Taxonomy and Categorization of Uncertainties in Space Systems with an Application to the Measurement...

American Institute of Aeronautics and Astronautics

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Taxonomy and Categorization of Uncertainties in Space

Systems with an Application to the Measurement of the

Value of Adaptability

Alejandro Salado1, Roshanak Nilchiani

2 and Mahmoud Efatmaneshnik

3

Stevens Institute of Technology, Hoboken, NJ, 07030

Space systems face multiple types of uncertainties from the design phase through

production, testing, launch, operation and retirement of the space system that challenge the

mission success in multiple dimensions and aspects. Therefore proper identification,

classification, categorization and management of uncertainties are necessary in

understanding the environment that space systems are embedded and also essential in

identifying the adaptable designs, architectures, or solutions. Given the ever increasing

dynamic environment of current space systems, sources of uncertainties are considerably

diverse and therefore make proper identification and management a crucial part of design

and operation of adaptable and Flexible Space Systems. This paper aims on a thorough and

holistic taxonomy and categorization of space systems uncertainties for the purpose of

keeping track of uncertainties and facilitate their prioritization, management, scenario

building and appropriate modeling during the entire life cycle for the purpose of designing

Adaptable and Flexible Space Systems. Several major types of uncertainties were organized

into five major groups including policy, service performance, organization, technology and

market; which are derived from the stakeholders and mapping the space system context.

The taxonomy has been defined ensuring completeness and coherency. Then various

classification types based on uncertainty dimension, being exogenous or endogenous, level of

complexity and other classification types are presented. This research also addresses the

peculiarities of the space systems according to their type of mission and customer.

I. Introduction

NCERTAINTY is the hallmark of all complex systems. It is the lack of complete knowledge of the state of a

particular system in the present or in the future. Adaptability, flexibility and other systems -illities are devoid

of meaning in a hypothetical deterministic world. Real world exists in the context of uncertainties of various kinds

that dramatically affect all natural and man-made systems. The concept of uncertainty has been discussed and

studied in various domains of knowledge including Physics, Statistics, Economics, Finance, Insurance, Engineering,

Information Sciences, Philosophy, Psychology, Sociology and many more.

A thorough study of the concept of uncertainty in space systems as well as other domains of knowledge indicates

that a detailed differentiation between various types of uncertainties is required and a comprehensive classification is

lacking. Space systems, like any other natural or man-made system, behave and respond differently when facing

various types of uncertainty. Therefore a comprehensive classification is a must. The next step, which involves using

various modeling techniques to model uncertainty, also needs the classification step in order to know what type

modeling is most appropriate.

1 Doctoral student, School of Systems and Enterprises, Castle Point on Hudson, Hoboken, NJ 07030. AIAA

Member. 2 Assistant Professor, School of Systems and Enterprises, Castle Point on Hudson, Hoboken, NJ 07030. AIAA

Associate Member. 3 Assistant Research Professor, School of Systems and Enterprises, Castle Point on Hudson, Hoboken, NJ 07030.

AIAA Member.

U


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Furthermore current prospect in design techniques for systems level consider the treatment of uncertainty as a

leverage to assess the value of different candidate solutions1-7

. Consequently a common taxonomy that provides a

comprehensive yet usable set of uncertainties is required so that a high level of confidence can be achieve in such

trade-offs.

This paper is organized as follows. Section II provides a review of existing literature on the topic of uncertainties

in space systems, with emphasis on their management at system level, and synthesizes their limitations. Section III

presents several classifications that can be used to understand uncertainties from different perspectives. The

taxonomy proposed in this paper is presented in section IV and is validated against assessed limitations in section V.

The paper finishes giving a short summary of the conclusions and a proposal for future work in section VI.

II. Literature Review

Uncertainty research has traditionally focused on uncertainty and risk reduction8,9

and uncertainty modeling8.

Such approach has resulted in classification of uncertainties based on the physics behind the uncertainty, as for

example the classification described by Thunnissen and Tsuyuki9:

Recent trends look at uncertainties as opportunity/threat elements, trying to incorporate uncertainties into the

evaluation of system level trade-offs so that systems are designed to cope with uncertainties rather than simply

limiting the designers to achieve their mere reduction1-7

. Under this new scope the use of the classification presented

here before despite being useful for modeling of uncertainties is not convenient to perform trade-offs. McManus and

Hastings10

propose to classify uncertainties from designer perspective and conclude that the right approach is to set

the knowledge base as the measurable element. In this way they identify five types of uncertainties, namely: lack of

knowledge, lack of definition, statistically characterized phenomena, known unknowns, and unknown unknowns.

These are further described in the next section.

In the new scenario of evaluating the effect of uncertainties at the system level has brought in interest in non-

technical uncertainties, which had traditionally been ignored, such as political uncertainty, development uncertainty,

requirement stability uncertainty, or market uncertainty7,11-13

. A review of existing literature shows that there is no

consensus on which uncertainties need to be considered for system level trade-offs. For example, Hastings et al.

make a selection of 10 uncertainties7 while Yao et al. use a combination of those uncertainties as well as several

technical ones11

. Furthermore, their selection follows the knowledge and expertise of the engineers at hand. Several

limitations of these approaches are listed in Table 1.

Uncertainty

Ambiguity Epistemic Aleatory Interaction

Model Phenomenological Behavioral

Approximation errors

Numerical errors

Programming errors

Design

Requirement

Volitional

Human errors

Figure 1. Thunnissen and Tsuyuki classification of uncertainties9


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An interesting element of the collection proposed by Hastings et al

7 is the differentiated collection of

uncertainties according to the life-cycle phase, which implies that the success of a system does not only depend on

how it performs, but that it begins at the very beginning of the design phase.

III. Classifications of uncertainties

Uncertainties can be classified differently depending on the aspect of the uncertainty that is of interest. In this

section, several types of classification are presented. Some exist in literature of the various domains of knowledge,

and some we have created the classifications such as Dimensions, and Nature of uncertainty. Figure 2 shows a brief

set of our various types of classifications, which will be discussed next.

A. Dimensions

Dimension classification addresses how the uncertainty occurs in temporal and physical dimensions.

Uncertainties in space systems can be categorized based in three dimensions: the timeframe and the lifecycle phase

in which they manifest themselves, the level of physical detail and abstraction in which they appear, and the type of

Table 1. Main limitations of existing collections of uncertainties.

