傳統汽車在未來情境中之潛在代替品分析
全文
(2) Analysis of Potential Substitutes of the Traditional Automobile on the Basis of Future Scenarios Master’s Thesis. Thesis to achieve the degree of “Master of Arts” at the Munich Business School & “Master of Business Administration” at the National Taiwan Normal University. Submitted by: Philipp Leistner Agricolastraße 55c 80686 Munich. Course of studies: Master of Arts in International Business Master of Business Administration First Advisor: Prof. Dr. Heiko Seif (Munich Business School) Second Advisor: Dr. Alexander Suhm (Munich Business School) Third Advisor: Prof. Dr. David Chao (National Taiwan Normal University). Date of Submission: 6/29/2018.
(3) 2. Abstract German cities and municipalities are facing strong challenges. More than 75 % of the German population lives in cities and the trend of urbanization is continuing. Due to this development, cities are becoming increasingly crowded, infrastructures reach their physical limits and the environment is suffering heavily. Many cities are already facing these problems by trying to avoid further growth of these grievances by developing, for example, so-called ‘smart city’ strategies. One major reason for these issues is mobility. Currently, the privately-owned and fuel-powered car is one of the most popular vehicles for movement in cities. Moreover, most public buses are still powered by fuel, too. Even though industry and governments are pushing the development of e-mobility and the expansion of a charging infrastructure for electric vehicles, the process has not progressed enough to convince the population to fully switch to electric-powered vehicles and ensure convenient transportation. This thesis is elaborating in which direction urban mobility might develop in the next ten years by creating different future scenarios. These scenarios will be created on future projections done from two focus groups that elaborate on expectations surrounding urban mobility from the user’s perspective.. As a result of the analysis of current and future mobility, it can be said, that the style of locomotion in cities will change from an individual journey to a multimodal, efficient and seamless travel, where the one and only purpose is to reach the destination.. Keywords: future, mobility, Germany..
(4) 3. Table of Contents Abstract ........................................................................................................................ 2 Table of Contents ......................................................................................................... 3 Index of Figures ........................................................................................................... 5 Index of Tables ............................................................................................................. 6 Index of Appendix........................................................................................................ 7 1. Introduction .............................................................................................................. 8 1.1 Relevance and Problem ...................................................................................... 8 1.2 Target and Research Questions .......................................................................... 9 1.3 Structure ........................................................................................................... 10 2. Terminology ........................................................................................................... 11 2.1 Traditional Automobile .................................................................................... 11 2.2 Substitute .......................................................................................................... 11 3. Basic Examination of Scenario Planning ............................................................... 12 3.1 Definition and Historical Background ............................................................. 12 3.2 Scenario Funnel ................................................................................................ 13 3.3 Phases of Scenario Planning ............................................................................ 15 4 Scenario Planning.................................................................................................... 26 4.1 Scenario Preparation ........................................................................................ 26 4.1.2 Project Description .................................................................................... 26 4.1.2 Decision Field Analysis ............................................................................ 27 4.2 Scenario Field Analysis ................................................................................... 31 4.3 Scenario Prognosis ........................................................................................... 38 4.3.1 Key factor No. I: “Digital Transformation” .............................................. 39 4.3.2 Key factor No. II: “Urbanization”............................................................. 42 4.3.3 Key factor No. III: “Individualization” ..................................................... 45 4.3.4 Key factor No. IV: “Anthropogenic impact on the environment” ............ 48.
(5) 4 4.4 Scenario Development ..................................................................................... 52 4.4.1 Consistency Analysis ................................................................................ 52 4.4.2 Scenario Formulation ................................................................................ 54 4.5 Scenario Transfer ............................................................................................. 58 4.5.1 Recommendations for Cities, Municipalities and Politics ........................ 58 4.5.2 Recommendations for Car Manufacturer .................................................. 62 5. Conclusion and Outlook......................................................................................... 65 5.1 Conclusion ....................................................................................................... 65 5.2 Outlook............................................................................................................. 67 Sources ....................................................................................................................... 69 Appendix .................................................................................................................... 78.
(6) 5. Index of Figures Figure 1: Scenario Funnel .......................................................................................... 13 Figure 2: Eight steps of the Scenario Technique ....................................................... 16 Figure 3: Phases of Scenario Planning ....................................................................... 17 Figure 4: Consistency Analysis Diagram ................................................................... 23 Figure 5: Percentage of Transport Performance ........................................................ 28 Figure 6: Percentage Use of Different Means of Transportation on a Daily Basis.... 29 Figure 7: Number of Electric Cars in Germany, from 2008 to 2018 ......................... 31 Figure 8: Degree of Urbanization in Germany from 2006 until 2016 ....................... 35 Figure 9: Survey in Germany Regarding Attitude Towards Mobility ....................... 38 Figure 10: Consistency Analysis of Future Projections ............................................. 53.
(7) 6. Index of Tables Table 1: Frequency of Different Vehicles in Percentage ........................................... 30.
(8) 7. Index of Appendix Appendix I: Documentation of Focus Group Discussions……………………….....78 Appendix II: Handout Focus Group Discussions…………………………………...89.
(9) 8. 1. Introduction The first paragraph shall give the reader the necessary information about the intention of elaborating this study as well as the used structure. The first subphase of this chapter introduces to the identified problem and is explaining the relevance of this topic. The second subphase is defining the overall target of this work and is presenting the research questions that this work is trying to answer. The last subphase will constitute the structure which will be applied for answering the research questions from paragraph 1.2.. 1.1 Relevance and Problem The world is facing an environmental crisis (Vester, 1990, p. 15) and ongoing urbanization (UN DESA, 2018). According to the UN DESA (2018), more than 75 % of the German population currently lives in cities – and the population density of cities is further increasing. This permanent growth of cities is causing air and water pollution, the extinction of flora and fauna, soil erosion, etc. (Vester, 1990, p. 15). Cities and municipalities already face different challenges as housing shortage and overloaded infrastructure resulting from these problems as well as an ongoing lack of space in urban areas (Siemens AG, 2017). These problems arose not least because of the automobile (Sumantran, Fine, Gonsalvez, 2017, p. X and 49). According to Sumantran et al., cities have been designed for cars, whereby they actually should have been designed for people (2017, p. X). And even though society is aware of cars being one of the main causes of environmental problems, they still possess a high value and status in society due to their high flexibility – especially in Germany (Kirchbeck, 2017).. To maintain and increase the quality of life in cities, the auto industry, governments, cities and municipalities have already started to identify and implement certain strategies (Siemens AG, 2017). City administrations and politicians, in collaboration with the automobile industry, have started to enact certain regulations for “(a) reducing toxic and harmful pollutants by lowering tailpipe emissions; (b) improving fuel efficiency, thereby reducing dependence on fossil fuels and lowering CO2 emissions, and (c) improving the reuse and recycling of materials used in cars […]” (Sumantran et al., 2017, p. 50). Hence, new mobility concepts already were invented – such as car or bike sharing – that are partially electric (Brúch, 2015; Jaekel, 2013, p. 5; Verband der Automobilindustrie e. V., 2018). Due to the situation, holistic solutions that are.
