunism (Kumar, Anirudh Thorbole and Gupta, 2024). The need to protect transaction-specific assets provides strong incentives for internalization.
In particular, in the semiconductor industry, one sees the recent emergence of sourcing relationships in which chip designers and manufacturers enter co-development arrangements with suppliers where the concern seems anything but the protection of assets. Rather, specific assets are exposed to tremendous hazards, but at the same time, suppliers and manufacturers embrace rich learning opportunities that cannot be obtained separately. These co-development activities proceed in loosely structured alliances where received theory would suggest that vertical integration might be the norm.
In the semiconductor industry, environmental, technological and human factors have combined to encourage the emergence of intermediate forms of governance, such as buyer-supplier technology collaborations (Luo et al., 2022). The following sections start by discussing the relevant environmental and technological factors, which include (i) limited firm capabilities, (ii) advanced packaging as a bottleneck complementary asset and (iii) system interdependencies. It is argued that each o f these factors leads to time-critical "knowledge failures" and "valuation uncertainties" that can be better overcome with bilateral external technology sourcing than with either markets or hierarchies (Luo et al., 2022).
(Forster et al., 2013) provides key insights into supply chain characteristics of the semiconductor value chain and how effectively there could be a collaboration between automotive and Semiconductor domains. As we observe in the diagram below[REF], the Automotive supply considers the silicon suppliers in the tier 2/tier 3/tier4 spaces based on the overall level of integration they provide to OEMs.
The semiconductor industry is characterized by four types of companies: Integrated Device Manufacturers (IDMs), fabless design firms, foundries and outsourced assembly and test companies (OSATs).
IDMs:
IDMs are companies vertically integrated across multiple stages of the value chain. It was the predominant business model at the beginning, but then the high investments in R&D and capital expenditures led to the emergence of the fabless-foundry model. IDMs accounted for about the 70% of the global semiconductor sales in 2019 [REF]. Fabless firms are companies specialized on the design stage, outsourcing the fabrication as well as the assembly and testing activities [REF]. Fabless firms accounted for almost 30% in 2019.
IDMs account for 98% of memory chip sales and 75% of DAO sales. Instead, the layer specialization model accounts for 47% of logic chips sales and 79% of logic chips production capacity
Foundries:
Foundries are companies specialized in manufacturing activities, addressing the needs of fabless firms and IDMs [1]. This specialization allows them to diversify the risk associated with large upfront capital expenditures required to build up the manufacturing capacity across many design firms and IDMs. Currently, foundries account for 35% of total manufacturing capacity [REF].
OSATs:
OSATs provide assembly, packaging and test services to fabless firms and IDMs.
Fabless Design Firms:
Fabless semiconductor companies design and sell semiconductor chips and hardware devices but outsource the actual fabrication (or manufacturing) of these chips to specialized manufacturers called semiconductor foundries, and these companies concentrate on innovation, design for the specific intended applications, be it microcontrollers for EVs, AD or IVI applications. (Lemaitre, 2024) Auto-original equipment manufacturers (OEMs) are increasing their investment in automotive semiconductors as they move deeper into the supply chain to gain competence and ensure they have the components they need for electrification and advanced driving systems.
Conclusion Literature Review
Ensuring the resilience of the semiconductor supply chain is essential for the future of SDVs. By addressing the challenges outlined in this literature review, the automotive industry can mitigate risks, reduce vulnerabilities, and secure the long-term success of SDV technology. Future research should focus on developing innovative solutions, strengthening international cooperation, and promoting sustainable and ethical practices in the semiconductor industry
By addressing these challenges and embracing emerging technologies, the semiconductor industry can play a pivotal role in shaping the future of mobility and ensuring the success of SDVs.
This research uses a secondary qualitative research approach to examine the strategic issues and possibilities of addressing the challenges of synchronizing the semiconductor supply chain with the demands of SDVs. The second method of collecting qualitative data is secondary data analysis, in which the researcher compiles and integrates data obtained from other sources like journals, business literature, cases, and statistical publications. It enables the acquisition of a deeper understanding of the context of the available knowledge, as well as thematic and interpretative analyses.
This type of research is more appropriate for investigating rich contextual and multifaceted problems. In this context, secondary data enriches the study with a rich set of viewpoints and findings published in the literature and industry overviews and analyses. This design allows for the appraisal of refinements in issues affecting the supply chain, partnership initiatives, funding patterns, and geopolitical dynamics of the semiconductor and automotive industries.
The rationale for using the secondary qualitative method is informed by the fact that abundant quality data are available from various sources and that the problem being addressed is global and cuts across disciplines. Therefore, This approach is efficient and cost-effective in addressing the research question since the process of data gathering is not undermined by logistical challenges associated with primary data collection. Secondly, the carry-out of secondary data analysis makes it possible to ensure that the information used in the analysis is up-to-date and includes all findings in the particular field of study.
Experimental Hypothesis
The experimental hypothesis for this study is that the successful alignment of semiconductor supply chains with SDV requirements is influenced by effective collaboration between semiconductor manufacturers and automotive OEMs, investment in advanced technologies, and mitigation of geopolitical risks.
Null Hypothesis
The null hypothesis posits no significant relationship exists between the alignment of semiconductor supply chains and factors such as collaboration, investment trends, and geopolitical considerations.
