INOM
EXAMENSARBETE INDUSTRIELL EKONOMI, AVANCERAD NIVÅ, 30 HP
STOCKHOLM SVERIGE 2021,
SUSTAINABLE MOBILITY SCENARIO MODELING
Evaluating Future Resilience of Modular Concepts for Electrified Trucks
RIKARD BODÉN
SIMON BJÖRKVALL
SUSTAINABLE MOBILITY SCENARIO MODELING
Evaluating Future Resilience of Modular Concepts for Electrified Trucks
by
Rikard Bodén Simon Björkvall
Master of Science Thesis TRITA-ITM-EX 2021:322 KTH Industrial Engineering and Management
Industrial Management
SE-100 44 STOCKHOLM
SCENARIOMODELLERING FÖR HÅLLBARA TRANSPORTER
En metod för att framtidssäkra modulära koncept för elektrifierade lastbilar
av
Rikard Bodén Simon Björkvall
Examensarbete TRITA-ITM-EX 2021:322 KTH Industriell teknik och management
Industriell ekonomi och organisation
SE-100 44 STOCKHOLM
Master of Science Thesis TRITA-ITM-EX 2021:322
SUSTAINABLE MOBILITY SCENARIO MODELING Evaluating Future Resilience of Modular Concepts for
Electrified Trucks
Rikard Bodén Simon Björkvall
Approved
2021-06-11
Examiner
Pernilla Ulfvengren
Supervisor
Lars Uppvall
Commissioner
Modular Management AB
Contact person
Colin de Kwant
Abstract
Today, one of the greatest concerns for companies is how well their business will fit their future markets.
However, predicting how the future will unfold is almost impossible for many industries, but companies that fail to prepare their products for future markets will most likely face substantial problems.
Consequently, many companies have drawn their interest to product development strategies that cope with an unpredictable future, and research has highlighted Modularization as one such strategy.
Nevertheless, there are no current methods that integrate future studies into the modularization process.
Besides, there are no methods that evaluate the resilience of modular configurations against future scenarios.
In the absence of such methods, this study targets the gap between future studies and modularization.
The objective is to explore how scenario modeling can be used in the modularization process to evaluate the fitness of modular configurations against future conditions. The study scope is a simplified inter-urban transport mission with a particular focus on battery-electric and fuel-cell electric trucks. To meet the objective, this study builds upon a scenario framework from previous research that provides possible but yet distinctive futures within the transportation industry.
Further, the future scenarios are bridged to the modularization process by transitioning the most important customer values from the scenarios to measurable design variables. Subsequently, by assigning weights to the customer values in accordance with scenario narratives, the overall efficiency of 42 unique modular configurations could be evaluated against the presumed importance of future customer values. Those findings were used to assess the relative performance of modules with respect to multiple futures and to provide reflections on the most and least robust modular design and configuration choices across multiple futures.
In summary, the contribution from this method is shown to be two-fold. On the one hand, the model can provide insights and directions on the future resilience of modular concepts in the early stages of product development processes. On the other hand, it can be used in recurring performance assessments of modular configurations and guide optimization of module variants to prepare modular product configurations for multiple scenarios.
Key-words: Scenario Modeling, Futures Studies, Modularization, Inter-Urban
Transportation, Product Development
Examensarbete TRITA-ITM-EX 2021:322
SCENARIOMODELLERING FÖR HÅLLBARA TRANSPORTER
En metod för att framtidssäkra modulära koncept för elektrifierade lastbilar
Rikard Bodén Simon Björkvall
Godkänt
2021-06-11
Examinator
Pernilla Ulfvengren
Handledare
Lars Uppvall
Uppdragsgivare
Modular Management AB
Kontaktperson
Colin de Kwant
Sammanfattning
Ett av de största bekymren för företag idag är alltjämt hur bra deras verksamhet kommer möta framtidens kundbehov. Emellertid är det nästan omöjligt att förutspå hur framtiden kommer utvecklas inom många branscher, men samtidigt möter företag överhängande operationella förluster om de misslyckas med att adressera framtidens behov. Föga förvånande har många företag börjat intressera sig för flexibla produktutvecklingsstrategier som kan hantera en oförutsägbar framtid och tidigare forskning har belyst Modularisering som en sådan strategi. Däremot finns det i dagsläget inga nuvarande metoder som tar hänsyn till framtidsstudier i modulariseringsprocessen. Dessutom verkar det inte finnas några metoder som utvärderar effektiviteten hos modulära konfigurationer med avseende på olika framtidsscenarier.
I avsaknad av sådana metoder riktar sig detta examensarbete mot gapet mellan framtidsstudier och modularisering av produkter. Syftet är att undersöka hur scenariomodellering kan användas i modulariseringsprocessen för att utvärdera robustheten hos moduler gentemot olika framtidsscenarier.
Studien är avgränsad mot inter-urbana transportuppdrag med ett särskilt fokus på batteridrivna och vätgasdrivna lastbilar. För att uppnå forskningssyftet bygger uppsatsen på scenariomodellering från tidigare forskning som bidrar med en uppsättning av möjliga men ändå distinkta framtidsscenarier.
Vidare kunde framtidsscenarierna sammanlänkas med modulariseringsprocessen genom att extrahera de viktigaste kundvärdena från framtidsscenarierna och översätta dessa till mätbara design variabler.
Därefter kunde den totala effektiviteten för 42 unika lastbilskonfigurationer utvärderas mot framtida kundvärden genom att tilldela kundvärdena olika signifikansnivåer baserat på framtidsscenarierna. Dessa resultat användes för att bedöma konfigurationernas relativa prestanda mot olika framtidsscenarier.
Resultatet användes också till att samla in data om modulernas robusthet och sedermera analysera lämpligheten hos enskilda moduler.
Sammanfattningsvis bedöms bidraget från metoden vara av dubbel karaktär. Å ena sidan kan metoden ge insikter om den framtida lämpligheten hos modulära koncept i ett tidigt skede av produktutvecklingsprocessen. Samtidigt kan metoden användas i återkommande utvärderingar av modulkonfigurationer och som ledsagning för att optimera modulvarianter och förbereda modulära produktkonfigurationer mot flera framtidsscenarier.
