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81 Seiten, Note: 1,3
1.2 Purpose Stateme
1.3 Research Questi
1.4 Research Des
1.4.1 Literature Analys
1.4.2 Semi-structured Intervie
1.4.3 Application Scenar
2. Automotive Indus
2.1 Supplier Structur
2.2 Value Cha
2.3 Industry Challeng
3. Blockchain Technolo
3.1 Historical Origin
3.2 Blockchain Funct
3.3 Types of Blockcha
3.3.1 Public Blockch
3.3.2 Consortium Blockch
3.3.3 Private Blockchain
3.4 Distributed Network Architect
3.5 Consensus Mechan
3.5.1 Proof of Work
3.5.2 Proof of Stak
3.5.3 Practical Byzantine Fault Tolerance Algorit
3.6 Limitations of Blockch
3.6.1 Technical Limitations
3.6.2 Nontechnical Limitation
4. Smart Contrac
4.1 History of Smart Contract
4.2 Contemporary Understandi
4.3 Smart Contracts Functional
4.3.1 Applying Business Logi
4.3.2. Design Pattern
4.3.3 External Data Fe
4.3 Limitations of Smart Contracts
5. Analysis of potential Applicati
5.1 Interview Evaluati
5.2 Application Scenario Design
5.3 Application Scenario 1 – Digital Vehicle Logbo
5.3.1 Textual Descriptio
5.3.2 UML Diagra
5.3.3 Critical Questi
5.4 Application Scenario 2 – Rewarding Mobility Platfo
5.4.1 Textual Descriptio
5.4.2 UML Diagra
5.4.3 Critical Questi
5.5 Application Scenario 3 – Additive Vehicle Manufactur
5.5.1 Textual Descriptio
5.5.2 UML Diagra
5.5.3 Critical Questi
6.1 Impact on Business Mod
6.2 Answers to Research Questio
6.3 Research Design Revi
6.4 Future Wor
Appendix: Interview Guideline .
This thesis aims to evaluate potential areas of application of Blockchain and Smart Contract technology within the automotive industry. In addition to the characterization of the automotive industry, fundamental aspects of both technologies are assessed as well as their current limitations.
In order to evaluate areas of application, three specific application scenarios are designed and assessed in detail to give examples on how Blockchain and Smart Contract technology can add value in the automotive industry. The designed application scenarios introduce a Blockchain and Smart Contract based digital vehicle logbook to record vehicle usage date, a rewarding mobility platform that grants its users incentives for conscious mobility behavior, as well as an application scenario to enable additive manufacturing of vehicle components with an innovative compensation model for suppliers.
The findings show, that both technologies have a direct impact on business models and are enablers to realize new products, digital value chains and realize new revenue streams.
List of related keywords:
- Smart Contract
- Automotive Industry
- Digital Vehicle Logbook
- Rewarding Mobility Platform
- Additive Vehicle Manufacturing
Figure 1: World motor vehicle production and sales units (Source: OICA, 2018 p. 1)
Figure 2: Supplier pyramid of the automotive industry (Source: based on Nieuwenhuis & Wells, 2003 p. 151)
Figure 3: Shared ledger characteristics in distributed networks (Source: based on Brennan & Lunn, 2016 p. 41)
Figure 4: Process of applying business logic to Smart Contracts (Source: based on Cermeño, 2016 p.
Figure 5: Digital vehicle logbook application mockup
Figure 6: UML use case diagram digital vehicle logbook
Figure 7: UML activity diagram digital vehicle logbook
Figure 8: Rewarding mobility platform application mockup
Figure 9: UML use case diagram rewarding mobility platform
Figure 10: UML activity diagram rewarding mobility platform
Figure 11: Additive vehicle manufacturing system illustration
Figure 12: Additive vehicle manufacturing chaincode - request 3D data
Figure 13: Additive vehicle manufacturing chaincode - request manufacturing permission
Figure 14: UML use case diagram additive vehicle manufacturing
Figure 15: UML activity diagram additive vehicle manufacturing
Table 1: list of related keywords
Table 2: Use case characteristics
Table 3: Contractual terms rewarding mobility platform
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The global economy is digitizing alongside the rapid developing technologies, digital value chains and process automation. Like many industries before, the automotive industry faces the challenge of disruption, driven by digitization across the value chain and changing customer expectations. A central aspect and major trend in this context is increasing connectivity and machine to machine communication. The connected vehicle is the logical next step that consumers expect from mobility as a service in the era of internet and communication. The automotive industry however struggles to adopt as many car manufacturers, also known as original equipment manufacturers (OEMs), pursue their century old business models and established structures. The market environment is changing. New market entrants with technological expertise explore the mobility business and are siphoning off parts of the value chain with digitized business models. Blockchain and Smart Contracts are expected to enable new business models and automated processes on a scalable level and offer value in large network constructs.
A Blockchain is a decentralized network technology, developed to enable the Bitcoin cryptocurrency. The interest in the technology has increased since its introduction in 2008 and accelerated with the reinvention of Smart Contracts. The possibility to embed self-executing, autonomous acting programs into a Blockchain solution attracted public interest and enabled first successful niche solutions. The reasons for the emerging interest in the technology are its features of providing security, data integrity or decentralization to potentially enable new business structures and models.