This research is underpinned by an interpretivist research paradigm, which aims at interpreting the research phenomenon in its context. Interpretivism is highly applicable to the qualitative research as it looks for the rationale behind action, interaction and choice based on an assessment of existing information and literature. This philosophy is appropriate for this study because it is anchored on the secondary qualitative research design where the aim is to make sense of available pattern, themes and ideas from other sources.
By adopting the interpretivist paradigm, the emphasis is on identifying how these related stakeholders, including semiconductor producers and automotive original equipment makers (OEMs), respond to these supply chain challenges and coordinate their actions. The plan here does not seek to provide general findings that can be applied to every situation but give well-grounded insights that can be enriched from the context towards the understanding of the topic of concern. The study benefits from the interpretivist philosophy to adopt a critical perspective on the existing literature and search for patterns that can help to make strategic decisions in the semiconductor and automotive industry.
The method of data collection used for this study excludes any primary data collection and uses secondary sources of information to analyse the semiconductor supply chain’s suitability for the new class of vehicles known as software-defined vehicles. This approach involves the collection of information from various and reliable sources, secondary sources that include, text books, journals, articles, reports, case studies, governmental publications and other market research. Thus, the use of secondary data is suitable to answering the research questions since it consolidates ideas and information from various sources.
The data collection process starts with evaluating qualitative and quantitative sources: ‘semiconductor supply chain,’ ‘software-defined vehicles,’ ‘trends in the automotive industry,’ and ‘geopolitical implications for semiconductor production.’ In order to find relevant articles, reports and datasets, search engines, databases and industry-specific sources including IEEE Xplore, Science Direct and Statista. Also, the data from the reports of such companies as McKinsey, Deloitte, or governmental institutions is considered to make sure the information provided is really accurate.
After finding resources, credibility, relevance, and currency are analyzed. The findings are specific to the most recent data available in the publication, that is why the primary sources are articles of the peer-reviewed journals and reports that discussed recent changes in the semiconductor and automotive industry. In the case of each source identified, the information is analyzed to ensure that essential data is pulled together to create a larger database. The data is therefore grouped under themes making it easier to address each of the set research questions systematically.
Secondary data offers a cheaper and effective tool for addressing the objectives of the study than using primary data which would have its own problems of data collection. This approach is particularly appropriate given the international and interdisciplinary focus of the research question because the study can benefit from previously accumulated findings that might be hard and time-consuming to gather through research. The study codes information from multiple credible sources in a way that guarantees a total understanding of the issues and trends in case semiconductor supply chains align with SDV requirements.
Thematic analysis is used in this study to examine and explain patterns in the secondary data that has been collected for this research. This study benefits from thematic analysis because it is a suitable method for analyzing the collected literature and reports to identify the themes pertinent to the topic of supply chain alignment for integrated semiconductor systems in SDVs.
The first phase involves assimilation which involves a detailed scan through the journals, magazines, industry reports, government publications etc available as secondary data in order to understand the material and its context. This step is taken as a way of developing a good understanding of the materials, before looking at thematic issues. After this, the key ideas and concepts are noted and highlighted whereby the data is divided into segments for analysis. These codes reflect ongoing concerns and important aspects concerning semiconductor planning, cooperation, financing, and conflict.
After coding the data, the proceed to the process analyze the data by forming larger categories from related codes. For instance, persisting conversations on supply chain issues, relationships between semiconductor makers and automotive original equipment manufacturers OEMs, and geopolitics are drilled down into broad classes. All identified themes are discussed and analyzed in order to make sure they are backed up by sufficient data and reflect the goals for the study adequately. Most related or related ideas are combined, and terminally abstract themes are either removed or redefined.
These final themes are then enunciated and culturally labelled to ensure that they are responsive to the research objectives and questions. The themes include semiconductor supply chain disruptions, the future coalition of industries, semiconductor investment and technology focus split, and geopolitical impacts on the semiconductor value chain. To support these themes in the analysis, supporting evidence is derived from the reviewed data sources to bring credibility in the analysis process.
As a result, the following ethical considerations are critical in this research to improve the objectivity, validity, and credibility of the study and to uphold participants’ rights, as well as anonymity (Ahmed, 2024). It is important to note that the survey follows the recognized ethical policies and practices for use with a human subject. Participating safety officers will have consent given before participating in the study to avoid any misunderstanding of the intention and the voluntary nature of the survey (Ahmed, 2024). All personal information and survey answers will be kept private, and survey data will not include personal identifiers. The handling of data will align with the country's data protection laws, and all information obtained throughout this study will be stored securely for the exclusive use of this study. The participants will have the freedom to withdraw from the study at any given time with no reason being required, and directions on how to do so shall be well described.
Nevertheless, this study has limitations that might reduce its scope or generalizability. While the employed sample is consciously chosen to include only professionals in the semiconductor or automotive field, it is still possible that not all worldwide regions or organizational positions are equally represented in their attitudes (Sinclair et al., 2021). Moreover, sample selection might be post-controlled as survey data collection methods produce response biases due to participants giving answers that are acceptable to the larger society or lacking sufficient understanding of technical questions.
