Business model innovation in the automotive industry: a whole new world.

Keywords:

Abstract:

Digital transformation in the automotive industry does not only affect the products and value creation of a car manufacturer, but also its business models. The example of the mobility providers Tesla and Waymo shows that the number of ‘data-driven companies’ has increased rapidly in recent times and that their value is threatening to increasingly outstrip that of traditional car manufacturers. So far, little research in economic geography has been done on the significance of digital business models in the automotive industry (i.e. Szalavetz 2020, Winter 2020, 2010, Nicholson et al 2017, Sturgeon 2008).

The business logic of traditional car production typically follows the linear model of the value chain (Porter et al. 2015, p. 98ff.). Companies process input goods in several stages into higher-value end products and sell these to consumers. They generate added value by controlling a sequence of activities that build on each other. Markets that function according to this principle are also referred to as one-sided markets. In contrast, platform markets are multi-sided markets characterized by the so-called network effect (Rochet and Tirole 2016, p. 645). This means that the more participants of a certain group are on the platform, the more attractive a platform is for another group of market participants. Once a critical mass is reached, however, it forms a highly networked value-added system that significantly increases the opportunities for market transactions and significantly reduces transaction costs. If a company succeeds in positioning itself successfully as a platform provider and in operating a platform business model, it can achieve a dominant market position by acting as an intermediary between the producers and consumers of the market. For a pipeline company like a car manufacturer, this represents a serious risk as it may lose the direct interface to its customers (Cusumano et al. 2019, p. 75).

Today, the classic business logic in the automotive industry is being broken up by data-driven companies and digital mobility platforms. Tesla is one of the first data-driven companies in the mobility sector to focus on using big data to their advantage. A digital platform is a marketplace that connects suppliers and consumers as well as any other vendors via the Internet and enables value-adding interactions between them (Parker et al. 2016). This transformation implies that a growing proportion of the manufactured cars is now smart and supplemented by the collection, storage, analysis and evaluation of data. These upcoming smart mobility products change the business logic of entire industries and markets by being carriers of digital platforms, enabling data-driven services and creating digital innovation ecosystems. On this foundation, completely new business models can be created in the automotive industry. The starting point of these new business models is the orientation towards the customer with his individual needs. Instead of selling a physical product like a vehicle, data-driven companies aim to offer customers an adequate range of product-service-systems at any time and any place.

One example of how these ideas are implemented today in the mobility sector is ‘mobility as a service’: In order to get from A to B, people may use an APP on the smartphone combining different means of transport, like car-sharing or public transportation. The user can choose if he wants to take the quickest or the cheapest connection. These mobility services need data about the products, their use and about the consumers. Additionally, they need data from other sources like traffic data, movement data or weather data. Furthermore, they must be able to extract valuable information from it via data analytics or machine learning in real-time (Muro et al. 2019). Beyond the digitally enhanced vehicle, mobile devices play a central role in gathering this information and data about how a certain service is used.

To sum up, the business activities of car manufacturers traditionally focus on products and product-related services. Data-driven mobility providers have already connected their ‘smart products’ to the Internet and t collect and evaluate the corresponding data. On the other hand, the speed with which business models must change are still underestimated (Kagermann et al. 2018). Car producers need to adapt to the changes induced by new market entrants in order to secure future business success and stay competitive.

The aim of this paper is to intensify the debate on business model innovation in the automotive industry.
This contribution is empirically based, relying on primary data collected through 160 qualitative guided interviews with executives and experts from China, Germany, Japan, Korea, United Kingdom and the US conducted between September 2015 and December 2018.