Recent years have witnessed a paradigm shift in the understanding of vehicles and mobility as a service in the automotive market. The old, established design approach that OEMs pursue has detrimental effect on the environment and the capacity of urban areas. Blockchain and Smart Contracts are assessed as potential enablers of the internet of things and a shared economy.
This thesis provides a detailed analysis of the automotive industry, its value chain structure and industry challenges. Furthermore the concepts of Blockchain and Smart Contract technology are characterized by reviewing relevant literature. A series of interviews with industry- and technology experts and the theoretical foundation of the literature review is then applied to design three specific application scenarios for the application of Blockchain technology and Smart Contracts in the automotive industry.
The automotive world is challenged by a major disruption that could change the face of a whole industry. Technology-driven trends revolutionize how industry players respond to changing customer behaviour and needs. New players with cross-industry backgrounds enter the market and introduce innovative products and services. Traditional business models are under pressure to exceed the impact of technological disruption.
Advancements in technology, engineering and performance continuously lead to faster and smarter vehicles. OEMs steadily increase the efficiency of production processes, expanded model range and facilitated the concept of mass customization. While these advantages and progress seems to pay off quickly, industry experts agree that the automotive industry is likely to see more change in the next ten years, than it has seen during the last 100 years. Those changes are primarily driven by global digitization and new disruptive business models, at which most OEMs are likely to struggle with adoption and implementation (Holweg, 2008).
The automotive industry is entering an era of digitization that will not only transform the OEMs, but will also fundamentally change its surrounding supplier industry. Future mobility concepts will be part of the internet of things to offer beneficial developments for users, urban areas, manufacturers and will enhance the industry’s value chain. Vehicles will need to interact with their environment in the near future (Kienbaum, 2016; Roland Berger, 2016). New digitized services emerge with the connected nature of vehicles and established services are obliged to increase the integration of digitization. In order to successfully adapt to the changing market dynamics, OEMs and suppliers need to integrate technological means and know-how to their competence portfolio.
Blockchain technology and Smart Contracts both feature a highly disruptive character to accelerate innovation and change within the industry. The technologies received significant recognition in recent years as essential building blocks of the new technological infrastructure in the era of connected vehicles and mobility as a service concepts. Combining the technology’s characteristics of automation, transparency, data integrity and secure transaction processes results in a vast number of concepts of service offerings within and around the future vehicle.
Blockchain technology and Smart Contracts are emerging technologies in their early stages of developing mature solutions to meet industry standards. Nonetheless, recent history of similar industries has shown that ignoring disruption and avoiding adaption to a changing market environment can potentially contradict the deceptive sense of “too big to fail” industry leaders.
As of today, the majority of research is focussed on Blockchain and Smart Contracts in the context of cryptocurrencies. The field of the technologies’ disruptive potential to enable new business models and streamlined processes is still nascent.
This thesis aims to evaluate three potential scenarios for the application of Blockchain technology and Smart Contracts in the automotive industry. The identified application scenarios will then be assessed in detail.
The first stage of systematically mapping the thesis is the definition of the research question. Creswell (2014) suggests to formulate a central research question (CRQ) that narrows down into several associated sub research questions (SRQ). The central research question represents a broad view of exploring the central subject of study while the sub research questions narrow the view into areas of focus (Creswell, 2014). In approaching the subject, the main research question is operationalised into three associated sub research questions that will be answered in this thesis:
What are areas of potential application of Blockchain and Smart Contract technology in the automotive industry?
Why should Blockchain and Smart Contracts be considered as beneficial technologies?
What characterizes the automotive industry and which challenges do exist?
What is the theoretical foundation of Blockchain and Smart Contract technology?
Blockchain and Smart Contracts are emerging fields of scientific research. Most early publications base their research on available white papers, practitioner-oriented sources and online journalism. The number of publications in peer-reviewed journals and scientific literature in general is rather limited. With arising academic interest, more publications focussing on both technologies are surfacing in the years 2016, 2017 and 2018 and established as common points of reference.The following Chapter defines the research design used to conduct this study.
The approach is explained beginning with a description of the data collecting process, followed by explaining the choice of designing application scenarios and conducting expert interviews.
The main research method focusses on the analysis of relevant literature. However, the access and availability might be limited, as Blockchain and Smart Contracts are rather young fields of interest in academic research. In addition to subject related books and print publications, online data as well as statistics constitute a significant part of literature and information. Common online based libraries, search engines and literature collections are used to retrieve relevant literature (EBSCO publishing, Genios, Google Scholar, Wiso). The libraries and search engines were chosen due to their prominence and accessibility. A list of keywords, covering the subject area is identified and used during literature analysis to collect available sources.
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Table 1: List of related keywords
The list of keywords in the table above illustrates the initial starting point, which is gradually and iteratively extended through the analysis of the identified literature. The first 50 search results and online sources for each keyword are analyzed and examined according to their relevance and subject relation.
The collected literature consists of subject related books, published academic articles as a source of latest information within a field of research, as well as related articles as a reinforcing frame of reference.
Multiple semi-structured interviews are conducted with industry- and technology-experts. The prepared interview guideline consists of an outline of topics and eight main questions with open follow-up questions, designed to promote the conversation flow and to receive open formulated answers. The implementation is dependent on how the interviewee responds to the questions and topics laid across by the researcher. The interview meetings are conducted online via Skype, Google hangouts and Facetime with a duration of approximately 45 minutes each. Respecting the will of anonymity of some interview participants, no personal information is presented when evaluating the interview results or referencing to statements.
