The automotive development process  a real options analysis

Introduction

Problem Statement

The objective of this research will be to solve the following primary problem:

How is it possible to value, control, and optimize the automotive product development process?

In order to answer this question the following subproblems j will be sought answered:

1 What are the key characteristics of the automotive development process and what models of design are available for the development process?

2 What role does the automotive development process play in helping the developing company attain a sustainable competitive advantage in the marketplace?

3 What are the relevant real options available to management in the development process, and how are these valued from the perspective of the developing company?

4 How can management structure the development process in order to maximize its value?

=See Leedy (1980, p 57) for a discussion of the use of subproblems in solving a main problem piecemeal

The above problems will now be elaborated on:

The thesis focuses on the automotive development process, emphasizing the interconnection between "valuation" and "control," where any action influencing cash flows directly impacts the process's value Consequently, it is essential to address control and valuation concurrently Additionally, the optimization aspect aims to enhance the development process's value by determining the most effective setup.

This article aims to analyze the automotive development process, highlighting it as the primary unit of analysis for subsequent research It portrays the automotive development process as an interconnected system of decisions, emphasizing the critical decisions and events that significantly impact the overall development effort Additionally, the article presents various models of automotive development, comparing their key assumptions and performance metrics.

This article explores the connection between the automotive development process and the corporate objective of maintaining a competitive advantage in the marketplace It begins by comparing the two main perspectives of strategic management analysis: strategic fit and strategic stretch, highlighting their core assumptions and characteristics The analysis then focuses on how management can leverage the automotive development process to achieve both strategic fit and strategic stretch Emphasis is placed on empirical studies that demonstrate how individual automotive companies gain a competitive edge through their development processes.

This article aims to explore and compare different types of managerial flexibility in the automotive development process through a real option valuation model It begins by examining the influence of financial markets and the foundational assumptions necessary for modeling real options based on general valuation principles Subsequently, it presents a tailored real option model designed specifically to assess the automotive development process.

This article aims to evaluate the overall automobile development process to enhance its value for the company The key method employed is a valuation model specifically designed for this analysis, which was established in relation to subproblem 3 By utilizing this model, the study seeks to identify and compare the value of different compositions of the development process, ultimately aiming for an optimal approach.

Delimitation

In answering the problem statement, the thesis will be delimited by the following points:

The focus of this analysis is the automobile development process, specifically optimizing the use of existing technologies within this framework While the preceding research and subsequent production processes are not explicitly examined, it is assumed that a defined technology freezer is in place Additionally, the output of the development process is expected to align with the overall outcomes of automotive development.

Organizational theory significantly impacts understanding the development process within companies, particularly in relation to organizational structure and individual behavior This article will explore how these factors influence development, assuming that individuals involved are acting in the best interests of the company's owners This perspective highlights the importance of information processing during the automobile development process.

The development process of automotive products involves significant technical complexities that are crucial to consider This thesis will address essential technical details and specifications when relevant, while primarily focusing on the main characteristics of the automotive product In certain sections, in-depth technical and process-related aspects will be intentionally minimized to maintain clarity and coherence.

2 Clark and Fujimoto (1991, pp 18-22) make a similar assumption in their empirical research of the world auto industry.

Methodology

This section outlines the methodology utilized in the thesis, referencing key works by Brodbeck (1968), Morison (1993), and Davis and Parker (1997) The aim is to establish a foundational rationale for the chosen approach to address the primary issue at hand Overall, the field of business administration serves as the framework for tackling this central problem.

The primary objective is to enhance the company's market value, reflecting the principles of liberal economics that allow free market forces to dictate resource allocation both in the broader economy and within individual firms.

The thesis addresses the automotive development process by adopting a broad theoretical perspective that encompasses both engineering sciences and business administration This comprehensive approach draws on theories from engineering, strategic management, and financial economics, with a particular focus on finance Notably, the theory of real options is emphasized for its potential to provide new insights into managing the automotive development process, serving as the foundational theoretical framework for the arguments presented in the thesis.

The data basis of the thesis consists of both quantitative as well as qualitative data From the above-mentioned theories, the theories and models from strategic

3 This is primarily done in answering subproblem I of the problem statement

The analysis focuses on addressing subproblems 2 to 4 of the problem statement, emphasizing the use of qualitative data in management In contrast, theories and models from engineering and business administration rely heavily on quantitative data Given the significant emphasis on the theory of real options and the critical aspect of valuation within the thesis, quantitative data will serve as the primary source of information.

This thesis primarily utilizes secondary data, both quantitative and qualitative, sourced from literature on automotive development and strategic management While specific primary data is acknowledged as significant for practical recommendations, the focus is on a deductive approach using hypothetical data Various assumptions and models are outlined, and conclusions are drawn through deductive reasoning supported by illustrative hypothetical data The main contribution lies in the development of models that can be applied in practice, emphasizing that the calculations rely heavily on hypothetical figures The specific numerical values are less critical than the overall goal of creating a framework to address the primary issue This work precedes any potential positivistic empirical research aimed at validating the presented models.

After establishing the theoretical framework and data foundation of the thesis, it is essential to examine the critical elements of reliability and validity concerning the chosen methodology, as depicted in Figure 1.

The reliability of a thesis is determined by its capacity to consistently yield the same results under identical conditions (Johnson and Harris 2002, pp 102-103) This principle is illustrated by the use of theoretical quantitative models from financial economics, which provide high reliability through consistent outputs given fixed inputs, such as option pricing models Conversely, models from strategic management exhibit lower reliability due to their qualitative nature, making it challenging to mathematically substantiate the strategic significance of specific business processes The strategic management literature often employs varied terms for closely related concepts, which can lead to confusion To enhance the thesis's reliability, it is essential to incorporate stringent definitions and clear arguments when utilizing strategic management models.

Reliability is a crucial aspect of assessing the validity of a thesis, which determines whether the research accurately measures what it claims to measure (Johnson and Harris, 2002, p 103) High validity cannot exist without high reliability; if both are lacking, it reflects the lowest quality of research, as illustrated in Figure 1 Ultimately, validity serves as the key benchmark for evaluating the quality of presented research.

To ensure the thesis aligns closely with the intended focus, extensive consideration has been given to enhancing its validity The theoretical framework is intentionally broad, allowing for an analysis that encompasses both qualitative and quantitative models as well as performance drivers Given the complexity of the automotive development process, it cannot be adequately assessed using quantitative models alone Thus, a comprehensive analysis must integrate both qualitative and quantitative variables In this thesis, qualitative models from strategic management and engineering validate the quantitative model framework derived from financial economics, which serves as the primary analytical tool.

The thesis's validity is strengthened through a combination of deductive and inductive reasoning Deductive arguments are utilized to construct analyses based on established theories, assumptions, and prior results, providing a solid theoretical foundation This approach allows the thesis to build upon and contribute to existing theories by proposing new and significant causal relationships Conversely, inductive arguments are employed to generalize findings from various case studies and research within the automotive industry In this context, validity aligns with the concept of "external validity," as referenced by Leedy.

(1990, p 37) for a discussion of external validity

The validity of the thesis is contingent upon two key factors: the reliability and validity of individual works and the applicability of their analytical objects to the current issue This raises important considerations regarding the validity of secondary data used in the thesis Furthermore, the reliance on quantitative modeling in the automotive development process presents challenges, including assumptions about causal relationships and the scarcity of relevant quantitative and financial market data necessary for accurately valuing this process.

The Automotive Development Process

The Setting of the Automobile Development Process

The present-day automobile consists of up to 20,000 separate components (Liker et al

1996, p 168) There are therefore roughly 400 million possible interactions between the components 7 This product complexity is compounded by a multitude of factors within

7 20,000 X (20,000 - I ) = 399,980,000 and outside the developing organization The process of developing an automobile could, therefore, be a voluminous task

In their work dealing with product development Clark and Fujimoto (1991) rank a product in general along two dimensions: internal and external complexity (see Figure

Internal complexity in a product is influenced by factors such as the number of unique components, the production process, internal product interfaces, and the technological trade-offs among those components In contrast, external complexity is shaped by the variety and quantity of performance criteria, as well as the degree to which customers prioritize both measurable and nuanced aspects of the product.

Figure 2: Internal and External Product Complexity

(e.g., conventional packaged Products goods) (e.g., quartz watch, audio equipment)

Complexity of Product-User Interface

The automobile, positioned in the upper right quadrant of the product complexity grid, represents one of the most complex products due to its high internal and external complexity This complexity arises from numerous advanced components, like electronic systems, necessitating meticulous management attention, as they significantly affect the final vehicle's characteristics Additionally, the multifaceted interactions customers have with the automobile further complicate management considerations As noted by Clark and Fujimoto, coordinating the entire vehicle development process is extremely challenging, akin to solving a vast simultaneous equation system This complexity profoundly influences the automotive development process, making it essential to identify and analyze key performance drivers Schwartz emphasizes that R&D project analysis is one of the most challenging investment issues, with decisions made during development greatly impacting total market costs Notably, up to 80% of total automotive product costs are determined during this phase, highlighting the necessity for efficient management of the automotive development process as a critical driver of corporate performance.

The thesis focuses on the critical aspect of valuation within the automotive development process, emphasizing the need to analyze decision-making impacts on cash flows and risk structures A comprehensive outline of this process is essential for creating an effective valuation model, as the complexity of automobiles significantly influences these financial elements A valid valuation model must accurately represent the key cash flows and risks associated with development, necessitating the identification of crucial performance drivers Given the intricacies of automotive products and the challenges faced during development, this chapter proposes a framework to effectively capture the main effects on cash flows and risk, serving as a foundation for the subsequent analysis.

The Automotive Development Process

To start with, the overall aims of the automobile development process are pointed out and discussed Wheelwright and Clark (1992) mention three imperatives (see Figure 3) for the automobile development process 9

Figure 3: Three Imperatives of the Automobile Development time productivity integrity

Source: adapted from Wheelwright and Clark (1992)

8 The actual valuation model is developed and presented in chapter 4

9 Similarly, Clark and Fujimoto (1991) list three objectives: Total product quality, lead time, and productivity

The automotive development process must prioritize three key objectives: accelerating the car's development time, maximizing resource efficiency, and ensuring the final product meets or surpasses customer expectations.

The objectives of the automotive development process are closely tied to the value it generates, particularly regarding the timing and magnitude of cash flows These objectives are interdependent and can fluctuate based on the development process utilized Therefore, a deeper exploration of this process is essential.

The automotive development process is often portrayed as linear due to its sequential decision-making structure, where decisions made at one point influence future options Various authors outline different generic product development processes, yet they all maintain a similar foundational structure, with differences arising in the level of detail provided Ulrich and Eppinger (2000) identify five major phases in their generic development process Additionally, it is important to recognize that choosing not to make a decision at a given time can also be a significant choice, as further explored in chapters 4 and 5.

Figure 4: The Five Phases of Product Development

Source: adapted from Ulrich and Eppinger (2000)

The initial phase of concept development begins by identifying the needs of the target market, focusing on the customers Following this, one or more concepts are created and subsequently tested to ensure they meet market demands, as outlined by Ulrich and Eppinger.

The product development process is defined as "a description of the form, function, and features of a product" (2000, p 17) It consists of several phases, starting with system-level design, which outlines the automobile's architecture, subsystems, specifications, and their interfaces, ultimately producing a geometric layout The detail design phase follows, refining the specifications to enable the production of unique parts for the vehicle The testing and refinement phase focuses on constructing prototypes to ensure the automobile meets customer requirements The final phase, production ramp-up, signals a commitment to mass production and the official end of the development process While the phases are illustrated sequentially, they often occur simultaneously and are interdependent, creating a development process characterized by loops Additionally, the management of information is crucial alongside the physical development of the automobile.

Clark and Fujimoto (1991) conducted extensive research on the global automobile industry, introducing a model that integrates information into the automotive product development process This model, illustrated in Figure 5, consists of three interconnected processes: product development, production, and consumption The product development process closely resembles the generic development process shown in Figure 4, where "System-level design" and "Detail design" are analogous to "Product plan" and other stages within the framework.