(10) 9 able to increase the efficiency of existing infrastructures by using modern technologies need to be further developed and implemented (ADL & eco, 2017, p. 6).. Through the interaction of smartphones or digital infrastructure and mobility, traveling already has become more flexible and individual by providing a multimodal range of different means of transportation. Business models are changing from the focus of producing transportation modes or providing one specific transportation system for interaction. between. industries,. communities. and. users. (Verband. der. Automobilindustrie e. V., 2018).. 1.2 Target and Research Questions The primary objective of this scientific paper is the elaboration and demonstration of potential future developments in urban mobility. By applying the scenario technique from Gausemeier, Fink and Schlake, three different future scenarios will be created that form the basis of the ensuing recommendations for two parties – on the one hand the car manufacturers, and on the other cities, communes and governments. In the elaboration of this work the focus is on individual movement in large cities in Germany. According to the Federal Republic of Germany, a large city is a condensed settlement with more than 100,000 inhabitants (Gabler, 2011). Distribution and transportation of goods and other objects than people are not part of this work. In addition to these distinctions, the time horizon of the research has been determined at ten years from 2018.. For conducting this work, the following research questions were formulated as a basis: •. What will mobility in Germany look like in ten years?. •. How will people living in large German cities travel in ten years?. •. Which technologies will be state of the art in ten years?. •. What do cities need to change in order to be prepared for future urban mobility developments?. •. What changes do providers of transportation need to undertake?. •. Will the traditional automobile still play a central role in urban mobility?. •. Which concrete recommendations for cities and manufacturer or traditional cars can be derived from future scenarios?.
(11) 10 1.3 Structure This paper is divided into five chapters. The second chapter provides terminological definitions that need to be clearly defined to meet the actual objective. In this chapter, the knowledge required to set the right frame of understanding will be defined.. After the definition of certain terminologies, the methodology, the structure and concept of this scientific work will be presented. First, the scenario technique in general will be presented and its historical background provided, followed by an explanation of the scenario funnel. In the last part of the third chapter, different approaches of scenario techniques will be shown and their different steps explained in detail. Following the introduction of different scenario techniques, one approach will be chosen. This chapter also argues for certain modifications of the approach of the scenario technique that were done by the author. Amongst other adjustments, this section also explains the procedure of focus group discussions that were conducted as one part of the scenario creation.. The fourth chapter deals with the actual performance of the scenario technique, resulting in different recommendations basing on the identified scenarios. This section executes each single step that was predefined. An additional part of this phrase is the identification of key factors that influence mobility the most, as well as presenting of their current situation. The current situation sets the starting point for the research thereafter. Afterwards, each key factors’ future development along specific characteristics will be elaborated upon. These elaborations form the basis of each scenario.. In the fifth and last chapter, this work will be reflected upon and a conclusion resulting from the elaborated recommendations formulated. Finally, the author will draw up a potential outlook on urban mobility..
(12) 11. 2. Terminology In this section, two terminologies – the traditional automobile and substitute – will be define, or order to establish the right parameters regarding the context of this work.. 2.1 Traditional Automobile The traditional automobile stands for individual, automatic locomotion (Schmidt, 1999, p. 22). It is the epitome of individualization and flexibility (ibid., p. 38) and its usage is a mixture of purpose and pleasure (Pelters, 2009, p. 116). The automobile – more commonly known as the car – is used to bridge the distance between, for example, home and work or other destinations in daily use. On the other hand, the car also can be used for leisure time and activities (ibid.). Schmidt (1999, p. 22) stated that the automobile often becomes equated with mobility. “The automobile has been central to human mobility systems for over hundred years” (Sumantran et al., 2017, X).. When defining the traditional automobile in the context of this work, it is a fuelpowered vehicle that is privately-owned and used on a daily basis for leisure or business. The traditional car is an isolated room on wheels (Jaekel, 2013, p. 120 & 130).. 2.2 Substitute Black et al. (2017) suggested that “at an informal level, one good or service is a substitute for another if it can be used to satisfy the same need, or at least a similar need.” In the field of study of this work, a substitute of the traditional automobile is any means of transportation that can carry people or goods from one point to another like the automobile does. In this study, the focus remains on mobility in cities. A substitute of the traditional automobile is, for example, public transportation. Public busses or trains enable the user to travel from one point to another. Similar to busses and trains are taxis, transporting people and goods to any destination on demand. Furthermore, fully autonomous or flying cars are also seen as substitute of the traditional automobile. Finally, walking, bicycling and driving a scooter are substitutes of traditional automobiles in terms of travel and as means of transportation in cities, too..
(13) 12. 3. Basic Examination of Scenario Planning In this chapter, the term scenario planning will be defined and historical derived. After this, the scenario funnel will be explained, followed by a short introduction to different scenario planning methods from different authors. At the end of this chapter, one approach will be chosen as basis of this work. The different phases of this specific type scenario planning method will be explained in detail.. 3.1 Definition and Historical Background The term scenario planning, scenario analysis or scenario thinking – as it also is named in different sources – is a strategic method for companies, governments, people, institutions, etc. to think about the future in a structured manner. Hence, scenario planning is a strategic planning method.. Scenario originally comes from the Latin word scena, which translated means ‘scene’ (Soanes, 2004, p. 1284). Soanes (2004, p. 1284) defined the term scenario as “a written outline of a film, novel, or play giving details of the plot and individual scenes. A setting, in particular for a work of art or literature.” This definition shows the background from the theater and film production. Gausemeier, Fink and Schlake (1996, p. 90) defined scenario as a generally understandable description of a possible situation in the future based on a network of complex influencing factors. Bea and Haas (2017, p. 304) further described scenario as a description of future developments of projections with alternative frame conditions.. In the early 1950s, Herman Kahn, an American futurist (Bea and Haas, 2017, p. 303), used the term scenario as a description for military experimental games (Reibnitz, 1991, pp. 11 f.). Kahn was working at the RAND Corporation, an institution for futurology, founded by the American Defense Department (Gausemeier et al., 1996, p. 91). In the 1960s, Kahn switched to the Hudson Institute, a conservative institute for public policy research. At this time, he and Anthony J. Wiener developed “scenario writing”, a method for scenario creation that they published in a study in 1967 (ibid., p. 92). This was the transfer of the scenario from the theater into economics and social science (ibid., p 91). In the early 1970s, scenario planning began to boom because of the oil crisis and its economic effects – prior to this, basic prognoses were sufficient. Due to the upcoming uncertainty based on the oil crisis, companies started to think.
(14) 13 more and more about the future and how to possibly predict it in certain manner, wanting to be prepared for potential oil scarcities and other unexpected events. Companies such as Royal Dutch (Shell) started to use scenario planning. Shell is seen as the pioneer in terms of scenario planning: they started to create future scenarios with a more qualitative approach than used earlier (ibid., p. 12; Bea/Haas, 2017, p. 308 f.). Later, with the introduction of the scenario technique or scenario planning, scenarios became popular and were used by other companies, too. First, it was used by industries harmed from the oil crisis, such as oil companies, the chemical or automobile industry. (Reibnitz, 1991, p. 13). 3.2 Scenario Funnel Figure 1 shows a simple illustration of the Scenario Funnel. The small circle on the left shows the starting point of the scenario development, the present. The bigger circle in the right side shows the future. The funnel, with its development from a small to a big circle, displays the uncertainty and complexity of the future. The further away from today the considered future is, the more uncertainties and unexpected developments can occur. Within that time horizon, the number of potential influence factors – like competitors, regulations, economic situation, etc. – increases more and more, which implies an increasing amount of prospective developments and potential scenarios. Currently, those influence factors are comprehensibly assessable. (Reibnitz, 1991, p. 26). Figure 1: Scenario Funnel (own illustration in the style of Reibnitz, 1991, p. 27). As it can be seen in Figure 1, different scenarios can be build up within the Scenario Funnel. The horizontal line in the middle of the funnel explains the Trend Scenario. A.