The interview participants were acquired using e-mail and phone number contact information, as well as social media profiles (LinkedIn, Xing, Twitter). As the subject of research can be described as nascent or very limited, a total number of nine interview participants is determined to ensure a solid foundation and multiple sources of information (Creswell, 2014).
The method of semi-structured interviews is chosen with the objective to develop an in-depth perspective, a profound understanding of the subject and gain impulses to design the potential application scenarios of Blockchain and Smart Contract technology in the automotive industry.
Three individual application scenarios are designed when approaching the objective of this thesis. The application scenarios are designed to show exemplary areas of application of Blockchain and Smart Contracts in the automotive industry. The choice of method is based on the ability to explore potential areas of application in a real life context.
Bryman (2004) draws attention to the fact that case studies are considered a “soft” method as there is no mandatory processing system given. However, according to Stuart et al. (2009) the model is appropriate in case of sparse and imbalanced literature, which is the case concerning Blockchain and Smart Contracts. Additionally, the research results are strengthened by the development of three individually designed application scenarios instead of a weaker one application scenario study.
The application scenarios are designed according to the unified modeling language (UML) standard in graphic illustration and textual description, which serves as a general purpose modelling language to specify and document the artifacts of a software system. The UML standard is the method of choice for illustrating the use cases as it is an established standard language to express a system functionality from the actor’s view (Rumbaugh, Jacobson, & Booch, 1998).
The automotive industry is a key sector of the global economy. Contributing 6.8% to the European GDP and employing 5.7% of the European workforce in 2016, it is one of the leading manufacturing industry sectors (Acea, 2016). The automotive sector employs engineers, designers and researchers and is closely linked to its surrounding service environment categorized by tier segments. Due to its size and economic importance, the sector is effected by global cyclical fluctuations and crises.
The industry continued international market growth and reached a global sector value of 1,010 billion USD in 2017 (Euler Hermes, 2017). The three key markets for new vehicle sales remain China, the United States and Europe. The markets recovered after sales decreased in 2008 and fell to 65 million units worldwide in 2009 as a reaction to the global financial crisis (OICA, 2018).
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Figure 1: World motor vehicle production and sales units (Source: OICA, 2018 p. 1)
Due to its strong connection to related industries, the automotive sector shares growth with the finance, insurance, transportation and many other industries (OECD, 2017). Vehicle manufacturing concentrates on the Asian market with China claiming a share of 29.5% to the global motor vehicle production of 2016 (OICA, 2018).
The leading OEMs face a vast number of opportunities and threats of today’s market dynamics. Electrification and technological advancements are shaping transportation efficiency and product portfolios while customers long for on demand mobility solutions and convenience in after sales service. Although these challenges may increase the risk of future profitability, the overall perspectives for the automotive sector is good (Kienbaum, 2016; Roland Berger, 2016).
KPMG’s Global automotive executive survey (2018) reveals that the majority of automotive C-level executives see mobility as a service, market growth in emerging markets and connectivity / digitization among the top five trends driving the industry towards 2025. The survey furthermore reveals the awareness that 83% of OEM executives anticipate a major business model disruption over the next five years. This chapter is designed to provide a detailed overview of the automotive industry as a whole, its unique supplier structure, value chain and the challenges of modern market dynamics.
The supplier structure of the automotive industry is categorized by cooperations based on services or physical products. Service providers are referred to as independent contractors or development partners which are usually involved in development processes and categorized as tier 0.5 supplier. Suppliers of physical products in the automotive value chain are categorized by tier segments one to three, depending on the level of added value, type of supplied product and commercial distance to the OEM. The segmentation can be visualised as a pyramid, as illustrated in the figure below and consists of tier one, two and three suppliers with product categories ranging from simple parts to components and modules (Nieuwenhuis & Wells, 2003; VDA, 2012).
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Figure 2: Supplier pyramid of the automotive industry (Source: based on Nieuwenhuis & Wells, 2003 p. 151)
Manufacturers have significantly reduced the number of direct suppliers, categorized as Tier 1 to build sustainable vertical partnerships. That allows OEMs to make use of synergy effects and operate in cost efficient supply networks and consistent quality. The close interaction and linked value creation between OEMs and suppliers created an intense interrelation of economic dependency. In consequence, both parties become vulnerable in economic crises (Wells, 2010). Many suppliers suffer from increasing price pressure exerted by OEMs and are forced to go out of business or merge under economically different circumstances (Hung Vo, 2016). Findings from the Fraunhofer Institute Austria (2013) point out a clear trend of consolidation and reduction in consequence of increasing predatory competition among automotive suppliers worldwide. The total amount of independent automotive suppliers worldwide reduced from around 30,000 in 1988 to 5,600 in 2000 and is expected to further decrease by about 50% until 2025 (Fraunhofer, 2013). Suppliers that are able to keep up with increasing requirements in price and know-how face high potential for long term growth (Hung Vo, 2016; Roland Berger, 2016; VDA, 2012).Research findings show, that suppliers with increasing IT and software competencies face higher growth opportunities than the industry average prognosis. Suppliers that do not revise their products and core competencies are likely to loose competitive strength. Hence, know-how in software and digitization becomes a significant hurdle for growth and market share (Kienbaum, 2016).
The following part of this chapter characterizes the individual tier segments in further detail.