Nyckelord: Scenariomodellering, Framtidsstudier, Modularisering, Inter-Urban
Transport, Produktutveckling, Lastbilar
Contents
1 Introduction 1
1.1 Background & Problematization . . . 1
1.2 Research Purpose & Research Questions . . . 3
1.3 Research Setting & Research Scope . . . 3
2 Literature Review 4 2.1 Future Studies & Scenario Modeling . . . 4
2.2 Trends in Future Transportation . . . 7
2.3 Key Design Variables for Inter-Urban Transportation . . . 8
2.4 Electrification of Truck Transportation . . . 9
2.5 Modularization . . . 12
3 Scenario Framework 15 3.1 The Future Scenarios . . . 15
3.2 Extraction of Future Customer Values . . . 17
3.3 Business Landscape & Network Structures . . . 17
3.4 Scenario Weighting of Future Customer Values . . . 18
4 Method 20 4.1 Research Design . . . 20
4.2 Research Process . . . 21
4.3 Data Collection . . . 23
4.4 Quantitative Model . . . 24
4.4.1 Assumptions & Calculations . . . 25
4.4.2 Modules & Module Variants . . . 25
4.4.3 Property Levels . . . 28
4.4.4 Total Cost of Ownership . . . 29
4.4.5 Infrastructure & Fuel Prices . . . 32
4.4.6 Overall Equipment Effectiveness . . . 36
4.4.7 Sustainability . . . 38
4.4.8 Evaluation of the Key Customer Values . . . 40
5 Findings 42 5.1 General Findings . . . 42
5.1.1 Findings on Total Cost of Ownership . . . 43
5.1.2 Most Robust Configurations in all Scenarios . . . 44
5.1.3 Most Robust Configurations per Scenario . . . 45
5.1.4 Worst Configurations in all Scenarios . . . 47
6 Analysis & Discussion 49 6.1 Analysis . . . 49
6.1.1 General Analysis of Robustness . . . 49
6.1.2 Analysis of Robustness in all Scenarios . . . 49
6.1.3 Analysis of the Transport Mission . . . 51
6.2 Discussion . . . 53
6.2.1 Revisiting the Research Questions . . . 53
6.2.2 Discussion of Method . . . 55
6.2.3 Discussion of Sustainability Aspects . . . 56
7 Conclusions 58 7.1 Concluding Remarks . . . 58
7.2 Academic Implications . . . 58
7.3 Practical Implications . . . 58
7.4 Future Work . . . 59
A Modular Configurations 69
B Modular Function Deployment 70
List of Figures
1 The Relation Between Future Studies & Scenarios [(Eriksson and Simme, 2020)] 5
2 Framing by Scenario Axes [(Eriksson and Simme, 2020)] . . . 6
3 Overview of the Generic Product Development Process (Eriksson and Simme, 2020) . . . 7
4 Four Possible Scenarios by Pernest˚al et al. (2019) . . . 16
5 Network Structures & Transactions in the Four Scenarios [Source: Lina Simme, Modular Management AB] . . . 18
6 Overview of the Research Process . . . 21
7 Overview of the Literature Review . . . 22
8 Overview of the Bibliometric Analysis . . . 23
9 Overview of the Methodology . . . 24
10 The Considered Modules in this Model . . . 26
11 Bottom-up Classification of Properties . . . 29
12 TCO Breakdown . . . 30
13 OEE Breakdown . . . 38
14 Sustainability Breakdown . . . 39
15 Overall Efficiency in Relation to Evaluation Dimensions . . . 41
16 Overall Efficiency of Configurations in the Four Scenarios . . . 42
17 TCO Efficiency of the Configurations in the Four Scenarios . . . 43
18 OPEX of the Configurations in the Four Scenarios . . . 44
19 Best Modules in All Scenarios . . . 45
20 Best Modules in Social Engineering 2.0 . . . 46
21 Best Modules in Green Circle . . . 46
22 Best Modules in Partnership Society . . . 47
23 Best Modules in Bathing in Data . . . 47
24 Worst Modules in all Scenarios . . . 48
25 (a) CO2-emission of Configurations (b) Energy Consumption of Configurations 50 26 TCO Analysis for Gothenburg - Boras . . . 52
27 (a) CAPEX for Gothenburg - Boras (b) OPEX for Gothenburg - Boras . . . . 52
28 Modular Function Deployment Process [Modular Management AB]. . . 70
List of Tables
1 Importance of Key Customer Values in the Four Scenarios . . . 192 Level of Importance in Percentage . . . 19
3 Properties of Engine Modules . . . 26
4 Properties of Battery Modules . . . 27
5 Properties of Hydrogen-Fuel Cell Modules . . . 27
6 Properties of the ERS Module . . . 27
7 Capital Expenditures of Main Components . . . 31
8 Driver Costs & Service Costs . . . 32
9 Remote Controlling of Autonomous Trucks in the Four Scenarios . . . 32
10 Hydrogen Costs & CO2-emissions in the Scenario Model . . . 33
11 Cost of Electricity Production and CO2-emissions of Electricity Sources . . . . 34
12 Electricity Sources in the Four Scenarios . . . 34
13 Production Cost of Electricity in the Four scenarios . . . 34
14 Tax & Fee Associated with Electricity Cost . . . 34
15 Total Electricity Cost . . . 35
16 Utilization & Costs of Fast-Charging Stations . . . 35
17 Costs of Utilizing Electric Road Infrastructure . . . 36
18 Electric Road Coverage . . . 36
19 Overview of Fuel Prices in the Scenario Model . . . 36
20 CO2-emissions per kWh Electricity Produced in the Four Scenarios . . . 39
21 Description of Module Drivers . . . 71
Acknowledgement
This study would not have been possible without the guidance and support from several people. First of all, we want to express our gratitude to our supervisor Arne Erlandsson at Modular Management, for your willingness to share your expertise and your constant availability. The Sustainable Mobility Scenario Modelling (SMSM) project leader, Colin de Kwant, for believing in our work and your valuable feedback, steering the project in the right direction. Lina Simme, whose Master’s Thesis was the starting point for our project, for your support and insights. We would also like to thank representatives from the automotive industry for your valuable feedback through our meetings. No one mentioned no one forgotten.
Your expertise, guiding feedback, and fruitful discussions have been of great value to this thesis.
We would also like to express our gratitude to Lars Uppvall, our supervisor at KTH, for his valuable input during discussions at our meetings. Your perspective and experience from both academia and business have been valuable for us in this thesis. Further, we would like to thank our peers at KTH, for your support, feedback, and valuable critique.
Finally, we would also like to express our thankfulness to our beloved friends for making lemonade out of lemons, particularly during this challenging Covid-19 pandemic. Last but not least, a special thanks to our families for their uncompromising support in our everyday life during these years. We would not graduate without your support.
Rikard Bod´en & Simon Bj¨orkvall Stockholm, 20210611
1 Introduction
This chapter aims to introduce the reader to the overall business context within the trans- portation industry and the current and future challenges. Subsequently, the reader is also introduced to the role of innovation strategies, modularization, and future studies have within product development. Accordingly, the chapter continues by shedding light on areas where further research is required, and lastly, the purpose of this study, the research questions and the study’s scope are presented.
1.1 Background & Problematization
One of the greatest concerns for many corporations is how well their business will fit future markets. However, predicting future directions is challenging, particularly for industries sub- ject to significant disruptive change and unpredictable market dynamics. Consequently, more and more firms have recently drawn their interest to embark on a flexible innovation strategy to quickly adapt to fluctuating market dynamics and diverse future scenarios (Eriksson and Simme, 2020). One such industry that faces substantial transformative pressure is the market for commercial vehicles, where significant changes are on the verge of changing the way of inter-urban freight and transportation (Ghandriz, 2020).
While researchers anticipate a fundamental transformation of the freight and transport in- dustry in the following decades (Jentzsch et al., 2019), strong lock-in effects are present in the transport system, and the transport industry is subject to slow changes (Bernardino et al., 2015; Kostiainen and Tuominen, 2019). The current transport system is the outcome of a century of incremental innovations along an innovation path characterized by fossil-fuel dependency (Kostiainen and Tuominen, 2019). Consequently, the system lock-in becomes a barrier for industrial innovation that will require vast amounts of new capital investments in infrastructure (Bernardino et al., 2015). As a result, the transportation industry has been mentioned as one of the most significant obstacles towards sustainable development (Dominkovi´c et al., 2018).