(15) 14 trend scenario can be perceived as exploration of the current situation (ibid., p. 28). Reibnitz (1991, p. 28) advised against trend scenarios, because they mislead to just expect this one specific future, where not much needs to be changed: the frame conditions stay similar and not much unforeseen will happen. Reibnitz (1991, p.28) further mentioned that this is exactly the problem with trend scenarios. If, against the expected trend scenario, some disruptions occur, societies, companies or people will be confronted with incidents they are unprepared for. As Zerres, Wolf and Zerres (2014, p. 19) state, it is highly unlikely, that no disruptions will occur in the predefined time horizon. For that reason, it is recommended to use trend scenarios only for shorter forecasting horizons, where the amount of possible unexpected disruptions is limited.. Beside the trend scenario, two extreme scenarios can be seen in Figure 1. Those two scenarios form the borders or the frame of the scenario funnel. In different sources, those two extreme scenarios are also labeled as best-case or worst-case scenario. Because those two labels are judgmental in a certain sense, they are named as extreme scenarios in this work. The judgment of each scenario is in the hands of the reader or creator of the scenarios and depends on his purpose and set of criteria. Extreme scenarios picture as much as possible development potential to present the most extreme outcome (Gausemeier et al., 1996, p. 241). Extreme scenarios are extreme, visionary and rather unlikely pictured. Their purpose is to show the most extreme future dimension to avoid unexpected surprises. Trend scenarios, on the other hand, are more feasibly created, enjoying a higher acceptance rate than extreme scenarios because they are much closer to the present and thus more tangible. (ibid., p. 224 f.). Figure 1 shows three different scenarios: Trend Scenario, Scenario A, and Scenario B. Scenarios A and B are just two out of plenty possible scenarios within the frame of the topic. Reibnitz (1991, p. 28) recommends the creation of two scenarios, which need to fulfill the following three criteria: •. Highest level of coherence and consistency within a scenario (the different developments within the scenario may not cancel each other out). •. Each scenario should have the highest level of stability. Stability means that the scenario is not going to collapse as soon as small shocks occur, but rather resists them..
(16) 15 •. Both final chosen scenarios should have the highest possible dissimilarity, meaning that each scenario should be able to place at one of the extreme borders of the funnel.. These three criteria confirm the statement of Reibnitz, that only two scenarios are enough, and a trend scenario is not necessarily needed. In this work, against Reibnitz, a trend scenario will be created in addition to two extreme scenarios.. 3.3 Phases of Scenario Planning Scenario Planning is subdivided into different phases. However, the number of phases varies between different authors. In the following, the two most common models will be introduced shortly. The first model is from Ute von Reibnitz, who divided the process of Scenario Planning into eight phases. Gausemeier, Fink and Schlake on the other hand, separated their model into five phases. In this work, the model of Gausemeier, Fink and Schlake will be applied and explained in greater detail. The proceeding of Gausemeier et al. will be modified at some specific points. Those modifications are explained in the appropriate position. In the following, the eight phases of von Reibnitz will be introduced shortly but not in detail.. Ute von Reibnitz (1991, p. 30 ff.) Reibnitz subdivided the process of scenario planning into eight phases. As Figure 2 shows, those phases were Task Analysis, Influence Analysis, Trend Projection, Bundles of Alternatives, Scenario Interpretation, Consequence Analysis, Analysis of Disruptions and the Scenario Transfer..
(17) 16. Figure 2: Eight steps of the Scenario Technique (Reibnitz, 1991, p. 30). In the following, those different phases will be shortly explained:. 1. Analysis of the present situation of the object of examination (company, strategic business unit, group of products, etc.) 2. Definition and evaluation of external influence factors, such as competitors, technology, economy, society, etc., that in the future might have an impact on the object of analysis 3. Development of descriptors (parameters), that describe the present condition, based on the identified influence factors from phase 2 4. Proof of consistency of the identified alternatives in phase 3. The results are consistent, but with stable different scenarios 5. Scenarios will be created and interpreted, based on the outcomes of phases 3 and 4 6. Deduction and evaluation of opportunities and threats for companies followed by the identification of suitable measures 7. Identification and evaluation of potential internal and external disturbing factors, and identification of measures to prevent those disruptions 8. Formulation of strategies and establishment of a measurement and monitoring system. Gausemeier et al. (1996, p. 101) The Scenario Planning process from Gausemeier, Fink and Schlake was subdivided into the following five phases: Scenario Preparation, Scenario Field Analysis, Scenario Prognostic, Scenario Development and Scenario Transfer. As Figure 3 shows, phases.
(18) 17 2 to 4 were more critical and marked as Scenario Creation (Gausemeier, 2004, p. 55). Figure 3 also gave information about the results of each phase, and the outcomes reached from the Scenario Base up to Strategies (ibid., p. 55).. Figure 3: Phases of Scenario Planning (Gausemeier/Fink/Schlake, 1997, p. 6). Scenario Preparation (Phase 1) According to Gausemeier et al. (1996, p. 125), phase 1 was subdivided into two different parts: project description and decision-field analysis. The project description gave basic information about the scenario planning; the project definition consisted of a description the kind of scenarios that would be created (extreme or trend scenario) and how (for example, if there was a team creating the scenarios or whether the author himself created them). Additionally, this phase answered the questions on what time horizon the scenarios would be made and what the core questions were that should be answered by the scenario planning. As Gausemeier, Fink and Schlake (1998, p. 6) mentioned, the second part of the scenario preparation – the decision-field analysis – was for analyzing the particular object of the scenario. Those objects could be companies, products, societies, technologies, etc. At that point, the current situation of the object would be analyzed. According to Gausemeier et al. (1998, p. 6), “the result.
(19) 18 of the scenario preparation is called scenario base, because it is the starting point of the scenario creation (phases 2 to 4) and transfer (phase 5).. Scenario Field Analysis (Phase 2) As shown in Figure 3, the scenario field analysis is the first phase of the actual scenario creation. The target of the second phase is the identification of key factors forming the basis of the scenario creation. Gausemeier et al. (1996, p. 167) subdivided this main phase into the following three subphases: creation of areas of influence, creation of influence factors, and identification of key factors.. Creation of areas of influence The scenario field is subdivided into different areas (ibid., p. 167). Gausemeier et al. (1996, p. 171) distinguished the areas of influence between steering (internal) and environment area (external). Steering areas, such as company divisions as marketing, research and development or human resources can be influenced by the scenario object/company and environment areas while technology, markets, demography, cannot be influenced by the scenario object/company.. Creation of influence factors At this point, for each influence area different influence factors will be identified. Those influence factors have to have the characteristic of explaining the current condition as well as the opportunities of future developments of the different areas of influence. (ibid., p. 167) After identifying the influence factors, those factors will be described without evaluation and finally verified. The result is a catalog of influence factors that describes the scenario field (ibid., p. 173). “The identification of influence factors is a creative process […]. This process can be supported by specific methods like brainstorming, brainwriting or the 6-3-5 method” (Gausemeier et al., 1998, p. 8).. Identification of key factors The last step of phase two is the filtration of the most suitable influence factors from the rest (Gausemeier et al., 1996, p. 189). Most important for this filtration is the direct and indirect influence analysis. The difference between the direct and the indirect.