Tier 0.5 suppliers
The tier 0.5 supplier segment covers a variety of service providers and contractors that are not responsible for supplying a physical product, but offer services such as consulting, development know-how or process experience that contributes to the vehicle development. OEMs tend to outsource in-house responsibilities around the manufacturing process to tier 0.5 suppliers when reaching a critical level of complexity (Nieuwenhuis & Wells, 2003; VDA, 2012).
Tier 1 suppliers
Companies that supply pre-assembled product modules directly to the assembly line of the OEM are referred to as the tier 1 segment. Tier 1 suppliers are often located nearby or inside the factory plant of the OEM to streamline logistic processes and to be time and cost efficient. Product modules are usually delivered just-in-time or just-in-sequence to maintain a constant level of production volume at the OEM assembly line. In most cases, the tier 1 supplier itself relies on a complex network of tier 2 and tier 3 suppliers and is specialized in the upstream assembly process. A tier 1 supplier usually forms a long term strategic alliance with the OEM. Pursuing a single- or dual sourcing strategy, the OEM often transfers a certain level of know-how, engineering competencies and quality control which are not considered as core competencies of the OEM. In consequence, tier 1 suppliers are highly involved in development and innovation processes (Nieuwenhuis & Wells, 2003; VDA, 2012).
Tier 2 suppliers
The second segment is considered as suppliers of product components, which differentiate from module products by a lower level of complexity. Tier 2 suppliers are responsible for producing product modules to customer specifications and are consequently not involved in the vehicle development process. Sourcing strategies on the tier 2 segment vary from domestic, dual or multiple sourcing as the switching costs are manageable. Tier two suppliers primarily serve OEMs and tier 1 suppliers (Nieuwenhuis & Wells, 2003; VDA, 2012).
Tier 3 suppliers
Tier 3 companies are referred to as the suppliers of raw, or close to raw materials and primarily serve tier 2 and tier 1 suppliers. Hence, the participants operating on the lowest of tier segments are specialized in certain products and materials but often very diverse in supplying different industries. The supplied products are highly standardized and there is no type of involvement in the vehicle development process. The companies deliver less critical components and are characterized by a lower level of dependence as they are usually not very closely connected directly, but operate through upper tier suppliers. Business on a tier 3 level is characterised by high standardization, volume sales and price pressure (Nieuwenhuis & Wells, 2003; VDA, 2012).
The highly competitive market environment forced OEMs to constantly increase the level of vertical integration along the value chain. The level of integration and intensity of relationship varies across the tier segments from strategic partnerships to mergers and acquisitions. The OEM obtains a key role in that structure, coordinating upstream processes and the level of added value on each step.
Multiple independently operating companies form a complex network along the vehicle development and manufacturing process which is characterized by high interdependence and efficiency. The actors within the value chain predominantly pursue a focus strategy and offer products or services that are highly advanced and specific to a certain industry segment or step within the automotive value chain (Holweg, 2008). Specialization is a key factor to success across all tier segments. The close partnerships of highly specialized organizations often result in consolidation among the individual segments to increase cost- and time efficiency and gain market share (Fraß, 2012). The supplier industry highly depends on the growth and economic wealth of the car manufacturers.
To deliver produced vehicles into the markets, OEMs cooperate with national sales companies (NSC) for downstream distribution of vehicles, spare parts, wholesale and retail finance. NSCs operate through a network of dealerships, which are independent franchise organizations. Dealerships vary in size and service portfolio. Some provide full service processes from selling vehicles to maintenance and repair, while others are specialized in providing repair services and offering service parts. As the automotive industry constantly increases brand consolidation, many dealerships are dual-branded, meaning they offer their services for multiple brands of the group (e.g. Skoda, Volkswagen and Audi as part of the Volkswagen AG). The dealerships can also be involved in the end-of-life vehicle recycling process of used cars (Holweg, 2008; Wells, 2010).
Various studies conducted by consulting companies and industry experts point out the shift of revenue streams across the automotive value chain. This shift is highly related to changing customer expectations and the consumer’s requirements to vehicles and mobility in general. It is known that consumer’s behaviour evolves to a utility driven consumption of on demand products and services (NTT, 2017). The prestige of owning premium vehicles, luxurious materials and excellence in engineering tend to decline in consumer focus compared to rising demand for convenient services like mobile payments, connectivity or machine to machine communication when drawing a picture of scaleable mobility services. The good of transportation degenerates to a hygiene factor while consumers show willingness to pay for digital services and on demand functionality (Kienbaum, 2016; NTT, 2017).
Traditional business models
The traditional business model of automobile manufacturers, providing vehicles to growing economies was developed almost a century ago (Nieuwenhuis & Wells, 2003). Although global car sales continuously grew by 3.42% on average between 2005 and 2014 (OICA, n.d.), the overall market growth in car production has been just below 2% since 1975. The majority of the developed markets (e.g. Europe or North America) are matured or even saturated today (Holweg, 2008).
Traditional business models are mainly driven by similarity in product design, a high level of standardization and benefits from economies of scale. The average time to market for a new vehicle line respectively adds up to six years from conception phase to the market release (Kienbaum, 2016; McKinsey, 2015). Car manufacturers are focussed on gaining market shares by reducing the number of platforms while increasing the total number of models and target a boarder customer basis. Holweg (2008) argues that ignoring inevitable market signals and market challenges by remaining with the current business model of volume production and fight for market share, rather than innovation, will not lead to long term success.