"Product design" respectively in Figure 5 The "Testing and refinement" phase in Figure

The testing activities outlined in Figure 5 are integral to each stage of the production process, which follows the "Production ramp-up" phase depicted in Figure 4 The consumption process highlights the customer's experience with the completed automobile, emphasizing the importance of addressing both "existing" and "potential" customers in this phase.

12 The term "loop" is later in this chapter understood as one or more "design-build-test" cycles

13 Of the three mentioned processes, the product development process is the primary focus of this thesis

Figure 5: The Development Process as an Information System i Product development process uoll Pr uo llPr u~ Pr essll concept plan ~ design design

9 :::i Simulation of production and consumption process i~:: v ~ v v iii Consumption process

Product function Actual customer satisfaction

Existing customers ii Production process

II II < structure r u onll process 9

Source: adapted from Clark and Fujimoto (1991, p 23)

Figure 5 illustrates the generation and transmission of information throughout the automotive design and production processes As the design evolves, essential information is created and communicated, which is crucial for the vehicle's production Additionally, during the consumption phase, customers gain insights about the product through their interactions Consequently, the amount of information expands at each stage of the process.

The progression of automotive design is illustrated in Figure 5, where the development process is viewed as a simulation of future production and consumption The steps in the development process, depicted in the upper section, align with the corresponding phases in the lower section, highlighting their parallel nature This simulation allows management to assess the current state of automotive design against various simulated outcomes Consequently, the insights gained from the simulation at different time intervals are crucial for optimizing the development process.

The "Product concept" phase focuses on understanding and integrating future customer satisfaction into the product design In the "Product plan" phase, engineers outline the styling, layout, and key platform features of the automobile to ensure it meets desired functions The "Product design" phase involves detailing all technical aspects of the vehicle, while the "Process design" phase addresses production logistics, including factory layouts and standard operating procedures Ultimately, the entire product development process begins and ends with the customer, highlighting that the market serves as the final evaluator of the company's efforts.

The information perspective in automobile development shifts the focus from the physical product to the vital information required for its creation This involves managing the interdependencies throughout the development process, enabling management to effectively oversee linkages The goal is to produce a vehicle with high integrity, which is influenced by two key value drivers: external and internal integrity External integrity assesses how well the automobile meets customer requirements, such as design and interior layout, while internal integrity evaluates the compatibility of product functions and structure, ensuring safety in crash tests Ultimately, the challenge lies in designing a development process that yields a car with both high internal and external integrity efficiently, a central theme in numerous studies on automotive product development performance.

To comprehend the automobile development process, it is beneficial to view it within a "design space," a concept introduced by Coyne et al (1990) to establish a theoretical framework for enhancing automobile design Ward et al (1995) emphasize that for any design challenge, there exists an unknown "design space" encompassing all potential values for every parameter involved An illustration of this design space can be found in Figure 6.

The article illustrates three sets of designs (t, t+1, and t+2) within an unknown design space, reflecting the evolving options considered by a developing company over time At time t, the company evaluates design A, which features a distinct combination of parameters By time t+1, the search expands to include design B, and at t+2, design C is assessed The company's objective is to map and compare these feasible designs to identify more optimal parameter combinations This process transforms automobile development into a problem-solving endeavor, characterized by the challenge of bridging the gap between the current design and a more optimal solution A key aspect of this search is the inherent uncertainty, as the outcomes of the design exploration cannot be predicted with complete confidence.

At the core of the problem-solving process is the design-build-test cycle (refer in general to Coyne et al 1990) illustrated in Figure 7

Figure 7: The Design-Build-Test Cycle

The design-build-test cycle is an iterative approach to design that begins with a set of design parameters, followed by prototype construction and testing The test results are compared against design objectives, primarily focusing on achieving internal and external integrity and effectively simulating production and consumption processes If discrepancies arise, management can modify the design and restart the cycle, leading to increased detail with each iteration This cycle is crucial for generating information throughout the automotive development process and serves as its fundamental building block To optimize the automotive development process, mastering the design-build-test cycle is essential, benchmarked against specific criteria.

A crucial aspect of the design-build-test cycle in automotive development is the creation of prototypes, which reflect the current state of automotive design and serve as representations of potential mass-produced vehicles These prototypes act as information-generating assets, allowing management to update design specifications after each cycle Consequently, the design-build-test cycle focuses on one prototype at a time, with each iteration leading to a new alternative in the design space Prototypes function as both technical tools and essential management resources for overseeing the development process Wheelwright and Clark (1992) emphasize the significance of prototyping in this context.

"Senior managers, functional heads, project leaders who do not understand and fully utilize the power of prototyping unintentionally handicap their efforts to achieve rapid, effective, and productive development results."

Present Value Method for the Automotive Development Process

Valuation plays a crucial role in the automobile development process, serving as a central theme of this work As outlined in the problem statement, this topic will be examined in greater detail in chapters 4 and 5 While various investment valuation methods exist, the present value method is the most widely accepted, as highlighted in equation (2.1) (Brealey, Myers, and Allen 2005).

(2.1) NPV=-IơPV(FCF)=-Iỡ_, n E(FCF') t=1(1+ rt) t where

Io = Investment Costs at time 0

FCFt = The Expected Free Cash Flow at time t rt = Discount rate at time t

The present value method comprises three key components: Free Cash Flow (FCF), the discount rate (rt), and the initial investment (Io) FCF reflects the cash return from an investment, such as an automotive development process, at a specific time (t) The discount rate accounts for the opportunity costs of the invested capital, while the present value (PV) indicates the maximum price an investor is willing to pay for the investment If the PV of expected cash flows surpasses the investment outlay, the investment is deemed favorable, resulting in a positive Net Present Value (NPV) Conversely, if the PV equals the outlay, the investor remains indifferent, leading to a zero NPV Finally, if the PV falls short of the investment cost, the method advises against investing, resulting in a negative NPV.

The Net Present Value (NPV) is a valuable metric that indicates the increase in an investor's wealth, reflecting how much a company's development process, such as that of an automobile manufacturer, enhances its market value.

To maximize the value of the development process, a strategic approach is essential for the success of the developing company The present value method will serve as a key reference point in discussions regarding valuation.

Models of Automotive Development

This article introduces and compares three key models of automotive development, focusing on their use of prototypes to navigate the design space within uncertain environments Building on earlier discussions about design exploration, the significance of prototypes, and the dynamic nature of the development process, this analysis highlights how these models adapt to challenges in automotive innovation.

To illustrate various development models, an analogy to the automotive development process is useful Liker et al (1996) provide an example involving scheduling a meeting for a group of employees The first strategy involves the organizer selecting a convenient time, inviting participants one by one, leading to multiple feedback loops as attendees propose alternative times, often resulting in a lengthy and inefficient process without guarantees of finding an optimal time The second strategy aims to expedite this process by either holding a preliminary meeting to decide on a meeting time or imposing a specific time on participants, which typically leads to a sub-optimal outcome The third strategy addresses the information gap by collecting available meeting times and preferences from all members, allowing for the identification of an optimal meeting time through the intersection of their schedules If no common time is found, the set of options can be expanded, mirroring the feedback loop of the first strategy.

Finding an optimal meeting time parallels the design space exploration in automobile development Sobek, Ward, and Liker (1999) identify three distinct paradigms in the automobile development process, summarizing the research on effective strategies for this complex task.

2) point-based concurrent engineering, and

Each has its counterpart introduced with the strategies for finding a meeting time This is summarized in Table 2

Table 2: Analogy between the Optimal Meeting Time and the Search through

Strategies for automobile development Strategies for finding a meeting b'me

Point-based serial engineering Invite people one at a time

Have a meeting to find a date Obtain set of available meeting times

In the succeeding sections, each of the three strategies for automobile development will be outlined (refer in general to Sobek et al 1999 and Liker et al 1996)

The point-based serial engineering process serves as a foundational framework for optimizing development processes, as highlighted by Papalambros and Wilde (1991) This method closely mirrors the standard automotive development process, as shown in Figure 4 Additionally, Figure 10 provides a visual representation of the structure of the point-based serial engineering development process.

Figure 10: Point-Based Serial Engineering

I Styling !_.~1 Marketing ~ ~1 Body I_~1 Chassis ~-~I Manufacturing ]

The automobile development process begins with styling, which conceptualizes a design based on optimality criteria This design is then evaluated by marketing, leading to a critique that prompts revisions in styling The iterative process continues as the design is reviewed by the body, chassis, and manufacturing functions, each providing feedback that requires further adaptations This sequential flow highlights the collaborative nature of design refinement, ensuring that each department contributes to the final product's optimization.

The point-based serial engineering approach highlights the challenges of coordinating meeting times due to limited information sharing among participants across departments Each function operates within its own segmented information set, undergoing a design-build-test cycle based on specific criteria Consequently, design changes create a feedback loop that can restrict feasible alternatives for other functions As noted by Ward et al (1995), the intricate interconnections of designs make it difficult to assess the impact of changes on previous decisions With increasing interdependencies among development participants, the number of feedback loops rises, potentially leading to prolonged problem-solving processes.

The point-based serial engineering strategy focuses on the development of a single primary automotive design at any given time Design changes arise from feedback loops, resulting in modifications that remain closely tied to the original design This approach is termed "point-based" as it emphasizes the review of one major design at a specific moment in the development process.

The point-based development process is commonly known as a "hill-climbing" approach, which simplifies management from an engineering strategy perspective This method allows for straightforward oversight of the point-based serial development.

The point-based concurrent engineering process, a derivative of the point-based serial engineering development method, will be discussed in the following sections This approach is currently prevalent in the automobile industry, as highlighted by Clark and Fujimoto (1991).

The primary goal of the point-based concurrent engineering process is to enhance problem-solving efficiency compared to point-based serial engineering Development time is significantly influenced by interdependencies and delayed feedback loops To mitigate the adverse effects of these delays on development lead-time, the concurrent engineering strategy emphasizes advancing feedback loops by promoting parallel processing of design, build, and test phases across all functions involved in the development process As a result, feedback from all participating functions is received earlier, improving overall project efficiency.

Figure 11: Point-Based Concurrent Engineering

In the design-build-test cycle illustrated in Figure 11, the styling function actively seeks input on design feasibility from marketing, body, chassis, and manufacturing teams Each of these functions conducts their own independent design evaluations, contributing to a collaborative assessment of the styling design This process highlights the importance of concurrent feedback across all development functions, ensuring a comprehensive evaluation of the design from multiple perspectives.

Concurrent processing of design-build-test cycles across various functions reduces the negative impact of segmented information on development lead time This approach enhances integration among functions in point-based concurrent engineering compared to serial point-based engineering, particularly in the information management process From a management perspective, greater attention is needed to oversee the interconnections between functions, as they operate simultaneously Unlike serial point-based engineering, which investigates only one design at a time, both methodologies fall under the same point-based paradigm for managing the automotive development process.

The set-based concurrent engineering process, more extensive than traditional point-based automotive development strategies, represents a fundamentally different paradigm in the automotive industry, notably implemented by Toyota This approach allows for the exploration of a broader design space, enhancing the development of optimal designs in uncertain environments By initiating the process with multiple design alternatives developed in parallel, the set-based strategy progressively narrows down options through various automotive development phases This proactive exploration of design alternatives is crucial for advancing the automotive development process.

Figure 12: Set-Based Concurrent Engineering (i)

Set-based concurrent engineering strategy X based concurrent engineering strategy Y

Figure 12 illustrates two set-based concurrent development strategies, X and Y, with strategy X exploring the design space more extensively than strategy Y due to its higher initial intersection on the ordinate As development progresses, suboptimal design alternatives are eliminated, resulting in the negative slope of the lines representing both strategies Additionally, the horizontal line starting at 1 on the ordinate symbolizes the point-based concurrent engineering strategy Figure 13 provides an alternative representation of the set-based concurrent development strategy.

Figure 13: Set-Based Concurrent Engineering (ii)

Start of End of development development

Figure 13 illustrates two set-based concurrent development strategies, X and Y Strategy Y examines design alternatives B and C, while Strategy X explores a broader range, including A and D alongside B and C Over time, the number of developed alternatives decreases until only one remains, guided by criteria focused on achieving high external and internal integrity This management-driven reduction process leads to the final implementation of a single design alternative, represented in Figure 13 as either A, B, C, or another option.