(20) 19 influence analysis is whether it examines the direct or indirect relationship between different influence factors (ibid., p. 191).. In this work, phase two is performed differently. Instead of the following three phases according to Gausemeier et al. – creation of areas of influence, creation of influence factors and identification of key factors – the assumption is made that relevant megatrends relating to mobility are used as key factors. Megatrends are used as key factors, since certain megatrends consistently appear during literature research. For example, urbanization appears in different sources as an influencing factor of mobility (Jung, Kraft, 2017, p. 227; Popp, Schüll, 2009, p. 320; Phleps, Feige, Zapp, 2015, p. 98). Nonetheless, the more detailed explanations and answers to the question why certain megatrends were used as key factors and what their impact is on mobility will be presented in section 4.2, when the scenario field analysis is performed.. Phase two is performed in one single phase with two subsections. First, megatrends will be defined and introduced. Second, relevant megatrends that have an impact on mobility will be chosen, followed by an explanation. Afterwards, those chosen megatrends will be presented in their current situation.. Scenario Prognostic (Phase 3) In the third phase the actual foresight happens, as Gausemeier et al. (1996, p. 221) stated. During phase three, the key factors that result from phase two will be analyzed and their different future developments elaborated. Furthermore, those future prognostics – as Gausemeier et al. (1996, p. 221) called them – will be mentioned and justified. This is one of the main steps during the scenario planning, given that these future prognostics constitute the basis of the subsequent future scenarios (Gausemeier, 2004, p. 56). This phase is subdivided into two different phases: preparation of key factors and creation of future projections (Gausemeier et al., 1996, p. 248).. Preparation of key factors In the first sub phase, the dimensions or characteristics of each key factor are prepared and defined. It is essential to form those dimensions in a manner in which they are able to describe the current as well as the future situation of the key factor (ibid., p. 228). Gausemeier et al. recommended finding two characteristics per key factor. More.
(21) 20 characteristics would make it more difficult to comprehend and visualize the key factors (ibid., p. 229). After identifying the characteristics, each key factors’ current situation will be captured and specified. The analysis of the current situation is a crucial moment because it constitutes the starting point of the future scenario. For a later evaluation and classification of each scenario, the definition of the current situation need to be consistent and comprehensible. (ibid., p. 230). Creation of future projections In the second sub phase of Scenario Projection the analysis starts (ibid., p. 248). Gausemeier et al. (1996, p. 248 f.) subdivided this phase into four different steps: •. The first step is the determination of potential future projections through: extrapolation of the evolution, hyperbolism of the development, conscious speedup of the development, conscious involvement of developments of the environment and development of future projections out of processes.. •. During the second step one to three future projections will be selected. Key factors with just one projection are referred to as uncritical key factors and the rest of them as critical key factors.. •. The third step assigns an occurrence probability to each critical future projection.. •. The fourth and last step is the formulation and justification of each future projection. The thorough formulation of the future projections is crucial for the later understanding and confirmability of the different scenarios.. Against the approach of Gausemeier et al., the second subphase – the creation of future projections – is performed by two focus groups in two group discussions. According to Kosow, Gaßner, Erdmann and Luber from the Institute for Future Studies and Technology Assessment (2008, p. 56), trend-based scenario techniques and key factorbased scenario techniques as well as systematic-formal scenario techniques and creative-narrative scenario techniques can be mixed with each other or influence each other. In this context, the creation of future projections is conducted with a creativenarrative technique. The focus groups consist of current and future customers of transportation, like a private car, shared car, bicycle, public train and bus, etc. For the group of experts, people were consciously chosen that on the one hand have the.
(22) 21 attitude of a “first mover” and an “early adopter”, but on the other are “normal” citizens that use different kinds of transportation vehicles every day. It was consciously decided to use people that do not have a specific connection to mobility, such as employees of Original Equipment Manufacturers (OEMs) or city administrations. The purpose was to choose people that can represent the populace but also have a creative and resourceful mindset. In this context, this means the participants are willing to use new innovations right from the beginning or shortly after launch and also think about prospective developments. The opposite of a first mover is someone that is waiting until a product is made to perfection and field reports are available. For the context of this work, innovators were needed, that are willing to use creative techniques and are able to think one step further. The two groups consist of people aged 23 to 33. All participants currently use different kinds of transportation – for example, privatelyowned car, bicycle, shared car and public transportation. The professions of all participants were different and a mix of business consultants, an entrepreneur, a patent attorney, a medicine student, an assistant of a Chief Executive Officer from a mediumsize company, an employee of a car rental and an employee of startup for “smart parking”.. For reasons of acknowledgment and comparability, the focus group totaling eight participants will be split up and two separate workshops with four members each will be conducted. With this method the results of each workshop can be compared and verify each other. As method for the projection of each key factor the 6-3-5 method will be applied. The 6-3-5 method is a creative technique that is similar to brainstorming (Schawel, Billing, 2014, p. 301). Since the creation of future projections is a creative task – especially when thinking about extreme scenarios – this method is suitable. Typically, 6 people will be asked to create 3 solutions within 5 minutes. After five minutes the sheets will be swapped with each other. During the next five rounds, everyone has to further develop the ideas from his antecessors. After 30 minutes, 18 solutions will be created and developed by all six participants (Gausemeier et al., 1996, p. 180). Since in this work four key factors with four dimensions each form the basis of the scenario creation, this method will become adjusted to 4-4-10: That means that each participant will become one key factor and has to fill out all four dimensions. After ten minutes each participant will pass his results over in clockwise direction to the next member and receives another key factor from another member in return. This.
(23) 22 action will be executed until each participant had filled out each single key factor once. After 40 minutes, 80 results or key factor outcomes and 16 future projections will be created. The advantage of this method is that within a short time period an array of future projections will be created, and each participant has to think about each single key factor – even when some of them not belong to his field of expertise (ibid., p. 185). After the execution of the 6-3-5 method (4-4-10 method), all outcomes will be presented and discussed by the group and afterwards consolidated into one graph.. Scenario Development (Phase 4) The goal of phase four is the creation of significant scenarios, based on the identified future projections from the previous phase (Gausemeier, 2004, p. 58). Gausemeier et al. (1996, p. 251 ff.) are subdividing this phase into the following four subphases:. Bundling of projections First, combinations of different future projections from different key factors will be made. According to Gausemeier et al. (1996, p. 253), exactly one future projection from each key factor needs to be used for the bundling. These bundles need to be consistent and plausible, which will be verified by a consistency and plausibility analysis (ibid., p. 251).. Formation of raw scenarios After the bundling of projections, the consistent bundles will be consolidated to raw scenarios, according to its similarity. This consolidation will be made by a cluster analysis. At the end of this step the raw scenarios will be specified in a raw scenario catalog (ibid., p. 217).. Future mapping The future mapping is used to visualize the different future bundles and scenarios in a future space.. Scenario description The description of the scenarios is the last step of the scenario creation. At this point, the different scenarios will be described and interpreted in detail. “The scenario.