The quantitative method may compromise detailed information on collaboration and strategy in the semiconductor and automotive industries while only providing sensible tendencies and relations. Further, since the study collects only the primary data, the researchers do not incorporate extensive longitudinal or historiographical data that may facilitate a deeper understanding of the industry’s development (Hopwood, Bleidorn and Wright, 2021). Some of the findings may also be time-sensitive since the geopolitics and economics that define the semiconductor supply chain relationships are also ever-evolving (Hopwood, Bleidorn and Wright, 2021).
Finally, this study relies on SPSS for analysis of collected data and the reliability of the tool and its outcomes is taken for granted. Some of the sources of error include Technical errors while analyzing, which could distort the results of the whole analysis process (Paul and Barari, 2022). While these limitations have been identified, they do not undermine the findings of this study but provide comes within the survey to suggest areas for future investigation and bettering.
This chapter describes the methodological approach used to study the semiconductor industry strategies' congruence with software-defined vehicles (SDVs) requirements. It started by explaining the quantitative research design in as much as it depicted the research as highly structured and formal. Research questions were stated as a means of framing the study and as a way of offering a focus on the analysis areas of challenge, collaboration and investment. The chapter also presented the choice of positivist research philosophy, which was considered appropriate for collecting the empirical findings and maintaining the reliability and validity of the study.
This chapter gives the study conclusion and recommendation with regards to the identified themes from the study’s thematic analysis. The study focuses on the disruption of the semiconductor supply chain with reference to software-defined vehicles, Sectifed challenges, cooperation approach, investment patterns, influence of geopolitics, and the shift to the SDVs. In this respect, the analysis based on synthesizing secondary qualitative data accumulation will develop a systematic view on the dynamics of the semiconductor and automotive industries’ environment.
Theme 1; Challenges in the Semiconductor Supply Chain for SDVs
The value chain of the semiconductors that make up SDVs has become very intricate which poses a problem for evolving technologies. These hurdles arise mostly where the car maker relies on imported semiconductors and as the automobile technology progresses requiring better chips, there is every reason for these microprocessors to ride on advancing technology.
As stated by Aityassine et al. (2022), the disruptions that occur in the supply chains contain aspects that are essential in carrying out industries, particularly semiconductors most efficiently. They argue that expansion with less diversification in the supplier networks binds industries and firms to risk areas, including geopolitical risks, natural calamities, or shifts in demand. There are further risks due to the regional focus of semiconductor manufacturing – Taiwan and South Korea. This centralization of the production capacity makes the industry vulnerable to bottlenecks and delays both aggravated by high demand for the products in automotive, consumer electronics, and healthcare industries.
Liu and Chen (2022) explain extensive decision dilemmas in semiconductor value chain and mainly address the issues of intellectual property rights management and material supply. They explain the complex form of interconnection between fabless design firms, foundries, and IDM that creates a long and complex supply chain. To SDVs, it brings the inconvenience of slow inclusion of advanced technologies in automotive solutions because OEMs depend on tiered supplies for crucial components. In addition, Liu and Chen note that technological development in semiconductors is brisk, leading to frequent production method updates, which pressure supply chain configurations.
Competition and supply cuts in this industry are well illustrated by the current global chip shortage occasioned by COVID-19. Sinclair et al. (2021) explore the reasons as to how economic pressures and risks compounded and exacerbated the fragility of sthe upply chain during the COVID-19 pandemic, resulting in numerous production disruptions in many sectors. These disruptions slowed the deployment of other innovations connected to SDVs, such as ADAS as well as in-car connection offerings. According to the authors, achieving these goals is possible only through planning and supply chain diversification – two issues that necessarily entail long-term strategies.
There are other structural problems, which create the basis for the supply chain in the semiconductor industry, but geopolitical factors are just as central to this whole process. Liu and Chen (2022) explained how trade restrictions and sanction disrupt the flow of semiconductor components around the world. For example, the trend of tariffs and sanctions, such as the US–China trade war, has unsettled industries’ supply chains due to restrictions on accessing fundamental materials and advanced technologies. Such geopolitical tensions put additional complexity to the companies: they change their supply chain strategies and start developing manufacturing facilities to reduce risks.
Another issue that reinforces the high capital intensity of semiconductor manufacturing is a challenge. New players are deterred by the customers and sizeable financial assets needed to set up modern fabrication facilities, as observed by Aityassine et al. (2022). A high concentration level within specific companies like TSMC and Samsung makes the semiconductor industry's supply chain rigid. They argue that this is particularly disadvantageous to SDVs made of specialized chips designed specifically for automotive purposes due to this inflexibility.
The problems of the semiconductor supply chain for SDVs originate from the structural uncompetitiveness, geopolitical factors, and increasing complexity of the technology landscape. These should be met by different coordinated strategies across all chain-affiliated industries to improve the supply chain defence and develop the various capacities of manufacturing and efficiency. The findings made by Aityassine et al. (2022), Liu and Chen (2022), and Sinclair et al. (2021) outline the aspects that require focus to implement semiconductors into SDVs effectively. By managing these challenges, the two industries can develop a stable and flexible supply chain to meet the growing needs of the fast-growing SDV market.
Theme 2: Strategic Collaboration Between Semiconductor and Automotive Industries
Key stakeholders such as semiconductor makers and automotive OEMs must therefore form strategic partnerships to manage the complexities of coordinating supply chains with the requirements of SDVs. It also maintains and encourages the development of research partnerships to support advances in supply chain management and the timely implementation of the newer semiconductor devices in automotive applications.