Comparative Analysis of the Point- and Set-Based Models of Development

This article compares point-based and set-based models of automotive development, focusing on their influence on managerial decision-making It analyzes how each model affects flexibility, uncertainty, and investment costs, with a comparative overview provided in Table 4.

Table 4: Comparison of Point- and Set-Based Development

Development strategy Point-based Set-based

Level of integration needed low high

The comparison of point-based and set-based strategies in automobile development reveals distinct approaches to achieving the same goal The point-based strategy prioritizes "doing it right the first time," urging management to concentrate resources on the most optimal design from the outset (Ward et al 1995, p 48) In contrast, the set-based strategy acknowledges multiple design possibilities alongside the optimal choice, promoting a broader exploration of the design space by developing several alternatives concurrently during the initial stages of the process.

The "Level of integration needed" highlighted in Table 4 pertains to the firm-specific capabilities essential for implementing either a point-based or a set-based development strategy A set-based strategy is inherently more complex than a point-based strategy, requiring a "high" level of integration due to the simultaneous management of multiple projects This approach necessitates oversight of various projects at once, increasing the likelihood of interactions among the design alternatives being developed, which complicates the management process Consequently, there is often a correlation matrix among the design alternatives that is not indifferent from zero Subsequent chapters will explore the capabilities related to the automotive development process, with Chapters 4 and 5 focusing on intraproject correlations within a valuation model.

One significant distinction between the two development strategies is the investment costs involved, as highlighted in Table 4 The set-based strategy entails two main investment components: first, the parallel development of multiple alternatives, which leads to higher proportional costs compared to point-based development; and second, an upfront investment to establish the necessary design information, requiring the development of an automotive platform to support various design alternatives Although this comprehensive exploration of design options can be beneficial, it also results in substantial costs, making set-based development appear inefficient, as ultimately only one alternative is implemented Consequently, most automotive firms tend to favor point-based development processes.

The significance of "uncertainty" and "flexibility" in development strategies largely hinges on the chosen approach Uncertainty is inherent in the development process, influenced by the evolving information landscape, which can lead to actual outcomes diverging from expected results When only one design alternative is available, management must adhere to the results of the design-build-test cycles, as seen in point-based development strategies.

In the following chapters, we will argue that the automotive industry's unique traits of uncertainty and flexibility are crucial value drivers that can enhance and streamline the development process The lack of flexibility in design choices arises when only a single design alternative has been developed.

The set-based development process offers significant advantages, as it provides management with the flexibility to select the most valuable design alternative at the end of the development phase This approach allows for the abandonment of poorly performing designs in favor of more promising options The effectiveness of this flexibility is influenced by the correlation between design alternatives, with a correlation matrix playing a critical role in outlining potential evolutionary paths for each alternative Further modeling is needed to understand how managerial flexibility within the set-based strategy affects the overall value of the development process, particularly when design alternatives exhibit uncertainty and mutual correlation.

2.5.4 Trade-off between Investment Costs and Flexibility to Switch

The comparison between point-based and set-based development strategies reveals a significant trade-off While the set-based approach offers management the flexibility to choose among various developed designs, it also tends to require more resources To maintain this flexibility, management intentionally incorporates adaptable elements into the development process, which results in higher investment costs This trade-off is visually represented in Figure 18.

19 The actual outcomes depend on the evolution of the technical and market uncertainties for each design alternative

Addressing this challenging trade-off necessitates a structured framework for effective illustration This framework is introduced in Chapter 4 and applied in Chapter 5.

Figure 18: Trade-off between Znvestment Costs and Flexibility to

Value of flexibility to switch

The primary goal of management is to enhance the value of the automotive development process, as illustrated in Figure 18, which highlights the technical and market uncertainties influencing the value of various design alternatives This approach to value creation is integral to calculations such as Present Value (PV) Additionally, the uncertainties associated with each design alternative impact the value of the flexibility to switch among them, a factor often overlooked in traditional valuations of the automotive development process While the ability to switch designs enhances value, pursuing multiple design options simultaneously incurs higher investment costs, creating a trade-off Consequently, management must determine the number of design alternatives to develop in parallel, ranging from one to several This decision reflects the contrasting point-based and set-based paradigms in automobile development, where the former focuses on a single design and the latter on multiple concurrent designs Ultimately, Figure 18 serves as a foundational reference for a forthcoming valuation model of the automotive development process, which will be detailed in Chapter 4.

(z.z) ENPV=-I o +PV (FCF) +Value of flexibility where

ENPV = Extended Net Present Value

Io = Present value of expected investment outlays

Flexibility in an uncertain automotive development environment holds significant value, yet it incurs costs Management must decide the extent of flexibility to integrate into the development process, considering rising investment costs The value of this flexibility is influenced by the types and correlations of uncertainties inherent in the development process Thus, the trade-off can be framed as balancing technical and market uncertainties against the investment costs associated with automobile development.

This chapter outlines the framework for the automobile development process, which will be further analyzed in subsequent chapters The primary goal of this analysis is to create a quantitative valuation model that effectively assesses the interdependencies within the automobile development process.

Chapter Summary

This chapter explores the complexities of automobile development, highlighting its intricate structure and function It outlines three distinct methods for developing an automobile, emphasizing the product's internal and external complexities The primary challenge for automotive companies lies in delivering vehicles efficiently while maintaining high productivity and quality standards.

The automotive development process heavily relies on understanding the technical and market prospects of vehicles, making information about uncertainties more critical than the physical product itself To effectively generate this information, it is essential to explore the design space through targeted design-build-test cycles that utilize prototypes as tools for information generation Therefore, management should prioritize the strategic use of prototypes to thoroughly investigate design alternatives and navigate the complexities of the development process.

Chapter 2.3 presented three models of automotive development, which were introduced by means of an example: finding an optimal meeting time The simplest development model is point-based serial engineering According to this model management chooses the current most optimal design and develops it through several sequential iterative cycles The second model is point-based concurrent engineering It also chooses the current most optimal design for development However, it is developed employing concurrent feedback cycles from all participating development functions and as such presents a logical optimization of the serial development process The third and final development model is set-based concurrent engineering The differences to point-based concurrent engineering consist in the size of the set of design alternatives developed in parallel and the implicit notion of a modular product architecture The concept of a development funnel was applied to illustrate the managerial effects of set-based development It was shown that there are two main managerial implications pertaining to managing the development funnel: opening the mouth of the funnel and killing non- optimal design alternatives Finally, in chapter 2.4 the point- and set-based models of concurrent engineering were compared according to their performance on the important dimensions of: exploration of the design space, level of integration, investment costs, accommodation of uncertainty, and managerial flexibilities provided Based on these performance criteria, a trade-off was identified between a more extensive exploration of the design space and the higher investment costs associated with developing more than one design alternative in parallel This trade-off is modelled explicitly in chapter 4, and chapter 5 presents numerical examples of how to determine an optimal size of the set of design alternatives to develop in parallel.

Competitive Advantage and the Automotive Development Process

Achieving a Competitive Advantage Utilizing the Development Process

The corporation's strategic objective is to establish a sustainable competitive advantage in the market, as noted by Rumelt, Schendel, and Teece (1991) This advantage is defined in relation to competitors within a specific market segment, allowing a company to provide superior products or services that others cannot match effectively Strategic management can thus be viewed as the creation of market imperfections, leading to a sustainable competitive advantage characterized by the ability to generate long-term economic rents The measurement of superiority is based on how effectively the company addresses critical success factors (CSF), which represent the most significant buying criteria for customers in that market segment.

21 Sustainability is understood here as referring to the long term effects of strategy I.e a sustainable advantage is a durable advantage

Rents, as defined by Shapiro (1991), refer to the ability to earn returns that surpass the opportunity cost of capital, with real markets, like automobiles, exhibiting these rents more frequently than financial markets The level of market perfection plays a crucial role in determining the attainable rents, which are ultimately reflected in the equity market price of a company Hall (1993) emphasizes that a sustainable competitive advantage arises when companies consistently produce products that align with the key buying criteria of their target market, including factors such as price, reliability, functionality, aesthetics, and image.

3.1.1 The Organizational Process of Sequential Choice

According to Mintzberg (1978), strategy is an organizational process that involves a series of sequential choices regarding resource allocation This means that management aims to outperform competitors in meeting Critical Success Factors (CSFs) by strategically investing resources over time Time is a vital factor in resource management, as decisions are not made all at once; instead, management retains the flexibility to adapt investment choices as opportunities arise, allowing for deviations from the initial strategy.

Strategic management literature offers various models that guide corporate management in sequentially investing resources to create value These models are divided into two main groups: the first focuses on the external environment of the corporation, while the second emphasizes the internal environment Historically, research in strategic management began with a focus on external factors, but recent studies have increasingly centered on internal dynamics This article will briefly elaborate on these models to enhance understanding.

The level of uncertainty in a project influences the choice between making all decisions upfront or making them as needed When uncertainty is absent, both approaches yield the same outcome However, incorporating flexibility in decision-making amidst uncertainty creates a positive option value, which is a crucial concept explored further in this thesis Subsequent sections will focus on defining and applying these central concepts.

In the 1960s, the Boston Consulting Group introduced the growth matrix, connecting market growth to a firm's relative market share, emphasizing the importance of the external environment During the 1970s and 1980s, industry structural analysis models emerged, focusing on how corporations should allocate resources within specific market contexts, notably highlighted by Porter's "Competitive Strategy" in 1980 Shapiro (1989) further advanced this strategic management perspective by incorporating dynamic modeling of competitive forces These external management strategies emphasize achieving competitive advantage through privileged market positions, often referred to as strategic "fit." This involves aligning corporate resources with identified market opportunities, as noted by Stalk, Philip, and Shulman (1992), and Johnson and Scholes (2002).

The second group of models centers on a corporation's available assets and their application in essential business processes, known as the Resource-Based View (RBV) This approach highlights the significance of the corporation's internal environment.

"emphasizes building competitive advantage through capturing entrepreneurial rents stemming from fundamental firm-level efficiency advantages." (Teece, Pisano, and Shuen 1997, p 510) This indicates that competitive advantage (rents) is achieved

24 Analogous to the dynamic programming approach known from decision-making theory

Teece, Pisano, and Shuen (1997) differentiate their approach from the capabilities-based perspective, aligning with Peteraf's (1993) definition of "resource-based" to include both corporate assets and capabilities This approach emphasizes the importance of effectively matching these resources to environmental opportunities, which Peteraf describes as a strategic "stretch." Management should not only leverage existing resources in current markets but also focus on expanding their resource base to explore new markets Ultimately, a company's competitive advantage hinges on its strategic choices in competition.

The concepts of industry structural analysis and the Resource-Based View (RBV) are crucial for understanding and enhancing the automobile development process This process primarily involves identifying market needs and leveraging corporate resources to meet those needs, firmly placing it within the RBV framework Kogut and Kulatilaka (1994) advocate for integrating industry structural analysis with the RBV to assess corporate resource investments effectively Similarly, Peteraf and Barney (2003) emphasize that Resource-Based Theory complements, rather than replaces, traditional industry-level analytical tools, such as Porter's five forces and game theory This integrated approach will be applied in the thesis, particularly in the valuation model of the automobile development process, with a detailed exploration of the RBV to follow.

The Resource-Based View (RBV) highlights two key concepts essential for achieving competitive advantage: resources and capabilities Resources, defined as "assets that a firm owns or has access to" (Pandza et al., 2003), can be tangible or intangible and are firm-specific assets with monetary value Examples include technology, finances, reputation, and organizational culture Capabilities, on the other hand, are defined as "a set of business processes strategically understood" (Stalk et al., 1992), encompassing actions and structures vital for competitive advantage They include processes such as creating quality, flexibility, and market responsiveness Ultimately, organizational resources serve as the foundation for developing capabilities, illustrating their interconnectedness in fostering sustainable competitive advantage.

According to Pandza et al (2003), the unique characteristics of resources and capabilities make them difficult to trade or replicate, leading to expected performance differences among firms This concept is central to the Resource-Based View (RBV) of corporate strategy, which posits that a company’s competitive advantage stems from its distinctive resource base, shaped by its unique business processes Consequently, the uniqueness of these resources and capabilities makes it challenging for competitors to imitate them.