Full Text:

28th International Gerpisa Colloquium / Call for papers / Virtual conference
Corresponding/first author: Dr. Johannes Winter, winter@acatech.de
Business Model Innovation in the Automotive Industry: a whole new world.
By Johannes Winter, acatech – National Academy of Science and Engineering
Keywords: Automotive Industry, Business Models, Business Model Innovation, Automation, Autonomous Systems, Smart Services, Digital Transformation, Mobility provider
 
Table of contents
0. Summary 3
1. Business model innovation in the automotive industry: a literature review 4
2. Methodology 5
3. The digital transformation of the mobilty sector 6
3.1 Trend #1: Data drives the transformation of the mobility sector 6
3.2 Trend #2: Digital business models complete outdated product offers 7
3.3 Trend #3: Co-evolution and collaboration in the automotive industry 8
3.4 Trend #4: From optimized production to data-driven business model innovations 8
3.5 Trend #5: From car manufacturer to mobility provider 9
3.6 Discussion: Trendsetting questions for automotive companies 10
4. A look ahead: Europe's path in the digital age 10
5. Conclusion: no competitiveness without transformation 11
6. References 13
0. Summary
Digital transformation in the automotive industry does not only affect the products and value creation of a car manufacturer, but also its business models. The example of the mobility providers Tesla and Waymo shows that the number of ‘data-driven companies’ has increased rapidly in recent times and that their value is threatening to increasingly outstrip that of traditional car manufacturers. So far, little research in economic geography has been done on the significance of digital business models in the automotive industry (i.e. Szalavetz 2020, Winter 2020, 2010, Nicholson et al 2017, Sturgeon 2008).
The business activities of car manufacturers traditionally focus on products and product-related services. Data-driven mobility providers have already connected their ‘smart products’ to the Internet and t collect and evaluate the corresponding data. On the other hand, the speed with which business models must change are still underestimated (Kagermann et al. 2018). Car producers need to adapt to the changes induced by new market entrants in order to secure future business success and stay competitive. The aim of this paper is to intensify the research related debate on business model innovation and the digital transformation in the automotive industry. This contribution is empirically based, relying on primary data collected through 160 qualitative guided interviews with executives and experts from China, Germany, Japan, Korea, United Kingdom and the US conducted between September 2015 and December 2018.
 