Furthermore, the transport industry is a key emitter of CO2, accounting for approximately 22% of the total global energy-related CO2 emissions (Dominkovi´c et al., 2018; Kluschke et al., 2019), and the number is expected to grow unless actions are taken (Transport and Envi- ronment, 2020). Several political responses address climate change; the Paris Agreement, and United Nations Sustainable Development Goals (SDG) are two examples. Alongside, most European truck manufacturers have committed to abandon their dependency on fossil fuels, and two of the most promising new alternatives are battery electric vehicles (BEV) and fuel- cell electric vehicles (FCEV) (Heid et al., 2018; Lee et al., 2018). Although electrification has gained increasing attention from policymakers, the way forward for manufacturers remains uncertain and subject to many changing external factors. On the one hand, customers to the truck manufacturers struggle with techno-economic uncertainties regarding the technological development of powertrains and their corresponding infrastructure, influencing the cost of ownership. At the same time, manufacturers are put under political pressure to reduce emis- sions rapidly. However, yet policymakers have left manufacturers without any perspicuous
plan for the future development of sustainable infrastructure.
Accordingly, many manufacturers are concerned with what innovations to invest in to stay relevant in the future. In addition, mega-trends such as digitalization and urbanization are drivers of change for evolving customer behaviors and goods flow (Keseru, Coosemans, and Macharis, 2021; L’Hostis et al., 2019; Pernest˚al et al., 2019), creating opportunities for new business models. Consequently, firms might have to innovate their value offering as margins and profits are shifting throughout the value chain (Jentzsch et al., 2019). If manufacturers fail to predict the future and address the unfolding scenarios, the business will likely face substantial losses in the form of decreased competitiveness. Previous research on future studies for inter-urban transportation has tried to address the challenges with this and underpins that there exist numerous scenarios that can unfold in the future (L’Hostis et al., 2019;
Keseru, Coosemans, and Macharis, 2021; Pernest˚al et al., 2019). Furthermore, re-design of heavy-weight long-range vehicles is especially challenging since trucks rely on product platforms designed to be used for decades (Eriksson and Simme, 2020; Dominkovi´c et al., 2018), and development takes many years (Greene, Ogden, and Lin, 2020). At the same time, manufacturers need to forecast well ahead to ensure that their products are relevant on the market once they are launched and the many years that follow (Eriksson and Simme, 2020).
Hence, it is becoming increasingly important to think about possible long-term scenarios that can help manufacturers to prepare their products for the future (Banister and Hickman, 2013).
One way to prepare for future changes is to use scenario modeling, where certainties and un- certainties outline different futures. While scenario-building is a tool for modeling the future, it must also be evaluated correctly to assure that proper measures are taken. One way of evaluating future scenarios is to identify and extract key variables and investigate how they are interlinked. However, future scenarios are likely to provide different needs for different customers. Hence, another central challenge for manufacturers is to provide customized solu- tions from a wide range of products in a cost-efficient way. Regarding this, modular product architectures can be viewed as potential solutions by optimizing the trade-off between prod- uct differentiation by increased fit to customer needs and economies of scale augmented by product standardization (G. Schuh, Rudolf, and Vogels, 2014). Key variables with consistent values influenced by identical or similar customer requirements can advantageously be com- bined into modules. By developing resilient platform designs for modules that share interfaces, modular up-gradation can enable assembly adjustment to fit the customer requirements that originate from different future scenarios.
Considering the many uncertainties of the future, developing a resilient innovation strategy capable of coping with technological innovations and changes in customer values is a complex task. Additional complexity is added by the fact that re-designing of trucks takes years (Greene, Ogden, and Lin, 2020) and that the product platforms are designed to be used for decades (Eriksson and Simme, 2020). Moreover, it takes time for structural changes to gain traction due to the resistance of change caused by industry inertia, stemming from strong lock-in effects (Kostiainen and Tuominen, 2019), ultimately leading to implementation times that can take decades (Bernardino et al., 2015). Therefore, manufacturers need to adopt
methods and structural approaches to ensure that their products are relevant on the market once they are launched and the many years that follow (Eriksson and Simme, 2020).
Although modularization can be viewed as one flexible strategy for manufacturers to prepare for different future scenarios, it remains challenging to develop resilient modular architectures that answer up to multiple scenarios. Despite the emerging need, research on how future sce- narios can be integrated into the modularization process remains scanty. In addition, there is no best practice available on evaluating the efficiency of modular configuration against future scenarios. In the meantime, it becomes difficult to design resilient modular product architec- tures in the absence of methods for assessing the efficiency of future modular architectures.
Hence, this thesis targets the gap between future scenario studies and how they can be used in the modularization process.
1.2 Research Purpose & Research Questions
The purpose of this thesis is to explore how future studies and scenario modelling can be combined with modularization. The targeted result is a systematic method on how scenario modeling can be incorporated in the modularization process to enable development of future- resilient modular product platforms. To meet the objective of this study, the following two research questions were developed:
RQ1: How can scenario modeling support product system design to meet the uncertainties of diverse future transportation scenarios?
RQ2: How can key variables from future scenarios be used to measure the efficiency of modular architectures in the transportation industry?
1.3 Research Setting & Research Scope
This thesis is one part of an ongoing research project at ECO2 Vehicle Design Research Center, within the project ”SMF ECO2 Sustainable Mobility Scenario Modelling”, focus- ing on environmentally and economically sustainable vehicle development. The research is multi-disciplinary, undertaking a holistic perspective by focusing on strategically essential connections between vehicles and society. The overall project purpose is
”(...) to enable, develop and explore scenarios for changing and diverse future urban and inter- urban area transportation cases to evaluate innovative and transformative mobility solutions, platform design strategies, product configuration, upgrade and evolution options” (Vinnova, 2020).
The scope of the study is delimited to electrified medium-duty trucks and heavy-duty trucks for inter-urban transportation with corresponding power infrastructure. The work focuses on sustainable transport, but only environmental and financial sustainability is considered. The third aspect, social sustainability, is not included in the scope, although recognized as both essential and influenced by the two others.
2 Literature Review
This chapter begins by first reviewing the field of future studies and scenario modeling, simply because that is where this thesis takes off from. Secondly, a review on future trends within the transportation industry is provided to contextualize the future studies, and thirdly, a review on key design variables for inter-urban transportation are presented. Then, a section about the electrification of trucks follows before the chapter ends with a review of the modularization literature.
2.1 Future Studies & Scenario Modeling
Future studies is an umbrella term describing different methods of systematically exploring the future to understand the development of variables (Eriksson and Simme, 2020). Futures studies have long been used to establish a plan for the future that minimizes the surprises and facilitates for managers to keep different possibilities in mind while planning for the future (Mietzner and Reger, 2005). The overall purpose of future studies within innovation processes is to improve the understanding and gather indications on emerging directions for innovations (Adegbile, Sarpong, and Meissner, 2016). Also, by exploring the future in a structured way, B¨orjeson et al. (2006) claim that actors can develop flexible strategies so that products can be resilient to future developments, which should be of interest from a product design perspective.
In the meantime, the concept of futures studies has been called a fuzzy multi-field, and methods for future studies can be classified as either forecasting or foreseeing (Eriksson and Simme, 2020). Forecasting is predictive, often considering one future that it aims to portray accurately. Foresight is, in contrast, more accepting to a range of possible futures (Mietzner and Reger, 2005), implicitly assuming that underlying dynamics are unknowable (Klooster and Asselt, 2006). Thus, by sharing the view that the future is inherently uncertain and developments and trends can take either direction for the future, foresight with its view of several different possible futures could enable a better chance of portraying the actual future (Eriksson and Simme, 2020).
However, foreseeing the future can be challenging for various reasons. One reason that is portrayed by Klooster and Asselt (2006) is that different and often conflicting perspectives on how the future may unfold can be legitimate. Also, Dunphy, Herbig, and Howes (1996) says that the critical path to create a successful industrial innovation can be understood as a series of macro-and micro-dependent sequential steps. However, it is widely acknowledged that the link between cause-and-effect is often elusive; hence, performing future studies is even more complex since many relationships, that may seem to have developed continuously in retrospect, often follow a non-linear pattern in the future (Klooster and Asselt, 2006).