(24) 23 description can be completed by the identification of disruptive factors or events, robustness, and sensitivity analyses” (Gausemeier et al., 1998, p. 13).. Similar to the second subphase of phase 3 (creation of future projections), phase four is – contrary to Gausemeier et al. – realized in a creative-narrative approach. That means that the consistency analysis is not performed in a consistency matrix – as Gausemeier et al. describe it – but during a workshop with a focus group through brainstorming and discussion. As described in phase three, these participants will create the future projections of each key factor and, according to the fourth phase, connect these projections to create consistent scenarios. This consistency analysis is done by allocating each projection into one of four quadrants described through the axes Progression and Complexity of realization, as it can be seen in Figure 4. The value of each parameter can be high or low.. Figure 4: Consistency Analysis Diagram (own illustration). Progression describes the degree of change that each single future projection means for the world and its mobility. Complexity of realization describes the complexity of the realization and development of each projection. Figure 4 pictures two main directions – on the one hand the present, when progression and complexity of realization are low, and on the other the future, when both axes describe a high value. The advantage of this participative-communicative method is that different sources of.
(25) 24 information and creative input of different people will be included. Each participant has different background knowledge and fields of interest (Kosow et al., 2008, p. 54 f.). By means of this more creative and intuitive approach the amount and the range of potential future projections will become greater than when the author would perform this himself. To ensure a high quality of outcome of the group discussions, the participants will be chosen carefully.. The result of the focus group discussions will be several possible scenarios that potentially could occur. In collaboration with the focus group, three final scenarios will be created, by allocating each projection into one quadrant. The projections, that are assigned to the quadrant of low and low value describes the trend scenario. Contrary to that outcome, the projections that are assigned to high and high or high and low value form the extreme and visionary scenarios. Out of all potential scenarios, one trend and two extreme scenarios will be chosen. These three scenarios will be described and interpreted in detail, similar to the fourth sub phase of the approach of Gausemeier et al.. Scenario Transfer (Phase 5) According to Gausemeier et al. (1996, p. 321), the previously created scenarios form the basis for essential decisions companies or cities have to make. Gausemeier et al. divided this transfer from scenarios to strategic business management into three different steps:. Impact analysis In the framework of the impact analysis the consequences of each scenario are determined. Resulting from this analysis, strengths and weaknesses of the scenarios can be identified, later leading to strategies, measures and recommendations. During this analysis, disrupting events can be included to align the strength and weakness analysis as well as the potential strategies. (ibid., p. 321).
(26) 25 Contingency planning Based on the strengths and weaknesses, concrete measures will be derived, that use the strengths at its best and minimize the identified risks. The identified measures form different contingency plans for each field. (ibid., p. 339). Robust planning During the last step, the previously created contingency plans will become conjoined to robust plans (strategic plans), which will meet different scenarios (ibid., p. 381).. Different to the approach of Gausemeier et al., in this work the scenario transfer will be executed within one step. Recommendations will be derived from the identified scenarios. These recommendations address cities and automobile manufacturer, both as mobility provider. The target of this chapter is to form the basis for cities and manufacturer to derive future strategies from these recommendations..
(27) 26. 4 Scenario Planning In this chapter, the scenario planning described in the previous chapter will be performed and described. Certain steps deviate from the approach of Gausemeier et al. according to the specific explanations in Chapter 3. The change of single steps was made to switch from a pure systematic technique to a mix of systematic and creativeintuitive techniques. Using a creative and intuitive approach, a greater amount of potential future outcomes will be created and evaluated, thereby leading to a greater range of projections.. 4.1 Scenario Preparation 4.1.2 Project Description Companies as well as cities and municipalities are faced with steadily changing surrounding conditions via endogenous and exogenous influence factors, such as regulations, technological developments, changing mindset of the population, environmental events, etc. As a result of this complex and dynamic situation where unexpected and unforeseen events can occur, companies, cities, municipalities and governments need to think about potential future developments sufficiently early in order to be reasonably prepared. This effects, among others, mobility and, in particular, the automobile industry. In this respect, questions that need to be answered and this study attempts to answer are: What will mobility in Germany look like in ten years? How will people living in large German cities travel in ten years? Which technologies will be state-of-the-art- ten years from now? What do cities need to change to be prepared for the future developments of urban mobility? What changes do providers of transportation need to undertake? Will the traditional automobile still play a central role in urban mobility? Which concrete recommendations for cities and manufacturer or traditional cars can be derived from future scenarios? It is not this work’s intention to forecast how the world will look like in ten years and how mobility and transportation or peoples’ needs might change. With striking and visionary future scenarios it shall be attempted to prepare companies, cities, governments and individuals for potential future changes. These stakeholders need to align their future strategies and strategic decisions by examining future developments. As a result of the process of scenario creation, three different scenarios will be created: one trend.
(28) 27 scenario and two more extreme, visionary scenarios, that strikingly will demonstrate two possible future developments.. 4.1.2 Decision Field Analysis In this study, the following two decision fields will be analyzed: car manufacturer and cities. These two decision fields constitute the basis for the resulting recommendations. Car manufacturer includes OEMs as car producer and OEMs as provider of car sharing and rental cars. Cities, on the other hand, are providers of public transportation like trains, subways, buses, etc. The following description of the current situation of each decision field will be examined and presented together, since their separation is a smooth transition. Both cities and manufacturer offer transportation alternatives to the citizenry. On the one hand, even though OEMs are the manufacturer of cars, they face the same customers as transportation alternatives provided by cities. On the other, OEMs are extending their product portfolio from just producing and selling cars and bikes to offering services as car sharing or car renting.. On average, people are stuck in traffic in Los Angeles 5.5 days per year (Sumantran, Fine, Gonsalvez, 2017, p. 3). That, of course, is not surprising, considering what commuting in big metropolises looks like today. As Sumantran et al. (2017, p. 3) further mentioned, the average speed that people drove on the Interstate 101 was 17 mph, while 65 mph is allowed. This is one aspect of the world’s current mobility situation. Another feature – even though it is mainly a problem in Asian, especially in big metropolises like Beijing, Shanghai or New Delhi – is smog (ibid., p. 5). “Smog is the outcome of a variety of industrial and automotive emissions that releases nitrogen oxides into the atmosphere, […]” (ibid., p. 6).. But this is just one aspect of the present. In the following, the current situation of transportation in German cities is presented and proven by some statistics. Figure 5 shows the dispersion of the different transportation facilities with its percentages used in Germany. It can be seen that 76 % of the passenger transportation in 2010 was made by car and, in comparison, 7 % by train and bus, respectively. Locomotion by bicycle and by foot represented 3 %, respectively..