Liu and Chen (2022) especially consider cooperation and collaboration in R&D of semiconductor manufacturing for automotive OEMs. In their case, co-development initiatives help both industries to synchronize their technology plans to ensure that the developed semiconductors address SDVs’ needs, including high computing, power efficiency, and durability in harsh climates. This way, semiconductor companies can avoid compromising on the end product and come up with solutions that will work best for SDVs during the design phase, which cuts down their time to market while at the same time improving the performance of these vehicles.
Ahmed (2024) was able to explain on how trust and transparent communication enhance successful collaborations. Reliability is established as one of the founding blocks of partnership, especially in industries, which are as critical as semiconductors and automobiles. Since relationships are built with trust, communication between various individuals and companies is more likely to be open, thus facilitating more synchronous effort, goal orientation, and creative solutions. Such transparency can also reduce future conflict and guarantee that corresponding parties understand overall objectives.
Mirzayi et al. (2021) suggest improving conceptual frameworks for information exchange to raise industry cooperation. Based on their previous studies on reporting guidelines in microbiome investigations, the authors of the paper propose that such models might also apply to semiconductor-automotive relationships. These frameworks would outline methods of transmitting technical specifications, research and development status and supply chain management information, guiding the various individuals and corporations that comprise the SDV development process.
The changing nature of semiconductor technologies makes it crucial for semiconductor and automotive firms to coordinate because of the technical challenges that arise. As Pan and Zhang stated in 2021, AI and the related ML are critical to making collaborations more effective when performed. Leveraging AI potential, a demand can be predicted, supply chain disruptions can be observed, and real time decision making can be initiated and provided which would lead to the increased effectiveness and dependability of the supply chain. Securing these technologies assists in improving communication between semiconductor manufacturers and OEMs as they address the growing market of SDV.
There is also a long-term partnership in the form of supply contracts and ventures. Liu and Chen (2022) have noted that automotive original equipment manufacturers (Automotive OEMs like Tesla and BMW have directly sourced direct semiconductor manufacturers for such components. These kinds of agreements contribute to risk sharing and reduction in the supply chain, but also enable significant co-investments in new technologies, including SiC/Si and GaN semiconductors that are fundamental to the next generations of SDVs.
However, integrating these collaborations depends on the following challenges that must be met for the collaboration to work. As Ahmed sees it in her manuscript published in 2024, contradictory motivations and distrust negatively impact the possibility of stalling collaboratories. With this in mind, corporations need to balance their objectives and outcomes where the objectives themselves are unmistakably understood and where there are well-defined communication and governance frameworks. Moreover, the development of joint R&D infrastructure and human capital building can enhance the capacity of the collaborative environment to maintain the industries’ competitiveness and foster innovation.
Theme 3: Investment Trends and Technological Prioritization
Semiconductor skills’ investment patterns and technological agendas are essential in meeting Software-Defined Vehicle (SDV) requirements. Chief among these factors are smart semiconductors used in functionalities that include autonomous driving, human convenience, and energy conservation; these trends have triggered increased investments in innovation and technology.
Liu and Chen (2022) review the specific changes in the technological environment, which are the focus of key players in the development of new semiconductor products. Based on patent data analysis, authors pointed out that current and future developments in patents concentrate on higher-tech components, silicon carbide (SiC) and gallium nitride (GaN), which are crucial to computing processing in SDVs. SiC and GaN materials are also superior in efficiency and thermal management over conventional silicon-based chips used in electric vehicle powertrains and sophisticated driving help systems. These technologies are chosen because of their capability to fulfill the high-performance expectations for automotive applications, such as being robust in harsh conditions and having high data rates.
Lutfi et al., (2023) discuss on how data analysis using analytic modules and artificial intelligence affects investments in industries with semiconductor dependency. Large data processing and semiconductor technologies in the automotive industry lead to predictive analysis, instant decision-making, and improved end-user experiences. Managers are escalating expenditure to investing on AI for semiconductors that are fundamental for autonomous driving and V2X applications. Approaches the industry assigns to these technologies stem from their focus on functionalities enabled by software that optimizes the scalability and flexibility of SDVs.
Pan and Zhang (2021) elaborate on their paper regarding the impact of R&D and the essence of investing in the semiconductor industry. From their review, it becomes clear that resources should be directed to evolving technologies, including machine learning algorithms and AI-based processors that enhance vehicle performance besides allowing features, including autonomous navigation. The authors state that it is not only the competitive imperative for such technologies but also the audacious business strategy for meeting the escalating consumer demand for innovative and connected vehicles.
Investment dynamism has also been shaped by the higher integration of semiconductor makers with automotive original equipment makers (OEMs). Liu and Chen (2022) highlight that current key automotive manufacturers, including Tesla and Volkswagen, have direct sourcing relations with the semiconductor suppliers and co-invest in R&D centres and production capabilities. These partnerships guarantee reliable access to important components as well as generate new solutions in such sectors as chip optimization and system design. These forms of co-investment eliminate the need to obtain supplies from third parties and facilitate the establishment of exclusive technologies specific to self-driving vehicles.
