ultra large scale systems design using list

8/14/2019 Ultra Large Scale Systems Design Using LIST

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Designing Ultra Large Scale Systems

Can Lean Inventive Systems Thinking (LIST) Help?

Navneet Bhushan and Karthikeyan Iyer

Crafitti Consulting Pvt Ltd, (www.crafitti.com)Emails:[email protected],[email protected]

1B-401, Akme Harmony,Sarjapur Outer Ring Road,

Bangalore 560037, INDIA

Abstract

The challenges of designing the needed Ultra Large Scale (ULS) systems are beyond

the methods and techniques that humanity currently knows of. These systems are

characterized by extraordinary decentralization, inherently conflicting, unknowable and

diverse requirements, continuous evolution and deployment, heterogeneous,inconsistent, and changing elements, erosion of people/system boundary, normal failures

and new paradigms for acquisition and policy. Today the largest systems being designed

are what in the US military parlance are called System of Systems (SoS). The current

cutting-edge SoS are characterized by operational and managerial independence of

elements, evolutionary development, emergent behavior and geographic distribution.

ULS will be SoS at Internet Scale. This is not a simple matter of extending the current

approaches as P.W. Anderson in his 1972 classic paper described More is Different. We

have definitely come a long way from that time in our approaches to design systems. Yet

predominantly our approaches continues to be constrained by the analytical and logical

thinking (analogical thinking) that we have perfected over past centuries.

The research agenda proposed to design ULS systems includes Human Interaction,

Computational Emergence, Design of all levels, Computational Engineering, Adaptive

System Infrastructure, Adaptable and Predictable System Quality, Policy, Acquisition and

Management. In this paper we explore the suitability of different thinking dimensions for

designing ULS Systems these are Lean Thinking, Inventive Thinking and Systems

Thinking. Our hypothesis is that these thinking dimensions need to play a much larger

part than the current analogical thinking that we are used to, in order to design these

highly complex systems of the future. We propose our framework named the Lean

Inventive Systems Thinking (LIST) as a possible approach to design such ULS Systems.

Keywords: Ultra Large Scale Systems, Systems Thinking, Inventive Thinking, Lean

Thinking, Lean Inventive Systems Thinking.

1. IntroductionThe ability to reduce everything to simple fundamental laws does not imply the ability to start from

those laws and reconstruct the universe says P.W. Anderson in his classic paper titled More is

Different[1]. He further states, The constructionist hypothesis breaks down when confronted with the
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twin difficulties of scale and complexity. The behavior of large and complex aggregates of elementary

particles, it turns out, is not to be understood in terms of simple extrapolation of the properties of a few

particles. Instead, at each level of complexity entirely new properties appear, and the understanding of

the new behaviors requires research (fundamental). We are standing at an important point in our

history when this century of complexity will lead to extremely large scales systems designed by humans.

The scale is the new frontier. These systems called the Ultra Large Scale (ULS) [2] systems, demands

unprecedented capabilities from human minds to design, operate, control and manage these systems.

The ULS systems are characterized by increasing interactions, dependencies, couplings or connections in

not only the depth of existing dimensions but in increasing the number of connection dimensions

manifold. The complexity of the ULS systems will be increasing manifold as it demonstrates the complex

behavior associated with complex systems such as a natural eco-system. Dealing with this level of

complexity needs new methods of study or solving problems. These methods need to be not only aware

of but thrive and exploit very nature of complexity. This nature is characterized by indeterminacy, non-

linearity, chaos, adaptation, self-organization and distributed intelligence [3]. The connotation of difficult

or hard to describe leads to a system being viewed as complex. Further complexity can be considered asa contradiction of distinction and connection [3].