(29) 28. Figure 5: Percentage of Transport Performance (Hütter, 2013, p. 7). In comparison to Figure 5, Figure 6 shows what kind of vehicle the people that live in big cities (this statistical survey was made in Germany’s ten biggest cities) in 2017 used on a daily basis. With 25 % each, cars and subways are the leader of this comparison, followed by the public buses, with 24%. Commuter trains and urban railways both routinely have a percentage of 18 % of the daily traveler, while train is used by 7 % of daily commuters. Since the metropolitan areas are growing bigger and bigger the number of commuters is in particular increasing steadily. According to Andreas Weck (2017) 18.4 million people in Germany commute. Rents in cities as Munich or Berlin are increasing constantly, so people have to move farther away from the city center to afford living, with the result of regularly commuting to their workplace. Weck (2017) further mentions that 26 % of the working population has a minimum commute of one hour. The ADAC e.V. (2018a) said 68 % of the working population is driving to work by car, and if the distance is greater than 25 km up to.
(30) 29 84 % take the car. ADAC (2018a) further mentions that most of those commuters drive alone – the average number of people in a private car is 1.18.. Figure 6: Percentage Use of Different Means of Transportation on a Daily Basis (Statista, 2017). This statement gets underscored by Table 1. As can be seen in this table, carsharing has not yet fully arrived in Germany. 1 % percent of the interviewed persons use carsharing on a daily basis or several times a week. But in contrast, according to this statistic to date 79 % have never used carsharing. This statement was confirmed by the Bundesverband CarSharing e.V. (2018), which stated that 2.1 million users are registered on 165 different carsharing platforms – 3.8 % from a total of 55.9 million licensed drivers in 2017 (Statista, 2017). The Bundesverband CarSharing e.V. further mentioned that 17,950 carsharing cars are available that combine free-floating and station-based cars. In comparison, 52 % of driving people use their private car every day while 14 % never use private vehicles– or rather don’t have one. Even though people in Germany still prefer driving by themselves in their privately-owned car, the kind of car they are driving is changing..
(31) 30. Table 1: Frequency of Different Vehicles in Percentage (Statista, 2018b). Corresponding to the Kraftfahrt-Bundesamt (2018), Germany has 46.5 million registered cars. This includes gasoline and diesel engines, hybrid and natural gas cars as well as electric cars. As in Figure 7 it can be seen that in 2018 53,861 electric cars were registered. This is a percentage of 0.1 % from the total of 46.5 million cars registered in Germany. To increase this number, the German government is offering different subsidy programs. Among other things, the purchase of electric cars is supported by the government – a grant of up to € 4,000 per car is paid. (Schwarzer, Breitinger, 2016; Bundesministerium für Wirtschaft und Energie, n.d.) Moreover, the government is providing € 300 million for the development of a charging infrastructure (Bundesministerium für Wirtschaft und Energie, n.d.). Olivier Reppert, CEO of the German carsharing provider Car2Go, states, that carsharing can help promote the development of Germany’s e-mobility market (Michael, 2018). Electric carsharing might help people to get to know electric cars better without commitment at a start and might push the extension of a charging infrastructure, Reppert further states. Currently, 10 % of all carsharing cars are electric (Bundesverband CarSharing e.V., 2018). That is 100 times as much as in the private car sector (when comparing percentages). The change that Olivier Reppert mentions is currently needed more than ever, given the numerous scandals surrounding diesel engines that have occurred in the past few months and years. A couple of German car manufacturer were accused of falsifying the exhaust emission data of their diesel cars. Since then, a ban on diesel.
(32) 31 cars in cities like Munich, Berlin or Hamburg is being discussed and has already been partially executed (Koch, 2018).. Figure 7: Number of Electric Cars in Germany, from 2008 to 2018 (Kraftfahrt-Bundesamt, 2018). One step further in the direction of modern mobility is autonomous driving. Germany and the United States are pioneers of autonomous driving in terms of technology (Roland Berger GmbH, 2017, p. 7). Germany has its strengths in the technological know-how and is already using different automated driving functions in their cars. Nevertheless, the government and its formalities slowdown the further development and implementation of autonomous features (ibid., p. 9). But one pilot project for autonomous driving in Germany has been successfully running since October 2017. Deutsche Bahn, Germanys main train provider, has started a project with an autonomous driving public bus in Bad Birnbach, a small town in Bavaria (Fuhrmann, 2018a). These buses drive very slowly and only short distances – but it is a start. Since this project has been successful and without accidents, another field test will be conducted in Hamburg (Fuhrmann, 2018b).. 4.2 Scenario Field Analysis As described earlier in this study, megatrends will be used as key factors. The reason is that megatrends are more or less influencing the world, behavior and thinking – they influence people, societies, politics, economies and cultures (Gatter, Schuldt, Varga,.
(33) 32 2014, p. 6). But megatrends are, on the other hand, influenced and created by people, societies, politics, economies and cultures. Megatrends don’t need to be predicted because they are already there. They mark the change, that has been, is and will happen. Megatrends embrace several decades and influence people and the society. (Müller, 2017) Müller (2017) further stated that megatrends are not dependent on technologies or tech trends. According to Horx (2007, p. 1), megatrends are the ambassadors of change. Horx (2007, p. 1) defined four general conditions of megatrends: •. Impact: A megatrend should last at least for 30 years. •. Ubiquity: A megatrend creates signals in all areas of life, is omnipresent, and develops factors in the economy, living environment, consumption, etc.. •. Universality: A megatrend has an underlying global character, even when its outcome is different in different cultures and religions. •. Robustness: A megatrend tolerates backlashes, without losing its dynamic. As the Zukunftsinstitut GmbH (2016a) stated, megatrends change the world, they influence people and embrace all areas of the society. For that reason, certain megatrends are chosen as key factors. There are many different compilations of megatrends. Each institute for futurology naming and defining megatrends is naming and focusing on different megatrends. Most of these outcomes are similar but differ in their definition or the number of megatrends listed. In this work, single megatrends were chosen from Z_punkt GmbH and Zukunftsinstitut GmbH. Out of the bunch of megatrends, digital transformation, urbanization, individualization and anthropogenic impact on environment were chosen as key factors, that have the biggest and direct influence on mobility. Z_punkt GmbH actually defines the last megatrend differently – the right designation would be “anthropogenic damage of environment”. Nevertheless, since this already has a negative implication it was changed to “anthropogenic impact on environment” by the author. Though this change, this megatrend can be observed more objectively. These megatrends also influence each other in a certain way – but this is not the focus of this work. Megatrends that were not taken into account were, for example, globalization, healthcare, or mobility. In the eyes of the author, mobility is not a megatrend but includes different megatrends that influence and shape it. For example, there are different sources that state, that mobility is influenced by megatrends, such as urbanization or digital transformation (Z_punkt.