The increasing globalization of markets, scale advantages and model diversity will lead to shorter model life cycles, increasing production volume and higher competition. By pursuing traditional business models, automakers will inevitably run out of strategic options to obtain market shares (Holweg, 2008; Kienbaum, 2016; Nieuwenhuis & Wells, 2003). Wells and Nieuwenhuis (2013) further argue the importance of product innovation in emerging markets like India and China with economic growth.
The majority of OEMs today rely on aftersales business and financial services as profitable revenue streams. Around 18% of industry profits result from original part distribution while 14% root back to financial services. Only 3% of total profits result from new car retailing (Nieuwenhuis & Wells, 2003). Industry experts agree, that pursuing conventional business models will sooner or later result in continuously shrinking profit margins, overcapacity and increasing production complexity across the whole industry (Holweg, 2008; Nieuwenhuis & Wells, 2003; Roland Berger, 2016).
Economies of scale is a core aspect of conventional OEM business models. Establishing production plants and efforts in research and development require large capital investments which consequently pushes OEMs to consolidate resources. Growing through mergers and acquisition is a preferred way to quickly generate synergy effects and gain market shares. This development resulted in giant multi-branded OEMs of which the biggest ten account for 67% of total vehicle production worldwide in 2016 (OICA, 2018).
A study conducted by KPMG (2010) investigated mergers and acquisition activities in European automotive markets and confirmed the continuing global trend of brand consolidation. Between the years 1950 and 2000, independent vehicle manufacturing brands consolidated from 11 to three in Germany, 20 to two in France, 20 to zero in the United Kingdom and 19 to one in Italy.
Industry experts expect the consolidation trend to continue, however there is likely a shift to happen in mergers and acquisition between traditional OEMs towards new OEMs and non-automotive players (Kienbaum, 2016; KPMG, 2010).
A key driver of mergers and acquisitions is the cost saving effect of multi branded platforms. When manufacturing vehicles, multiple brands and models can be derived from a single platform, which increases the effect of economies of scale across the whole value chain as it saves resources in research, development and tooling. As a result, development phases are compressed and more products can be created at lower costs per model. General Motors demonstrates the effect of platform architecture by deriving 63 variants of pickups and sport-utility vehicles from the GMT-800 model (Well, 2010).
Shifting customer needs
According to recent publications by the United Nations department for economics and social affairs (n.d.) the world population is expected to grow from 7.6 billion in 2017 to 9.8 billion in 2050. Growth is mainly driven by emerging countries and accelerates global demographic change that shifts the customer basis from the babyboomer generation towards the generations y and z (Koushik & Mehl, 2015). The digital natives, a phrase coined by Marc Prensky (2001) describes a certain group across the generations y and z that is accustomed to the use of internet, computers and mobile devices from early age (Prensky, 2001). It is expected that their exposure and personal understanding of technology will have a massive impact on the global economy and consequently the automotive industry. Customer needs and expectations change towards an “asset-light” world with a shared economy mind set and industries have to adapt with product innovation and entirely new business models (Koushik & Mehl, 2015). Although availability of passenger cars, the driver’s license and mobility are still demanded, the millennial generations in urban areas are already changing their transportation patterns. The market for car sharing services is expanding at annual growth rates of 30% and reached a market size of USD 1.2 billion (Global Market Insights, 2018). Studies conducted by Roland Berger (2014) and Kienbaum (2016) in the European passenger transportation market confirmed the growing acceptance of a shared consumption culture and transportation as a service models in industrialized nations. The increasing urbanization rate is a key factor which requires OEMs to shift towards innovative mobility concepts. The share of citizens living in urban areas crossed the 50% mark in 2008 and continues a long term upward trend. Urbanization in developed markets is even ahead of that trend with 76% in Germany, 80% in France and 80% in Spain for 2016 (The World Bank, n.d.). Consumers shift towards more sustainable products and purchasing behaviour in form of on demand, utility-driven transportation that is tailored to meet individual consumer needs. Technology plays a major role in that context as a variety of new revenue streams emerge in digital services (Koushik & Mehl, 2015).
Digitization of the value chain
Telecommunication technology and mobile devices are continuously exploring new areas of application. By utilizing the internet as a way of transferring, processing and sharing information, countless possibilities to improve consumption and product experience emerge. It is considered a crucial aspect of contemporary business models. Highly successful startups like Uber and Lyft have proven that using technological advancements to create innovative products and serve a rapidly growing customer segment pays off with multi-billion dollar valuations (Koushik & Mehl, 2015).
OEMs can generate value through digitalization in two areas, digitalize internal operations and digitalize the customer experience. Both areas show potential contribution to higher profitability and the foundation for new digital business models. The partial digitalization of the existing value chain is critical to realize the full potential of digital value creation. A study conducted by Accenture highlights the positive impact of digitizing internal operations. Gissler, Oertel, Knackfuß, and Kupferschmidt (2016) argue that by the year 2020, OEMs can increase their profitability by 36.3% when accelerating digitization of core competencies (e.g. research & development, manufacturing & supply, marketing & sales or aftersales) and supporting corporate functions.