1. Business model innovation in the automotive industry: a literature review
The business logic of traditional car production typically follows the linear model of the value chain (PORTER et al. 2015, p. 98ff.). Companies process input goods in several stages into higher-value end products and sell these to consumers. They generate added value by controlling a sequence of activities that build on each other. Markets that function according to this principle are also referred to as one-sided markets. In contrast, platform markets are multi-sided markets characterized by the so-called network effect (ROCHET et al. 2016, p. 645; TIWANA 2013; RYSMAN 2009, p. 125). This means that the more participants of a certain group are on the platform, the more attractive a platform is for another group of market participants. Once a critical mass is reached, however, it forms a highly networked value-added system that significantly increases the opportunities for market transactions and significantly reduces transaction costs. If a company succeeds in positioning itself successfully as a platform provider and in operating a platform business model, it can achieve a dominant market position by acting as an intermediary between the producers and consumers of the market. For a pipeline company like a car manufacturer, this represents a serious risk as it may lose the direct interface to its customers (CUSUMANO et al. 2019, p. 75).
Today, the classic business approach in the automotive industry is being broken up by data-driven companies and digital mobility platforms (KAGERMANN et al. 2018). Tesla is one of the first data-driven companies in the mobility sector to focus on using big data to their advantage. A digital platform is a marketplace that connects suppliers and consumers as well as any other vendors via the Internet and enables value-adding interactions between them (PARKER et al. 2016). This transformation implies that a growing proportion of the manufactured cars is now smart and supplemented by the collection, storage, analysis and evaluation of data. These upcoming smart mobility products change the business logic of entire industries and markets by being carriers of digital platforms, enabling data-driven services and creating digital innovation ecosystems. On this foundation, completely new business models can be created in the automotive industry. The starting point of these new business models is the orientation towards the customer with his individual needs. Instead of selling a physical product like a vehicle, data-driven companies aim to offer customers an adequate range of product-service-systems at any time and any place.
One example of how these ideas are implemented today in the automotive industry is ‘mobility as a service’: In order to get from A to B, people may use an APP on the smartphone combining different means of transport, like car-sharing or public transportation. The user can choose if he wants to take the quickest or the cheapest connection. These mobility services need data about the products, their use and about the consumers. Additionally, they need data from other sources like traffic data, movement data or weather data. Furthermore, they must be able to extract valuable information from it via data analytics and machine learning in real-time (MURO et al. 2019). Beyond the digitally enhanced vehicle, mobile devices play a central role in gathering this information and data about how a certain service is used.
So, if data is the raw material of the 21st century (MUNDIE 2014), its transformation into valuable knowledge becomes a significant manufacturing process. When ‘everything as a service’ is the preferred delivery model, highly automated cloud centers become major global manufacturing sites. The competitive question of the future will be: will the intelligent vehicle become the platform for innovative services like route optimization, infotainment or automatic parking? Or will intermediaries such as fleet operators offer individually tailored mobility services to the user that can also be used across all modes of transport?
To sum up, the business activities of car manufacturers traditionally focus on products and product-related services. Data-driven mobility providers have already connected their ‘smart products’ to the Internet and t collect and evaluate the corresponding data. On the other hand, the speed with which business models must change are still underestimated (KAGERMANN et al. 2018). Car producers need to adapt to the changes induced by new market entrants in order to secure future business success and stay competitive. The aim of this paper is to intensify the debate on business model innovation and the digital transformation in the automotive industry.
2. Methodology
This contribution is empirically based, relying on primary data collected through 160 qualitative guided interviews with executives and experts from the automotive industry in China, Germany, Japan, Korea and the US conducted between September 2015 and December 2018. Data was collected through exploratory, semi-structured guided interviews based on existing studies, publications and projects concerning the relevant research and innovation areas in the automotive industry and the mobility sector. The qualitative questionnaire and the interview guidelines contained prompts and key questions for guiding the conversations, together with quantitative elements.
The in-depth interviews were transcribed and analyzed using ‘Qualitative Content Analysis’ (GLAESER et al. 2004). The study forms part of a research project carried out at acatech – National Academy of Science and Engineering, funded by the German Ministry of Economic Affairs and Energy.
The study consists of five major parts. First and second part deal with the problem statement and the methodological approach of the paper. The third part demonstrates the rise of the platform enterprises and the transformation of the automotive industry. The fourth part tries to reply to what Europe has to do in order to be competitive with platform companies and new market entrants. Finally, some conclusions and outlook follow.
3. The digital transformation of the mobilty sector
Our discussions with international representatives of the automotive industry have confirmed the assumption that digital technology platforms are becoming the dominant marketplace for new business models. Digital platforms have a booster effect on productivity by creating transparency and more efficiently connecting actors, capital, and resources – and with almost unlimited reach via the Internet. Based on empirical evidence, five trends can be deduced that stand for an increase in the significance of digital offerings in the automotive industry and strengthen the transformation of this key industry:
3.1 Trend #1: Data drives the transformation of the mobility sector