Nonetheless, the field of future studies has in recent times seen a remarkable upsurge in the number of scholarly papers reporting a positive influence of future studies on innovation (Adegbile, Sarpong, and Meissner, 2016). Also, research says that a holistic view of the future and its evolvement can be obtained by performing scenario modeling, one technique of strategic foresight. The scenarios can also be used to provide insights about cause-and-effect
sequences. This would enable companies to look at their organization from a holistic view in terms of the future and map the causes and effects that different developments would have on their company, improving the prospects of production of products which can withstand the future (Eriksson and Simme, 2020).
Previously, it has been claimed that scenario modeling is the archetypical product of futures studies because it embodies the central principles of thinking profoundly and creatively about the future (Bishop, Hines, and Collins, 2007), whilst visioning multiple plausible futures, not just the expected (B¨orjeson et al., 2006). However, the development of scenarios can be based upon different approaches and techniques (Bishop, Hines, and Collins, 2007). Similarly, Miet- zner and Reger (2005) argue that scenarios are either exploratory or normative. Exploratory scenarios aim to outline various plausible trajectories of development without the intention to represent the most probable course of events (predictive scenarios) or to assess how a prefer- able scenario can be achieved (normative scenarios) (Pernest˚al et al., 2019; B¨orjeson et al., 2006). In rhetorical meaning, exploratory scenarios seek answers to the question ”what will happen?” by starting in the present. Normative scenarios, in contrast, are developed with their starting point in the future, with a scenario typology related to the question ”how can a specific target be reached?” (B¨orjeson et al., 2006).
Figure 1: The Relation Between Future Studies & Scenarios [(Eriksson and Simme, 2020)]
Accordingly, to frame different but yet possible scenarios, Klooster and Asselt (2006), describe a method called Scenario Axes, developed as a tool of portraying the future during a set of workshops. The method involves scenario participants to identify the two most important drivers for the future. They further state that the drivers of impact should be the ones that correspond to most uncertainty and impact, and that these should then be plotted as two crossing axes; see figure 2 below. This method is also apparent in Bishop, Hines, and Collins (2007) where the two driving forces are said to constitute strategic uncertainties.
Figure 2: Framing by Scenario Axes [(Eriksson and Simme, 2020)]
Regarding scenarios, Klooster and Asselt (2006)’s problematization of future studies and conflicting perspectives are, on the one hand, still justified. But, scenarios should provide strategists with various possible futures and not aim to forecast the actual future. The purpose of a scenario is at a meta-level, and as the driving forces correspond to substantial impact with high uncertainty, scenarios can advantageously be used to dramatize trends and alternative futures (Mietzner and Reger, 2005; Pernest˚al et al., 2019). Hence, from an organizational perspective, scenario modeling can aid organizations to cope with the future (Adegbile, Sarpong, and Meissner, 2016). Scenario modeling does not only allow firms to plan proactively by evaluating the implications of future events, but it can also help companies detect and avoid arising problems or crises (Mietzner and Reger, 2005). In addition, if the scenarios are created in workshops (as suggested by Eriksson and Simme (2020) and Klooster and Asselt (2006)), firms immediately gain access to multiple stakeholders and their interests (Drew, 2006; B¨orjeson et al., 2006).
However, to link the insights from the scenario modeling, it is important to realize where and when the scenario modeling and evaluation of product innovations are particularly valuable.
Looking at a generic product development process, with steps described in figure 3, the value creation is mainly in the “fuzzy-front-end”. Those is the steps that precede the actual development of a new product and where the focus is to develop concepts and an action plan for the business case.
Figure 3: Overview of the Generic Product Development Process (Eriksson and Simme, 2020)
Further, by recalling that scenario modeling does not necessarily portray the most likely fu- ture, but a plausible yet distinctive one, scenario modeling can serve as a basis for concept creation in the product development process, (Pernest˚al et al., 2019). Research has pointed out that scenarios within product development processes can provide insights on changing customer needs and ideas on how un-articulated customer needs could be addressed. Exclu- sive knowledge about customers and methods for anticipating future customer needs thereby becomes a competitive advantage. However, the innovation agenda in incumbent automo- tive firms reaches far into the future, making it more difficult to sufficiently model (Ruff, 2015).
2.2 Trends in Future Transportation
The study of the future in a particular industry is often far more abundant than the industry itself as it stretches beyond industry boundaries. In Eriksson and Simme (2020), it was out- lined that the central core of futures studies is about the future of humanity in general. This is to some extent supported by Ruff (2015), who argue that changes in the mobility of persons and goods are shaped by social megatrends on a “macro level”, e.g. by political, economic, in- frastructural, social and cultural conditions. Regarding this, prior research generally confirms that truck industry will be impacted by many megatrends in the society and that the industry face considerable industrial transformation during the next decades (Sidbrant and B¨orjesson, 2018; Liimatainen, Kallionp¨a¨a, et al., 2014). At the same time, road freight are considered to be a highly complex system as it is constituted by many elements influencing each other both directly and indirectly on different levels (Alonso Raposo et al., 2019). Meaning that the future of transportation becomes challenging to predict under influence of megatrends.
Yet, several researchers have approached the issue by outlining plausible future scenarios that encompass certain development and uncertain development (Sidbrant and B¨orjesson, 2018;
Pernest˚al et al., 2019; Keseru, Coosemans, and Macharis, 2021; Liimatainen, Kallionp¨a¨a, et al., 2014; DHL, 2012).
Regarding the certain development for future inter-urban transportation, literature of the field tend to agree that macro-trends such as increasing urbanization, digitalization, and sustainability will play a significant role (Pernest˚al et al., 2019; Keseru, Coosemans, and Macharis, 2021; Keese et al., 2018; Trpevska, 2016; DHL, 2012). Several authors predict
the demand for trucks and transportation to grow due to an increased global population, technological advancements, and increased interest for sustainability (Berger, 2018; Trpevska, 2016; International Transport Forum (ITF), 2019a).
Literature also highlight trends on a meso-level, e.g. changing customer behavior, changing lifestyles, and increasing e-commerce as a major influence to future transportation (Pernest˚al et al., 2019; Ruff, 2015; Keseru, Coosemans, and Macharis, 2021). Yet, disagreement is visible in the literature as some argue that growing e-commerce and consumption is likely to play an increasingly dominant role in the logistics landscape (International Transport Forum (ITF), 2019b; Keese et al., 2018; Pernest˚al et al., 2019). Other says that the demand for freight transportation has been rising for many years and growing transport demand are not so influential (Trpevska, 2016).
Moreover, the study from Sidbrant and B¨orjesson (2018) shows that there are more certainty around the macro-level than on the micro-level. Thus, the literature seem to know what to expect, but not in detail how trends will hit and to which extent. The main uncertainties that imprint the future scenarios are related to the political decisions on data utilization and the industry perspective on dominant future fuel for long haul transportation. Accord- ingly, Pernest˚al et al. (2019) framing their scenarios by outlining sustainable development and data sharing as the most influential uncertainties in the future. In contrast to Sidbrant and B¨orjesson (2018) and Pernest˚al et al. (2019), it is suggested by Keseru, Coosemans, and Macharis (2021) that the individual lifestyle and consumption should be regarded as one of most important key uncertainties. The other uncertainties are policies and regulations, which supports that politics is a common denominator that is associated with uncertainty. One plausible reason is outlined Meersman and Voorde (2019), who claims that regulatory pushes are dynamic, and as a result, their impact is hard to estimate.