Limitation Justification

Lack of

completeness

Uncertainties are identified without following a structured process. Instead

brainstorming or field knowledge are used. Therefore it is not possible to ensure that the

selected uncertainties are the right ones (validation of the selected uncertainties) or their

completeness.

Lack of

weighting

Different uncertainties have different effects on the system and therefore the

criticality of their impacts shall be reflected on the trade-offs. Although it can be argued

that weighting is part of the design process and not of the framework, the high

complexity of decisions in space systems due to the amount of decision takers involved,

it is recommended to provide a “default” weighting system embedded on the framework.

Lack of

distinction based

on mission type

Similar justification with regards weighting, since impact of uncertainties may be

perceived differently depending on the type of mission.

It can also be the case that some uncertainties exist for some missions and not for

others (particularly when moving down in the architectural hierarchy).

Lack of

distinction based

on customer

Similar justification with regards weighting, since impact of uncertainties may be

perceived differently depending on the customer.

It can also be the case that some uncertainties exist for some customers and not for

others (particularly when moving down in the architectural hierarchy).

Limited to space

segment

Segments others that the space segment are not considered in the categorizations.

However, they may have a major contribution to the end value for money of space

systems, particularly during operations.

Mix of different

abstraction levels

For example considering high level uncertainties like cost or market and low level

ones like semi-major axis or inclination11

. Makes it difficult to measure and use

uncertainties at the right level of abstraction. Threatens the completeness of the

uncertainties taken into account.

Lack of

organizational

uncertainties

Project related organizational aspects can affect the successful completion of a space

system, e.g. leave of key personnel, adequate project organization, unsuitable supplier

selection, etc. are not addressed.

Uncertainty

Classification

Dimensions Objective vs.

Subjective

Endogenous vs.

Exogenous

Roots and

Sources

Nature of

uncertainty

Figure 2. Classifications of uncertainties


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change (object of variation) that creates the uncertainty. For example, Launch Failure is a valid uncertainty only

within the timeframe of launch to successful insertion of the space system, whereas orbital debris uncertainty affects

a space system once it is launched and deployed and until the end of its operation.

B. Objective Vs. Subjective

Objective vs. Subjective classification addresses the way in which the uncertainty is estimated.

Objective uncertainties are those subjected to rules that remain relatively constant over time (in relation to the

lifetime of the space system), often follow a statistical probabilistic distribution of an uncertain physical

phenomenon, and therefore can be studied and estimated with a high degree of confidence. They include

epistemological and ontological based uncertainties such as subsystems reliability, space debris, or availability,

which often follow the probabilistic laws of physics.

Subjective uncertainties are those for which the rules may change dramatically and therefore high confidence

levels cannot be reached when estimating them through in-depth studies. Some examples of this type of

uncertainties include market uncertainty, project schedule uncertainty, and technological development uncertainty.

C. Endogenous Vs. Exogenous

Another form of categorization is based on the boundaries of the system under study. Based on the boundaries

drawn, uncertainties can be exogenous to the space system, such as market uncertainties and probability of collision

with space debris, or endogenous, such as part, subsystem, and instrument failure on board of the spacecraft.

D. Roots and Sources

McManus and Hastings10

propose five categories to classify uncertainties according to their roots, namely:

Lack of knowledge, which includes facts that are not known, or are known only imprecisely.

Gathering this type of knowledge can reduce the uncertainty.

Lack of definition, which exists when the elements or attributes of a system are not specified.

Statistically characterized phenomena, which represent elements that cannot always be known

precisely, but that can be statistically characterized or bounded.

Figure 3. Dimensions classification of uncertainties

Figure 4. Endogenous vs. Exogenous uncertainty classification description


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Known unknowns, which refer to those uncertainties that are identified, but that cannot be reduced

beforehand.

Unknown unknowns, which refer to emergent behaviors of a system, i.e. there is no awareness of

their existence until they actually occur.

These roots have different levels and depth of uncertainty. For example, the uncertainty associated with the lack

of knowledge and definition is often much less than the uncertainty associated with unknown unknowns. Figure 4.6

conceptually shows the depth of uncertainty in these five defined categories of uncertainty.

E. Uncertainty nature

This type of categorization of the uncertainty is based on the rules and dynamics inherent to the nature of

uncertainty under study. We define three major categories: Simple, Complicated, and Complex.

The Simple category consists of the types that follow the single cause and effect. A complicated uncertainty

follows a series of cause and effects and in a way is a superimposition of some simple processes. These types of

uncertainties are often easier to model in a statistical manner. Examples of complicated/Simple Uncertainties include

reliability of subsystem or the space system, orbital debris, or availability. If they are modeled well, an agent that is

interfering with the problem at hand to address the effect of uncertainty can resolve and respond to the uncertainty.

In contrast, Complex uncertainties are often governed by very different internal dynamics. An adaptable

response implemented by an agent to reduce the complex uncertainty, can change the uncertainty profile and

magnitude, or even initiate emergence of a new type of uncertainty. Complex uncertainties are often governed with

circular causality and feedback loops. Circular causality in essence is a sequence of causes and effects whereby the

explanation of a pattern leads back to the first cause and either confirms or changes that first cause. They can show

chaotic behavior, a small change in the cause implies dramatic effects or unforeseen types of problems. Complex

uncertainties are also associated with emergence, unpredictability and entropy. Figure 5 shows a conceptual figure

of the Complex uncertainty concept.

Complex uncertainties are understudied and less

understood in various domains of engineering. A

void of research on modeling and understanding

these types of uncertainties exists. The current

existing literature puts the emphasis on reduction of

complexity in its modeling, and many use expert

judgment or opinion in characterizing these types

of uncertainties. Examples of complex uncertainties

include technological obsolescence uncertainty,

technological development uncertainty, schedule

uncertainty, policy and economic uncertainties, and

many more. The complex uncertainties show the following behavior in nature:

Dynamic: The complex uncertainty profile changes in time and is dynamic. For example, an action taken

by an agent to reduce the funding uncertainty can start a chain of events with feedbacks in the system,

which can have an adverse effect in resolving the uncertainty.

Governed by feedback: There are often several feedback loops and nested loops with reinforcing or

balancing nature in the system.

Nonlinear: An interference in the uncertainty doesn’t have a linear correlation to the magnitude of

interference

Adaptive and evolving: complex uncertainties adapt themselves to the solutions that the agent provides

them. They can show resistance to the solution provided by the agent to resolve the uncertainty.

Time lag: there is often a time lag between the action of the agent and behavior of the complex

uncertainty. The time lag creates the illusion of dissociation between the action of the agent and the

complex uncertainty.