As our expert survey shows, data become more and more independent economic goods, have a value and are base of innovative and profitable business models. Once they have left factory, smart cars are still connected via the internet and exchange massive volumes of data during their use. These big data are refined into smart data, which can then be used to control, maintain or enhance and improve smart products and services. They generate the knowledge that forms the basis of new business models.
The consolidation and refinement via real-time analytics and artificial intelligence is usually done in data-rich digital platforms, which will soon be the predominant marketplace. Quite a few automotive companies have already connected smart products to the internet and have started collecting and evaluating data. Ideally those platforms should combine device management with easy connectivity, data storage systems and an App Store open for customized data-driven services provided by an open digital ecosystem. The quality of the digital innovation ecosystem and how fast it can be established will be crucial for a successful implementation of new data-driven business models (see figure 1). In addition, several challenges must be answered regarding financing, reliability, data security, IPR (Intellectual Property Rights)-protection, and finally standardization.
Figure 1: The architecture of data-driven business models
3.2 Trend #2: Digital business models complete outdated product offers
Following the principle of the circular economy, digital sharing platforms can contribute to greater efficiency and sustainability along the entire product life cycle by making better use of the capacity of cars, machines, and apartments. Some platforms have the potential to affect established business models. Examples include online agency services for passenger transport, in the accommodation segment, and streaming services for music and films. Networking effects represent one characteristic of platforms. The more actors are connected through the platform, the more the participants profit from its use and the more attractive the platform becomes for new customers and providers. Following the principle of the circular economy, digital sharing platforms can contribute to greater efficiency and sustainability along the entire product life cycle. The rapid and sustainable growth of platforms is a decisive factor in their competitive success. In recent years, American and Chinese companies such as Amazon and Alibaba, Google and Baidu, as well as Facebook and Tencent, whose business models are based on digital platforms, have had enormous success in the B2C sector. These kinds of platforms are now also emerging in the business-to-business sector (B2B). With these platforms, the winner takes all principle is not equally inevitable, attributable to the significance of complexity and domain knowledge. In addition to the advantages stated, platform markets also have structural weaknesses, for example, concentration tendencies towards monopolization through scaling and networking effects, which is creating problems for social networks as Facebook. In this way, competition law is also facing new challenges. If data power tends to strengthen existing market power, it must be clarified when the ‘misuse’ of data power requires regulation.
3.3 Trend #3: Co-evolution and collaboration in the automotive industry
The interviews showed that no single Original Equipment Manufacturer (OEM) possesses the necessary know-how to be permanently successful in the digital age. Through co-evolution and collaboration, companies can jointly offer complementary solutions for their customers and boost their competitiveness. If several innovators work together successfully in the environment of a platform, innovation ecosystems emerge. Global competition will change through the rise of digital business models and platforms: it primarily takes place between digital innovation ecosystems – no longer just between individual companies. Here lie opportunities for start-ups and SMEs to introduce their highly specialized skills into these ecosystems without having to accept a greater entrepreneurial risk by having to establish their own platforms.
The development towards hyperconnectivity, autonomy, and increased human-machine interaction is inspiring automotive companies to design their core processes more efficiently and finish products and services digitally (KAGERMANN et al. 2018). This development will take a rather evolutionary course. Data-driven business models, platform markets, and digital ecosystems, on the other hand, have a disruptive effect. Current business models can be cannibalized within very short time spans, no matter which industry. This new view of the economy is unfamiliar for many ‘traditional’ car manufacturers and suppliers (COOPER et al. 2020). Established business models and previously successful companies are being challenged by start-ups but also by companies outside the industry – especially by large online corporations.
3.4 Trend #4: From optimized production to data-driven business model innovations
The boundaries between producing trades, service companies, and the IT and Internet industry are becoming blurred. Car manufacturers and their suppliers need new skills, for example in the areas of IT security and artificial intelligence supported data analysis. Even if many companies have already connected their ‘smart products’ to the Internet – they also collect and evaluate relevant data. It is often underestimated how fast and radical current business models need to change. the speed and radicality with which current business models must change is still underestimated.
Figure 2 shows what such a process from optimized production to data-driven business model innovations could look like. Connectivity and real-time responses around the original product or service are followed by the optimization and efficiency on the product and process level, including new after-sales services. The expansion of the business model towards products as a service and value-¬added services transforms the company into a service organization. Via the new digital business, the company is ultimately developing into a platform company and/or participant in a digital ecosystem.
Figure 2: From optimized production to data-driven business model innovations in the automotive industry (own fig.)
3.5 Trend #5: From car manufacturer to mobility provider
While the best networks today have latency periods of ten to fifteen milliseconds, the upcoming 5G mobile communications standard offers almost real-time mobile Internet availability. Data latency, the time that passes between data retrieval and data provision, will be reduced to just one millisecond in the future. 5G is fast, instantaneous, energy-efficient, and reliable – a fundamental requirement for the next generation of cars and mobility services.