2.3 Key Design Variables for Inter-Urban Transportation
Regarding key design variables for long-haul inter-urban transportation, the reviewed liter- ature mainly revolves around the range, payload capacity, available infrastructure, vehicle performance, overall usability, and cost of ownership, see for instance Ghandriz (2020) and Sim et al. (2019). For long-haul transportation, it is important for the customer to not be interrupted by many charging stops (Ciarapica et al., 2012; Kadapala and Sj¨oberg, 2019).
Accordingly, many researchers shed light on the present range-anxiety among transporters, arguing that the range is one of the most critical key variables for long-haul transportation (Ciarapica et al., 2012; Ghandriz, 2020). The range is often discussed in relation to the availability of infrastructure (Forrest et al., 2020; Ghandriz, 2020), illustrating their interde- pendence.
Further, Silvas et al. (2016) says that design choices for vehicle architectures depend on the intended transport mission, and there will always be a trade-off between key variables.
For instance, if the energy storage is too small, it does not only lead to less range, but for batteries, it may also lead to sub-optimal harvesting from the energy grid, implying longer charging times (Tran et al., 2018). On the other hand, solving the range-anxiety by including
larger energy storage would negatively impact the payload capacity, another key variable for long-haulers. The long-haul transportation is also said to be more technically challenging than urban transportation since the trucks are driven longer and often require a larger payload capacity (Earl et al., 2018). However, the payload capacity is constrained by legal regulations which set payload boundaries for haulers.
An additional key variable that is frequently mentioned is the fuel-consumption (Forrest et al., 2020; Ghandriz, 2020; Kadapala and Sj¨oberg, 2019), which is interlinked with range and pay- load capacity. In turn, the fuel-consumption is directly impacted by the vehicle powertrain, source of energy, and weight - which is affected by all modules in the architecture. Another common denominator that is affected by all modules is the total cost of ownership (TCO), which encompasses capital expenditures (CAPEX) and operational expenses (OPEX). While CAPEX is strongly influenced by choice of powertrain, investments in fuel-saving compo- nents can pay off quickly (F. M, Lehmeyer M, and Lienkamp M, 2017). Similarly, Silvas et al. (2016) suggests that even minor changes in components can decrease fuel-consumption, which significantly reduces OPEX over time. However, Kadapala and Sj¨oberg (2019) says that a trucks’ overall usability is the main determinant for hauler’s profitability. Hence, the vehicle performance cannot be neglected in the search for the lowest fuel-consumption and lowest TCO. Analysis of vehicle performance should be constrained to specific vehicle per- formance requirements such as acceleration and speed (Diba and Esmailzadeh, 2020). Sim et al. (2019) summarizes this multi-objective problem by claiming that fuel-consumption and vehicle performance are, on the one hand, always dependent on the individual modules. On the other hand, component-sizing always affects the TCO and the purchase-decision of con- sumers.
While many previous papers regarding key variables and evaluation of transportation and trucks revolves around TCO, a recent study from Mu˜noz-Villamizar et al. (2018) demon- strated how a measure of overall equipment efffectiveness (OEE), derived from the manufac- turing industry, could be used to evaluate the effectiveness of urban freight transportation systems. The original definition of OEE, comprises the overall effectiveness into three cat- egories: quality (Q), performance (P) and availability (A). Originally, The quality rate Q denoted the relationship between the number of units produced and the number of units pro- duced that meet specifications. In manufacturing processes, the performance rate P indicates the actual deviation in time from the ideal cycle time. Availability A measures the total time that the system is not operating due to break-downs, set-up and adjustment, and other stoppages. Thus, the OEE metric leads to the manufacture of only good parts Q as fast as possible P and with no stop time A.
2.4 Electrification of Truck Transportation
Two of the most promising technologies mentioned in current literature regarding electric propulsion of vehicles are battery-electric and hydrogen fuel cells (Heid et al., 2018; Lee et al., 2018). Fuel cell technology builds on the principle of converting hydrogen and oxygen into electricity, where H2O is the only emission (Thomas et al., 2020).
In 2020, the EU announced a hydrogen strategy in which the EU will invest 430 billion euros in hydrogen by 2030 (Fossilfritt-Sverige, 2020), with increasing interest from the transportation industry as a result (Gangloff et al., 2016; Forrest et al., 2020; Kast et al., 2017). Even though hydrogen fuel cells are seen as a versatile future energy carrier, deployment for fuel cell vehicles remains relatively small and geographically dispersed (Greene, Ogden, and Lin, 2020; Gangloff et al., 2016). Nevertheless, research concludes that hydrogen is perhaps the only viable zero- emission option for hard-to-decarbonize applications, including long-haul freight transport.
(Greene, Ogden, and Lin, 2020). Meanwhile, hydrogen fuel cell technologies are promising;
their performance and feasibility remain relatively unclear compared to other powertrains (Kast et al., 2017; Forrest et al., 2020).
Diversity in literature is apparent when comparing the feasibility of battery-electric powered trucks. According to Monios and Bergqvist (2019), there is little to no doubt that electric batteries will be the dominating fuel source in the medium term. Other scholars maintain a more restrained view of the future. Forrest et al. (2020) conclude that, compared to lighter vehicles, MDT/HDT’s travel considerable larger distances and carry heavier loads, making the transition into EV’s more challenging (Earl et al., 2018). Liimatainen, van Vliet, and Aplyn (2019) agree that the possible benefits depend on each route’s drive-cycle and charac- teristics but show that battery-electric trucks are already viable for a large part of the current transports conducted using MDT. Most of the literature agrees that BEV is an emerging area of research and a promising strategy to reduce emissions (Forrest et al., 2020) and that battery-electric propulsion should not be dismissed (C¸ abukoglu et al., 2018). Currently, the few battery-electric trucks on the market have a range up to 200 km (Monios and Bergqvist, 2019; Anderhofstadt and Spinler, 2019), where manufacturers aim at reaching 300 km in the near future (Liimatainen, van Vliet, and Aplyn, 2019).
Recent advancements in both battery technology and fuel cells have increased focus on feasi- bility in light duty vehicles, creating synergies for the medium and heavy-duty sector (Forrest et al., 2020). One major factor is the improvement in the energy density of batteries, which is expected to continue to improve even more in the future (C¸ abukoglu et al., 2018), with a lower price as a consequence (Liimatainen, van Vliet, and Aplyn, 2019). The optimal choice of technology is debated among researchers, but a key factor among literature is the range of vehicles. FCEV’s chief advantage over BEV is the more extensive range and shorter refuel- ing time, comparable to conventional diesel trucks (Fossilfritt-Sverige, 2020; Lee et al., 2018;
Greene, Ogden, and Lin, 2020; C¸ abukoglu et al., 2019). Accordingly, (Greene, Ogden, and Lin, 2020) conclude that the higher utilization of the vehicle, the greater hydrogen’s advan- tage, making MDT/HDT an attractive platform for a hydrogen fuel cell propulsion.
Vehicle Solutions
The current debate in the literature is not just about one or the other technology; several studies focus on hybrid combinations. Both battery- and fuel cell technology are likely to be co-evolved in the future. FCEV’s are categorized as either fuel cell- or battery-dominant (Lee et al., 2018). Battery-dominant FCEV’s rely on a relatively large-capacity battery, where fuel cells serve as an auxiliary power source (Kast et al., 2017). The opposite, a fuel cell–dominant
FCEV, is primarily powered by fuel cells, where additional power is received from a compa- rably smaller battery (Lee et al., 2018). Hybrid solutions can eliminate problems related to diverse freight scenarios and ambiguity in range. Still, drawbacks include increased weight and reduced cargo space due to the many components required (Forrest et al., 2020).