Counterintuitive: Cause and effect are distant in time and space. High leverage points are often not

obvious.

Policy Resistance: “Common sense” solutions often don’t work; many obvious solutions fail or actually

worsen the problem.

Figure 5. Schematics of the concept of Complex

Uncertainty.


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IV. Development of a taxonomy for uncertainties

Taxonomy for system trade-off has been developed to cope with the limitations presented in section II, ensuring

completeness of the uncertainties and applicability of the taxonomy to any space system. The taxonomy is

independent of the classifications presented in the previous section. It is not intended to classify types of

uncertainties, but rather to provide a structure in which the uncertainties space systems face can be rationally

organized.

The taxonomy is composed of 27 uncertainties organized in 5 groups and addressed at a space system level, thus

it includes the space and launch segments, the ground segment, and the mission segment. The selection of

uncertainty categories has been done bearing always in mind the ultimate use of the taxonomy, which is assessing

value of a solution within a trade-off among different space system alternatives.

A. Application to the design of adaptable and flexible systems

As presented in the introductory section system attributes like adaptability or flexibility are only meaningful in

the context of uncertainty. In fact, the need to produce adaptable and flexible systems stems from coping with

uncertainty. Consequently, the better the uncertainties are modeled, estimated and ultimately understood, the better

the value of different candidate systems can be assessed.

Due to the ever-increasing complexity of space systems and the amount of interaction they have with other

systems, isolated modeling of discipline uncertainties is not sufficient for evaluating candidate systems. Instead the

design team should acquire a complete understanding of the influence a particular uncertainty has on the overall

system. This can be facilitated by gathering together the following elements:

Taxonomy that structures uncertainties from a system point of view and that ensures completeness of the

uncertainty identification process.

Description of positive and negative consequences of the uncertain event to the system.

Mathematical description of the uncertainty (modeling technique, conveyed information, and model).

Interdependencies between the different uncertainties, understood as those that are modified after an

uncertain event occurs (complex uncertainties).

Importance of a particular uncertainty within the range of uncertainties for the particular system of

interest. For space systems this is mainly driven by the type of mission and the type of customer.

B. Taxonomy

The idea behind providing taxonomy to organize uncertainties is ensuring completeness and providing a

common reference under which different systems can be evaluated. In order to achieve this goal the taxonomy is

structured following a sequential conceptualization of the elements that affect the end value of a space system.

These elements respond to five questions that incrementally challenge the value of the system:

1) Is it allowed to build and use the system?, which addresses the Legality of the system of interest.

Uncertainties responding to this question are labeled “Policy uncertainties”.

2) Is it feasible to build and use the system?, which addresses the feasibility of having the system. Due to the

nature of space system uncertainties responding to this question are technological and therefore have been

labeled as “Technology uncertainties”.

3) Can we build and operate the system?, which addresses the actual capability of industry and government to

implement the system and make it usable. Uncertainties responding to this question are labeled “Capability

uncertainties”.

4) Does the system operates within the initial specified performance level?, which addresses the level of

functionality and performance the system offers once the system is operational with respect to its intended levels.

Uncertainties responding to this question are labeled “Service performance uncertainties”.

5) Is the system successful?, which addresses the actual use or recognition of the system once it is operational.

Uncertainties responding to this question are labeled “Market uncertainties”.

The proposed categories are hierarchical with respect to value provided by the system in the sense that the upper

levels are enabled by the lower ones. This enabling mechanism shall not be understood as sequential in time,

because uncertainties in each category may affect the system at different phases of the system life cycle.

Table 2 presents the proposed taxonomy and provides additional description of each of its categories.


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Because the selection of categories for uncertainties cover all aspects of how the value of a system may be

affected, virtually any specific uncertainty a space system would face could be mapped to one of the categories.

Therefore the taxonomy provides the means for structurally identifying uncertainties in a system and thus guarantees

that every aspect related to its value is evaluated. Consequently the taxonomy increases the confidence in the

evaluation of all uncertain events that ultimately may affect the value of a system.

C. Types of missions and customers

Space systems are well compartmentalized, i.e. there are conceptually few and well-known uses for space

systems. The characteristics, challenges, and objectives of each use are generally shared between the majority of

space systems for a particular use, resulting in similar concept of operations. These uses are traditionally recognized

as types of space missions. Furthermore the economics of the space market make those characteristics, challenges,

and objectives differ according to the purpose of each specific mission. Therefore uses of space systems can also be

further determined given the type of customer acquiring the space system.

In the proposed categorization uncertainties are weighted according to the type of mission and customer for

which the space system is developed. This differentiation in importance for the uncertainties increases the quality of

the analysis when determining value of adaptable and flexible systems, when several uncertainties are treated

simultaneously. It shall be noted that although the table in Appendix provides specific figures for the particular

weights these are only informative and the determination of the actual value for a specific space system is left open

to each project so that the values of the actual customer can be properly captured.

Table 3 lists the types of missions that have been considered as part of the present research. They are the result

of analyzing the concepts of operations and mission objectives of existing space systems and grouping those that are

similar in approach and purpose.

Table 2. Description of the taxonomy categories

Category Description

Policy

uncertainties

Uncertainties related to law and regulation that impact the system. Most common

examples include ITAR, EO laws, or ITU frequency allocation.

This type of uncertainty has not really been explored in the available literature. When

discussing Policy uncertainty, it is normally related to government funding, which in this

taxonomy is allocate to market.

Technology

uncertainties

Uncertainties that are related to the availability of technology or technical solutions.

Most common examples are obsolescence, state-of-the-art, achievability, Technology

Readiness Levels (TRL’s), System Readiness Levels (SRL), etc.

Capability

uncertainties

Uncertainties that are related to the capability of the project team and may impact the

development or the operation of the system. Most common examples include supply

chain performance, complexity of operations, directives to use specific suppliers, loss of

key personnel, inadequate personnel, etc.

Uncertainties falling under this category have not been addressed by existing

literature, despite the fact they are usually the major contributor to cost overruns, schedule

delays, and technical non-compliances in space systems.

Service

performance

uncertainties

Uncertainties that are related to the impacts of bringing the system into real-life

operation. They could be defined also as uncertainties included in the design by definition

(performance based on probabilities). Most common examples may include reliabilities,

availabilities, transmitted power, degradation, lifetime, orbit accuracy, fuel usage,

radiation, atmospheric effects, network load, integration to other systems, etc.