The interviews showed that there is a clear trend towards autonomous systems in the automotive industry, even though the corona crisis has led to greater cost discipline and new strategic considerations. Autonomous cars independently and in a fashion tailored to the situation achieve a pre-defined objective, without human control or defined action plans. The central components of autonomous systems are sensors, self-regulation, and actuators. The self-regulation of autonomous mobility systems is facilitated through elements of perception and interpretation, planning and plan identification, learning and reasoning, as well as communication and collaboration (NICHOLSON ET AL 2017). Thanks to the enormous progress in AI, it is now possible to gain valuable information and findings in real time from data obtained through sensors. These data also serve as training material for self-learning and autonomous systems, which recognize the structure of their environment themselves and generate their own knowledge base that can be continuously updated during operation. Self-driving public transport shuttles, mobile service robots in rehabilitation centers and care work, as well as smart home technologies are just some examples of autonomous, adaptive systems that are taking on increasingly complex tasks in all areas of work and life.
3.6 Discussion: Trendsetting questions for automotive companies
The study results raise several key questions to which companies should devote more attention in future. For these key questions will also determine the extent to which classic car manufacturers will be able to survive in the digital age and maintain their dominant position even against new market participants and tech giants from the US and China:
a) Companies from automotive industry should consistently examine how they are developing their existing business model and how they can innovate fundamentally?
b) How can the company's products such as cars, systems, components and parts be permanently connected to the Internet?
c) Does the analysis of real-time usage data of the products bring new knowledge that can be exploited or marketed?
d) Can products (passenger cars, light-duty vehicles, trucks, motorcycles etc.) also be offered as "mobility as a service" market services?
e) How can even elements of the value chain be completely digitized? And would this digitization replace the control points in favor of your own company?
To sum up, small and medium-sized enterprises as well as large companies of the automotive industry should focus on those areas in which they can create the highest added value. It is crucial that the data, which the products generate during their operation, is available to the respective company for refinement.
4. A look ahead: Europe's path in the digital age
There are excellent and promising approaches for a future-oriented innovation policy in many European countries. In Germany, a network of company representatives, scientists, trade unionists and politicians developed in 2013 a ‘digital journey framework’ (ACATECH 2013). This was an innovation strategy designed for lead companies of each size to achieve a high degree of digitization and to save their competitiveness, especially in key industries such as the automotive industry. Multi-stakeholder platforms are founded to guarantee the exchange of the knowledge between car makers, suppliers, research institutions, non-governmental organizations, policymakers and trade unions. Enterprises share their best practices of the company’s digitalization and help small and medium-sizes companies on their way into the digital era. Politics support their development with public funding and focused research projects. Moreover, companies are offered different programs to gain knowledge regarding digital business models and its implementation funded by the government, trade unions, associations and so on. Thus, business model innovation is all about networking and exchanging knowledge and experiences with all parts of the economy. At the same time, new business models are emerging where borders are being crossed and completely new challengers are entering the competition arena. For the European car industry, these new challengers are Tesla and Waymo on the one hand, but also the many (electrical and autonomous) car producers and suppliers from emerging markets and new industrial powers such as China and India.
Moreover, especially for European countries, it is important that digital technologies are adopted by businesses in order to grow labor productivity and to benefit from the potentials of online commerce. Europe would benefit from the next wave of digitalization to develop specific digitization plans for the car industry. It is important to think about investment in developing a strong Europe-wide ecosystem of digital innovation hubs. In several regions digital manufacturing platforms have already been developed to help digitize the manufacturing process. Europe could also benefit from creating the right conditions for private investments to improve the digital infrastructure. However, Europe must overcome shortages in IT-skills of the citizens. According to the Digital Economy and Society Index (DESI), about 20 % of the European population has never used internet. That also limits the possibilities offered by the digital economy and society.
5. Conclusion: no competitiveness without transformation
As the empirical results of the qualitative study show, Europe’s small and medium-sized enterprises play a crucial role in the innovation process of the automotive industry. These often family owned companies distinguish themselves from other companies through an extraordinary level of specialization, know-how and innovative capacities. Often, they are world leaders in their niche markets – and literally hiding in many of the small towns and villages throughout Europe. Although mainly unknown in public, besides multinational corporations and state-owned companies, small and medium-sized enterprises form an essential backbone of the European economy and are actively contributing to the industrial transformation process in the car industry. Europe’s strong automotive, machinery and plant manufacturing companies and their know-how in embedded systems as well as automation engineering are reasons for the continent’s pole position in the international race towards the fourth industrial revolution. These so-called hidden champions have also been successfully introducing information and communication technologies for several decades.
To sum up, the business activities of car manufacturers traditionally focus on products and product-related services. Data-driven mobility providers have already connected their ‘smart products’ to the Internet and collect and evaluate the corresponding data. On the other hand, the speed with which business models must change are still underestimated (Kagermann et al. 2018). Car producers need to adapt to the changes induced by new market entrants in order to secure future business success and stay competitive.
 
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