Das et al. (2020) discusses the concept of Connected Mobility, where vehicles communicate among themselves and infrastructure, creating an opportunity for smart EV charging man- agement. Connected mobility enables both load management optimization, affecting grid sta- bility and overload, as well as optimization for the driver’s convenience when deciding where to charge. Other applications are vehicle fleet management, increasing vehicle utilization rate and goods efficiency.
The main challenges for hydrogen as a fuel are low energy density, and the high production cost (Kast et al., 2017). Hydrogen is commonly stored in a gaseous state in pressurized tanks at either 350 or 700 bar (Gangloff et al., 2016). One method of increasing the energy density is to liquefy the gas, but the process is more expensive and less energy-efficient than compressed gas hydrogen production (Fossilfritt-Sverige, 2020; Gangloff et al., 2016; Lee et al., 2018).
Infrastructure
Whether looking at battery-electric or hydrogen fuel-cell technology, an absence of infrastruc- ture for charging and refueling is seen as one main barrier for a transition to electric vehicles across literature (Monios and Bergqvist, 2019; Forrest et al., 2020; Anderhofstadt and Spin- ler, 2019). Low deployment of EV’s, strong lock-in effects, and low profitability creates little incentive to construct charging stations, while at the same time, these are the very reasons customers are hesitant to EV’s. This is referred to as the ”chicken-or-egg problem”, where the two aspects are highly dependant on each other and neither can spur without the other (Greene, Ogden, and Lin, 2020).
Power infrastructure investments are capital intensive and characterized by a high risk for investors, discouraging actors from investing (Fossilfritt-Sverige, 2020). Research further em- phasizes that escaping the current petroleum lock-in is a multi-decade transition period that requires reliable and durable politics that provide the necessary trust in the market (Greene, Ogden, and Lin, 2020). The EU’s hydrogen strategy is one example of this, creating synergies across different industries enabling economies of scales and shared risk-taking (Fossilfritt- Sverige, 2020). The infrastructure’s backbone is expected to be hydrogen supply and demand hubs referred to as ”hydrogen valleys” (A. Wang et al., 2020). These clusters are gradu- ally expected to be leveraged in the early markets (Kast et al., 2017) and then evolve into a mature pan-Europe hydrogen infrastructure by 2040 (A. Wang et al., 2020). The de- velopment of hydrogen cluster hubs is already under discussion in Sweden, where ports in Gothenburg and Stockholm are seen as strategic placements due to their traffic variation, providing emission-free solutions suitable for both the road transport- and the sea freight industry (Fossilfritt-Sverige, 2020).
Infrastructure for BEV’s can be divided into two categories, stationary and dynamic. Station-
ary infrastructure refers to plug-in solutions using cables, where the vehicle must come to a stop while charging. Dynamic infrastructure refers to various forms of electrified roads, where charging is done while the vehicle is moving (Chen, Taylor, and Kringos, 2015). FCEV’s can only benefit from stationary charging, but with the advantage of shorter fueling times. Sta- tionary charging builds upon the idea of strategically placed charging stations or chargers at logistic centers. The latter can sufficiently meet the demand when distances are shorter, and vehicles stay parked for most of their time, allowing for slow charging (Greaves, Backman, and Ellison, 2014). On the other hand, long-haul transportation spends most of their time on highways, with little possibility to stop for more extended periods.
Dynamic charging refers to the principle of providing power to the vehicle while in motion, and it is considered to be an emerging technology with several ongoing research projects (Q.
Wang, Berlin, and Meijer, 2019). Two methods are currently under development; conduc- tive charging, where physical contact between the vehicle and power supply is required, and inductive charging, where the power transmission is done wireless (Das et al., 2020). Both methods require costly installations as equipment must be either embedded in the existing pavement or installed along the road. Conductive dynamic charging is considered a more mature technology compared to inductive dynamic charging as it is similar to what has been used by the railway industry for a long time (Connolly, 2017; El-Shahat et al., 2019).
2.5 Modularization
In recent years, competition has intensified for many companies through increased variation in customer demand that increases the pressure on firms to deliver customization at lower costs (Shamsuzzoha et al., 2019; Seiler and Krause, 2020). Therefore, much attention has lately been drawn to the importance of modular product architectures by both academia and industry (Kashkoush and ElMaraghy, 2017; Williamsson, Sellgren, and S¨oderberg, 2018). A modular product architecture is frequently described as a design or platform with standard interfaces that enable interchangeable modules to create various products (Y. Liu, Zhang, and Z. Liu, 2011; Williamsson, Sellgren, and S¨oderberg, 2018; Shamsuzzoha et al., 2019). To create interchangeable modules, Erixon (1998) published a method called Modular Function Deployment (MFD), which has been included in most of the papers on modularization since then.
It is evident that the reviewed literature recognizes modular product architectures as a promising approach to manage extensive complexity and product variety meanwhile realizing economies of scale and product standardization (Seiler and Krause, 2020; Williamsson, Sell- gren, and S¨oderberg, 2018; Shamsuzzoha et al., 2019; Guenther Schuh, Riesener, and Breunig, 2017; Kashkoush and ElMaraghy, 2017; Bannasch, Rossi, and Thaidigsmann, 2017). Nonethe- less, to reach the overall success of a product, companies must strike the right balance between commonality and differentiation across products (Bannasch, Rossi, and Thaidigsmann, 2017).
The trade-off between product variety and product complexity is widely recognized in aca- demic literature. Although modularization can be considered a strategy for addressing this trade-off (Henriksson and Yxkull, 2017), the major issue is to balance all different customer demands against the capabilities and performance of the individual modules to determine an
optimal solution (Seiler and Krause, 2020).
To reach optimal product solutions, several studies has elaborated on methods for component optimizing in general (Y. Liu, Zhang, and Z. Liu, 2011; Kashkoush and ElMaraghy, 2017;
Guenther Schuh, Riesener, and Breunig, 2017). Mostly, the methods revolve around con- figuration algorithms that are referred to as “option-based algorithms” (Seiler and Krause, 2020). A few scholars has also used such methods to address modular solutions for FCEV, BEV and HDT (Diba and Esmailzadeh, 2020; F. M, Lehmeyer M, and Lienkamp M, 2017;
Sim et al., 2019; Williamsson, Sellgren, and S¨oderberg, 2018). Apart from Bonvoisin et al.
(2016), who partly touched upon unknown constraints when ”modularization for X” was in- troduced in their article, little research has been conducted on modular product architecture and future scenarios. It could however be argued that some scholars have addressed future scenarios since Diba and Esmailzadeh (2020), F. M, Lehmeyer M, and Lienkamp M (2017), and Sim et al. (2019), among others, have demonstrated component-optimization in vehicles partly based on futuristic assumptions about fuels and costs. Yet, as far as the authors know, none have tried to assess modular architectures’ overall efficiency based on key variables from future scenarios.