Defining new business models and digitizing the customer experience is the key to long term success for OEMs. Offering digital service platforms and on demand services through a digital ecosystem is just one way of redefining products (Gissler et al., 2016). Potential to create digital value also occurs in established and mature growing segments like the after sales service sector. Future retail, maintenance and repair services will highly rely on online communication, offering the customer individual service packages which are flexible in time and location. The increasing level connectivity of the vehicle allows over-the-air updates and maintenance, allowing OEMs to implement a new level of product ecosystem and service environment (McKinsey, 2015; NTT, 2017). Research findings reveal a growing customer segment with acceptance and demand for such digitized services (Kienbaum, 2016; McKinsey, 2015).
The majority of OEMs are already evolving their century old brandings from traditional car manufacturer to mobility provider to access emerging parts of the digital value chain. A survey conducted by KPMG reveals that 85% of OEM executives agree that on a long term basis, a digital ecosystem will generate higher revenues than the hardware of the car itself (KPMG, 2018).
In the recent past, a technology referred to as the Blockchain appeared to be a disruptive innovation with potential applications across all major industries. It gained public interest in 2008 as the underlying open source protocol to realise the Bitcoin cryptocurrency and was quickly adopted to establish alternative cryptocurrencies. Apart from this first utilization, many researchers see the technology as an enabler to redefine modern interaction, transaction and as the foundation of a revolutionized economy (Morabito, 2017; Swan, 2015).
To this day, there is no common definition of a Blockchain in scientific literature. Some researchers refer to it as a distributed data structure (Böhme, Christin, Edelman, & Moore, 2015; Finch, 2016) or the decentralization of markets (Swan, 2015) while others characterize it as a decentralized network of transactions (Morabito, 2017). Characteristics of Blockchain solutions in fact vary depending on their area of utilization, however a Blockchain in general combines all features of these definitions.
Simplified to its fundamentals, a Blockchain can be described as a log or ledger that documents all transactions among the participants of a network. All recorded transactions are linked and refer to its predecessor to form a sequence of transactions. Whenever a new transaction is added, the updated version of the ledger is shared with all participating entities across the network. Transaction verification is processed by the nodes of the network via a consensus mechanism. Once a new block of transactions is added to the chain, it cannot be removed or modified again. When transacting digital assets within a Blockchain network, no trusted intermediary or middleman is needed (Finch, 2016; Morabito, 2017; Swan, 2015).
The Blockchain is referred to as an emerging technology in its earliest stages of development and exploration. Aside from cryptocurrencies, no other area of application has yet achieved large scale recognition and economic impact. The main reason for that is the early stage of development and proof of concept of the technology, which has to compete with well-established systems and processes in many industries.
The prognosis is promising. Blockchain solutions that meet industry standards are expected to enter the market in five to ten years (Gartner, 2017). The market size for Blockchain applications increased from 241.9 million USD in 2016 to 411.5 million USD in 2017 and is expected to reach 7,683.7 million USD in 2020 (MarketsandMarkets, 2017).
The following chapter focuses on the key concepts of Blockchain, also referred to as distributed ledger technology, to create a detailed picture of how the technology works at its core.
The Blockchain technology was brought to life in 2008 when Satoshi Nakamoto published the whitepaper titled “Bitcoin: A Peer-to-Peer Electronic Cash System” and introduced Bitcoin to the public. Bitcoin is a peer-to-peer cryptocurrency that operates without financial institutions, arguably the root of modern Blockchain-based currencies and emerging Blockchain innovations. Nakamoto (2008) introduced the concept of identifying individual blocks within the Blockchain based on a hash that is created using the SHA256 algorithm on the header of each block (Antonopoulos, 2010). This mechanism of linking information based on a hash algorithm in fact refers to the concept of time-stamping digital documents (Haber & Stornetta, 1991). Although Nakamoto does not mention the exact designation in the original whitepaper, he mentioned that “blocks are chained” (Nakamoto 2008, S. 3), which led to the quickly settled name Blockchain. Although the technology was already introduced in 2008, Blockchain first experienced public interest when it appeared on the cover page of the magazine The Economist in October 2015, titled “The trust machine, How the technology behind bitcoin could change the world” (The Economist 2015, S.1).
The reason behind the design of Bitcoin was to perform three main purposes of traditional money transactions. It aims to simplify exchanges, eliminate centralized control, create a system of trust and store digital assets. However, Bitcoin is only the first of numerous possible application for Blockchain technology.
Morabito (2017) defines a Blockchain as “a distributed public ledger or database of records of every transaction that has been carried out and shared among those participating in the network” (p. 4). Each transaction of data in the public ledger needs to be authenticated by achieving consensus among the majority of members within the network. The Blockchain contains a verifiable record of every single transaction conducted within the network. Each block of data is linked to the previous block of data by using a timestamp that contains date, time and origin (William, 2016). The sequence of linking each block to its previous creates a chain reaching back to the very first block ever created, known as the genesis block (Antonopoulos, 2010). In consequence, individual users of the network are not able to modify any data within a Blockchain without validating it through the consent of the other members. Every time a new block is validated and attached to the Blockchain, it is broadcasted to the network and stored locally at the nodes. Once a block of data is attached to the Blockchain, information can never be erased (Crosby, Nachiappan, Pattanayak, Verma, & Kalyanaraman, 2016). Blockchains are often visualized as a vertical stack where the blocks are layered on top of each other where the first block, the genesis block, serves as the stacks foundation. The genesis block is the only block in the chain that doesn’t refer to a previous block as there isn’t any information or transaction to refer yet (Prusty, 2017).