Complexity has been defined as two or more distinct parts that are joined in such a way that it is difficult

to separate them. This characteristic of complex system led Ray Kurzweil [4] to state I know English but

none of my neurons do. Complexity generates an emergent behavior that is not exhibited individually by

any of the parts, partially or fully. Further the connotation of difficulty in understanding this emergent

behavior of complex systems springs from our classical methods of thinking [3]. These methods have

proven their worth in tackling the issues of classical science, problems and issues predominantly in a

world of distinct organization structures where the interactions between uniquely identifiable elements

were clear and unambiguous, relatively few and limited to specific dimensions. These methods werebased on reductionism or analysis, determinism, dualism, correspondence theory of knowledge and

rationality. However, the classical methods of dealing with mechanical systems and mechanistic

worldview pioneered by Aristotle and taken to their zenith by Newton, faltered in explaining the new

observations that started coming in early parts of last century. Further, the lessons from complexity

research initiated a worldview that real world is not the perfect, geometrical, ordered, predictable,

deterministic, rational construct that human mind, labor and ingenuity has created by engineering

perfect geometries that we see in all man-made physical structures. The nature turned out to be an

extremely creative and complex system, where dynamism and emergence are the norms. It is with

deeper study of nature of information; man started realizing the inherent complexity of the world that is

unfolding.

Our research indicates that the successful thinking dimensions that are working successfully in dealing

with complex systems in contrast to the classical analogical thinking are Lean thinking, Inventive thinking

and Systems thinking. We propose a framework for design of ULS systems using the integrated Lean

Inventive Systems Thinking (LIST). The paper is organized into following sections. In Section 2, we briefly

describe the challenges and needs of Ultra Large Scale Systems design. In Section 3, we describe

elements of design coming from Lean Inventive Systems Thinking. In Section 4, possible approaches for


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Ultra Large Scale Systems Design as emerging from LIST thinking are described. The paper ends with

conclusions and details of future research directions in Section 5.

2. Ultra Large Scale Systems Challenges and NeedsGiven the systems that we have built and which are continuing to scale-up in all walks of life, we arecloser to building larger and larger systems. There are needs for such systems to optimally utilize the

rapidly depleting natural resources and also to function in a highly connected world that we have created

for ourselves. Most of these systems are, be it web and computing infrastructure, supply chain systems,

healthcare infrastructure, military systems or government systems, software based engineering systems.

These systems are increasingly complex web of ultra-large, network-centric, real-time, cyber-physical-

social systems.

The ULS Systems will be system of systems at the Internet scale. Characteristics of ULS systems arise

because of their scale. These are unprecedented decentralization; inherently conflicting, unknowable,

and diverse requirements; continuous evolution and deployment; heterogeneous, inconsistent, andchanging elements; erosion of the people/system boundary; normal failures and new paradigms for

acquisition and policy. Table 1 gives a brief view of contrasts between present approaches and

characteristics of ULS Systems [6].

Table 1: Contrasting the ULS Systems needs and Current Approaches

ULS Characteristics Present Approaches

Decentralized Control All conflicts must be resolved and resolved centrally and

uniformly

Inherently conflicting,

unknowable, and diverserequirements

Requirements can be known in advance and change slowly.

Tradeoff decisions will be stable.

Continuous evolution and

deployment

System improvements are introduced at discrete intervals.

Heterogeneous, inconsistent,

and changing elements

Effect of a change can be predicted sufficiently well.

Configuration information is accurate and can be tightly

controlled. Components and users are fairly homogeneous.

Erosion of the

people/system boundary

People are just users of the system. Collective behavior of people

is not of interest. Social interactions are not relevant.

Normal Failures Failures will occur infrequently. Defects can be removed.

New paradigms foracquisition and policy

A prime contractor is responsible for system development,operation, and evolution.

The ULS systems will be artificial systems hence they differ from natural complex systems in fundamental

ways. Unlike natural systems that may evolve because of specific constraints or available paths, the

artificial systems are designed at least in principle, with a specific goal or function in mind. As Herbert


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Simon [5] describes, an artifact is an interface between inner environment and the outer environment.

The artifact tries to accomplish a goal or provide a function in the outer environment. This artifact can

have one of many possible internal environments to accomplish the same desired function in the same

environment. This is an important fact, as it indicates that theoretically infinite ways exist to construct or

design an artifact to accomplish specific function in specific environment. This is important; because this

fact creates an uncertainty and unpredictability, in the artificial world that we are living in as it leads

different actors to design different artifacts to achieve the specific function in multiple environments.

This is a dimension of complexity that needs to be understood and grappled with.