The design of modular products and architectures using key variables can be formulated as an optimization problem (Pattanaik and Jena, 2019). A component optimization problem generally deals with determining the values of key variables that minimize or maximize one or several objective functions with respect to a given set of constraints (Palani, Subramanian, and Chetty, 2019). However, due to the large number of variables, their diversity, and the multi- objective nature of the problem, it is a rather complex task (Silvas et al., 2016). Additional complexity is added for modular architectures since modules often comes with certain modular variety and different prioritization indices. Thus, the possible solution space can become almost immeasurable large when influential factors with strong dependency are varied in each iteration (Seiler and Krause, 2020). Also, such multi-variate problem generally consists of conflicting objective functions that require a trade-off solution (Palani, Subramanian, and Chetty, 2019), which cannot purely be addressed via a system of equations (Seiler and Krause, 2020). Instead, literature reveals that such multi-objective problems require an optimization algorithm that can deal with a large solution space.
When defining an optimization problem for vehicle design, the point of departure from a man- ufacturer perspective is to meet all legislative restrictions while creating a vehicle competitive on the market, appealing to customers and financially beneficial (Silvas et al., 2016). Hence, it often becomes a techno-economic trade-off problem, that origin from certain policy decisions.
Prior to this study, the search for alternative and optimal design solutions for vehicles and trucks has been investigated by several researchers (Eren and Gorgun, 2015; Tran et al., 2018;
Sim et al., 2019; F. M, Lehmeyer M, and Lienkamp M, 2017; Ciarapica et al., 2012), yet often with a narrow focus on specific architectures. For instance, Eren and Gorgun (2015) studied the techno-economic optimal sizing of Fuel Cell Battery Hybrid Electric Vehicles (FCBHEV).
Similarly, Sim et al. (2019) researched the economic optimal design solutions for FCEV while ensuring that there were no trade-off in performance. However, no cost associated with in- frastructure or downtime for charging are included in this model, which becomes necessary
when comparing TCO for different powertrains. In their model, a penalization factor to the cost function was included to penalize all solutions in which the pre-determined requirements were not met. Similarly, Ciarapica et al. (2012) considered a particular transport assignment with the objective to find optimal sizing of key components to minimize the TCO for a hybrid heavy-duty powertrain without impairing the performance requirements. Eren and Gorgun (2015) presented a study in which optimum sizing of components was analyzed with a given load profile and a multi-objective framework considering simultaneously the minimization of operating cost, weight and volume.
3 Scenario Framework
This chapter presents the scenario framework that has been extracted from previous research.
Then, the most influential customers values are derived from the future scenarios. The chapter also presents an interpretation of the business landscape in the future with respect network structures and transaction flows between actors. The scenario descriptions and interpretation of the future business landscape in each scenario are then used as a rationale to weigh the extracted customer value so that they receive a scenario-specific level of importance.
3.1 The Future Scenarios
This section presents four future scenarios for the transportation industry, developed in a previous research project (see Pernest˚al et al. (2019)). The scenario matrix is presented in figure 4 below and represents an outcome of a scenario modeling workshop series that involved more than 50 experts from more than 30 different organizations within the freight transport landscape. Further, the authors to the scenarios state that participating actors included transportation buyers, logistic service providers, road carriers, vehicle manufacturers, real estate companies, cities and regions, public bodies and authorities and researchers within transportation and logistics. The selection of participants was intended to cover a broad range of actors involved in, or interacting with, the freight transportation landscape.
These scenarios are selected as a foundation for this study because they reflect the litera- ture’s perception on trends, certain development, and uncertain development. In addition, the scenarios are of exploratory nature, which is particularly suitable when studying several interconnected systems, that is affected by internal and external trends and driving forces, and when time-horizon is ranges far into the future (B¨orjeson et al., 2006). Although the scenarios are profoundly developed, the originators underline that the four scenarios should not be seen as the most probable futures but rather as plausible futures with distinctive characteristics.
On the other hand, that is beneficial for a study like this since a clear distinction between scenarios is valuable for the analysis and can enrich the discussion on modularization.
Figure 4: Four Possible Scenarios by Pernest˚al et al. (2019)
Social Engineering 2.0
Characterized by a high focus on climate change and reducing environmental impact, So- cial Engineering 2.0 is a scenario where data sharing is limited. The low level of data sharing makes it difficult for new actors to enter the market. Existing actors are more likely to expand their existing collaborations with other major actors in favor of involving new businesses. The reduced data sharing is the result of both governmental regulations and consumers’ unwill- ingness to share their private information. High taxation on fossil fuels increases environ- mental sustainability, and as a result, fossil-fueled vehicles are almost non-existing. Although data-sharing is limited, digitalization is an integrated part of society, with plenty of informa- tion available. Regulations stating minimum fill rates and an overall willingness to increase transport effectiveness lead to cross-border collaborations between different actors. Major highways across Europe are electrified, which increases the share of battery-electric trucks.
However, due to diverse transport missions, a mix of electric and bio-fuel powered vehicles is still represented by the vehicle fleet.
Green Circle
Environmental sustainability and open data-sharing are both highly prioritized in the Green Circle scenario. On a European level, climate change receives attention at the expense of other social issues. Data-sharing is seen as positive, and regulations force actors to share their data on open platforms. New actors benefit from open data access and a high level of digitalization, and new and innovative solutions are challenging existing business models.
Rapid fluctuations are seen in the business market, where eco-friendly solutions can be seen to benefit. The focus on sustainability has made the public aware of transports, and with access to data, anyone can verify the sustainability of actors. An altered goods flow with an
increase in short-distance urban transports and decreased inter-urban transports are seen as a result.
Partnership Society
Economic instability, a reduced environmental focus, and increased focus on costs are char- acteristics describing Partnership Society. Economic viability is prioritized at the expense of environmental impact. The business climate is characterized by an unwillingness to share data with external actors, and information is kept within alliances formed by established busi- nesses, continuously building on existing relationships. Without access to data, new entrants find it hard to compete and break new ground. An increased goods flow results from in- creased trade, both on a global and national level. While technology, such as electric vehicles powered by solar energy, have decreased emissions per ton-km, the increased goods flow leads to greenhouse gas emissions on a level equivalent to 2019.
Bathing in Data
The main characteristics describing Bathing in Data are the high level of digitalization and fast-changing market dynamics. Climate change is not a top priority but benefit significantly from sustainable solutions stemming from open data-sharing and the amount of data available.
Actors across industries are connected and freely share data. An increase in e-commerce has led to an all-time high goods flow, focused on urban transportation. New actors offering flex- ible solutions with the customer in focus benefit from this new business climate. Companies do not necessarily own their vehicle fleet as innovative solutions focusing on transport-as-a- service (TaaS) offer new opportunities. A majority of the vehicles are powered by electricity, positively influencing the environment.
3.2 Extraction of Future Customer Values
When modularizing against the future scenarios there are, as the literature review revealed, many key variables to consider. By viewing the future scenarios from the customers (fleet owners) perspective, we interpret the main customer values to be cost-effective, sustainable, and efficient transportation. The rationale is that those three customer values not only encompass all four scenarios, but also includes more or less all of the key variables that were highlighted in the literature review. Although these customer values appear at a high level, they are also measurable in the modularization process. However, recalling that the scenarios in this framework reflects four possible but yet distinctive futures, it should be emphasized that the customer values are not equally important in the four scenarios. In fact, the level of importance is something that has to be considered to better understand the scenario performance of different vehicle solutions and this will described more in-depth in section 3.4.
3.3 Business Landscape & Network Structures
Further, the business landscape in the scenarios is analyzed to provide a deeper understanding of how business models are likely to develop in the future. To do this, the level of data-sharing
is used to frame network structures in the future business landscape, which can be leveraged to analyze transaction flows and understand future business models. In turn, such analysis is used as a rationale to justify the importance of customer values in each scenario. The network structures and transaction flows are described and illustrated in figure 5.