Market

uncertainties

Uncertainties related to “funding and revenues”, which may be impacted by business

case success or effects of internal and external competitors:

Commercial project: market capture, effect of other company putting the

system in place earlier or at lower cost, impact of competitors with same

service in other industry (e.g. terrestrial networks).

Government project: actual scientific return, competitors making funding

fluctuate (e.g. budget moved from Human spaceflight to Earth observation),

etc.


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Table 4 lists the types of customers that have been considered as part of the present research. They reflect the

typical customers existing in the space market, which are mainly defined by the system or customer objectives and,

more importantly, by the funding source.

D. Identification of uncertainties

Uncertainties may differ from project to project and it is virtually impossible to list all uncertainties a system will

face during its development and lifetime. However, the proposed taxonomy identifies 27 uncertainties that are

considered common to each and every space system and in addition, are considered sufficiently complete to

holistically evaluate candidate solutions at system level.

Identification of uncertainties has been done using a hybrid approach:

Top-down: in addition to evaluating the uncertainties inherent to the system itself, typical stakeholders for

space systems are also consulted. Such a process aims at providing completeness because all elements

affecting the success (value) of the space system are considered.

Down-top: uncertainties listed in existing literature, as presented in section II, have been gathered and

organized according to their applicability in terms of system decomposition. This step serves as

validation for the top-down approach.

Because of their nature uncertainties are described as positive and negative consequences for the system. These

are defined for different moments in time of the system life cycle. For simplicity under the scope of the present

research two phases are considered: development phase and operational phase (including launch, LEOP and IOT).

Table 5 lists the identified uncertainties with their correspondent stakeholder and provides a brief description of their

meaning. Table 6 provides a description of positive and negative consequences of the uncertainties when the

triggering event occurs during development phase and table 7 does the same for the operational phase.

Table 3. Mission types

Type of mission Description / Justification

Communications Provides any type of communication link using space assets, being them inter-

satellite or satellite-to-ground.

Navigation Provides positioning and navigation services to ground or space users. It shall be

noted that although its concept of operations falls under the ‘Communications’

paradigm, a dedicated category has been implemented because the actual constraints

may be different depending on the customer. For example, a degradation of the

availability or service reliability of a government owned satellite used for technology

development can easily not affect the mission objectives. However, those same

requirements for a navigation constellation must be fulfilled, since other systems may

rely upon them for their performance, including safety of people lives (for example

landing an airplane).

Earth Observation Provides information about the Earth using space assets.

Science Pursues the advancement of science beyond its boundaries using space assets. This

category includes the so-called ‘Exploration missions’ because both share similar

constraints and objectives, being usually the mission context identical.

Human Spaceflight Although Human ‘Spaceflight’ is nowadays directly linked to science, the fact of

humans being part of the system makes it peculiar enough to form a stand-alone type.

Table 4. Customer types

Type of customer Description / Justification

Commercial Projects funded by private companies, normally aiming at profiting from a service

provided using space assets.

Government Projects funded by public fund, i.e. eventually the tax payer, normally aiming at

stretching the boundaries of science or develop new technologies that could be used

by the private sector.

Military Projects funded by public funds, i.e. eventually the tax payer, normally aiming at

improving the military capacity of a country.

Specific characteristics of military projects such as for example handling of

classified information are sufficient to make it a stand-alone type of customer.


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The proposed taxonomy does not include the uncertainty of budget or funding fluctuations, despite it is usually

considered in existing literature. This decision has been taken by analyzing why budget may fluctuate (increase or

decrease) for a space system. When thinking in those terms budget fluctuation can be seen as a consequence of a

combination of other factors such as competition or policy. For example, a change in policy of how space must be

explored may result in shifting budget from human spaceflight to science. This effect could be seen as an increase in

competition. Consequently the taxonomy only accounts for the factors that are considered root causes, thus leaving

budget fluctuation out.

Table 5. Description of uncertainties.

Uncertainty Stakeholder Description

Policy

Export Law and regulations Export restrictions (e.g. ITAR).

Frequency

allocation Law and regulations Loss/availability of frequency channels to operate.

Mission-specific

regulations Law and regulations

Mission-specific regulations affecting operational concept and

implementation.

Disposal Law and regulations

Regulations on how to deal with the space assets once

operations are finished (e.g. debris management).

Technology

Obsolescence Technology Obsolescences.

Technology

readiness Technology Technology development risks (e.g. TRL).

System readiness

Technology/Manufac

turer Integration of technologies (e.g. SRL).

Capability

Supply chain Manufacturers/Policy Supply chain performance.

Cost

Manufacturers /

Customer Validity of the development and operational costs estimations.

Technical

capability Manufacturers Technical capability to develop the system.

Key people Manufacturers Dismissal of key people.

V&V Manufacturer Validity of the verification and validation program.

Design Manufacturer

Correctness of the subsystem designs towards system

objectives.

Requirements Customer Fluctuation of requirements.

Customer

involvement Customer Level and type of involvement of the customer.

Service performance

Reliability Customer System reliability (e.g launcher, component, ground, etc.)

Availability Customer Service availability.

Debris Space environment Probability of orbital debris colliding with the space segment.

Radiation Space environment Space radiation damages the space segment.

Weather hazard Earth climate Earthquakes and alike (Ground segment).

Lifetime Space environment

Lifetime of the system, normally limited by degradation of the

space segment.

Performance

Technology /

Manufacturers

Actual performance of the space system (e.g. receive power,

contact window, etc.).

Market

Market size User Size of the market targeted by the system.

Discount rate Customer Uncertainty of the prediction of the discount rate.

Competitor Competitor

Competition affecting the success of the project. It may have

different definitions dpending on the type of customer.

Market capture User Actual part of the market using the system.

Schedule Manufacturers Time to market.


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Table 6. Description of positive/negative consequences during development phase.

Uncertainty Positive consequence Negative consequence

Policy

Export

Access lower cost or better location launcher; use

of better componenets (improve performance or

reduce cost).

Limitation on the use of launcher leading to

redesign or requalification; multiple launcher

selection leading to overdesign; inability to use

specific components leading to overruns and

redesign.

Frequency

allocation Use a higher bandwidth.

Redesign to accommodate newer frequency;

improve filtering; narrow bandwidth.

Mission-

specific

regulations Open to increase intended service. Limiting intended service.

Disposal None. Redesign to fulfil debris regulations.

Technology

Obsolesc.

Use of newer technology that improves

performance.

Cost and schedule impact to find a replacement.

The later the obsolescence triggers, the worse the

imapct for the project.