The interplay of natural and artificial is another area that comes under the realms of ULS scale

complexity. Natural objects evolve through natural selection and based on the environment in which they

operate. The observations based on how the natural phenomena occur led humans to fields of natural

sciences. The industrial revolution started a focused direction towards the artifact sciences where

suddenly man-made objects became prevalent and useful with specific functions or goals to be achieved

in specific environments. Modern world characterized by artificial environments, virtual reality and

synthetic materials, has become more man-made than natural. Yet nature has not been tamed fully infact natures fury keeps on giving clear messages of the journeys that humankind has yet to perform, in

the form of earthquakes, hurricanes, floods, volcanic eruptions and multiple natural disasters that

happen in many parts of globe.

The ULS Systems research needed as described in [2] include 7 main fields. These are represented in

Table 2.

Table 2 Ultra Large Scale Systems Research Areas

ULS Systems Research Area Specific Sub-Areas

Human Interaction

Context-Aware Assistive computing Understanding Users and Their Contexts

Modeling Users and User Communities

Fostering Non-Competitive Social Collaboration

Longevity

Computational Emergence Algorithmic Mechanism Design

Metaheuristics in Software Engineering

Digital Evolution

Design

Design of All Levels

Design Spaces and Design rules

Harnessing Economics to Promote Good Design

Design Representation and Analysis

Assimilation Determining and Managing Requirements

Computational Engineering

Expressive Representation Languages

Scaled-Up Specification, Verification, and Certification

Computational Engineering for Analysis and Design

Adaptive System Infrastructure

Decentralized Production Management

View-Based Evolution

Evolutionary Configuration and Deployment

In Situ Control and Adaptation


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Adaptable and Predictable System

Quality

Robustness, Adaptation, and Quality Attributes

Scale and Composition of Quality Attributes

Understanding People-Centric Quality Attributes

Enforcing Quality Requirements

Security, Trust, and Resiliency

Engineering Management at Ultra-Large Scales

Policy, Acquisition, and

Management

Policy Definition for ULS Systems Fast Acquisition for ULS Systems

Management of ULS Systems

The increasing complexity either natural, artificial or a combination of both is an important fact of the

new world. The increasing scale of ULS scale systems is creating more complexity as is evident in multiple

dependencies, connections and unknown network effects the new pace is creating for humankind. Yet

there is a hope it is remarkable how much the human mind has been able to create and synthesize

especially in last 100 years or so. In fact, there is no gainsaying that artificial future is more likely than a

natural future at least next 50 years or so. This is possible and thinkable only because of the human

mind which has proven to be an extremely robust and comprehensive factory of new ideas, new

thoughts and new memes that are successfully implemented to create artifacts, synthetic environments,

and robust global artificial systems. This is the dimension of design thinking. In the next section we will

describe elements of design that we have culled out from Lean Inventive Systems thinking.

3. The Elements of Ultra Large Scale Systems Design A SystemsApproach

We do not understand complexity. This is an inherent property of reality and the nature shows umpteen

examples of the complex behavior as exemplified in the emergence of order in various seemingly simple,

local interactions with limited rules in various natural systems. Complexity emerges from dependencies

informational, control, decisions, structural and material dependencies. Connections create complexities

as well. Multi-dimensional dependencies create higher order complexities. How can one embrace

complexity rather than thinking or working for reducing complexity? How can one invent or innovate to

leverage the boundaries rather than always focusing on the core? It is at the boundaries that value

capture will have maximum returns. Value capturing and value creation are interrelated phenomenon

that one needs to maximize. Further complexity is not necessarily natural; in fact most prevalent form of

complexity impacting human-beings is artificial. The artificial things are synthesized by human beings.

The world around us is full of more artificial things than natural things. Starting from our morning alarms,

newspapers, radio music, television images and computer chat rooms we work through artificial things

till the night when we sleep in artificially heated rooms and beds.


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The very purpose of design is to reduce the complexity of what is abstract, by giving it some sort of

physical shape or form. The crystallization of thoughts into some structure, either on paper or on any

medium reduces complexity as perceived by the human mind structures make it easy for the brain to

memorize and retrieve information. By design and not by accident highlights the scientific, structured

aspect of design, where the system, its components and its behavior are completely understood, thereby

introducing the element of predictability into the proceedings. Ultra large scale systems design needs a

holistic systems approach incorporating the following key elements:

3.1.NeedsWhat the users of a product need from it is probably the most ambiguous question of all. User needs

stem from multiple intelligences

[19], namely linguistic, musical,

logical, spatial, kinesthetic,

inter-personal and intra-

personal. User needs also stem

from the gratification of the five

senses sight, sound, touch,

taste and smell. At product-user

interface points, the product has

the opportunity to beneficiallyimpact one or more senses or

intelligences. For instance, one

could look at the interface

points on a product use timeline

or life cycle similar to the Buyer


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Utility Map [18] and design the product to raise the bar of user experience (intelligences + senses) at each

of the interface points.