However, many difficulties persist in making investments correspond to technological development priorities. This characteristic means that semiconductor manufacturing is highly capital intensive and this does not afford firms much leeway in diversification of capital investments. According to Sinclair et al. (2021), adversative global and economic conditions expose organizations to financial pressures and tension that distort their sources of funding. All of these impose significant risks on innovators because of the increasing uncertainty in the global technological sphere for the benefit of long-term strategic planning and diversification of a manufacturing-driven industry.
However, the world's governments today have identified the importance of this industry and have put in place measures to encourage more investment. Liu and Chen (2022) explain that countries are developing manufacturing strategies such as the U.S CHIPS Act and The European Union’s semiconductor strategy to decongest their suppliers. These policies offer grants and subsidies for semiconductor industries to proceed with more and improved research, development and application of technology on SDVs.
Theme 4: Geopolitical and Economic Influences on the Semiconductor Industry
Semiconductors are a strategic element of the technological industry and the global circuit where economic and geopolitical factors impact production systems, supply chains, and technologies. All these factors impact on the readiness of the industry to deliver for software-defined vehicles (SDVs), thus posing some level of threat and opportunity for stakeholders at the same time.
Liu and Chen (2022) pointed out that the industry locations are highly clustered due to the fact that semiconductor manufacturing mainly occurs in Taiwan and South Korea. This traditional geographic centralization has implications for the presence of risk in the supply chain network, where interruptions in these supporting regions due to natural disasters, geopolitical hostilities, trade restrictions or other issues can lead to major delays and supply shortages. The recent trade war between the U.S. and China has intensified this situation with restrictions and control of technology transfers and export controls of the vital materials and components. Such tensions change the existing company approach to sourcing and the search for manufacturing bases to ensure supply chain reliability.
Supply chain risks in the semiconductor industry are stressed by the broader economic challenges, according to Sinclair et al 2021. The global manufacturing and supply network was exposed to severe disruptions as Covid-19 continue to unleash its influence at different phases across the globe, consequently disrupting the automotive business. Market volatility seen throughout the pandemic affected the sales demand and added to disruptions in production times. From the perspective of SDVs, these disruptions restrained the integration of pivotal semiconductor technologies that include ADAS and in-vehicle connectivity solutions.
These geopolitical and economic concerns are key when elucidating the Governmental policies and intervening factors needed to resolve them. For example, Liu and Chen (2022) described public policies, like the U.S. CHIPS Act, which desires to tackle supply chain vulnerability by subsidizing domestic chipmaking. In like manner, the European Union’s strategy on semiconductors is to strengthen production within the region in order to avoid over-reliance on Asia. These policies entail provisions for support of R&D and manufacturing as a way of enhancing on innovation in tandem with a strengthening of the supply chain.
According to Pan & Zhang (2021), geopolitics also encompasses competition over technology and traditional trade policy patterns. Semiconductor technologies are deemed useful for national security and economic competitiveness and so most countries try to develop or regulate them. This prioritization leads to an advancement of capital for quickly developing technologies, including artificial intelligence (AI) and quantum computing, which are applicable in SDVs. But competition for dominance also spoilers cooperation as businesses and governments lock horns in an effort to shield certain technologies and inventions or to deny others access to them.
Figure 4.1: Global semiconductor revenue
(Source: Kusum De, 2023)
According to Gartner, global semiconductor revenue is expected to decline 6.5% in 2023 to USD562.7bn. Market recovery is expected in 2024, with revenue reaching USD654.3bn, i.e., growing 16.3%. The conflict has intensified existing bottlenecks in the semiconductor supply chain and chip shortages (Kusum De, 2023). According to a KPMG study, 56% of semiconductor companies believed (prior to the conflict) that chip shortages will last until 2023.
Theme 5: Transition Toward Software-Defined Vehicles
This change aims at turning the vehicles themselves into software-defined vehicles (SDVs) with relevant outputs governing the car’s functions, connectivity, and experience. This shift requires a complete rethink of how semiconductor solutions are embedded in cars, focusing on scalability, modularity and synergy with semiconductor suppliers and automobile manufacturers or automotive original equipment manufacturers (auto-OEMs).
According to Ahmed (2024), the dependency on software, especially advanced ones, has transcended the establishment of vehicle design and usage. Earlier automobiles were mainly hard-ware-based systems in which most controller parameters were dictated by mechanical hardware. On the other hand, SDVs depend on a process of detaching software from equipment; the goals can be amended to increase utility, while modifying the hardware is not possible. Integrating post features like self-driving mode, predicting maintenance, and OTA solution necessitates the sturdy design of semiconductor systems with strong capabilities of high computing and real-time data processing.
It is identified that the profound implementation of Artificial Intelligence and machine learning has exclusively determined the advanced development of Self-Driving Vehicles (Elallid et al., 2022). The core components of AI and AD systems include semiconductors that are crucial to executing V2X and to creating unique customer experiences. These technologies are built on semiconductors that can enable large-scale ETL operations on data gathered from sensors, cameras, and others; hence, AI-optimised chips are high priorities for semiconductor makers. The shift to SDVs , therefore imposes unprecedented expectation on semiconductors that tests the envelope of computation and power consumption (Elallid et al., 2022).