(34) 33 GmbH, 2018; Jung, Kraft, 2017, p. 227; Reinz-Zettler, Kirchbeck, 2017). In the following, the attributes of the development potentialities of the four chosen megatrends will be explained in detail and their current situation presented.. Digital Transformation (Z_punkt GmbH, 2018) What is digital transformation? It is digital connectivity, big data, Internet of Things (IoT), artificial intelligence, robotic, etc. (Z_punkt GmbH, 2018). It is not possible to pinpoint one all-embracing definition of digital transformation. It is more that each stakeholder has its own definition of it. But generally, it can be said that digital transformation is the implementation of digital technology into peoples’ lives, businesses, societies, etc. (The Enterprisers Project, 2018).. But what kind of impact does digital transformation have on mobility and automotives? “The digital transformation of the automotive industry is, in effect, the innovative reassembly of customer and company resources, and of products and services, in order to grow value, revenue and efficiency via digital technologies” (World Economic Forum 2016, p. 7). Commuters increasingly tend to use smartphone apps to buy their bus ticket or check the weather before leaving the house or check the traffic of their daily route to work. This somehow seamless mobility where the user can spontaneously decide which vehicle to take or how to connect different types of transportation with each other is becoming of increasing importance (VCD Verkehrsclub Deutschland e.V., 2018). Currently, thanks to digital connectivity, it is possible to use a more flexible and cheaper version of taxis in big cities all over the world – for instance, Uber or a similar provider. Via smartphone app, people can call an Uber driver, track their current position, get a price for their ride and pay the trip. (Uber Technologies Inc., 2018) Besides this way of traveling, the number of alternatives to taxi driving is increasing in the past few months and years; without the digital transformation this would not have been possible. A similar example of the use of digital services is carsharing. Carsharing, as people know it, is almost impossible without a digitized world, including smartphones and internet connectivity everywhere and at every time. Available cars from carsharing provider get visible through a specific smartphone app, which each provider furnishes their customers. Similar to Uber, the user can book, pay, open and close the shared car with his smartphone. (DriveNow Gmbh & Co. KG, 2018) Beside those public examples, also privately-.
(35) 34 owned cars are becoming more digitized. The number of cars connected to the Internet for traffic updates and parking lot availability (BMW AG, 2018) or the navigation system and even messaging services as WhatsApp is increasing permanently (Apple Inc., 2018). Additionally, as mentioned earlier, autonomous driving, if in public buses or private cars, is gaining more importance and Germany is strengthen its position in the autonomous driving field (IMO Institut, 2017; Roland Berger GmbH, 2017, p. 7). Currently, totally autonomous driving is not allowed in Germany; however, German cars already have several semi-autonomous driving assistant features that were not possible without digital transformation (Roland Berger GmbH, 2017, p. 7 & 9). First examples are parking assistances, that autonomously park the car, active lane assists, that allow an autonomous lane change on highways or even a stop & go assist for traffic jams, that autonomously drives up to a certain speed (BMW AG, 2018). All those examples demonstrate, that the digital transformation is boosting but also changing peoples’ every day convenience.. Urbanization (Zukunftsinstitut GmbH, 2016a) Urbanization is the description of people living in cities instead of rural areas. It is the additional dimensioning and extension of cities, in terms of quantity, population and area size (Bähr, 2011, p. 1). According to Z_punkt (2018), 54 % of the population currently live in cities. In Germany, 76 % of the population in 2016 lived in cities, as it can be seen in figure 8. As Z_punkt (2018) further mentions, the worldwide percentage of people living in cities will rise up to 60 % by 2030. As a consequence of this development, the infrastructure in cities needs to be adapted and further improved..
(36) 35. Figure 8: Degree of Urbanization in Germany from 2006 until 2016 (World Bank, UN DESA, 2018). This development can be done through more efficiency in the use of resources and space. Cities will need to build resource and space-efficient buildings with a reduced use of scarce materials and the increased use of renewable energies (Riedel, 2016). This automatically leads, among other things, to the previous aspects of digital transformation and the use of, for instance, electric cars and carsharing instead of privately-owned diesel or gas cars. There is an ever-increasing number of urban dwellers that want to move and live freely – especially when thinking about the third megatrend, individualization, which will be talked about in greater detail in the next paragraph. But how will companies and cities prepare themselves for this constantly increasing number of urbanites?. Urbanization is, in the eyes of the author, one of the main influencing factors on mobility and urban life. For that reason, urbanization was chosen as one out of four key factors for the creation of future scenarios regarding the development of mobility. It is necessary to prepare cities’ infrastructures for the steadily increasing number of citizens. Cities become more crowded and in addition people want to be more flexible and get from one point to another faster and easier – they strive to increase their individuality (Proff et al., 2012, p. 7, 12).. Individualization (Zukunftsinstitut GmbH, 2016b): As already mentioned in the previous paragraph, the number of people living in big cities is increasing steadily, and with it the need and the desire for more flexibility and.
(37) 36 freedom. Individualization is the opportunity to freely choose where and how to live or which profession to practice. It is the process of an increase of freedom in each individuals’ personal development (Zukunftsinstitut GmbH, 2016b). As the Zukunftsinstitut GmbH (2016b) further states, the value of individuality that each single person gives to it has evolved. People are looking for individuality, they want to wear individual clothes that no one else is wearing, buy products that perfectly fits them (especially since the introduction of mass production) and want to go wherever they want to go anytime they want (Zukunftsinstitut GmbH, 2017, p. 6). Individualization as well as urbanization are causing some shifts within the behavior of consumers. These two megatrends extend the circle of consumers and the range of goods (Ewinger, Ternès, Koerbel, Towers, 2016, p. 6). Consumers have the desire to create their own product or want to be part in the process of the product designing (Buttkus, 2016, p. 3). Moreover, almost everyone has an individual digital identity these days on social media platforms like Instagram and Facebook or on online shops, where the shop recognizes each persons’ shopping habits and recommends new or other products according to these habits and interests (Müller, 2017).. Mayer (2017) suggests that individualization is one of the main influencing factors of town planning – and with that also of mobility. The formation of public spaces is impacted among others, by individualization (ibid.) – and individualization is, on the other hand, influencing the development of mobility and its demand (Zukunftsinstitut GmbH, 2017, p. 6; Köth, 2014). Nowadays, to be mobile is a prerequisite for social participation, self-fulfillment and individual success as citizens. Mobility determines if people become successful in private and professionally and each individual’s quality of life. The individuality of mobility is that people can freely choose which vehicle to use for which route or part of their route. Users of mobility have the desire to selfdecide when, where and how they want to go somewhere (ibid.) Today, the individualization of mobility is already happening. Through digitalization, it becomes possible to plan and create the travel via smartphone. Digital platforms enable the user to connect all different kinds of transportation with each other and choose the most suitable route, the basis of intermodal mobility. (Knie, n.d.) But as in Figure 6 can be seen, nowadays, 25 % of the people use their private car on a daily basis. Intermodal mobility is increasing its importance, but with 25 %, the privately-owned car is still.