Simultaneously, one

could look at the spatial

and qualitative interface

points.

User needs may also

vary over time or due to

change in context. This

leads to a multi-

contextual and evolving

map of needs. A multi-

level user needs

dashboard can serve as

a key tool in designingthe next level of user

experience.

3.2.Function (Inventive Thinking)Needs and functions are often interchangeably

used. It is common to hear that the function of a

product is to fulfill a certain need e.g. the function

of a washing machine is to fulfill the need of

cleaning clothes. While related to each other,

needs and functions differ in perspective an

emotional human-centric view vs. a parametric,

neutral view. One could argue that the function of

a washing machine is to wash clothes, not to

fulfill the need to clean clothes.

A single product may have several functions which

may be sorted based on priority or importance.Typically, one core function is enabled or

supported by supplementary functions. There may

be additional layers of derived/ dependent

functions.

Products can be synergistic when the cores attract (they are different from each other) and the outer

functional layers merge or fit well into each other. On the other hand, two products with repulsive cores


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(very similar to each other) will typically compete or in the unlikely event of a forced merger try to

subsume each other.

Function-focused design looks at two aspects:

1. Performing the function elegantly [7] (in the simplest manner possible minimal consumption ofresources and minimal harmful effects) [46][47]

2. Performing only what is necessary (eliminate unnecessary functions) [8]At the outset, there may be multiple design alternatives or paths, each offering a functional improvement

and therefore looking equally promising. How do we know which path to take? TRIZ offers clues and

directions.

TRIZ is a large collection of empirical methods discovered and invented through comprehensive studies of

millions of Patents and other inventions for problem formulation and possible solution directions. Reader

can refer to large body of knowledge at [46][47]. TRIZ clearly distinguishes two main parts of problem

solving problem description/definition and its solution.

Define, Describe, Analyze the problem from multiple perspectives, as deep and as wide as onecan go. This requires a focused discipline to not to jump to solution immediately TRIZ has

tools and Processes for Problem Definition

Find out the root contradiction and look at how the contradiction has been solved in the past Solve by exploring in multiple directions but start from the end result The Ideal Final Result

Focus on Functionality not features.

Some of the key design directions that emerge from TRIZ are:

1. Choose a design which eliminates one or more system contradictions (contradictions are pointswhere beneficial impact to one parameter causes a negative impact to another parameter)

2. Choose a design which moves the system to a higher level along known lines of systemevolution.

3. Choose a design which is moving towards ideality all benefits at zero cost.3.3.StructureOften, form (or structure) determines function,

the way the function will be achieved and the

constraints that the function will face. Structures

provide stability to systems, preventing descent

into randomness or chaos. The flip side is that

stability brings rigidity along, suppressing the

ability of a system to change, adapt and evolve.

Can structures overcome this basic contradiction

provide flexibility while retaining stability?


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Structures also have to evolve in step with needs and functions in order to enable systems to move to the

next level. The TRIZ lines of evolution also clearly point out the significance of increasing structural

flexibility to overall system evolution.

Why are specific system elements rigid?

a. Stability - They are elements where the most critical functions are performed and cannotafford to fail

b. Unpredictability They are not very well understood and are unpredictable, hence they aredeliberately operating within strict constraints

c. Dependency Too many other system elements are dependent on this element, hence itcannot be changed very easily and without pain

d. Insulation They are not very well connected and therefore do not have an incentive tochange or adapt to changes

e. Efficiency The elements are optimally structured to perform certain functions as efficientlyas possible

All of these reasons are fundamentally linked to the concept of system complexity as defined by coupling-

cohesion measures [32]. Tightly coupled structures tend to be rigid and therefore not amenable to

change. Cohesive system elements and loosely couple structures are conducive to change, without

compromising on stability. The root cause of high coupling and low cohesion is the lack of understanding

of structural relationships between system elements, the need for these relationships, when they are

activated/ de-activated and the strength of these relationships. The following steps are useful to move

towards increasing structural flexibility:

1. Find the areas of the system that are highly complex (tightly coupled, non cohesive) usingDependency Structure Matrices [48] and the System Complexity Estimator [12].