Technology

readiness Performance better than targeted.

Performance not achieved; cost overruns; schedule

delays.

System

readiness Integration of technologies

Performance not achieved; cost overruns; schedule

delays.

Capability

Supply chain

Having some elements earlier may allow for de-

risking activities.

Having some elements earlier may increase costs

due to storage.

Late deliveries have a negative impact on

schedule.

Cost Cost to develop the system is lower than expected.

Cost to develop the system is higher than

expected.

Technical

capability

Team performs better than expected and system is

developed with fewer errors thus leading to lower

costs; or achieve better performance than required.

Team does not have the capability to develop the

system and thus system does not achieve

performance; or it takes longer than required to

achieve it.

Key people

Some key people act as bottlenecks. Although at

short term it may seem as negative to the project,

substitution may lead to faster processes (removal

of bottleneck).

Difficult replacement leads to incapibility to

achieve performance (develop system).

V&V N/A N/A

Design

Smooth integration at system level, reducing

schedule/cost.

Difficult integration at system level with cost

overruns and schedule dealys.

Reqs.

Less stringent requirements leading to lower cost

and fastest time to market. Change requiring redesign of the system.

Customer

involvement

Customer supportive: allows manufacturer to do

the job and provides pragmatic expertise to add

value to the development.

Customer limitting: forces bouracracy without

added value and limits the manufacturer from

carrying ist job.

Service performance

Uncertainties related to service performance are not applicable during development phase because their trigerring event

can only occur once the system is operational (note: real performance).

Market

Market size N/A N/A

Discount rate N/A N/A

Competitor

Less competition than expected may lead to higher

funding.

More competition than expected may lead to

lower funding (government) and/or change in

requirements.

Market

capture N/A N/A

Schedule

Time to develop the system is shorter than

expected.

Time to develop the system is longer than

expected.


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Table 7. Description of positive/negative consequences during operational phase.

Uncertainty Positive consequence Negative consequence

Policy

Export N/A N/A

Frequency

allocation Use of higher bandwidth. Interference/jamming from another satellite.

Mission-

specific

regulations Open to increase intended service. Limiting intended service.

Disposal None.

Reduction in lifetime to accommodate disposal

needs.

Technology

Obsolescence

Improved performance; reduction of operational

costs in the long run. Upgrade costs; potential temporal loss of service.

Technology

readiness N/A N/A

System

readiness N/A N/A

Capability

Supply chain N/A N/A

Cost Profit is higher than expected. Profit is lower than expected.

Technical

capability N/A N/A

Key people N/A N/A

V&V

System survives/performes under potential non-

nominal situations.

System does not perform or survive under nominal

or almost nominal situations with respective

reduction in service provided.

Design N/A N/A

Requirements

New needs fulfilled by the system (potential

higher market capture or maintain it).

New needs not fulfilled by the system (potential

reduction in market capture).

Customer

involvement N/A N/A

Service performance

Reliability

Reduction on recovery actions leading to

reduction of operational costs.

Service degradation (even potential loss of

service); increase on operational costs for

increased recovery actions.

Availability Higher revenues. Service degradation leading to a loss in revenue.

Debris None.




Radiation None.




Weather

hazard None.




Lifetime

Increase on the depreciation time of assets

(revenues at USD 0 cost on assets).


service).

Performance Better performance than expected. Worse performance than expected.

Market

Market size

A market higher than expected may lead to higher

reveneus.

A market lower than expected may lead to lower

reveneus than expected.

Discount rate Higher profit. Lower profit.

Competitor

Less competition may lead to higher revenues or

funding.

More competition may lead to lower revenues or

funding.

Market

capture

More users than expected that may lead tl higher

revenues.

Less users than expected that may lead to lower

revenues.

Schedule N/A N/A


12

The identified uncertainties within the proposed taxonomy are applicable to any type of mission and customer,

yet market uncertainties need some discussion in order to understand their meaning outside the commercial

customer. Table 8 provides a description of the meaning each uncertainty belonging to Market category has to

different customers. Such definitions have been developed by exploring the uncertainties from a conceptual

perspective and not from their literal meaning, thinking about Market as the community of potential users of the

system, being these consumers in the commercial environment or scientists in the government one, for example.

As presented earlier uncertainties need to be prioritized according to stakeholder’s demands. For space systems

these are related to the mission type and the customer type. A prioritization matrix is proposed as part of the

taxonomy so that proper evaluation of candidate solutions can be performed. The matrix, presented in table 9, shall

be considered only for illustrative purposes since no evidence for actual figures has been used, besides the rationale

and experience of the authors.

Table 8. Definition of Market uncertainties for each type of customer

Market

uncertainty

Customer type

Commercial Government Military

Market size Size of the market

addressable by the system.

Scientific community

that could use the system.

Population that could

benefit from the system.

Total amount of military

conflicts.

Discount

rate

Opportunity cost of

capital.

Opportunity cost of

scientific or social revenue.

Opportunity cost of

upgraded military capacity.

Competitor New competitors

entering the market while

the system is being

developed or when

operational.

Other projects or market

segment getting public /

government interest that

make budget / funding

fluctuate (e.g. budget

moving from Earth

observation to Human

Spaceflight).

Other governmental

agencies. Governments may

transfer funding between

the different agencies

making the budget/funding

fluctuate.

Market

capture

Actual part of the

market using the system.

Actual part of the

scientific community using

what the system delivers.

Actual usage by

populations/agencies/etc. of

what the system delivers.

Amount of conflicts

(percentage) where the

system can be used.

Schedule Time to market. Time to bring the

system into operation.

Time to bring the

system into operation.


13

E. Uncertainty Interdependencies

As presented in section III, uncertainties of complex nature are those in which a change by an agent to resolve

and address the uncertainty at hand, potentially changing its probability distribution and trigger events, results in

emergence of new uncertainties. In space industry, we can observe many examples of complex uncertainties and

how they create a ripple effect in the larger system of systems level due to the interdependencies between specific

uncertainty types. In order to evaluate the impact of complex uncertainties on the value of a system, it is necessary

to understand how the different space systems uncertainties are related. The authors believe that correlation finding

and determining interdependencies between various types of space systems is a critical domain of research. The

initial studies of interdependencies between the space systems uncertainties of the proposed taxonomy by the

Table 9. Prioritization of uncertainties.