From its parent application Bitcoin, many different flavours of Blockchain solutions have evolved. The concept of public Blockchains has already become a very popular topic of technology discussion, while systems with permissioned access are still in an emerging stage of development (Buterin, 2015). This section focusses on the different characteristics of public, consortium and private Blockchains, which are also referred to as permission-less, federated and permissioned Blockchains (Voshmgir & Klinov, 2017). All types share the basic functions of a Blockchain, being decentralized peer-to-peer networks and maintaining replicas of the shared ledger which is kept in synchronisation through a consensus mechanism. Nevertheless there are big differences when assessed in detail (Jayachandra, 2017).
In a public Blockchain, the number, identity or origin of participants is not regulated. Anyone has permission to join the network, access data, transact assets and participate in the consensus process (Buterin, 2015). The consensus mechanism typically includes an incentive to encourage more participants to join the network and to validate transactions. The incentive usually comes in the form of a digital currency or token, that is granted to the miner after successful validation. While the members of public Blockchains typically participate via cryptographic keys and do not share identity related information, transaction details are transparent and accessible to everyone. This high level of transparency refers to the lack of trust among the members. Any member of the network is permissioned to access, read and write data. The main purpose of common public Blockchains is to securely perform peer-to-peer asset transactions in a network of unknown participants without the need of a trusted third party (Morabito, 2017).
A constituting element of public Blockchains is the cryptographic key mechanism. The technology utilizes asymmetric cryptography to identify and authenticate users and transactions. Public cryptographic keys act as account numbers. Only the one who possesses the corresponding private key can access the transferred asset that is associated with a public key account. In order to be valid, every transaction has to contain a digital signature that was created with the corresponding private key from one account to another (Drescher, 2017).
As the consensus models relies on monetary incentives when validating new blocks, public Blockchains operate on basis of crypto currencies. Each time a new block is generated, the miner receives a transaction fee as a reward for offering computer processing power to serve the network.
Public Blockchains are expected to have a highly disruptive impact on many industries as they create an effective peer-to-peer network and allow transactions without the need for intermediaries (Jayachandran, 2017). Public Blockchains are in general considered to be fully decentralized (Buterin, 2015). Popular examples of public Blockchains in practice are Bitcoin, Etherium, Monero, Dash or Litecoin (Buterin, 2015; Morabito, 2017).
Consortium Blockchains are partly private and public as they operate under the governance of a group. The consensus mechanism is controlled by a pre-selected set of nodes within the network. This hybrid form between public and private solutions is best explained by using a fictional scenario of a consortium of 15 financial institutes, each operating an individual node and of which 10 must digitally sign every new block for validation. On the other hand, it is essential to grant public or restricted permission to the customer basis (Buterin, 2015). Consortium Blockchains are faster, provide a certain level of transaction privacy and are common in the banking sector (Voshmgir & Kalinov, 2017).
Private Blockchains are commonly referred to as “enterprise solutions” as the network consists of a semi-trusted foundation. Each node has to verify its identity and needs permission to join the network. The participants of the network need to obtain certificates of permission that regulate the access and validation of data (Jayachandran, 2017). The governance structure plays a significant role in private Blockchains as individual entities and consortiums can grant or deny permission of further participants. Regardless of individual permissions, each node within the network plays an equivalent role in maintaining the ledger in a decentralized manner. Contrary to public Blockchains, authentication of the members is a crucial aspect of private Blockchains, while transaction details are only visible with permission. The characteristics of proven identity and encrypted transaction details is best explained using a fictional example of a business to business solution, where the seller of a good negotiates individual prices with multiple buyers. The seller is permissioned to access all transactions details, while the buyer’s access to transaction details is limited to transactions they were involved in.
Finding consensus in a private network also works considerably different compared to public networks. The consensus mechanism usually does not include an incentive in the form of a digital currency or token.
Private Blockchain solutions are valuable for solving efficiency, security and fraud problems within traditional processes and collaboration setups. Private Blockchains rebuild an existing business infrastructure and allow semi-trusted entities to collaborate using one Blockchain solution. Optimization aims at streamlining the processes, availability and integrity of data. The most common provider of private Blockchains is IBM.
The innovative core of the technology is the concept of a distributed ledger, which is stored and maintained by the computer network itself. It is essentially a decentralized database that keeps track of the transaction history and is shared among the nodes of the network. Every time the ledger is updated it gets broadcasted among the members of the network to ensure a single point of truth (Morabito, 2017). Even though the technology was initially conceptualized to enable Bitcoin, it has been abstracted to utilize any type of distributed database technology to record data in chained information blocks. This network model creates a censorship and tamper resistance system (Morabito, 2017; Brennan & Lunn, 2016). As the network does not rely on a central entity, it avoids the risk of a single point of failure. Each node runs the same software, ledger and consensus which makes them dispensable by design. The principle of chaining blocks differentiates Blockchain technology from other distributed ledger technologies (Roßbach, 2015). Networks in which each participant has equal access and permission to add information are referred to as peer-to-peer networks. In contrast, traditional centralized networks and databases rely on a single point of authority to manage and distribute information. As mentioned in section 3.3, the level of decentralization can vary, based on the three types of Blockchains used for individual solutions. The following figure illustrates the shared ledger characteristics of private, public and consortium Blockchains.