Yıldırım (2021) also found a similarity between the COVID-19 pandemic contingency and flexibility in SDV shift. The capability to expand and modify the software functionalities in as a result to users’ wants and needs and the improvements made on the technological front is fundamental to the success of the SDVs. This flexibility also applies to the manufacture of chips that must meet the layout of the software platforms that are bound to change (Morgan et al., 2021).
Liu and Chen (2022) confirmed that the way to convert to SDVs is by fostering the innovation of semiconductor design. Strategically, critical technologies like SiC and GaN semiconductor devices which can support high power and thermal management, are concentrated for their relevance to EVs and ADAS. Using such materials into SDVs offers a breakthrough in the efforts needed to source components that can satisfy the requirements of demanding software-controlled systems. Regarding SDVs, a transition also affects the economic and strategic orientations of the automotive and semiconductor sectors. Following their conceptual analysis, Sinclair et al. (2021) point out that economic factors which include increased costs of production, and supply chain disruptions, put pressures on SDVs scalability. However, the sustained development and profitability opportunities of software-centric mobility services stimulate further investment and cooperation.
The components manufacturers’ supply chain structure is highly centralized, geographically concentrated hence making it sensitive to the few a top supply chain players. To be specific, Liu and Chen (2022) explained that Taiwan and South Korea dominate the fabrications of semiconductor products, which leads to supply chain risks.” For example, the US-China trade war affected the global supply of semiconductor components, and the nature of the companies’ businesses compelled them to alter their supply chain strategies. These disruptions are quite significant for SDVs, which require professional semiconductors for aspects such as self-driving and networkability (Morgan et al., 2021).
COVID-19 pandemic set back these advances and exposed the geographic vulnerability of the supply chain industry. According to Sinclair et al., (2021), the disruptions that occurred during the pandemic, such as delays in working on scheduled production and shortage of certain parts impaired the progress of automotive technologies. The presented results are consistent with those of Aityassine et al. (2022), who stressed the importance of supply chain management in avoiding similar disruptions. Mitigating such risks calls for portfolio diversification in production facilities and putting money into regional manufacturing plants, as the U.S. CHIPS Act and the EU semiconductor plan have hoped to do (Utharala et al., 2022).
Strategic partnerships are a key enabler of addressing supply chain and technological issues. It pointed out that collaborations in R&D let the semiconductor makers and automotive Original Equipment Manufacturers (OEMs) work together to develop customized solutions based on SDVs. For instance, direct channel relationships with semiconductor providers have helped Tesla gain the cooperation necessary to build application-specific EV and autonomous driving hardware processors (Utharala et al., 2022). They help minimize use of third-party suppliers while encouraging innovation.
Ahmed (2024) differentiated between strategic partnerships and focused on trust and clarity of information exchange as their main factors. Thus, if organisations' goals are contradictory or not clearly defined, the implementation of SDVs may be slowed down. Mirzayi et al. (2021) pointed out that one of the promising strategies could be to use the existing standards when sharing information with others; this way, the processes would be easily aligned, and all the stakeholders would be consistent. It is mainly the case when to integrate one or another complicated technology, including AI-controlled semiconductors and ADAS systems (Jaeger and Dacorogna, 2023).
However, collaboration also has its drawbacks. The nature of the semiconductor industry is such that there is a significant capital investment and technological complexity, which acting as an entry barrier and reduces the variety of potential partners. Solving this necessitates multi-year contracts and largely co-funded developments of shared research and development infrastructures to coordinate technology planning properly.
Over the years, there has been an investment focus in the semiconductor industry on hi-tech products that conform to the system’s demands for SDV. Lutfi et al. (2023) also highlighted the growing importance of data analytics and, artificial intelligence for investment decision-making. Integrated circuits need semantic data processed in real time with artificial intelligence to boost the vehicle performance necessary for self-driving cars. Liu and Chen (2022) pointed out that with the improvements in material technologies, & more specifically, high thermal and electrical devices like SiC and GaN system devices used in the EV powertrain and high-performance computing systems.
It is noteworthy that priorities such as SiC and GaN are established for an apparent convergence with the automotive market and its tendencies towards electrification and networking (Mercier et al., 2024). These materials allow semiconductors to fulfill the high performance requirements of SDVs facilitating innovative aspects such as quick charging capability or enhanced entertainment solutions. Nonetheless, high costs of developed capital and technologies become an issue of contention; companies need to achieve the innovation of devices while keeping them affordable in the market.
Geopolitics are complex and affect the semiconductor industry in such a way that it cannot meet the set SDV needs. Liu & Chen (2022) insisted that semiconductors have become a critical industry that directly affects national security and international competition, which increases countries’ efforts in local production and research. The trade relations between the United States and China provide one of the best examples of how geopolitics affects international business by distorting global supply systems.
While economic pressures like instability in demand and increasing costs of production worsen these factors, according to Sinclair et al. (2021). The uncertainties which arose due to the COVID-19 pandemic resulted in interruptions in production and slower investments to essential technologies. These disrupts serve as a reminder of the requirement for supply chain flexibility to deal with outside disturbances but still continue production.
According to Pan and Zhang (2021), the threat also fuels innovation as countries strive to advance in technology. Due to this competition, new improvements have been made in artificial intelligence systems, quantum computing, and next-generation semiconductors, which are the core of the design of SDVs. However, it also has a negative impact, particularly in that has made it difficult to form alliances and strategic partnerships as companies and Governments protect their ideas and innovations (Emami et al., 2022).