2. Continuously re-architect the system to decrease coupling and increase cohesion.Another critical aspect of structure is the concept of centre centre of gravity or centre of control.

Centralized structures tend to have very tightly coupled central nodes or hubs. While centralized

structures offer a lot of stability, the hubs tend to become bottlenecks in complex, quickly changing

environments. Recent trends of system evolution seem to be pointing towards networked, distributed

architectures with distributed centers of gravity and distributed intelligence.

3.4.BehaviorWhile needs, functions and structure characterize the static system, the working system is characterized

by its behavior manifested in the way system elements come together to create a whole greater than the

sum of the parts. In real time, system elements interact between themselves and with external entities,

reacting differently to different stimuli and evolving in the process.

Human beings have typically designed systems that are based on centralized intelligence. This can

probably be attributed to the limitations of the brain to handle complexity [43]. On the other hand,

natural systems are evidently much more complex and display what we perceive as emergent behavior.


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They are so named because we are unable to draw logical threads from any central intelligence to the

behavior displayed by the elements of the system (in other words, behavior that cannot be explained by

logic). As systems grow larger, the cost of centralized control increases. Gradually, multiple centers of

intelligence emerge and finally, intelligence is so completely distributed that hubs cannot be easily

isolated.

While emergent behavior has been observed, it has never been designed. In order to design

emergence, the characteristics of emergent behavior and systems displaying such behavior have to be

understood in detail. Following are some of the key characteristics:

Synchronization

All systems (animate and inanimate) seem to have the innate tendency or ability to synchronize with the

surroundings [20]. In the absence of centralized control, there still seems to be a strong pull for elements

to work in tandem. Intuitively, it makes sense. Much more can be achieved with much less effort through

synchronized efforts. While, the initial belief was that this behavior is the result of complex intelligence as

pursued by game theory [22], the evidence of synchronized behavior in entities with very low intelligence

(fire flies) or with no intelligence at all (pendulums) has changed the assumptions somewhat. In small

systems, stable behavior can be achieved through stable structures and centralized logic. In large,

complex, non-linear systems, these measures are typically inadequate or ineffective. Observations from

natural system behavior show that the ability of system elements to synchronize (on their own) helps

maintain stability and prevents the system from collapsing (natural tendency to decay and degenerate

into chaos). While most designs are built with the objective of overcoming entropy through tighter

centralized control, natural systems balance the tendency of systems to move towards higher entropy

with the counter tendency to synchronize. There are clear parallels to be drawn with USAs war against

terror and potential alternative designs to combat terror.

How to design complex artificial systems with built-in ability to synchronize?

1. Stop trying to build more and more complex central brains. Distribute the intelligence.2. Even in highly complex natural systems, synchronization happens through simple rules of state

change or behavior change. These are preset responses to stimuli and may even happen without

any active intelligence. Simpler elements are easier to influence (social network theory) and

therefore contribute to synchronization much more effectively.

3. Sync happens around rhythms (or clocks). Clocks do not have to be centralized, but central clockscan play a major role in assisting synchronization (touched upon in greater detail in the chaos

section). The Takt time [8] concept also seems to be alluding to something similar.

4. There needs to be an innate reason to synchronize (although it is not essential for all elements toknow the reason). Sometimes, reason is so deeply entrenched in behavior, it ceases to be visible;

this can be dangerous (some rhythms are just impossible to get out of). If synchronization offers

benefits in reaching some common output, elements tend to synchronize over multiple iterations

of discovery.

5. All system elements need to be designed as learning systems. Synchronization has to be learnt;the time period of learning may vary.


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Chaos

Chaotic systems look chaotic at the outset the patterns of behavior are non-linear but not random [21].