Uncertainty (N) Communications Navigation

Earth

Observation Science

Human

Spaceflight

C G M G M C G M G C G

Policy

Export 3 2 1 2 1 1 2 1 1 1 1

Frequency

allocation 2 2 N 2 N 3 3 N 3 3 3

Mission-specific

regulations 3 3 N 3 N 1 2 N 3 1 1

Disposal 3 3 N 2 N 2 2 N 3 3 3

Technology

Obsolescence 1 1 1 2 2 3 3 3 3 2 2

Technology

readiness N 1 1 1 1 2 1 1 1 1 1

System readiness N 1 1 1 1 2 1 1 1 1 1

Capability

Supply chain 1 2 2 2 2 1 2 2 2 1 2

Cost 1 2 2 2 2 1 2 2 2 1 2

Technical

capability 2 2 1 2 1 2 2 1 1 2 2

Key people 3 2 1 2 1 3 2 1 1 2 2

V&V 2 1 1 2 1 1 2 1 2 1 2

Design 1 2 1 2 1 1 2 1 2 1 2

Requirements 1 2 1 2 2 1 2 1 2 1 2

Customer

involvement 3 2 2 2 2 3 2 2 2 3 2

Service performance

Reliability 1 2 1 1 1 2 3 1 2 1 1

Availability 1 3 1 2 1 1 2 1 3 3 3

Debris 1 2 1 2 1 2 3 1 3 1 1

Radiation 1 2 1 2 1 2 3 1 3 1 1

Weather hazard 3 3 2 2 2 1 2 3 3 3 3

Lifetime 1 3 2 3 3 1 2 3 2 1 2

Performance 2 1 1 2 1 2 1 1 1 2 1

Market

Market size 1 2 2 2 2 1 2 2 2 1 2

Discount rate 2 3 3 3 3 2 3 3 3 2 3

Competitor 2 3 3 3 3 2 3 3 3 2 3

Market capture 1 3 3 3 3 1 3 3 3 1 3

Schedule 1 2 2 3 2 1 2 1 3 2 3

Legend: N – Not applicable; 1 – High priority; 2 – Medium priority; 3 – Low priority.


14

authors are presented in a matrix form in Figure 6. Note that dependencies are one-directional: relation from A

(cause) to B (consequence) does not imply a relation exist from B (as a cause) to A (as a consequence).

Justification and clarification for such dependencies are provided in the appendix. We believe that an in depth

research on interdependencies of uncertainties, can guide us to be more informed about the probable consequence of

unfolding uncertainties and guide us to understand how an specific uncertainty can trigger other types. The

importance of this research also due to its application in calculating the value of Adaptability and Flexibility: The

scenarios that are building according to the interdependencies of space systems create a clearer picture of the options

and their sequence, and therefore, a more precise valuation of options in or on the space systems.

V. Validation: overcoming existing limitations

The proposed taxonomy has been developed aiming at mitigating the limitations that are present in existing

literature and that have been discussed in section II. Table 10 summarizes which elements of the taxonomy mitigate

such limitations.

Ob

sole

scen

ce

Tech

no

log

y r

ead

iness

Sy

stem

read

iness

Reli

ab

ilit

y

Av

ail

ab

ilit

y

Deb

ris

Rad

iati

on

Weath

er

hazard

Lif

eti

me

Perf

orm

an

ce

Mark

et

size

Dis

co

un

t ra

te

Co

mp

eti

tor

Mark

et

cap

ture

Sch

ed

ule

Su

pp

ly c

hain

Co

st

Tech

nic

al

cap

ab

ilit

y

Key

peo

ple

V&

V

Desi

gn

Req

uir

em

en

ts

Cu

sto

mer

inv

olv

em

en

t

Ex

po

rt

Fre

qu

en

cy

all

ocati

on

Mis

sio

n-s

pecif

ic r

eg

ula

tio

ns

Dis

po

sal

Obsolescence 11 12 21 41 100 110 79

Technology readiness 1 13 72 101 111 80

System readiness 2 73 102 112 81

Reliability 22 42

Availability 63 68 113

Debris 23 43 99

Radiation 24 44

Weather hazard 25 45

Lifetime 3 18 26 31 34 38 ? ? ?

Performance ? 60 64 69

Market size 27 46 52 65 135 82 92

Discount rate

Competitor 61 70 103 123 136 83 93

Market capture 28 47 53 66 84 94

Schedule 4 6 14 32 35 62 67 71 104 114 124 137 146 85

Supply chain 74 115 150 127 147

Cost 139 148

Technical capability 7 15 54 75 128 132 140 149

Key people 119

V&V 19 29 48 56

Design 20 30 49 57 129

Requirements 9 17 36 37 39 ? 58 77 116 120 130 133 96

Customer involvement 78 105 117 121 125 131 134 141

Export 5 10 40 55 107 106 118 122 126 142

Frequency allocation ? 108 143

Mission-specific

regulations ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 144 ? 88

Disposal 33 ? ? ? 145

Market

Capability

Policy

Capability Legal

Tech.

Service perf.

Columns are triggered by rows

Techn. Service performance Market

Figure 6. Uncertainties dependencies.


15

VI. Conclusion

The present paper begins with a review of classification of uncertainties in existing literature for space systems.

In particular, several classifications have been gathered that look at uncertainties from different perspectives, namely

dimension, objective vs. subjective, endogenous vs. exogenous, roots and causes, and nature. The authors also

contribute to new types of classifications of uncertainty in space systems.

A taxonomy that groups and identifies a complete set of uncertainties that are of use for the value assessment of

adaptable and flexible space systems has been discussed and developed by authors. The taxonomy is characterized

by two major features:

Structures the identification process of uncertainties, ensuring completeness and coherency.

Provides the necessary information package to use uncertainties in multi-disciplinary trade-offs at system

level for space systems.

The present discussion has shown that the proposed taxonomy mitigates the limitations when dealing with

uncertainties in approaches described in existing literature. We also have presented some initial results of our studies

in finding correlations and interdependencies between various space systems related uncertainties which are crucial

in building realistic scenarios that can be used for valuation of flexibility and adaptability in space systems.

Finally the present research supports proposes advances and future work on the following domains:

Collect evidence to provide validated figures on priorities of uncertainties.

Develop models for capability and policy uncertainties, which have not been explored so far in existing

literature, yet they are a major contributor in development success of space systems.

Explore the nature and dynamics of complex uncertainties.

More in depth studies of the uncertainty dependencies presented in this paper, correlation finding through

studies of more historical cases.