AI and machine learning were described by Pan and Zhang (2021) to be the key drivers of this shift. Auto-optimized chips work with inputs from sensors and cameras, and support flexible drive solutions and vehicle-to-everything (V2X) capabilities. Liu and Chen (2022) explained that new-age components like SiC and GaN were incorporated, which improved the general characteristics of semiconductors in SDVs.
Many economic considerations have to do with this process. I have found the same concern echoed by Sinclair et al. (2021) in their observation of AI applications that acknowledged the limitations of producing affordable devices due to the high cost of AI semiconductors and advanced materials. However, the long-term revenue generation prospects derived from subscription types of services and new functionalities are self-reinforcing to prop up higher investments in SDVs.
Chapter 6: Conclusion and Recommendations
5.1 Conclusion
The study investigated the strategic alignment of semiconductor supply chains with the needs of software-defined vehicles (SDVs), exploring five central themes: logistics issues, partnership management, funding dynamics, technological development, geopolitical aspects, and the shift toward software-based platforms in the automotive sector. The paper presents precise results in the dynamics of these factors and provides key insights into the prospects and problems for both the semiconductor and automotive industries.
One of the significant risks arising from the current supply chain configuration of the semiconductor industry, mainly focused in areas like Taiwan and South Korea, is a weakness to the automotive industry’s change-over to SDVs. Liu and Chen (2022) pointed out that a high degree of industry concentration and geopolitical risks, including the Trade War between the United States and China, increased supply chain vulnerability. These precursors became worse during the COVID-19 pandemic, as noted by Sinclair et al. (2021), disruption of production schedules, and delay in the incorporation of technology. Finally, the study suggests that to manage these threats, various semiconductor producers and automotive OEMs need to initiate diversification and localized production activities and thus to make use of the existing and potential governmental policies and regulations, such as the U.S. CHIPS Act and other similar propositions of the EU.
The nature of cooperation between semiconductor producers and automotive original equipment manufacturers became evident as a critical amplifier of the supply chain and technology disruptions. According to Liu and Chen (2022), co-development programs which enable the alignment of technological planning help the two industries optimise production. Some recommendations of Ahmed (2024) in building these partnerships include Building and maintaining trust and ensuring open communication. Mirzayi et al. (2021) identified standardized reporting frameworks can also improve collaboration through the provision of more precise specifications and updates of technical details. Nonetheless, there are recognizable issues, including the lack of goal congruence and proprietary technology issues ,which may hinder these relationships.
The trends in investment patterns and the technology hierarchy exercised enormous influence over the configurational adaptation of semiconductors to support SDV. The research revealed that the industry is concentrating on other material solutions such as SiC and GaN, both of which remain important for robust computing systems in SDVs. Specifically, in their paper, Liu & Chen (2022), the authors showed that these materials provide capabilities including charging and advanced driver-assistance systems (ADAS). Lutfi et al. (2023) pointed out that line-following semiconductors with artificial intelligence are increasingly shaping the systems responsible for autonomous navigation and real-time decision-making. Such trends have found support in the government. Further, Sinclair et al. (2021) noted that because of the economic pressures and geo-political risks, funding streams may be contingent, thus calling for companies to embrace long-term planning.
This paper aims to establish that political and economic forces exert pressure on the semiconductor industry in controlling SDV requirements. Struggles between countries for trading partners, export restrictions, and competition for supremacy in technology development disrupt partnerships and supply systems. Accordingly, Sinclair et al. (2021) and Liu and Chen (2022) highlighted these challenges require a transition towards regional diversification to cut down on source reliance. Though geopolitical tensions have raised incentives for developing new and more advanced technologies, the conflict and eagerness, together with pressures for controlling Intellectual Property Rights, have posed tremendous challenges to collaboration.
The shift towards software-defined vehicles is a radical change in the automotive domain. Ahmed (2024) explained how SDVs can be engineered without integrating hardware with software, thereby availing features like OTA updates, predictive maintenance, and connection. Pan and Zhang (2021) underscored how such functionalities are based on AI-optimized chips, especially in autonomous driving and V2X (Vehicle-to-Everything) applications. Nevertheless, there is high production costs of the advanced technologies, which Sinclair et al., (2021) observe raises the factor of affordability for price-conscious markets. This transition also presents a difficult challenge to semiconductor manufacturers’ ability to scale and adapt to new software platforms and to design chips that can support these new platforms.
5.2 Recommendation
Hence, companies in the semiconductor industry and automotive Original Equipment Manufacturers should consider de-risking their supply chains against issues of proximity and geopolitics (Steyn, 2024). This involves creating country-specific manufacturing plants in well-serene political and economic climates. The U.S. CHIPS Act and the European Union’s semiconductor strategy are, therefore quite solid ground upon which to encourage such diversification (Steyn, 2024). These policies should be relied on to foster distributed manufacturing approaches that help companies minimize their overreliance on places such as Taiwan and South Korea.