They hide certain patterns of behavior called attractors. Attractors are essentially axes of stability,

invisible at the outset but clearly borne out by macro behavior. Again, while natural systems are known

to display chaotic behavior, artificial systems are not typically designed for chaos. There are certain

traits that can be exploited for such design:

1. In chaotic systems, attractors manifest across scale (clearly, if they do not, system stability willget affected). In reverse, while designing large, complex systems, all elements need to gravitate

towards a common attractor (the behavior that needs to be manifested). For example, in a

complex, non-linear system such as the internet, if security is a characteristic that needs to be

implemented, it cannot be done by inserting some central security nodes into the network.

Secure behavior needs to manifest across scale (entire network, zones, local networks, smallest

subnet, router within a subnet, TCP/IP socket on a router etc.)

2. Attractors act as strong central rhythms or clocks. You can choose to design systems such thatthe central rhythm is beneficial to the objectives of the system. In the absence of such design,emergent attractors can often turn out to be out of sync with the system objectives (many

large organizations face this challenge when they grow in size and changes become more and

more difficult to incorporate [51]).

Balance

In the world of complex, chaotic systems, stability is not about being still rather it is about being on the

move constantly to avoid stagnation and to counteract the inevitable forces that prevent you from

remaining still. (Ever tried to balance a cricket bat on one of your fingers?) The key element is not

stillness but balance. For instance at a macro level, system behavior may be looked at as a function of

the following four elements and their individual traits:

1. Fire fuel, energy to run the system2. Water vitality or life of the system, ideas3. Air transports essentials to all parts of the system, enables change4. Earth structure and raw material, the basis of the systemIn centralized systems, specific parts of the system may be designated specific roles in alignment with

one of the four elements. The overall proportion of the elements is also controlled centrally and changes

over time. In complex systems, these elements stay in overall balance, present at all levels ranging from

micro to macro but in different proportions depending on the system need.

This is very close to Ayurveda [52] which prescribes mechanisms to sustain balance between the four

elements (as manifested in the human body as a system) and to prevent and cure imbalance, if any.

Consider an organization as a complex entity, sustained by motivators, thinkers, communicators and the

implementers representing fire, water, air and earth respectively. While some portions of the

organization will specifically need larger proportions of certain types (e.g. leadership, research, sales and


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engineering), sustained stability and growth requires an overall balance to be maintained. Any excess or

shortfall in any of the elements proves detrimental in the long run.

The concept of balancing key elements of a system which are manifested across scale (again falling back

on the chaos attractor theory) can be used as an alternative, simpler mechanism to design system

behavior (as compared to typical sequential, deterministic approaches).

4. The Process of Ultra Large Scale Systems Design A Lean Set-basedApproach

It is fascinating to note that while there are possibly millions of different products out there, there are

just a handful of different approaches to the design process.

Typically, product design has followed a convergent

approach to arriving at the right design, in other

words, survival of the fittest. Based on data

available at any given point in time, the best

alternative out of multiple alternatives is chosen as

the to-go approach. This chosen design then gets

fine-tuned over time, using the same survival of the

fittest approach iteratively. This is similar to

traversing a unidirectional tree (from root to

branches) with a single player; choose the best

looking branch at every node and hope to make

your luck as you go.

Fixing on one approach out of many early on in the life cycle has some clear pitfalls:

1. Always reaching Local OptimaAs a design matures, more data and information tend to become available. Often, there is the

feeling If I had known this earlier, I might have taken a different path! Decisions based on

incomplete data tend to lead towards local optima in many cases much inferior to global

optima [16].

2. Creates a false sense of simplification of complexityThe decision to take one path somehow erases the existence of other paths from the mind,

whereas in reality they are still there. This artificial simplification often results in all resources

getting expended in one direction making it more and more difficult to retrace and take

corrective measures as we walk deeper down a path.

3. Introduces artificial delayBeing forced to choose the best alternative may result in procrastinationLet me wait for more

information before I choose the path to move ahead. While this delay in action (making way for

rigorous analysis) may look justifiable, much of the analysis is only hypothetical (since the data


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required for real analysis can only be obtained a few steps down the line) and therefore not of

much use.