26 E.g the degree of integration (vertical and horizontal) with the environment

Valuing organizational capabilities requires aligning payoffs from these capabilities with market asset prices, a concept rooted in the duplication principle, similar to option pricing This thesis will further explore the unique aspects of corporations, particularly irreversibility and duplication Irreversibility arises from the reluctance or inability to alter existing organizational and technical investments, leading to a "sticky" resource base in the short term Teece, Pisano, and Shuen (1997) highlight that this inertia is partly due to the lack of time and suitable assets for change, emphasizing the complexity of business development The second unique aspect is the challenge of duplicating an organization's asset and capability structure, which creates a competitive advantage through a unique sequence of resource deployment decisions that competitors cannot replicate due to their own limitations Pandza et al (2003) note that duplication is hindered by the complex and ambiguous nature of organizational phenomena, where causal ambiguity and the multitude of performance determinants, combined with managerial bounded rationality, contribute to the sustainability of competitive advantages and the retention of economic rents.

The uniqueness of a corporate resource base leads to market imperfections, prompting research within the Resource-Based View (RBV) to focus on optimal sequential decision-making processes However, it is crucial to recognize that organizations must not only optimize their paths but also navigate the constraints imposed by them This necessitates an analysis of the compounding effects within the decision-making process Ultimately, the path-dependent business processes result in asymmetrical payoff structures that are distinctive to each corporation, highlighting their potential for unique advantages.

2s The terms "irreversibility" and "duplication" are chosen here due to their parallel use in option pricing, which will play an important role later in this work

29 Bowman and Hurry (1993, p 766) discuss organizational inertia in terms of real options

There is a significant correlation between the analysis of compound options in financial economics and corporate capabilities This article will delve deeper into the unique characteristics of these capabilities, highlighting their importance for corporations.

3.1.4 Meta-Learning and Absorptive Capacity

Tallman and Fladmoe-Lindquist (2002) define capabilities as "complex knowledge resources," emphasizing the connection between organizational information and capabilities The information available to an organization changes over time due to market and technical uncertainties, leading to different ex ante and ex post information sets (Pandza et al 2003) This evolving information is crucial for managerial decision-making, facilitating single-loop learning, which focuses on behavioral outcomes (Argyris 1983; Lei, HiLt, and BeLtis 1996) Consequently, an organization's ability to learn from and adapt to this changing information is vital for its capabilities As Bryan (2002) states, the strategic goal is to continuously adapt the organization to its dynamic environment, highlighting the importance of organizational flexibility in this process.

Empirical Research of Automotive Development Processes

The automobile development process exemplifies the essential characteristics required for achieving a sustainable competitive advantage, as outlined in the previous chapter This complex undertaking offers numerous managerial options that facilitate organizational learning and the enhancement of capabilities, highlighting the strategic importance of effective management in this industry.

In 1990, it was argued that the product development process plays a crucial role in enabling corporations to better meet their critical success factors (CSFs) compared to competitors, particularly through core competencies This article aims to analyze how the automobile development process can leverage strategic management models, especially the Resource-Based View (RBV), to attain sustainable competitive advantage The focus will be on selected empirical studies from the automobile industry, which will provide a foundational data basis for this analysis.

3.2.1 Using Prototypes to Achieve Internal and External Integration

Based on their extensive research in the global automobile industry, Clark and Fujimoto

In the competitive automotive environment, three main forces are at play: the rise of international competition, evolving customer demands, and advancing technology Internationally, U.S firms are losing market share to Japanese and European companies, highlighting the increasing global rivalry Customers now expect more than just good performance; they seek deeper satisfaction through design and features, raising the standards for market participation Additionally, while technology has improved, its impact is nuanced; companies must focus on effectively integrating technology into the development process and aligning it with customer requirements This integration of internal and external factors has heightened uncertainty in automotive development, making mastery of the development process essential for gaining a competitive edge Consequently, the development process has emerged as the focal point of competition in the industry.

Research on the automobile development process is limited due to the challenges of obtaining firm-specific information Key studies by Clark and Fujimoto (1991), Ellison et al (1995), and Sobek (1997) provide valuable insights into the objectives and business processes of major automotive companies across the US, Europe, and Japan These comparative analyses of 29 development projects and the processes at Chrysler and Toyota offer a rich data set for modeling the automobile development process By integrating these findings, the research aims to evaluate competitive advantages against empirical observations and ultimately develop a quantitative valuation model grounded in financial economics.

This article examines the practical application of three development models: point-based development, point-based concurrent development, and set-based concurrent development Concurrent development was first adopted by Toyota in the 1960s, followed by Mazda and Nissan in the 1970s and 1980s, with US and European firms embracing these models by the mid-1990s The focus will primarily be on the point-based concurrent engineering model and the set-based concurrent engineering model.

Clark and Fujimoto (1991) utilized a comparative methodology to evaluate companies based on three key objectives: time, productivity, and integrity in the development process, employing statistical measures Their research spanned from 1985 to the end of the 1980s and revealed that Japanese companies, on average, excelled in development productivity, outperforming a combination of European and US firms Additionally, they assessed the development lead time, further highlighting the competitive advantages of Japanese firms in the industry.

In the competitive landscape of product development, European and US companies typically initiate their processes an average of five years before market introduction, while Japanese firms can afford to start only 3.5 years in advance This leads to Japanese companies outperforming their European and US counterparts in both productivity and lead time However, when it comes to product integrity, there is no definitive regional leader; selected European and Japanese companies achieve the highest standards, with the remaining European firms sharing the second position.

US and Japanese companies are intertwined in rankings, demonstrating that it is feasible to produce high-quality and technically advanced automobiles without leading in either lead time or productivity metrics.

This article compares development performance through various development models, focusing on point- and set-based concurrent models The development funnel serves as a simplifying tool, illustrating how different development processes represent unique funnel shapes It is characterized by the number of design alternatives developed in parallel and how management narrows the funnel during the phases The number of prototypes created acts as an indicator of these design alternatives Notably, surveyed automotive companies do not exclusively use either the point- or set-based models; instead, they integrate both to varying degrees based on the specific project requirements.

Prototypes are crucial for exploring design possibilities, raising the question of their impact on development process efficiency Research by Clark and Fujimoto (1991) reveals significant variations in the number of prototypes developed by different companies, highlighting the importance of prototyping in enhancing performance in the development process.

Europe Volume producer High-End specialist

Source: Own creation, adapted from Clark and Fujimoto (1991, p 196)

Table 5 highlights the distinction between "Engineering prototypes," which are utilized during the development phase leading to the final automotive design, and "Pilot vehicle prototypes," which are constructed based on that final design Notably, while the average number of engineering prototypes is similar across most brands, European premium brands differ significantly Furthermore, US and European high-end companies produce pilot vehicle prototypes at rates two to four times higher than their Japanese counterparts This disparity suggests that Japanese prototypes serve as more effective problem-solving tools, contributing to their superior productivity and shorter lead times Ultimately, the iterative problem-solving process leads to a refined development funnel, eliminating less viable design alternatives.

Clark and Fujimoto (1991) highlight that an "integrated" development process is crucial for achieving competitive advantage by effectively narrowing the funnel Their research examines the roles of internal integration, external integration, and cross-functional business processes within the companies surveyed.

Table 6: Tntegrated Development and Specialization

Involvement of long-term 523 1190 863 partidpants

Source: adapted from Clark and Fujirnoto (1991, p 267)

Japanese companies excel in both internal and external integration, achieving superior automotive design characterized by strong technical consistency and a comprehensive understanding of customer buying criteria compared to US and European firms Notably, they engage fewer long-term project participants, suggesting that a cross-functional business process, which minimizes reliance on specialists, is crucial for this high level of integration Effective problem-solving processes must incorporate all relevant internal and external information, allowing organizations to fully leverage their combined information assets through a cross-functional approach.

Clark and Fujimoto (1991) highlight a strong positive correlation between integration levels and lead-time and productivity in automobile development, indicating that a consistent development process provides a competitive advantage They emphasize that management must focus on both the overall system and its individual components to achieve timely and productive automobile development Similarly, Wheelwright and Clark (1992) stress the importance of integration at the working level among diverse disciplines to enhance development performance They further assert that an integrated development process is crucial in dynamic business environments where rapid market changes and time constraints are significant competitive factors Conversely, in stable environments with well-defined customer requirements and clear functional interfaces, the need for such integration is less critical.

"functional groups may develop new products effectively with a modest amount of coordination through procedures and the occasional meeting." (Wheelwright and Clark

The uncertainty surrounding project value drivers necessitates a more integrated development process Analyzing the findings of Clark and Fujimoto (1991), it can be inferred that the significant positive impact of integrated development in automobile processes was influenced by the uncertain internal and external environments present during their research.

Toyota's Development System - A Resource-Based Analysis

This article will delve into Toyota's automotive development process through the perspective of strategic management, highlighting a gap in research that has primarily focused on the company's production system Notably, there has been limited investigation into the development processes at Toyota's Japanese headquarters Sobek will serve as the main source for understanding this development system.

(1997) In great detail he outlines the development process at Toyota and thereby provides the needed material for further analysis in this work

A recent overview is given by Spear (2004,)

Toyota's supplier relationships are distinctively characterized by a deeper engagement with its first-tier suppliers compared to other automotive manufacturers On average, the company sources 70% of its vehicle value directly from these suppliers A critical factor in Toyota's supplier selection is their ability to manage complexity and explore various design alternatives simultaneously As noted by a Toyota engineering general manager, only a select few suppliers can effectively navigate this ambiguity Consequently, Toyota adopts a differentiated approach, not treating all first-tier suppliers equally, which allows for a more innovative and flexible design process.

In 1997, suppliers like Nippondenso, known for their robust resource base, gained high regard within the industry As Toyota's relationship with its suppliers deepens through experience, the application of set-based communication and development techniques significantly increases This collaborative approach to set-based development is closely linked to a modular design framework, highlighting the necessity for Toyota to excel in systems engineering to create an optimal platform for effective cooperation.

Millman and Wilson (1994) suggest that there is a positive correlation between the nature of cooperation—whether adversarial or collaborative—and the degree of customer involvement.

Figure 28: Supplier Relational Development Model

Source" adapted from Millman and Wilson (1994), own creation

Millman and Wilson (1994) identified key account management practices in supply chains, exemplified by Walmart and its suppliers, which can be adapted to automobile development This adaptation highlights the relationship between the level of organizational involvement of automotive companies and suppliers and the implementation of set-based development practices Utilizing a platform architecture in automotive development necessitates extensive collaboration between companies to enhance integrity, which involves narrowing the development set while facilitating information sharing across all hierarchical levels Trust, cultivated through years of intertwined practices, is essential for these processes, leading to a scenario where companies "succeed or fail together" (Sobek 1997, p 38).

Figure 28 illustrates a cooperative model that enhances economic benefits by lowering agency costs through reduced behavioral uncertainty as relationships transition from adversarial to collaborative The three main issues stemming from information asymmetry—hidden characteristics, hidden actions, and hidden intentions—can be more effectively addressed by both parties as cooperation deepens This collaboration is essential for implementing a set-based approach in automotive development alongside chosen suppliers, as evidenced by Toyota's supplier relationships.

In particular, the problems of and possible solutions to hidden action, hidden information, and hidden intention, which all arise during a cooperation, shall be briefly discussed here

1) Hidden action could arise when the developing company cannot properly evaluate given cost estimates from the supplier, e.g., in the course of prototype construction and testing at the supplier Likewise a hidden information problem could arise when the developing company commissions the supplier to develop a particular module of the automobile due to the suppliers' expertise in the technology In this case the supplier could misuse its position as an expert in the technology in order to optimize its own profit In both cases moral hazard arises and seems to put the developing company at a disadvantage, thereby making the cooperation potentially adversarial As mentioned this type of relationship was often observed empirically in particular at US automotive companies This could be a reason for the relatively small supplier engineering participation at US companies observed in Figure 23

2) Hidden intention arises when a specific irreversible investment is made, which in effect induces a dependence on the other party In connection with the automobile development process this seems to be the case simultaneously at the developing company and at the supplier For example the developing company can instruct the supplier to develop a particular component for the automobile development project, and the investments in tools, knowledge, etc needed for the development cannot be applied for other potential customers This would create a hold up problem for the supplier as he has made an irreversible investment in specific assets, which can only be used for one customer E.g., after the irreversible specific investment the developing company could force the supplier to lower its price for the components by threatening to cancel the cooperation

A supplier can significantly hinder a developing company by creating delays and inefficiencies If the supplier refuses to cooperate, the company may be compelled to seek alternatives, which can negatively affect its development process and overall productivity.