Appendix

Table 11 lists the impacts uncertainties may have on the different segments of a space systems and which new

uncertainties might be triggered as a consequence to a change in the originating uncertainty. Note the following

legend: U – Utility; C – Cost; S – Schedule; S – Space Segment; G – Ground Segment; and M – Mission Segment.

Table 10. Mitigation of limitations in existing literature.

Limitation Mitigation in proposed taxonomy

Lack of completeness Structured taxonomy with conceptualization process of 5 steps. Use of

stakeholders and confidence review with existing literature (section IV.B and

section IV.D).

Lack of weighting The taxonomy includes prioritization of uncertainties based on customer type

and mission type (section IV.D).

Lack of distinction

based on mission type

The taxonomy includes prioritization of uncertainties based on customer type

and mission type (section IV.C).

Lack of distinction

based on customer

The taxonomy includes prioritization of uncertainties based on customer type

and mission type (section IV.C).

Limited to space

segment

Uncertainties have been identified analyzing a space system at the system

level, i.e. considering the traditional segments (section IV.B, section IV.D and

appendix).

Mix of different

abstraction levels

Structured taxonomy at system level. Hybrid approach to determine

uncertainties at the appropriate level (section IV.B and IV.D).

Lack of

organizational /

capability

uncertainties

Specific category in the taxonomy (section IV.B).


16

Table 12 provides justification and clarification for the uncertainty interdependencies listed in section IV.E.

Table 11. Effects of uncertainty on segments and triggering of additional uncertainties

Uncertainty (N)

Development phase Operational phase

Effect Segment Uncertainties

triggered

Effect Segment Uncertainties

triggered (group) U C S S G M U C S S G M

Policy

Export X X X X

Requirements/Supply

chain N/A

Frequency allocation X X X X X X Requirements X X Market capture

Mission-specific

regulations X X X X Market capture X X X X Market capture

Disposal X X X Requirements X X N/A

Techonlogy

Obsolescence X X X X N/A X X X X N/A

Technology

readiness X X X X N/A N/A

System readiness X X X X N/A Capability

Capability

Schedule X X X X N/A X N/A

Supply chain X X X X X Schedule X X X X X X Schedule

Cost X X X X X Schedule X X X N/A

Technical capability X X X X X X Performance/Schedule Performance

Key people X X X X X X Technical capability Performance

V&V X X X X X N/A

Design X X X X X

Schedule/Technical

capability

Requirements X X X X X Technology X X X X X Market capture

Customer

involvement X X X X X X

Schedule/Technical

capability

Service performance

Reliability N/A X X X X X N/A

Availability N/A X X X Market capture

Debris N/A X X X N/A

Radiation N/A X X X N/A

Weather hazard N/A X X X X N/A

Lifetime N/A X X N/A

Performance N/A X X X X Market capture

Market

Market size N/A X Availability

Discount rate N/A X N/A

Competitor N/A X X X Marlet capture

Market capture N/A X Availability


17

Table 12. Justification for uncertainty interdependencies.

ID Example ID Example

1

The later a technology may be available the higher the risk

of obsolescence for other parts of the system. Same in opposite direction. 76 N/A

2

The later a system may be available the higher the risk of

obsolescence for some parts of the system. Same in

opposite direction. 77

Change in requirement may lead to redesign, which may affect

schedule.

3

The higher the lifetime the higher the risk of obsolescence.

Same in opposite direction. 78

The level of customer involvement may lower or speed the

development process.

4 The longer it takes to put the service in operation the higher the risk of obsolescence. Same in opposite direction. 79

Risk of obsolescence may drive governments to limit their

exports of that product, or of the new substitutors in order to have technological advantage.

5

The higher the limitation in export regulation for key

technologies the higher the probability to use old technologies and therefore the higher the risk of

obsolescence. Same in opposite direction. 80

Export regulations may be affected by availability of new

technology in order to have technological advantage.

6

An increase on the time to market allows for more time to

develop new technologies and therefore the uncertainty of technology readiness may be lower. 81 Same rationale as for 80.

7

If the technical capability is not sufficient, the uncertainty

to achieve a specific level of technology readiness may be higher. 82

Goevrnments may want to limit a specific service in a region in order to limit their capabilities.

8 N/A 83 Same rationale as for 82.

9

Requirements may change toward more stringent

performance needs, which would result in new TRL

uncertainties. 84 Same rationale as for 82.

10

Export regulations may suddenly forbid the export of a specific technology and therefore its TRL would change

for that project. 85

The longer it takes to put the system into service the higher the

probability of law changes.

11

Obsolescence of a technology may change a TRL from 1 to 7, i.e. from available technology to new development.

Therefore their uncertainties are related. 86 N/A

12

Obsolescence of a technology or component may make a SRL change from available to not available. Therefore

their uncertainties are related. 87 N/A

13

The later a technology may be available the later a system

may be ready. 88

Mission specific regulations may limit use of foreigner

technologies.

14

An increase on the time to market allows for more time to

integrate new technologies and therefore the uncertainty of

system readiness may be lower. 89 N/A

15 Same rationale as for 7. 90 N/A

16 N/A 91 N/A

17 Same rationale as for 9. 92 The higher the market size, the higher the bandwidth and maybe there are not enough in the selected frequency channel.

18

The longer the service has to operate the lower its

reliability may be. 93

A competitor entering earlier in the marketplace may take the

frequency slot.

19

A wrong verification approach may result in early failures not identified and therefore the reliability uncertainty

might be affected. 94 Same rationale as for 93.

20 A wrong design may result in an actual lower reliability. 95 N/A

21

Obsolescences when in operation may result in longer

times to repair or upgrade and therefore availability may be

affected. 96

Change in requirements may lead to different frequency use,

which then may not be available.

22

The system availability dependes on the system reliability

and therefore their uncertainties as well. 97 N/A

23 The more actual debris in space the higher the risk of collapsing (lower availability) and the opposite. 98 N/A

24 Same rationale as for 23, but with radiation. 99

The higher the debris content, the higher the probability agencies

force to perform disposal operations.

25 Same rationale as for 23, but with weather hazard. 100 The higher the obsolescence, the riskier the supply chain.


18

Table 12 cont. Justification for uncertainty interdependencies.


26

The longer the service has to operate the lower its

availability may be, since for example more maintenance

might be needed. 101 Same for technology readiness

27

If the market size is bigger the system may be less available as it cannot cope with the entire demand for

service. 102 Same for system readiness

28 Same rationale as for 27. 103 Competitor may higher similar companies in the supply chain and affect ist performance.

29

Wrong V&V may result in differences for availability

"influencers" such as maintanance operations between

designed times and actual times (as an example). 104

Elongation or reduction in schedule may limit performance of

supply chain.