4.1.Set-based Concurrent EngineeringSet-based concurrent engineering [24][25][26][27][28][29][30][31] is a product development technique

invented by Toyota, which focuses on collaboration between different departments. The aim is at shorterdevelopment times with an increased quality level by improving collaboration and by parallelizing parts

of development process. In the traditional point based approaches the teams select an initial design

option and work on quickly producing it however, the design gets

modified as new information, experiences and requirements emerge

thereby creating what is called Design Churn effect. In this

scenario, the product remains in development phase for very long

period as the chain reactions created by many modifications to initial

design lead to continuous refinement and an evolutionary design

that keeps on going. This is the result of early design convergence

and action-oriented approaches most companies and management

gurus prophesize. In contrast Toyotas SBCE advocates slow

convergence strategy. SBCE processes starts with large design

alternatives covering broad design spaces and then slowly converges

to a possible design by eliminating the weakest alternatives rather than choosing one best alternative.

It is a counter-intuitive approach and looks paradoxical to people trained in the traditional point based

approaches. Various sets of alternatives are taken ahead for all parts of the product and the weakest

ones are eliminated as we move in the product development life cycle.

SBCE leading to slow convergence seems like an inefficient and expensive way to develop products,

however, Toyota creates new automobiles faster than industry average with less effort. It has beentermed as the Second Toyota Paradox as more time spent in early phases of the product life cycle leads

to less time spent in the overall product life cycle [26].

Although SBCE is known for many years and many research publications have described the process, it

has not been picked up by many companies as principles are counter intuitive and in time and budget

constrained commercial organizations, it becomes very difficult to not to show one design quickly so as to

show the development project is on the right track to the top management. The information, decision,

design and organization complexity also increases as SBCE as a process requires strict discipline in

following the process by everyone as there is no central control, it creates a self-organizing system.

Further, the SBCE principles dont describe specific methods, techniques, tools or frameworks for

execution. It is this important gap that Inventive Thinking approaches (TRIZ, function-based approaches)

and Systems Thinking approaches (holistic understanding of needs, function, structure and behavior)

serve to fill.

5. Linking the design elements with the LIST framework


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SBCE Steps Specific Actions TRIZ and Other tools

Mapping the

Design Space

Describe user needs In case of multiple needs carry out needs

interdependency analysis

Find out key functions to be performed

Understand structural complexities Understand behavioral complexities Function dependency analysis to find out

interdependencies

Can some high level functions specific tostrengths of different teams be identified

Let each team explore the specifications, needs,functions independent of each other

Each team explore design tradeoffs throughsimulations and their past observations

Each team should come up with their sets ofdifferent solutions with in the functional and

performance needs of the product

Problem Formulation andAnalysis

Value Stream [49]

Ideal Final Result (IFR)Why-what hierarchyNine windowsDependency Structure

Matrix (DSM) [48]

Function/Attribute AnalysisSystem Complexity

Estimator (SCE) [12][16]

S curve analysis Vedic Inventive Principles

[50]

Contradictions Technical/Physical

Trends of evolutionStriving for

Conceptual

Robustness

(Functional Team

level)

Design should remain functional after variationsin its environment

Vulnerability of system to changes in theenvironment should be minimized

Modularized Design with standard components

IFR AFD/Subversion Analysis Robust Inventive System

Design (RISD) [10]

DSMIntegration by

Intersection

(System level)

How are the parts integrated to meet at thepoint that will be regarded best solution

Find out overlap of feasible design spaces foreach sub component

Decisions about eliminating the weak designs

Decision DependencyMatrices (DDM) [13][15]

Analytic Hierarchy Process(AHP) [15][45]

Technical Contradictions /Inventive Principles

Establish

Feasibility before

Commitment

Multiple concepts developed using prototypingsimulation

The infeasible ones will be rejected rest all willcontinue to be developed

Decision theoreticprinciples [28][29]

AHP Closer to IFR

Conflict Handling Cooperative Conflict handling Which solution is closer toIFR?

DDM AHP

6. Conclusions and Further ResearchThe challenges posed by the Ultra Large Scale (ULS) Systems design are immense. These challenges are

unprecedented as well. Further the traditional methods that we have perfected and that have served us


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for many centuries are failing in designing such large scale systems. Taking the cues from research

agenda proposed by the ULS systems community, we propose in this paper a framework combining lean,

inventive and systems thinking, is a possible route. The framework that we termed LIST has elements of

natural evolution, design thinking, holistic thinking and inventive thinking. We propose to carry out

further research and development of the methodology for designing highly complex ultra large scale

systems.

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