The examples highlight a mutual hold-up situation in the cooperation between developing companies and suppliers, with the extent of this hold-up increasing as the relationship shifts from adversarial to collaborative Specifically, irreversible investments are closely linked to the type of cooperation, as evidenced by the high supplier participation rates in Japanese companies, which have successfully implemented cooperative designs that reduce agency costs stemming from information asymmetry To mitigate short-term opportunistic behavior, the economic viability of such cooperation relies on effective incentive structures As cooperation matures, behavioral uncertainty diminishes due to the growing size of specific irreversible investments made by both parties over time Furthermore, if the cooperation contributes to a capability that fosters competitive advantage, its value escalates, ultimately creating a win-win situation and further limiting behavioral uncertainty.

3.3.2 Unique Assets and Capabilities in Toyota's Development Process

Toyota's development practices can be effectively understood through the resource-based view, which emphasizes the importance of assets and capabilities The company utilizes prototypes to navigate the design space, often employing a set-based approach and collaborating closely with select first-tier suppliers These empirical insights serve as the basis for categorizing Toyota's resource base, highlighting the strategic management of resources in their development process.

Table 7: Classification of Toyota's Resource Base

9 Using prototypes to simulate production and consumption process

9 Integrating organizational learning into checklists

9 Communicating design alternatives in selection matrices

9 Prototypes (production system) 9 Platform development (systems engineering)

9 Extensive relationships with first Uer suppliers 9 Developing large sets concurrently

Utilizing suppliers' resource base in development process

Knowing when to narrow the development funnel

Table 7 highlights Toyota's unique assets and capabilities identified through empirical research conducted on-site at Toyota and within the automobile industry It focuses specifically on those assets that significantly contribute to Toyota's competitive advantage, distinguishing the company from its competitors Additionally, the table outlines the capabilities that, while they may enhance Toyota's short-term competitive edge, do not all equally support long-term sustainability in the competitive landscape.

The capabilities of organizations can be ranked using a framework akin to that proposed by Tallman and Fladmoe-Lindquist (2002) As illustrated in Figure 29, Toyota's competitive advantage is derived from a detailed analysis of their unique assets and capabilities utilized throughout the automobile development process.

Knowing when to narrow the development funnel

Integrating organizational learning into checklists

Communicating design alternatives in selection matrices

Using prototypes to simulate production and consumption process

Utilizing suppliers' resource base in development process

* N o t e : Toyota's assets are not depicted

Toyota's development process plays a crucial role in generating profits, which can be attributed to its strong component and architectural capabilities This profit generation is analyzed through the lens of strategic management literature, highlighting the effectiveness of Toyota's approach in enhancing its competitive advantage and financial performance.

Toyota's diverse capabilities, illustrated in Figure 19, enable the efficient use of resources, which is crucial for maintaining its competitive advantage The interconnectedness of these individual capabilities contributes significantly to Toyota's efficiency gains While Toyota may not possess the best assets in every category, it strategically deploys its resources and business processes to ensure that its vehicles excel in critical success factors within their target markets.

Chapter Summary

This chapter explores how organizations achieve competitive advantage through two primary models: strategic fit and strategic stretch The strategic fit model emphasizes industry structural analysis, concentrating on the external environment and guiding organizations in selecting their competitive arenas In contrast, the resource-based view (RBV) model focuses on the internal environment, highlighting how organizations can leverage their unique resources and capabilities to determine their competitive strategies.

The Resource-Based View (RBV) posits that organizations can secure Ricardian rents by leveraging firm-level efficiency advantages to better meet Critical Success Factors (CSFs) than their competitors This efficiency advantage comprises two key components: resources and capabilities, both of which are unique and difficult for competitors to replicate or acquire, thus strengthening the competitive edge.

Capabilities are crucial as they embody complex knowledge resources essential for an organization's growth Central to the sequential choice process is information, which drives decision-making An organization's ability to leverage and enhance its capabilities hinges on its absorptive capacity, influencing its learning processes Single-loop learning represents basic adaptability, while double-loop learning involves a deeper understanding of the learning process itself, leading to a compounding effect in sequential choice.

37 Due to the superior quality of the organization's factor inputs (in this case resources and capabilities)

Classifying capabilities into component and architectural categories is essential for understanding business processes Component capabilities refer to specific processes unique to a firm, while architectural capabilities leverage and enhance these components, acting as a platform for collaboration with suppliers This framework aligns with the Resource-Based View (RBV), which sees resources as foundational elements of component capabilities, and these capabilities, in turn, serve as the foundation for architectural capabilities.

Research in the automotive industry reveals two distinct archetypes of development processes The first, characterized by a focused approach and rapid narrowing of the development funnel, is prevalent in the US and Europe This derivative of the point-based concurrent development process emphasizes technical aspects of prototypes, leading to a functional development process with lower internal and external integration Conversely, the second archetype features a wide approach and gradual narrowing of the funnel, commonly found in Japan This set-based concurrent development process views prototypes as tools for organizational learning, fostering a cross-functional development approach that enhances both internal and external integration.

The findings suggest that automotive developers may gain a competitive edge by expanding their development funnel, which involves concurrently exploring a broader range of design alternatives A key factor in this approach is the early involvement of suppliers in the development process, notably observed in Japan, particularly at Toyota.

The automotive development process at Toyota exemplifies the set-based approach and has been thoroughly analyzed Empirical research conducted at Toyota in Japan revealed several unique resources and capabilities integral to its development process These capabilities were ranked, leading to the identification of three key component capabilities and four architectural capabilities.

Real Option Model of the Automotive Development Process

The Role and Structure of Financial Markets

The concept of the market is vital in financial economics and significantly influences strategy, particularly in the automobile development process.

4.1.1 The Discipline of Financial Markets

Asset prices in financial markets reflect crucial information regarding return and risk, serving as a key input in development processes By providing a benchmark for performance, the market assists decision-makers in effectively allocating resources among different design alternatives.

(1999, p 95) state that managers: "can incorporate the market's objective measures of value under uncertainty into their own strategic choices." In order to apply this

Incorporating market information is crucial for managers, as highlighted by Amram and Kulatilaka (1999) For instance, when developing an automobile engine, managers can utilize market insights on future gas prices and the volatility of long-term oil contracts to inform their internal forecasts of purchasing behavior This analysis allows for the estimation of expected free cash flows (FCFs) and the market value derived from the engine design Consequently, financial markets play a vital role in assessing the economic viability of various corporate strategies, ultimately benefiting owners by providing a clearer valuation framework.

Once management determines the value of an asset, they can utilize a recursive method to identify the best strategic choices By focusing on the timing and size of cash flows, management can align their strategies with the anticipated cash flows Implicit in any valuation is the optimal decision-making process that drives these cash flows Ultimately, the focus on this optimal strategy is crucial for understanding its managerial applications Additionally, financial markets provide valuable information and guidance for corporate decision-making.

4.1.2 Separating the Investment and Financing Decisions

The Fisher separation theorem, established by Fisher in 1930 and further explored by Copeland, Weston, and Shastri in 2005, posits that in complete markets, there is a critical distinction between owners' investment choices and their financing preferences This principle underpins the concept of market discipline, emphasizing the importance of separating these two aspects for effective financial decision-making.

Maximizing the total present value of free cash flows (FCFs) linked to an automotive development strategy aligns management with the interests of company ownership This approach effectively connects investors with varying preferences for investment timing and consumption size in the financial markets.

In ideal circumstances, financial markets serve as an equilibrium mechanism that simplifies the valuation of the automotive development process, guiding corporate resource allocation However, financial markets are rarely perfect or complete in practice, which imposes limitations on market discipline These limitations will be explored further in the discussion.

4.1.3 Financial Economics, Free Cash Flows, and Strategic Management

The automotive development process is traditionally viewed as a component of a company's overall strategy, necessitating a conversion from qualitative strategic management to quantitative financial economics for effective valuation This valuation relies on free cash flows (FCFs) as the primary input for models, emphasizing that the development process is a sequential decision-making framework generating a series of cash flows Additionally, the expected information that management anticipates learning about these cash flows is crucial, as the process also serves as an information-generating mechanism This parallels the concept of filtration in financial mathematics, where the sequential disclosure of information influences market prices Consequently, understanding the cash flows from the automotive development process is vital for accurate valuation.

Based on the above outline it is thus possible to mirror the automotive development process (an elemental part of strategic management) in the financial markets (Borison

In the context of strategy and finance, they represent two perspectives on the same issue, as emphasized by Myers (1984), who suggests that finance theory and strategic planning are essentially two cultures addressing a common problem This concept is illustrated in Figure 31, which analyzes the automobile development process through both strategic frameworks and financial market valuations The analysis specifically values the component and architectural capabilities derived from Toyota's development process, highlighting that the valuation of technology development centers on assessing the connections between various elements.

Valuing technology hinges on understanding the importance of linkages, as capabilities connect various business processes and assets within an unpredictable environment This connection highlights the intentional exercise of managerial flexibility that underpins these capabilities, as discussed by Kogut and Kulatilaka (2001).

In finance, a real option represents the counterpart to a capability, reflecting the "economizing of organizational intuition" (Bowman and Hurry, 1993) The concept was first introduced by Myers in 1977, highlighting managerial flexibilities in research and development and manufacturing The 1980s saw the emergence of academic articles on real options, notably by McDonald and Siegel (1986), Trigeorgis and Mason (1987), and Dixit (1989) The topic gained further traction with the publication of the first textbook on real option valuation by Dixit and Pindyck in 1994 Over the past decade, real options have become a standard topic in academic finance literature, including in the widely used textbook by Brealey, Myers, and Allen (2005).

The real option framework offers valuable insights into the Resource-Based View (RBV) from a financial markets perspective As highlighted by Peteraf and Barney (2003), understanding the limitations of the Resource-Based Theory (RBT) is crucial; it must be recognized as a resource-level and efficiency-oriented analytical tool to fully appreciate its contributions This article aims to utilize the real options framework to enhance the efficiency-based view of corporations as outlined in the RBV Kogut and Kulatilaka (2001) define a real option as an essential component in this context.

Real options represent a vital investment in both physical and human assets, allowing management to effectively respond to future uncertainties These intangible resources and capabilities are characterized by uncertainty, flexibility, and irreversibility, similar to corporate resources Like financial options, real options gain value from risky underlying variables and the managerial flexibility to adjust corporate strategies This approach mitigates downside risks while enhancing upside potential in cash flow distributions Real options are particularly effective in capturing asymmetric payoff structures, making them the preferred modeling instrument in processes such as automobile development.

Real options play a crucial role in guiding corporate resource allocation, similar to strategic management They enable management to view corporate strategy as a "portfolio of initiatives" rather than just a "portfolio of businesses" (Bryan 2002, p 19) This perspective allows business strategies to be modeled as "chains of real options" (Luehrman 1998, p 90), providing valuable insights into the corporate resource base from a shareholder's viewpoint According to Bowman and Hurry (1993, p 762), real options emerge from the interaction of an organization's investments, knowledge, capabilities, and environmental opportunities Consequently, they can help identify strategic applications of corporate resources in "white spaces" within the market landscape (Kogut and Kulatilaka 2001, p 744).

Understanding the relationship between information, capabilities, and real options is crucial, as capabilities embody complex knowledge resources Central to sequential choice, information influences an organization's ability to leverage and enhance its capabilities, which is largely determined by its absorptive capacity This capacity dictates how effectively an organization learns, beginning with single-loop learning, where flexibility is exercised through a single real option In contrast, double-loop learning occurs when an organization reflects on its learning processes, leading to a compounding effect in sequential choice as it engages with compound real options.