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Figure 3: Shared ledger characteristics in distributed networks (Source: based on Brennan & Lunn, 2016 p. 41)
Traditional systems rely on a single source of information and an authority that manages access to information through a server-client relation. Hence each client has to rely on the authenticity of data when accessing the server information. In a public system, every node within the network holds a copy of the ledger and is permissioned to add information. Validity of information is granted by the implemented consensus mechanism. Limiting the group of nodes with permission to access data results in a consortium or private system, depending on the availability of data to the excluded peers (Brennan & Lunn, 2016).
The security of the consensus mechanism is one of the basic, crucial aspects that require attention to detail when developing Blockchain solutions as it maintains the sanctity of the recorded data on the Blockchain. A Blockchain can only uphold its immutability and auditability when the underlying consensus model can resist failure and adversarial conditions (Baliga, 2017).
Consensus mechanisms have been a topic of active research in the past three decades (Baliga, 2017). Baliga (2017) states that a consensus protocol has three key properties to evaluate its applicability and efficacy.
- Safety – A consensus mechanism is graded safe if all nodes produce the same output that is valid according to the rules of the protocol.
- Liveness – A consensus mechanism guarantees liveness if all non-faulty nodes participating in consensus eventually produce a value.
- Fault Tolerance – A consensus mechanism provides fault tolerance if it can recover from failure of a node participating in the consensus.
In essence, the consensus algorithm ensures that the upcoming block within the chain is the one and only version of the truth and prevents adversaries from derailing the system (Castor, 2017). This section reviews the most relevant and common types of consensus models that are already used by popular Blockchain platforms.
The proof of work (PoW) consensus mechanism was initially proposed by Dwork and Naor (1992) and aims to suppress junk mailings. It introduces the idea that one entity is in charge of performing a task before receiving a service from another entity. The Bitcoin Blockchain later implemented the hashcash concept, introduced by Adam Back (1997). To add a new block to the Blockchain, each node has to prove that it has performed a certain amount of work. The PoW concept is most commonly known in practice on the Bitcoin Blockchain, where each participant of the network adds a piece of data, referred to as “nonce” to the block to form a “block + nonce dependency” (Morabito, 2017). A validating node has to find a hash value that is less than a certain number, referred to as the difficulty setting by the network. This task is performed by constantly recalculation the nonce, relating to the blocks hash. The difficulty setting is dynamically tuned by the Bitcoin protocol that currently ensures a production period for new blocks every ten minutes (Baliga, 2017). The first node to find a valid hash claims the mining reward as an incentive to perform the PoW and adds its proposed block to the Blockchain. The process of validating new blocks and solving the PoW puzzle to find a winning hash value is also known as mining. The PoW model is commonly used in permission-less Blockchain environments where any number of nodes can participate in the network and start mining. The PoW consensus model provides a high level of scalability as no knowledge or authentication is needed of any participants (Baliga, 2017). However, finding a winning hash ties up a lot of resources as it is very time intensive and requires powerful hardware.
In a proof of stake (PoS) consensus model, the process of creating and validating a new block is assigned to nodes of the network holding the highest stake of the network’s value in percentage. The algorithm assumes the largest stakeholders of the network to be good agents with high incentive to be loyal and find consensus (Finch, 2016). The model ensures, that the power of validating blocks depends on reputation within the network, rather than the computational power of individuals (Asharaf & Adarsh, 2017). Although PoS and PoW share the same hash function, the manner of applicability is different. Calculating a winning hash in a PoS consensus model is done in a limited search space, which makes it faster to solve comparted to PoW, which works in unlimited search space. PoS is commonly used in both, permissioned and permission-less environments where nodes in the network have varying roles and privileges over transactions. It overcomes the disadvantages of PoW as it replaces the mining process and does not require as much resources. The PoS algorithm eliminates the threat of future hardware centralization. On the other hand, the model enables only the most “valuable” nodes, with highest incentives, to control the consensus process. That dependence can potentially create a lack of trust within the network (Finch, 2016). To overcome this issue, it is common that the developer team of a Blockchain solution retains a high amount of coins.
Castro and Liskov (1999) introduced the Practical Byzantine Fault Tolerance algorithm (PBFT) as the first practical solution to achieve a consensus when facing the Byzantine failure. The problem of the Byzantine generals describes a situation of combat planning, in which three generals can only communicate using oral messaging and have to agree upon a common battle plan. However, one or more of the three generals may be traitors who will try to confuse the others. It is shown, that the problem is solvable only if more than two-thirds of the generals are loyal (Lamport, Shostak, & Pease, 1982). The PBFT mechanism adopts this situation of battle planning on the challenge of finding consensus in a network that may contain disloyal nodes. The model uses the concept of replicated state machine and voting by replicas for state changes to achieve consensus.
It also provides important optimization features, such as signing and encryption of messages that are exchanged between the replicas and clients, reducing the size and amount of messages exchanged, for the system to be practical when facing the Byzantine fault. In order to function, the algorithm requires “3f+1” replicas to be able to tolerate “f” failing nodes (Baliga, 2017). PBFT is most commonly used in semi-trusted networks where the consortium consists of known participating nodes with verified identities. The most popular application of the PBFT consensus model is the Hyperledger fabric project, a permissioned Blockchain solution (Baliga, 2017).
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