Figure 5: Network Structures & Transactions in the Four Scenarios [Source: Lina Simme, Modular Management AB]
In scenarios with a low level of data-sharing, alliances of major companies are expected to dominate the market and implying that the transportation is managed by the fleet owners.
In contrast, in future scenarios with open data-sharing, the market is assumed to be domi- nated by multiple smaller actors, not necessarily owning their vehicle fleet. Accordingly, it is reasonable to assume that economical aspects will be crucial for the fleet owners when the business models suggests fleet ownership. Likewise, economical aspects are considered less important in scenarios that correspond to increased collaboration and servitization enabled by open data-sharing.
3.4 Scenario Weighting of Future Customer Values
To consider the level of importance across all four scenarios, the customer values are weighted as; 1) not important. 2) important. 3) very important. The rationale to the scenario weighting is provided below per key customer value and with the perspective of a fleet owner.
Sustainability
Sustainability is explicitly a dimension in the scenario matrix, where its’ priority is deter- mined. Governmental organizations are seen steering companies to a more sustainable path, as to why in the scenarios Social Engineering 2.0 and Green Circle, sustainability is given the highest priority. In addition, a reinforcing effect is seen where customers prefer eco-friendly transportation and solutions in favor of fossil-fuel alternatives as society becomes very envi- ronmentally conscious. However, in Partnership Society and Bating in Data, sustainability is
only one of many other priorities, and hence, sustainability considered relatively un-important in comparison to the future scenarios where sustainability is top priority.
Transport Effectiveness
High transport effectiveness is deemed necessary when business models focus on services. If the trucks are leased, and fleet owners pay per transport, then trucks have to be capable of performing effective freight transportations in order for fleet owners to stay competitive.
While considered fairly important in all scenarios, the characteristics of Bathing in Data stand out regarding demand for increased vehicle utilization and transport optimization. Thus, transport effectiveness is deemed to have the highest priority in Bathing in Data, but only to be important in the other scenarios.
Total Cost of Ownership
Partnership Society and Social Engineering 2.0 are both characterized by a few large firms that collaborate in alliances, assumed to own their vehicle fleet, putting an increased focus on both CAPEX and OPEX, as to why TCO is given the highest prioritization in both scenarios. Green Circle and Bathing in Data are both characterized by a circular economy, specialized services, and a dynamic business environment, and therefore the importance of TCO is considered relatively low in those scenarios. A summary of the weighting are presented in table 1 below.
Table 1: Importance of Key Customer Values in the Four Scenarios
Key Customer Values Social Engineering 2.0 Green Circle Partnership Society Bathing in Data
Sustainability 3 3 1 1
Total Cost of Ownership 3 1 3 1
Transport Effectiveness 2 2 2 3
Once the key customer values are weighted as described above, each property can be divided by the sum of all values in the column that corresponds to a particular scenario. The results from this can be viewed in table 2 below. By doing this, an individual level of importance can be obtained. That ratio can then be used as input to the model to evaluate the effectiveness of a configuration based on scenario-specific values.
Table 2: Level of Importance in Percentage
Weighting Factor Social Engineering 2.0 Green Circle Partnership Society Bathing in Data
Sustainability Ws 37,50% 50,0% 16,67% 20,00%
Total Cost of Ownersship WT CO 37,50% 16,67% 50,0% 20,00%
Transport Effectiveness WT E 25,0% 33,33% 33,33% 50,0%
4 Method
In this chapter, the overall method that is used in this thesis is outlined. Beginning with the research design and then describing the research process before the data collection and the quantitative model is presented.
4.1 Research Design
Regarding the research design, an exploratory approach to address the gap between future studies and modularization was used, much due to the lack of current methods to evaluate modular architectures against future scenarios. Future studies and modularization are two distinctive fields. Future studies often tend to be more qualitative compared to modular- ization, where the focus is concentrated on product design. Nevertheless, modularization, according to the MFD method (see Appendix B), includes both qualitative and quantita- tive steps, why it has been used as a guiding framework throughout this research project.
While the MFD method was not followed step by step, it guided how scenario modeling, in general, can be used as a tool to support the product development process in meeting the uncertainties of diverse futures. In addition, this thesis aimed to explore how key variables from future scenarios can be extracted and used to evaluate the efficiency of modular con- figurations. Thus, to address the research purpose and research questions, this thesis was not only conducted with an exploratory perspective, as described by Saunders, Lewis, and Thornhill (2015) but also with a combination of mixed methods, described by Schoonenboom and Johnson (2017).
Mixed methods research is an approach that combines quantitative and qualitative research methods in the same research inquiry (Venkatesh, Brown, and Bala, 2013). This thesis uses this method because the scenario modeling is qualitative, whilst the evaluation of modular configurations needs to be performed quantitatively. Moreover, the qualitative scenarios serve as a foundation for the quantitative evaluation, and the sequence of using a qualitative method followed by a quantitative method is referred to as sequential exploratory (Saunders, Lewis, and Thornhill, 2015; Venkatesh, Brown, and Bala, 2013) and the quantitative method is said to be dependent on the qualitative analysis (Schoonenboom and Johnson, 2017).
Using mixed methods has, however, been subject to research critique due to the risk of diluting the value of the qualitative research in the quantifying process (Saunders, Lewis, and Thornhill, 2015). On the other hand, mixed methods can leverage the complementary strengths of qualitative and quantitative methods and offer greater insights on a phenomenon that each of these methods individually cannot offer (Venkatesh, Brown, and Bala, 2013). It should also be mentioned that mixed-method research does not have to have an equal weight of methods. Instead, mixed methods can be either qualitative dominant or quantitative dominant, yet a mix (Schoonenboom and Johnson, 2017; Saunders, Lewis, and Thornhill, 2015). The mix in this thesis was dominated by quantitative research. One advantage with that is that increased focus on the quantitative assessment can provide a more profound understanding of the studied phenomenon. In addition, increasing the share of quantitative research is also advantageous when exploring the relationship between variables, and this is
particularly valuable when the phenomena of complex nature and affected by many factors (Blomkvist, 2014), as in this thesis.
Regarding the view on the research process, the quantitative-dominant mixed method research is characterized by a quantitative and postpositivist perception while concurrently recognizing that the inclusion of qualitative research is likely to strengthen the research (Schoonenboom and Johnson, 2017). Further, the qualitative scenarios in this thesis originated from an ex- ploratory research approach (Pernest˚al et al., 2019), whilst the assessment of performance and comparisons of the efficiency of different modules corresponds to a more evaluative research approach according to Saunders, Lewis, and Thornhill (2015). In addition, the theoretical drive of sequential quantitative analysis can be viewed as evaluative, which is more of a deduc- tive approach (Schoonenboom and Johnson, 2017). Nevertheless, by continuously presenting intermediate results at stakeholder meetings, we were provided new qualitative input and en- couraged to return to the literature to collect more data when necessary, as the intermediate findings occasionally shed light on new paths. Hence, the theoretical drive is arguably more of an abductive approach, implying a continuous movement between data collection (litera- ture & stakeholder meetings and iterative testing (quantitative simulations). This research approach enabled us to make adjustments throughout the data collection process, pointed out as a key feature to theory-building by Eisenhardt (1989). The approach was further useful to systematically break down the complexity of integrating future studies in the modularization process.
4.2 Research Process
The overall research process for this thesis is illustrated below in figure 6. As a general scope of the project was already defined, the research process began with an initial literature review to explore the research field. The initial literature provided a more in-depth understanding of the transportation industry as well as guidance and inspiration to the more extensive literature review that was later conducted.
Figure 6: Overview of the Research Process