General Asset Valuation

The valuation process is a crucial aspect of real options, relying on a quantitative model framework This article establishes a theoretical foundation for asset and options valuation, primarily through the lens of financial economics, which encompasses the models and assumptions discussed The insights provided align with fundamental financial market principles found in standard financial economics textbooks and serve as a basis for the quantitative valuation model applied in the development process later in the article.

In finance, valuation entails comparing a specific asset to other marketable assets, which involves identifying a comparable marketed asset to determine the price of a new one (Luenberger 2001, p 2) Consequently, establishing a financial market model is essential, as it provides a crucial reference point for effective asset valuation.

This section utilizes foundational concepts from standard financial mathematics textbooks, including Duffle (1996) and Bingham and Kiesel (2004) Given the multiperiod nature of automotive development, a multiperiod securities market model serves as the basis for analysis A discrete-time state approach is selected for convenience, although similar models and concepts can be adapted to a continuous-time state-space framework The discrete-time state-space is particularly suitable for the valuation methods discussed in this thesis, especially considering the numerical approximation techniques outlined in chapter 4.4.

There are T+I trading dates ( t ) with t = 0,1, ,T For each point in time t there are K possible states of nature (co) The set of co make up the sample space ( n ) with

The sample space encompasses all possible scenarios at any moment, represented graphically as a grid of time and state space The probability measure, P, quantifies the likelihood of reaching a specific state, ensuring that the total probability across possible states equals one The dissemination of price-relevant information over time is characterized by a filtration (F t), which is essential for addressing pricing issues This filtration consists of a sequential accumulation of information, allowing market participants to have complete access to historical and current prices of N risky assets, aligning with the weak form of market efficiency Additionally, the market includes a risk-free bond (B t).

- / B t _ i >_ 0 being the risk-free interest rate from time t-1 to t (e.g., Duffle 1996, p 12) Finally, at any given state co and point in time t there are N risky assets

In the market, each risky asset S(t, co) for 1 < i < N follows a diffusion process, where price-relevant information is incorporated into its valuation Since new information is inherently random, the price movements of S(t, co) are also random, a concept supported by Samuelson's Proof.

1965) With regard to the automobile development process, the N risky assets function as important inputs to the valuation of the automobile development process because

The N risky assets represent a subspace within a Hilbert space, as discussed in Luenberger (2001) and Bingham and Kiesel (2004) These assets encapsulate crucial information regarding risk and return, highlighting the concept of market discipline.

An essential characteristic of asset prices in the market is the absence of arbitrage opportunities According to Smith and McCardle (1998), profit generation requires investment or risk-taking, meaning there is no "easy money" in securities trading Duffle (1996) outlines three conditions for arbitrage: the initial value must be zero, the investment must be non-negative, and the expected return must be positive.

In the context of portfolio valuation, Vo and ~ represent the portfolio values at Lime 0 and Lime 1, respectively An arbitrageur can establish a cost-free portfolio at Lime 0, ensuring that its value at Lime 1 is non-negative and has a strictly positive expected value When these conditions are satisfied, investors can achieve riskless profits With the assumption of perfect market access for all investors, they would quickly devise trading strategies to exploit any pricing discrepancies Consequently, the dynamics of supply and demand would drive asset prices toward equilibrium, eliminating arbitrage opportunities Therefore, for any valuation model to be deemed reasonable, the absence of arbitrage is essential.

The probability measure plays a crucial role in determining the movements of Sj(t,o) over time, with the real-world probability function for asset price returns denoted as P In specific valuation scenarios, such as valuing contingent claims, it is often beneficial to transition to an "equivalent martingale" or "risk-neutral" measure Q, as discussed by Duffle (1996, p 28) To understand this concept, it is essential to define a martingale: a stochastic variable Zt is classified as a martingale if certain conditions are met.

Market participants anticipate a positive growth rate for assets under the real-world probability measure P, indicating that the expected growth rate of Zt is O These stochastic processes, characterized by their tendency to increase over time, are referred to as submartingales.

(4.5) E[Z,§ IF,] > Z,, for all s, t > 0 or equivalently

(4.6) E~[Z,+,IF,] > Z,, for all s,t>_O to denote that the expectation is taken with respect to the real-world probability measure P Rephrasing equation (4.6) in terms of a risky asset S,.(t), one gets

The transition from the real-world probability measure P to the risk-neutral measure Q allows for the conversion of a submartingale into a martingale, as illustrated in equation (4.7) Specifically, let S(t) represent a discounted price process, following the framework established by Duffle (1996, p 29).

It can be shown that

Under the probability measure Q, similar to the real-world measure P, the probabilities must total 1, ensuring that Σ Q(~) = 1 Additionally, if the expected change in S, equals 0, then a specific equation must hold true.

In a risk-neutral probability measure Q, the discounted value of the risky asset S, using a risk-free bond as a state-price deflator, exhibits zero growth This indicates that, within the risk-neutral framework, the expected returns on risky assets are adjusted to reflect a neutral perspective on risk, leading to a stable valuation over time.

Assets are increasing at a pace that matches the risk-free rate, a significant finding for valuing risky assets This principle can also be utilized for assessing contingent claims, like options, to evaluate asymmetric payoffs during the automobile development process, which will be further explored in the subsequent sections.

4.2.4 Lattice of Asset Price Movements - The Binomial Tree

To value a contingent claim on a risky asset, further assumptions about its stochastic price process are necessary, as outlined in equations (4.4)-(4.10) The binomial model introduced by Cox et al (1979) describes asset price movements, where the risky asset \( S_t \) can either increase or decrease by constant factors \( u \) or \( d \) in each time period The model imposes restrictions on these factors, specifically that \( 0 < d < 1 < u \), ensuring that \( u \) and \( d \) are inversely related, allowing the binomial process to be recombining.

Option Valuation

This article outlines established models for asset valuation and their underlying assumptions, and will subsequently introduce a valuation model specifically tailored for the automotive development process, emphasizing the intrinsic real options involved.

4,3.1 Valuing Financial and Real Options 4s

The valuation models for financial options can be effectively applied to real options due to their similar structural characteristics There are two primary methods for valuing these options: analytical techniques and numerical techniques.

In some cases these overlap

Approximating Approximating differential equations stochastic processes

Implicit finite Monte Carlo differences simulations

Explicit finite Lattice differences* approaches*

Source: own creation, refer in general to Geske and Shasbi (1985) and Hull (2003)

Analytical techniques are effective for valuing simpler options, particularly European options with a single underlying asset A foundational example is the Black-Scholes model for European call option valuation, introduced in 1973 These analytical methods laid the groundwork for early academic contributions to real options, as highlighted in the works of Dixit and Pindyck (1994) and Trigeorgis (2000), which provide an overview of key analytical models Many of these approaches utilize Bellman's principles For a comprehensive introduction to real options, refer to Trigeorgis (2000).

The Principle of Optimality, as discussed by Dixit and Pindyck (1994), introduces a dynamic programming approach to decision-making However, when dealing with American options and options involving multiple underlyings, analytical solutions can become challenging or unfeasible This complexity is highlighted by Childs et al (1999), who propose a numerical method for valuing contingent claims during development processes.

Numerical techniques are commonly used to value options due to their versatility and ease of understanding These methods can be categorized into two main groups: the first focuses on approximating partial differential equations with difference equations, which can be solved under specific assumptions about the stochastic price process and boundary conditions The second group either approximates the stochastic price process or employs simulation techniques, which are straightforward but may not be applicable to all option types and can become complex "black boxes." A prominent example within this group is the binomial model developed by Cox, Ross, and Rubenstein (1979), which effectively values European options by approximating the underlying geometric Brownian motion This model also demonstrates that the Black-Scholes result is a special case of the binomial approach in the limit Lattice approaches, particularly the binomial process, are widely utilized by both academics and practitioners in the valuation of real options.

2000 and Copeland and Antikarov 2001) ~ The lattice approaches shall therefore by employed primarily to value the automotive development process

4.3.2 Real Options in the Automobile Development Process

This thesis has focused on identifying managerial flexibility, uncertainty, and irreversibility within automotive development models, highlighting their significance in revealing real options Chapter 3 defined the role of capabilities in this process, particularly emphasizing Toyota's development model and its unique capabilities Building on this, Chapter 4 connected these capabilities with real options, setting the stage to analyze the automotive development process through the lens of real options to identify the most valuable setup for development.

Identifying the key real options for analysis in the automotive development process is challenging due to numerous managerial flexibilities and uncertainties, leading to an infinite array of options to consider The 'true' value of this process, represented by the Expected Net Present Value (ENPV), encompasses all identified real options, making it a nearly impossible task However, approximating the value of these options is feasible Trigeorgis (2000) highlights that real option interactions occur when multiple options share the same underlying asset, influenced by factors such as option types (call or put), exercise timing, their relative financial position, and sequential order These elements significantly affect the probability of joint exercise and, consequently, the overall value of the options.

This article provides an approximation of the true value of the automotive development process by identifying key 'dominant' real options that represent the value of a specific automotive development setup The focus is on understanding the significance of these options in assessing the overall development process.

The real option valuation process at Toyota, illustrated in Appendix 3, serves as a key case study for identifying significant real options Chapter 3 details Toyota's component and architectural capabilities, while Figure 31 enhances this by incorporating financial market perspectives into Toyota's development process.

Figure 31: Toyota's Capabilities from the Real Options View

( leverage :.i < Component capabili#es" ~ building ii i" processes :! i i i Developing ! i large sets i concurrently i

Knowing when to narrow the development funnel

Integrating organizational learning into checklists

Communicating design alternatives in selection matrices

Using prototypes to simulate production and consumption process

Utilizing suppliers' resource base in development process

* Note: Toyota's assets are not depicted

The lower section of Figure 31 illustrates the capabilities of a 'real option to switch' and a 'compound real option.' To effectively leverage market discipline, the goal is to assess the value of these options by utilizing financial market prices for market risks and expert estimates for private risks, as referenced in the lower right quadrant of Table 8.

Carr (1993) highlights that the exchange option, allowing the owner to swap one asset for another, serves as a general form of various options, including call and put options In Figure 31, the compound real option, as proposed by Geske (1979), emerges as the most valuable due to its applicability across global corporate projects rather than being limited to a single automotive initiative This option is closely linked to innovation and sustainable competitive advantage, driven by architectural capabilities in platforms and supplier collaborations, particularly in uncertain and high-value contexts The dynamic nature of these architectural capabilities offers significant flexibility, enhancing the compound real option's value for corporations However, two key factors render the compound real option less relevant for this study, which focuses on automotive development Firstly, identifying the correct parameters for this option is challenging, as it stems from two dynamic capabilities that are inherently difficult to classify operationally Secondly, since the compound option pertains to all corporate projects due to the involved architectural capabilities, establishing a conversion key to allocate portions of its value to specific development projects proves difficult.

The real option to switch, highlighted as the final option in Figure 31, emphasizes Toyota's unique ability to manage the development funnel effectively This option is directly linked to individual automotive development projects and underscores the significance of Toyota's set-based development capabilities Consequently, valuing the option to switch emerges as a crucial element in Toyota's development process.

4.3.3 The Real Option to Switch in the Automobile Development Process Figure 15 showed the concept of modularity as applied to the automotive set-based development process In the example four design alternatives are developed in parallel Analyzed as a European option, the option to switch allows management to choose the most valuable of the design alternatives for implementation at the expiration date, e.g.,

One year prior to mass production, the option modelled is a call option, specifically understood as an American option to switch This allows management the flexibility to transition resources at any time during the automotive development process to the most promising design alternatives Both European and American options involve irreversible resource investments that are influenced by the evolving market and technical uncertainties associated with each design alternative.

Table 4 compares point- and set-based strategies within a real options framework, highlighting key variables This data is visually represented in Figure 18, which demonstrates the trade-off between the value of switching flexibility and the incremental investment costs associated with it The flexibility to switch is modeled as an option, with market and technical risks influencing its value for each design alternative Thus, the value of switching flexibility relies on underlying factors and two risk sources for each Notably, the point-based development process is a specific instance of the broader set-based process, occurring when the set size is one, as shown in Figure 32.