30 Same rationale as for 29, but related to the design. 105 Customer involvement may reduce supply chain performance.

31 The longer the service is in operation the higher the risk to be impacted by debris. 106

Export regulations may limit the use of some suppliers and put in danger supply chain.

32

The longer it takes to put the service in operation the

higher the risk of debris impact (because debris increases with time). 107

Export regulations may result in long lead times, influencing schedule.

33

Disposal needs may require maneovres through areas with

different debris content than expected during the design

phase. 108

Frequency allocation decisions may take long time, influencing

design decisions and thus schedule.

34 Same rationale as for 31, but with radiation. 109 N/A

35 The launch window influences the radiation levels, as they depend on the solar cycle. 110 The higher the obsolescence risk the higher the cost uncertainty.

36

New or different requirements may need going through

areas with different debris content. 111 Same for TRL

37

For example, a change in requirements to have less mass

may result in less protection against radiation. 112 Same for SRL

38 Same rationale as for 31, but with weather hazard. 113

If availability is lower than expected, more maintanance

operations than planned are required and therefore operational costs are increased.

39

New or modified requirements resulting in ground segment

locations with riskier weather hazards. 114 The longer it takes to develop the more expensive it is.

40 Export regulations may result in launching or installing the ground segment in locations with riskier weather hazards. 115

A non-working supply chain will imply additional costs in terms of controlling, overseeing, etc.

41

Early obsolescences once in operation may make the

system not upgradable or supportable and die before expected. 116

If requirements are unstable, cost cannot be determined, thus ist uncertainty is higher.

42

The lower tha actual reliability the higher the risk to

become non-operational before planned end of life. Same

in opposite directin. 117

The more the customer is involved the bigger the team in order to

accommodate analysis requests, progress reports, travel, etc.

43

The higher the debris content the higher the impact risk,

the higher the lifetime uncertainty. 118

Export regulations may imply over-purchasing, legal agents,

border taxes, etc.

44

The higher the radiation levels the less time the system can

perform in orbit with the required performance. Note that radiation affects lifetime, not performance itself. 119

Technical capability is determined by the key people of the project mainly.

45

Only applicable if there is only one ground segment in one

location and it is really work intensive for the system. 120

Technical capability is measured against the requirements to be

fulfilled.

46

If the system service beyond ist planned capacity, it may ware out its components faster and therefore reduce the

lifetime of the system. 121

Technical capbilitity is limited or augmented by the level and type

of customer involvement.

47 Same rationale as for 46. 122 Technical capability may be limited by the use of export-regulated technologies, knowledge, etc.

48

Wrong assumptions for the verification of the system

lifetime may result in a different actual lifetime. 123 Competitor may hire key people.

49 Same rationale as for 48, but with design. 124

The longer it takes the more difficult retaining key people is. For

senior people the higher the risk of severe illness or death.

50 N/A 125 Key people may be incompatible with counter-parts at customer.


19

Table 12 cont. Justification for uncertainty interdependencies.


51

Unplanned disposal maneovres may require fuel that is

taken out of the planned system lifetime. 126

Export regulations may limit the use of key people depending on

their nationality, etc.

52

Performance of the system may be degraded if more users have to be served. Even if availability is ok, parameters

such as latency might be affected.. 127

The capability of thhe supply chain may influence decisions that

jeopardize the validity of the VV program.,

53 Same rationale as for 52. 128 The VV validity< depends on the technical capability of the company designing the VV strategy.

54

The performance of the system depends on the technical

capabiltiy of the manufacturer. 129 The suitability of the design enables good VV.

55 Export regulations may limit the service from operating in various regions, limiting therefore the market size. 130 The stability and adequacy of the requirements enable proper VV.

56

If the verification is uncertain, the actual performance of

the system is also uncertain. 131

Customer involvement may jeopardize validity of the VV

program.

57 Same rationale as for 56, but with design. 132 Technical capability of the manufacturer enables good design.

58 Performance is measured against requirements. 133

The stability and adequacy of the requirements enable proper

desighn

59 N/A 134

Customer involvement may jeopardize validity of the design

program.

60

If the system is very good it may bring attraction from

more users than initially expected (the ones that were outside the market target) 135

New estimations on the market size may result in adaptation of requirements.

61

Competitors may support the increase of the market size

with the same rationale as for 60, or giving perhaps more presence. 136

Introduction of competitors in the market may result in adaptation of requirements to be more competitive.

62

If the system is late and is put into service once the market

is established, then the market size uncertainty wouild be lower. 137

The longer it takes to develop, the more probable stakeholders may change requirements.

63

The availability performance of the system may influence

competitor decisions to go into the same market. 138 N/A

64 Same rationale as for 63, but with performance. 139 Variation in cost may lead to modify (upgrade or waive) requirements.

65

The certainty of a market size may influence the

competitors to go into that market. 140

Technical capability of the manufacturer may lead to modify

(upgrade or waive) requirements.

66

If the market capture of the system is too big or too low

may influence the competiro to go or not to go into the

same market. 141

The more the customer is involved the more probable

requirements will evolve.

67

If time to market is too high, competitors may want to try

to advance into the market (fort example Microsoft over Apple). 142 Export regulations may result in updating requirements.

68 Availability influences customer satisfaction. 143 Same as for 142.

69 Performance influences customer satisfaction. 144 Same as for 142.

70 Competitors influence customer decision. 145 Same as for 143.

71 Time to market influence how many customers the competitor takes before the system is put into service. 146

Problems with schedule may drive the customer to become more involved, and the opposite.

72

Time to develop the system depends on availability of

technology. 147

Problems with the supply chain may drive the customer to

become more inolved, and the opposite.

73 Same as for 72, but with system readiness. 148 Problems with the technical capabiltiy may drive the customer to become more involved, and the opposite.

74

Performance of the supply chain influence the time to

market. 149

Problems with the cost may drive the customer to become more

involved, and the opposite.

75

Technical capability of the manufacturer influence how

long they need to develop the system. 150

Technical capability depends on the capabilities of the supply

chain.


20

Acknowledgments

The present research has been developed under the DARPA/NASA Ames Contract Number: NNA11AB35C on

the Fractionated Space Systems F6 project awarded to the Stevens Institute of Technology. The authors would like

to thank Dr. Owen Brown for his ideas and feedback on the elements presented in this paper.

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