The best way to facilitate the measurement of technological trends is to improve cooperation between semiconductor manufacturers and automotive OEMs. Co-development arrangements should be preferentially negotiated, allowing both industries to draw upon each other for support. Improved solutions that ensure transparent and clear communication between partners have been proposed by Mirzayi et al. (2021), including the establishment of standardized frameworks. It would be advisable to create various trust enablers like combined governing boards to delay or even prevent goal incongruity that may bring about conflicts during collaborative efforts.
This means the rising need for future-oriented technologies, such as silicon carbide (SiC) and gallium nitride (GaN), for semiconductors to enhance high-performance computing and energy efficacy (Del Giudice et al., 2021). To ensure the success of autonomous driving systems, vehicle-to-everything communication technology, and further enhancements, companies must invest a lot of money in research and development of unique AI-optimized semiconductors and next-generation materials (Del Giudice et al., 2021). Thus, governments and industrial players must cooperate in offering sweet deals to productivity, such expenses as a long-term technology guarantee.
It was found that supply chain technology can be advanced through artificial intelligence and big data analytics for demand forecasting, real-time monitoring and decision-making (Zamani et al., 2022). Both semiconductor makers and OEMs should devote resources into the development of systems to predict disruptions manage the inventory, and supply chain. These technologies can also be beneficial in creating agility in supply chains and ensuring they will not have great disruptions during volatile times.
Companies must lobby policymakers to ensure that favorable structures of free trade and protection of ideas occur over time (Antoine, Ece Özlem Atikcan and Adam William Chalmers, 2023). Together, international cooperation and mitigation of technical trade barriers stabilise global semiconductor supply chains. Industry associations should actively develop communications between the governments and the private sector to seek stable and reasonable regulation processes.
Automotive OEMs and semiconductor makers need to accept this shift to software-defined architectures by deploying and designing new-generation scalable, modular semiconductors and architectures (Zhao, Song and Liu, 2022). OTA updates, predictive maintenance, and improved user experiences are critical for SDVs, but semiconductors must be geared to deliver these. The combined effort of developers in writing software applications and engineers who design hardware should be improved to foster a strong link between software and hardware (Zhao, Song and Liu, 2022).
Obviomet connections of SDVs demand a qualified staff who can navigate through AI-controlled semiconductors, material science, and autopiloting. Both sectors must dedicate resources to the development of employees through training and education (Mukhuty, Upadhyay and Rothwell, 2022). Collaboration with universities and research centres helps develop partnerships for talented staff while meeting the demands for these technologies.
Consumers are shifting toward CAVs connected, autonomous, and sustainable vehicles, and firms have to adapt. Market the results of the present study point out several directions for future research and industry developments (Verma et al., 2021). This is one believable direction for further investigation: perspectives for developing the roles of the emerging markets in such segments as semiconductor and automotive. Subsequent papers could explore the ability of certain locations like India, Vietnam, and Brazil to diversify the supply chain and boost technology advancements of SDVs.
Employing survey and interview methods with key informants from the semiconductor manufacturing firms, original automotive equipment manufacturers, and policy-making institutions would bring richness and real-life application of the issues at hand (Wang, Zhao and Gu, 2021). Such studies could also involve an appreciation of unique cases where such industries have collaborated and an analysis of strategies enhanced for transfer from industry to industry.
cesses and consumer studies should regularly dictate the organization's research, development, and products (Wang, Zhao and Gu, 2021). It will become important again to meet consumer needs with the right semiconductor technologies to remain competitive for mortal SDV market.
5.3 Limitations
The current research offers significant contributions to understanding how the supply chain of semiconductors can be matched with SDVs. Though the research findings are worth noting, some limitations can be considered. The study mainly uses secondary qualitative data, which reduce the range of possible empirical evidence (Gebhardt, Spieske and Birkel, 2022). Although the data sources comprise journals and reports, biases or lack of literature could affect the results. Further, the growth of both the semiconductor and automotive industries is dynamic. As such, other trends that may have emerged within the study period may not be included.
A similar limitation is the regional coverage of the analysis. The study focuses on global trends, although the latter focuses on countries such as Taiwan, South Korea, the United States, and Europe. The presence of other regions, including South East Asia, Africa, and South America, may not be well defined in terms of the semiconductor supply chain or the SDV (Wong et al., 2024). Additionally, the thematic organization of the study may obscure various underlying necessary relationships within the industries because of the all-encompassing themes (Wong et al., 2024).
Secondary sources dominating the study negate the possibility of obtaining first-hand information on strategic management decisions and operational issues affecting the companies. It could also have used primary and secondary data to make the findings even more robust. Finally, the dynamism of technological fields in both industries makes forecasting over long periods difficult because new technologies, policies, or markets may appear or change instantly (Autor, 2022).
5.4 Future scope
The results of the present study point out several directions for future research and industry developments. This is one believable direction for further investigation: perspectives for developing the roles of the emerging markets in such segments as semiconductor and automotive. Subsequent papers could explore the ability of certain locations like India, Vietnam, and Brazil to diversify the supply chain and boost technology advancements of SDVs (Sun et al., 2024).
Employing survey and interview methods with key informants from the semiconductor manufacturing firms, automotive original equipment manufacturers, and policy-making institutions would bring richness and real-life application of the issues (Frijters and Krekel, 2021). Such studies could also involve an appreciation of unique cases where such industries have collaborated and an analysis of strategies enhanced for transfer from industry to industry.
References
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