Figure 32: The Option to Switch in the Automobile Development

I Value of Option to Switch

IOnOer' 'no 'l IOoOe ' 'no ' I IOoOe ' ,no o, I jJ- A-,

The Option to Switch as a Multivariate Contingent Claim

This model builds upon the frameworks established by Childs, Ott, and Triantis (1998) as well as Childs and Triantis (1999), focusing on the valuation of the option to switch.

For a comprehensive analysis and a computer program related to the model developed by Childs, Ott, and Triantis (1998), refer to Sorensen (2001) This model can be viewed as an extension of Boyle's framework, highlighting its complexity and the presence of multiple underlying factors.

The 1988 model builds upon the Cox, Ross, and Rubenstein (1979) binomial method to value contingent claims involving two underlying assets This approach retains the essence of the original method while expanding its applicability to accommodate multiple underlyings, offering a more versatile framework for valuation.

To effectively evaluate the automobile development process within the specified timeframe, it is crucial to establish general assumptions that serve a dual purpose These assumptions not only ensure the accuracy of the valuation model's structure but also set minimum requirements, allowing the thesis to concentrate on the model's framework.

1) First, markets are presumed incomplete I.e., risk-neutral valuation is possible only partly and under the assumption that company ownership is risk-neutral towards private risks The further the distance of a business process from the financial markets, the more difficult it is to span FCFs with marketed assets The assumption about risk- neutrality towards private risks is a convenient assumption to make because it puts aside the need for a specification of risk-adverse preference functions, which is a non- trivial task, particularly when there are several owners with varying risk-preferences In addition, the assumption about risk-neutrality towards private risks is widespread in the real-option literature and often employed

2) Second, for the purpose of this work, financial markets are assumed perfect The implications of e.g., taxes and financial market structure are disregarded The model could be extended to coincide with these imperfections Still, this assumption prevents the valuation model from becoming unnecessarily complicated and most likely has a minor effect on the issue of optimal development process structure on an overall level

48 Refer also to the assumptions made by Black and Scholes (1973, pp 640-641) in their seminal work on option pdcing

3) Third, financial markets are assumed arbitrage-free This is a necessary precondition for the martingale measure to be applied

4) Fourth, the stochastic properties of both the stochastic variables and processes in question are presumed stationary

5) Fifth, the risk-free rate ( r ) , the volatility of asset returns ( ~ ) , and the correlation (p,j) between assets i and j are assumed constant

4.4.2 Boyle, Evnine, and Gibbs ( 1 9 8 9 ) - An n-Dimensional Lattice

Contingent claims in the financial industry often depend on multiple sources of uncertainty, a concept that also applies to the automobile development process Analytical solutions for these claims are scarce, necessitating the use of numerical techniques The option to switch between different underlying assets is crucial for modeling, as it reflects management's flexibility in decision-making Boyle, Evnine, and Gibbs (1989) introduced a model for numerically valuing the option to switch among n underlying assets, where each design alternative or prototype serves as an underlying asset Similarly, Kamrad and Ritchken (1991) developed a technique for valuing multivariate claims, enhancing the BEG approach by allowing for horizontal jumps in the model However, the BEG model will be utilized here due to its potential to provide insights into the valuation and optimal structuring of the automobile development process, particularly regarding the trade-off between investment costs and the value of flexibility.

The BEG model, similar to the standard binomial tree introduced by Cox, Ross, and Rubenstein in 1979, comprises two key components: jump amplitudes and jump probabilities These components are essential for ensuring that the mean and standard deviation of the discrete distribution align with those of the continuous distribution The model's lattice structure facilitates the analysis of both European and American options There are two primary development approaches for the BEG model: one involves fixing the jump probabilities and solving for the jump amplitudes, while the other, adopted by Cox, Ross, and Rubenstein, involves fixing the jump amplitudes and determining the jump probabilities to achieve convergence.

Based on the analysis presented in Table 8, this work adopts an integrated approach Consequently, the underlying asset, denoted as S, is defined as the present value of expected cash flows derived from the development process, assuming no management flexibility to alter the original plan Initially, it is presumed that each underlying asset adheres to geometric Brownian motion.

(4.25) dS, = ,u,S, dt+ cr, S, dz, i s i < n where

Sj = the current price of asset i

Pi = the drift of the process for asset i dzj = a Gauss-Wiener process

Ptj = the instantaneous correlation between dzj and dzj, 1 s i s ] s n

In the automotive development process, the stochastic progress of the design alternative value is represented by L The subsequent step involves defining the differential equation that the derivative value must fulfill For a single underlying asset, Black, Scholes, and Merton utilize the stochastic differential equation, as detailed in Merton's 1973 work.

The value of a derivative must satisfy specific conditions, as indicated by the formula (4.26) where f represents the derivative's value Garman (1976) and Cox, Ingersoll, and Ross (1985) expanded upon the Black-Scholes-Merton (BSM) differential equation, leading to the formulation of a stochastic differential equation (refer to equation (4.27)) for derivatives with payoffs that depend on multiple underlying assets.

(4.27) rf af af 1 ~p~a, ajS, Sj aS, a&

In the context of the BSM stochastic differential equation, the investor's risk preferences, represented by /~j (refer to equation (4.20)), lose significance since ,t~ is known This highlights the established connection between CCA and DP, as indicated in equation (4.22).

In the automotive development process, the value of the option to switch between multiple design alternatives is crucial Specifically, in Toyota's approach, this value represents the capability leverage processes that enable the selection of the most advantageous design option among the available alternatives This highlights the effectiveness of the set-based approach in optimizing the development process.

Assets that follow geometric Brownian motion exhibit a lognormal distribution at any given time, leading to a multivariate lognormal distribution for multiple assets Instead of directly solving the differential equation with the appropriate boundary conditions, BEG utilize the method proposed by Cox, Ross, and Rubenstein (1979) to discretely approximate the stochastic processes of the underlying assets The objective is to ensure that the discrete distribution approaches the multivariate lognormal distribution as the limit is reached To achieve this, the discrete approximation must align with the corresponding moments of the continuous-time state-space stochastic process.

The following additional notation is introduced in accordance with the approach taken by BEG:

T = time to option maturity in years

N = number of time steps into which the time T is divided h = T / N ; length of a time step

.9~u~ = asset value after one up-jump

.9~d t = asset value afcer one down-jump

/~j = r - 1/2cz~ = drifc-rate of continuous Iognormal distribution

BEG provides the suitable jump probabilities and sizes for the scenario when n=-2, while offering a general formula applicable to the n-asset case The up and down jump sizes, denoted as u and d, respectively, adhere to the notation established by Cox, Ross, and Rubenstein (1979), as illustrated in equation (4.11) For the n-asset scenario, the formulas are generally outlined.

Chapter Summary

This chapter explores the significance of financial markets in assessing the automotive development process, highlighting how the prices of marketed securities reflect crucial information for corporate strategy valuation A real option valuation model can elucidate the firm-specific efficiency advantages, framing corporate decision-making as a series of free cash flows (FCFs) for valuation purposes Central to this approach is the identification of comparable marketed securities that mirror the payoffs from the development process However, the open systems nature of strategic management presents challenges in quantitatively modeling all aspects of sequential choice By employing simplifying assumptions, it is feasible to capture the essential causalities through a real option model, ultimately aiming to optimize the automotive development process setup for maximum value.

The chapter's second part introduces a general valuation framework applicable to any claim, demonstrating that under no-arbitrage conditions and complete markets, a unique equivalent martingale measure exists The value of a claim is determined by its expected, discounted value based on this martingale measure When the market price of risk is known, the martingale approach aligns with the value derived from dynamic programming However, in incomplete markets, private risks emerge that are not covered by available securities, particularly evident in automotive development where unique, non-duplicable capabilities create distinct payoff structures In such scenarios, a unique martingale measure is absent, leading to potential tracking errors when attempting to replicate payoffs To assess these private risks, it is assumed that the owners of the automotive development company are risk-neutral regarding them.

This chapter's third part provides an overview of option valuation techniques, emphasizing the identification of relevant real options for valuing the automotive development process Building on the analysis of Toyota's capabilities, the option to switch emerges as the most significant real option in automotive projects This aligns with practical observations of development funnels, where broadening the funnel's entrance allows for a greater selection of design alternatives, enhancing the value of the option to choose.

The core challenge lies in identifying the optimal size (n) of the initial set of design alternatives, which is influenced by the underlying values of the design process.

This chapter's fourth part introduced the BEG model, which represents the choice among n design alternatives as a multivariate contingent claim, with the set size limited to a maximum of n = 3 for clarity The equivalent martingale measure was calculated for scenarios with n = 2 and n = 3, and the resulting lattices were illustrated for n = 1, n = 2, and n = 3 Additionally, the cash flow structures applicable to the real option model were presented for each case of n = 1, n = 2, and n = 3.

Optimizing the Automotive Development Process

Value Drivers in the Automobile Development Process

Table 11 shows the base case values of the model variables utilized in the following sections

Table 11: Case Data for the Automobile Development Process

Model Variable Base Case V a l u e Descdption

The development process spans a single time unit, defined as the interval from to to tl For simplicity, the total development time is considered constant across all calculations.

During the total development process the underlying variable(s) jump at time to and to.~

The risk-free interest rate For reasons of simplicity it is assumed constant throughout the development process

The exercise price of the European option to switch at time t represents the present value of costs associated with implementing a design alternative For simplicity, it is assumed that the exercise price is consistent across all design alternatives, meaning the base case data considers it as an at-the-money option.

The value of asset/(/= 1, 2, 3) at to

The standard deviation of returns of asset i (/= 1,

2, 3) For reasons of simplicity they are assumed constant throughout the development process

The correlation coefficient between assets i and j (~ ]" = 1, 2, 3) For reasons of simplicity they are assumed constant throughout the development process

This article analyzes the impact of varying the variables S, , or,, and p~ on option value, with results displayed in tabular format in Appendix 4 The findings are primarily illustrated graphically within the text.

5.1.1 The Option Value in the Point-Based Development Process

The option value in the point-based development process emerges when the present value of free cash flows (FCFs) from a specific design alternative exceeds the associated implementation costs.

The standard deviation and the current value of the design alternative significantly influence the likelihood of exercising the option to implement the design alternative, thereby impacting its current value in the point-based development process Figures 39 and 40 illustrate how both the standard deviation and the current value of the design alternative affect these dynamics.

Figure 39: The Effect of Volatility with One Design Alternative

Source: own creation, see also Appendix 4

In point-based development, the option value increases linearly with the standard deviation of the design alternative, indicating that higher-risk projects significantly contribute to the expected net present value (ENPV) The option value illustrated in Figure 39 will be used as a benchmark for analyzing the set-based development process, allowing for a comparison of option values between set-based and point-based approaches.

Figure 40: The Effect of Underlying Value with One Design Altemative

Source: own creation, see also Appendix 4

Once the underlying asset's current value exceeds 150, the likelihood of the option being exercised increases significantly, leading to a linear rise in the option's value.

5.1.2 The Option Value in the Set-Based Development Process

In the case of set-based development there is an additional parameter, which plays a crucial role This is the correlation coefficient p~

Figure 41: The Effect of Volatility with Two Design Alternatives

Source: own creation, see also Appendix 4

Figure 41 illustrates how option value is influenced by equal increases in the volatilities of two underlying assets, highlighting five correlation levels ranging from -1.0 to +1.0 The option to switch is significantly impacted by both rising project volatility and changes in the correlation coefficient between the two design alternatives At a correlation of +1.0, the designs behave identically, resulting in no substantial difference from a point-based development process However, as the correlation shifts from +1.0 to -1.0, the option value increases markedly, demonstrating a 'portfolio effect' in set-based development This change in correlation coefficients reflects the exploration of diverse design spaces and marketing strategies When the design alternatives stem from different technical approaches targeting distinct market conditions, a decrease in correlation is anticipated Consequently, management's flexibility at the end of the development phase to select the optimal project enhances the maturity of automotive design, leading to a significantly higher option value in financial terms.