(38) 37 the main transportation vehicle in Germany (Deutsches Zentrum für Luft- und Raumfahrt e.V., n.d.).. Anthropogenic impact on the environment (TRENDONE GmbH, 2018) The natural environment is suffering subsequently from the actual development of the human way of life (Z_punkt GmbH 2018). Z_punkt GmbH also states that power stations, industrial plants, traffic systems and agriculture are the main reasons for this damage and all are caused by humans. The increase of carbon dioxide (CO2) emission and with it the greenhouse effect are caused in particular by human activity (Sumantran et al., 2017, p. 46). Sumantran et al. explain, that “the accumulation of CO2 acts as a thermal blanket, causing the Earth to retrain more heat from the sun” (2017, p. 46). This leads to an increase of the average global temperature that “is expected to seriously disrupt agriculture and food supplies and lead to sea level increases that would inundate many coastal cities” (ibid.). According to Tim Schröder from Siemens AG (2009, p. 4), 80 % of the greenhouse gas – of which CO2 accounts for a main part – comes from cities. He further stated that cities consume 75 % of the energy. Due to this situation, cities account for climate change the most (Schröder, 2009, p. 4). Nevertheless, under the assumption of urbanization, cities will continuously grow in the future, further increasing those numbers – unless some arrangements are made. Moreover, scarcity of resources caused, for example, by industrial production, growing population, growing of wealth in certain countries and globalization, needs to be observed as well (Bretschger, 2008, p. 1 ff.). Currently, resources are used and consumed faster than they can grow again – the world has a resource deficit (Bartoschek, 2017).. As Figure 9 shows, for 55 % of the participants of this specific survey, the privatelyowned vehicle is important. Compared to that, 25 % of the participants said that driving a car is bad for the environment. Figure 9 also shows that about 50 % of the asked persons said that a car stand for independence and freedom. These two are directly connected to the previous paragraph, where the megatrend individualization was introduced. It can be seen that – at least for German people and its culture – cars are still important, despite emissions being a common issue regarding the environment (Bundesministerium für Umwelt, Naturschutz und nukleare Sicherheit, 2017)..
(39) 38. Figure 9: Survey in Germany Regarding Attitude Towards Mobility (Statista, 2018a). Anthropogenic impact on the environment is one of the main influence factors on mobility, because transportation is one of the premises for economic and social development. Furthermore, the need of mobility will increase, and the importance of cars will remain high for the German population (Bundesministerium für Umwelt, Naturschutz und nukleare Sicherheit, 2017). However, the available resources are limited, and the damage is high. As Sumantran et al. (2017, p. 49) said, “the current mobility architecture imposes an unacceptably high cost on the environment, health, and the global economy.” In the past, even with some ups and downs, energy was affordable. This affordability was one of the main drivers of growth of the global economy (ibid.). According to Sumantran et al. (2017, p. 49), 40 % of the global energy source is crude oil and, among others, the fuel-powered car as it is common these days depends on crude oil. For that reason, different new technologies and energy sources need to be identified and implemented (Bundesministerium für Umwelt, Naturschutz und nukleare Sicherheit, 2017). For example, the German city Munich has set a goal to reduce its CO2 emission about 50 % by 2030 compared to 1990, Schröder says (2009, p. 4 f.).. 4.3 Scenario Prognosis After the description of the characteristics of each key factor their future projections will be described. These projections are resulting from the conducted focus group.
(40) 39 discussions, as described in paragraph 3.3. The results of these two discussions are consolidated in table “Documentation of Focus Group Discussions” in Appendix I.. 4.3.1 Key factor No. I: “Digital Transformation” In this work, in which the focus is on mobility and its future development, digital transformation will be explained and analyzed by the following four attributes: artificial intelligence vs. human intelligence and central vs. decentral. According to Copeland (2018), artificial intelligence (in the following also shortened by AI) is “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.” Artificial intelligence describes independent learning and the ability of logical thinking to solve problems (Onpulson, 2018). Onpulson (2018) further defined AI as the link between computer science and cognitive psychology. Human intelligence (in the following also shortened by HI) is, on the other hand, the “mental quality that consists of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts […].” (Sternberg, 2018) Human intelligence is the cognitive ability to deductive reasoning and abstract thinking, planning, problem solving and to learn from experiences (Franken, 2007, p. 27). In this work and within the context of digital transformation, central stands for a centralized control of, for example, mobility or systems – one central position or unit controls a wide range of systems. The opposite of this is decentral, where each system has its own power and authority, in a sense autonomous or with a controlling unit per system. Merriam-Webster (2018) defines it as “the delegation of power from a central authority to regional and local authorities.” In this context, decentral mobility is for example an autonomous driving vehicle (Flämig, 2015, p. 387).. Projection A “Knight Rider” Locomotion in big cities is decreasing through digital transformation. As a result of a digitized world, the way of working has changed and with it so has mobility: from driving to the office every day to working at home and being connected with all other colleagues. In case the need of traveling or driving arises anyway, this locomotion has become partly autonomous. Through the rapidly developing digital world in which more and more things are connected with each other, vehicles have become autonomous and can control themselves instead of by a central unit. Cars and other.
(41) 40 vehicles as small helicopters or flying vehicles as Lilium jets, “the world’s first electric vertical take-off and landing jet” (Lilium GmbH, 2018), are communicating with each other and have the ability to learn from the habits of their owner or user. Vehicles are connected to the calendar of their user and can plan the pick-up times according to their schedule. Furthermore, cars are able to autonomously plan when to be cleaned or visit the dealer for maintenance, without the need of an individual. Resulting from this autonomy, vehicles are able to plan routes for their user. Through the use of big data, mind reading and deep learning, the system knows what kind of destination the user wants to stop by and is planning the route by itself. Depending on the congestion of the routes and destinations, the vehicle autonomously chooses which stop to make first and how to get there. For people with no privately-owned vehicle, a wide range of autonomous vehicles like buses, cars/taxis, flying taxis, etc. are available, depending on the users’ needs, time schedule and purpose of travel. These different types of transportation can be called, payed and planned via a smartphone app that learns the users’ preferences and habits.. Projection B “New Normality” When thinking about locomotion nowadays, people get from A to B by driving their privately-owned car, bicycle or motorcycle, using public transportation as buses or trains, using shared vehicles as bikes or cars, or by calling a taxi. Through digital transformation driving has become a little bit more autonomous since systems as lane assist or cruise control have been increasingly developed during the past few years. With sensors all around the car, they can drive autonomously in cities, but the driver still needs to pay attention, choose the route and be able to interfere. Manufacturers are implementing new kinds of steering into cars. These cars are not equipped by a regular steering wheel but rather with sensors and augmented reality with which they can control the car by gestures or verbalization. A display s projects stores, museums and other potential destinations onto the windshield while passing by to give the driver all necessary information about his current position. In addition, people, cars and environment are connected to each other, so that besides destinations friends will be shown that are along the route, too..
Outline
相關文件
Wang, Solving pseudomonotone variational inequalities and pseudocon- vex optimization problems using the projection neural network, IEEE Transactions on Neural Networks 17
Define instead the imaginary.. potential, magnetic field, lattice…) Dirac-BdG Hamiltonian:. with small, and matrix
Monopolies in synchronous distributed systems (Peleg 1998; Peleg
Corollary 13.3. For, if C is simple and lies in D, the function f is analytic at each point interior to and on C; so we apply the Cauchy-Goursat theorem directly. On the other hand,
Corollary 13.3. For, if C is simple and lies in D, the function f is analytic at each point interior to and on C; so we apply the Cauchy-Goursat theorem directly. On the other hand,
õT¤_ .â·<íËju, Data Access Component Module 2FíŠ?. âÀÓ“, ©ø_ method úk’eé query v,
y A stochastic process is a collection of "similar" random variables ordered over time.. variables ordered
Time constrain - separation from the presentation Focus on students’ application and integration of their knowledge. (Set of questions for written report is used to subsidize