Figure 42: The Effect of Relative Volatility with Two Design

0 Volatility ratio o.oo o.so 1.oo z.5o 2.oo (a,/,72)

Source: own creation, see also Appendix 4

Figure 42 illustrates how varying volatilities in design alternatives impact option value With a low volatility ratio, design alternative one is overshadowed by asset two However, as the volatility ratio exceeds 1.5, the option value shifts to favor design alternative one due to its increased likelihood of implementation Notably, regardless of the volatility ratio, the option value remains greater when comparing two design alternatives to a point-based development alternative.

40) Again, the the correlation coefficient plays an important role in determining the value of the development process

Figure 43: The Effect of the Underlying Values with Two Design

Value of Underlying Assets (Sl = S2)

Source: own creation, see also Appendix 4

Figure 43 illustrates that the option value increases significantly as the current value of design alternatives rises, with the highest option values achieved under specific conditions (P12 < 0, 51 > 350, and S2 > 350), effectively doubling the ENPV of the development process Higher starting values for design alternatives lead to a greater likelihood of achieving substantial underlying values, which supports the implementation of these designs However, the difference in outcomes between higher starting values and the point-based development process shown in Figure 40 is minimal, as management often opts for a point-based approach when it is likely to yield above-average results Additionally, the correlation coefficient is a crucial factor in determining the development process's value; for instance, with underlying values at 400, shifting from +1 to -1 correlation can enhance the project's value by over 33%, indicating that lower correlations can significantly boost expected value.

Figure 44: The Effect of Relative Project Value with Two Design

Source: own creation, see also Appendix 4

Figure 44 demonstrates the increasing option value of design alternative one as it gains significance over design alternative two, leading to a rapid rise in the likelihood of its implementation This shift is attributed to the high initial value of design alternative one, which minimizes the difference in option value within a point-based development process, as it remains suboptimal to transition to design alternative two.

Figure 45: The Effect of Project Volatility with Three Design

Source: own creation, see also Appendix 4

Increasing volatility significantly enhances the option value in a set-based development process with three design alternatives When there is perfect positive correlation, the option value remains similar to that of a point-based development process However, as the standard deviations of the design alternatives rise, the option value increases linearly This indicates that a set-based development approach becomes increasingly valuable with higher volatility and lower correlation levels From a management perspective, it is crucial to prioritize design alternatives that differ in both technical and market aspects at the outset of the development process This strategic focus greatly influences the value of the option to switch Notably, at a volatility level of 0.8, the option value can increase by approximately 300% when the correlation between design alternatives shifts from +1 to -1.

Figure 46: The Effect of Relative Project Volatility with Three Design

Source: own creation, see also Appendix 4

Figure 46 shows an increase in option value compared to the two-design case in Figure

42 Again, the requirement is that the design alternatives do not have a perfect positive correlation, in which case there is no value-added of developing sets concurrently (compare with the option values in Figure 40)

Figure 47: The Effect of the Underlying Values with Three Design

Source: own creation, see also Appendix 4

The parallel development of three design alternatives significantly enhances option value, particularly when these alternatives exhibit less than perfect positive correlation (p < +1.0) This leads to option values that rapidly exceed the intrinsic value of the individual design alternatives Ultimately, the ability to switch between these design options contributes substantially to the overall value of the automobile development process.

Deriving an Optimal Development Process Setup

The following sections shall be devoted to a direct comparison of the point- and set- based models of automotive development The objective is to determine an optimal development process setup

5.2.1 The Option Value of the Number of Design Alternatives

Figure 18 depicts the trade-off between the value of the option to switch and development costs, which is modeled and solved to determine the optimal number of design alternatives.

Figure 48: The Effect of the Number of Underlyings and their

Volatilities on the Option Value

Source: own creation, see also Appendix 4

Figure 48 illustrates that the option value rises as the number of concurrently developed design alternatives increases A set-based development process offers a higher option value, particularly when there is uncertainty regarding the future value of the design alternatives For instance, with a volatility of 0.8, the option value can increase by approximately 100% when management opts for a set-based approach (n = 3) compared to a point-based approach (n = 1).

Figure 49: The Effect of the Number of Underlyings and Their Values on the Option Value

Data: CT 1 =Or 2 = Cr 3 = 0.4, /O12 = P13 = /023 = 0, X = 100, T= 1, N = 2, rr =0.05

Source: own creation, see also Appendix 4

Figure 49 recapitulates the previous results that the values of the underlying assets generally increase option value In addition Figure 49 clearly shows the value-added of developing in sets

5.2.2 The Optimal Development Process Setup: Linear Cost Structure

The focus has been on calculating the option value within various development process settings To determine the Expected Net Present Value (ENPV), it is essential to subtract the initial development investment costs, highlighting the trade-off between option value and these costs Management aims to design the development process so that the value of the option to switch exceeds the associated investment costs In this context, two typical cost structures characteristic of automotive development processes are considered.

1) The first is a linear cost structure I.e., the development costs rise linearly with the number of design alternatives This is the case when the variable costs associated with the automobile development increase due to the extra needed engineering hours, dies for prototype constructions, etc

2) The second is a cost structure influenced by a cost multiplier That is, the development costs rise more than linearly due to capacity restraints and organizational limitations at large E.g., increasing the set-size leads to certain inefficiencies, which make it increasingly prohibitive for the developing organization to work concurrently

An example of a linear development process cost structure is given in Figure 50

Figure 50: Automotive Development with a Linear Cost Structure

Number of design altematives Development costs = n 9 10

Figure 51 shows the option values for the number of design alternatives developed in parallel and their correlation coefficients

Figure 51: The Effect of the Number of Underlyings on the Option

Source= own creation, see also Appendix 4

In order to find the ENVP of the development process the linear costs structure in Figure

50 is subtracted from the option values in Figure 51 The result is shown in Figure 52

Figure 52: ENPV as a Function of the Development Process - Linear

-10-l o~ -2 ~ / Number of design p~ = - 1 0 • p~ = - 0 5 a p,j = 0 c P,j = 0.5 [] p~ = + i 0 n*= the optimal number of designs to developed concurrently

Data: Sl = S2= S3 = 100, a i =cT 2 = cr 3 = 0.8, X = 100, T= 1, N = 2, rr=0.05

Source: own creation, see also Appendix 4

To maximize the Expected Net Present Value (ENPV), management should determine the optimal number of design alternatives based on the ENPV curve's peak For probabilities p~ = -1.0 and p~ = -0.5, the ideal range is n > 3, while at p~ = 0, the maximum occurs at n = 3, indicating that three design alternatives should be developed in parallel Conversely, for p~ = 0.5, the optimal approach involves developing one to two alternatives In scenarios where p~ = +1.0, a point-based development process is recommended, as a set-based approach would lead to value destruction due to the redundant mimicry of design alternatives, eliminating the potential for valuable exploration of the design space.

5.2.3 The Optimal Development Process Setup: Non-Linear Cost Structure

Figure 53 shows the case of a non-linear development process cost structure

Figure 53: Automotive Development with a Non-Linear Cost Structure

Number of design altematives Cost multiplier = 50%

Development costs: n = l " -10 n : 2" -10 -(10 x (1+cost multiplier)) : -25 n = 3' -25 -(25 x (1+cost multiplier)) = -62.5

In the example provided, the variable costs associated with developing several design alternatives concurrently rise based on a selected cost multiplier, set at 50% This escalation in costs imposes a significant constraint on the development capabilities of the automotive company The impact of non-linear development costs on the expected net present value (ENPV) is depicted in Figure 54.

Figure 54: ENPV as a Function of the Development Process - Non-

Number of design alternatives p,~ = - 1 0 x pjj =-0.5 A p# = 0 r p # = 0 5 [] p#=+1.0

Source: own creation, see also Appendix 4

The non-linear cost structure has led to a decrease in the optimal number of design alternatives that can be developed simultaneously, as the costs associated with the development process outweigh the benefits of pursuing multiple options in parallel Specifically, for the range of -1.0 ≤ p/j ≤ 0, the ideal number of designs to develop concurrently is approximately n = 2.

For design alternatives with positive correlation coefficient the optimal number of designs is less, namely, n = 1.

Five Principles of Automotive Development

This article aims to outline key indicators that aid in selecting an automotive development process Building on the previous thesis, five fundamental principles of automotive development are identified and thoroughly discussed.

5.3.1 Capabilities in Platform Design and Developing Sets Concurrently

The cornerstone of automotive development lies in the integration of platform design capabilities and concurrent set-based development Achieving this requires modularity within the automotive product architecture, enabling the flexibility to switch between design alternatives The value of this flexibility is heavily influenced by the organization's proficiency in platform development, as exemplified by Toyota's systems engineering approach The overall architecture defines the boundaries for design exploration; thus, a poorly conceived product platform can severely limit valuable exploration opportunities Insufficient platform development capabilities lead to a development process akin to the outdated point-based model.

An organization's ability to concurrently develop sets of design alternatives is crucial for success in the automobile industry Companies that possess this capability can gain a competitive advantage, as it is challenging for others to replicate such complex knowledge Conversely, a deficiency in this capability can lead to a development process that mirrors the traditional point-based model, hindering innovation and efficiency.

The second principle of automotive development emphasizes that increased volatility leads to set-based development As the volatility of design alternatives increases, so does the value of the option to switch between them This principle aligns with findings from option pricing literature, suggesting that when automotive companies encounter heightened technical and market uncertainties, they should concurrently develop a broader range of design alternatives This is particularly relevant in situations where there is uncertainty regarding the feasibility of new technologies or upcoming environmental regulations, as these factors influence the option value by affecting the free cash flows (FCFs) of the automobile, ultimately impacting its expected value.

As the volatility of individual design alternatives decreases, the value of the option to switch diminishes When automotive companies encounter lower technical and market uncertainties, they should concurrently develop smaller sets of design alternatives This scenario typically arises when there is minimal uncertainty regarding the viability of technologies or when the market is stable In cases of zero volatility, the automotive development process becomes strictly point-based.

The third principle of automotive development emphasizes that lower correlation levels promote set-based development As the correlation coefficient decreases, the value of the option to switch increases, allowing for a more extensive exploration of the design space from both technical and market perspectives Designs characterized by low correlation levels tend to perform well across various scenarios, enabling automotive companies to shield themselves from technical and market volatility by concurrently developing larger sets This phenomenon, referred to as the portfolio effect in automotive development, represents a significant finding of this thesis.

The fourth principle of automotive development emphasizes that dominant design alternatives lead to point-based development A design alternative is considered dominant when the present value (PV) of its expected free cash flows (F-CFs) significantly surpasses its implementation costs, indicating a high net present value (NPV) In such cases, automotive companies should focus on developing a smaller set of design alternatives simultaneously For instance, if a particular technology has consistently demonstrated its effectiveness both within and outside the company over the years, and no other design alternative offers a comparable NPV, pursuing multiple concurrent designs becomes inefficient.

When no single design alternative stands out due to all options having a net present value (NPV) near zero, automotive companies should pursue the development of multiple design alternatives simultaneously In such scenarios, where no clearly advantageous design exists, it is prudent to focus on creating improved design alternatives to enhance potential value.

5.3.5 Capabilities to Manage Competent Suppliers

The fifth principle of automotive development emphasizes that managing competent suppliers fosters set-based development, leveraging specialization for sustainable product improvements and cost efficiency As development costs remain linear, the option to explore multiple design alternatives enhances value, allowing for extensive design exploration while keeping costs low This high degree of efficiency is evident even with numerous concurrent designs, a hallmark of long-term partnerships in the automotive sector, such as those seen at Toyota Successful implementation of this principle necessitates strong collaboration with skilled suppliers throughout all phases of the automotive product lifecycle.

When non-linear development costs are present, the optimal number of design alternatives significantly decreases This situation often arises in development processes with constraints that limit thorough exploration of the design space For instance, expanding the range of design alternatives can lead to increased development costs due to confusion and reduced overall efficiency.

Model Criticism and Future Research

This section addresses model criticism and explores future research opportunities It begins by evaluating the validity of the methodology and analyses used, emphasizing the importance of understanding the impacts of the assumptions and models applied Following this, the discussion identifies potential areas for future research based on the findings of this study, highlighting promising fields that warrant further investigation.

Conclusion