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System Thinking for Business Plan Validation

HELSINKI UNIVERSITY OF TECHNOLOGY

FACULTY OF INFORMATION AND NATURAL SCIENCES

DEPARTMENT OF MATHEMATICS AND SYSTEMS ANALYSIS

MAT-2.4108 INDEPENDENT RESEARCH PROJECTS IN APPLIED MATHEMATICS

11TH OF NOVEMBER 2009

MAUNO TAAJAMAA

57894B

Mauno Taajamaa 11th of November 2009 57894B

Table of Contents

1 Introduction ................................................................................................................... 1

2 Business Plan Validation in ICT-sector – Some Literature Remarks and

Discussion .............................................................................................................................. 2

3 Context and Goals for This Study ................................................................................... 4

3.1 General Description ............................................................................................................................. 4

3.2 Ecosystem Definition E1 Milestone in Detail ....................................................................................... 5

3.3 Criteria for Evaluation Different System Approaches .......................................................................... 7

4 System Approaches and Applicability to Business Plan Validation ............................... 8

4.1 System Thinking in General .................................................................................................................. 8

4.2 Discussion about Appliance of System Concepts in This Work .......................................................... 10

4.3 Models and Methodologies from System Approaches ...................................................................... 11

4.3.1 Introduction .......................................................................................................................................................11

4.3.2 Contingency Theory ...........................................................................................................................................11

4.3.3 System Engineering and System Analysis ..........................................................................................................12

4.3.4 System Dynamics ...............................................................................................................................................14

4.3.5 Organisational Cybernetics ................................................................................................................................16

4.4 Evaluation of the Models ................................................................................................................... 20

5 Conclusions and Considerations .................................................................................. 22

Mat-2.4108 Independent Research Projects In Applied Mathematics 1


1 Introduction Business plan is one of the necessary elements of a starting new business by

defining formally the business idea with its goals and methods achieving those

purposes. It is a useful and integral tool for any kind of business , including also non-

profit entities, as a way to map out the sought-after future (Ford, Bornstein and

Pruitt 2007).The construction and critical evaluation of a business plan requires

careful consideration of its relative strengths, shortcomings and risks. The likelihood

of success depends on both the organisational activities and structures as well as on

the organization’s environment, including the customers and competitors (e.g.,

Porter, 1985). Thus, the business plan needs to be considered as part of a bigger

picture. This process can be called systems thinking which “means an effort to "look

at the whole" of an issue, e.g., to include the entire relevant problem environment in

one's definition of a design problem” (Ulrich 1993, 583). One way of applying

systems thinking is to consciously use what Jackson (2000) calls “systems

approaches”: problem solving approaches that build on the basic idea of systems

thinking.

There are several analysis and validation tools of business plans in the field of

business strategy. The goal of this study is to present a number of systems

approaches that could complement these tools. The starting point of this study is

that business plan validation could benefit from a holistic perspective. System

thinking is a discipline that studies complex systems from a holistic perspective, and,

thus, sounds promising.

Scope of this study is to examine the problem of business plan validation by focusing

on system thinking models as tools or as solutions for it. This work is done for Tieto-

ja viestintäteollisuuden tutkimus TIVIT Oy/Ltd based on their need for more robust

business validation tools.

Tieto- ja viestintäteollisuuden tutkimus TIVIT Oy/Ltd is one of the Finnish Strategic

Centres for Science, Technology and Innovation (SCIS) and is one part of the SHOK-

programme focusing on the area of information and communication industry and

services (ICT). Its main task is to run globally active research programs in co-

operation with leading Finnish universities and companies. It defines its goal as “to

create new growth based on know-how, knowledge and innovation by accelerating

the utilization of the latest research results in global business” (Paajanen 2009).



In the first phase all development ideas are gathered into four current Strategic

Research Agenda’s (SRA’s), which define the themes in which all research and

business development are run. Main idea of TIVIT is to shorten the throughput time

from research to actual business. For this purpose it has developed an “ecosystem

creation process”.

The goal is to present multiple system thinking models and see how they can be

used in the validation process, especially in the context of the TIVIT Ecosystem

creation process phase E1. To achieve this target a brief overview of the business

plan validation field is presented, then the context of the study is illustrated by

introducing the ecosystem creation process, and then finally the chosen models of

system thinking or system approaches are reviewed and evaluated based on the key

factors found on the ecosystem creation process. At the end conclusions and

considerations about the findings are presented.

2 Business Plan Validation in ICT-sector – Some Literature Remarks and Discussion

In the following chapters are presented relevant approaches to business plan

validation from literature sources.

Schumpeter (1934) defined innovation as a new value creation through

development in technology and its discontinuous change. He identified as sources of

new value the introduction of totally new products or production methods, the

creation of new markets, the discovery of new supply sources and finally

reorganisation of industries. These are quite general by nature, but can be used as

starting point in the validation by analysing the business plan’s main characters

versus these attributes.

Porter (1985) presents what is today a widely used perspective of so called value

chain analysis, which first identifies the firm’s functions and then the economic

implications for these functions. The key in his theory is the value adding

performance in every step of the value chain, which can be accomplished by first

defining the strategic business units, then critical activities, products and services to

offer and finally determining the value of these activities. This analysis should give



answers to what activities should the firm do and how it will be competitive in the

industry it is in (so called competitive advantage). The value adding function leads

the company to differentiate, by making each of its activities to lower customers cost

and raise company’s performance. The value chain analysis can easily be used as a

business plan validation tool by simply analysing the value adding performance of

the business plan in each of its functions.

Amit and Zott (2001) derive from their comprehensive case-study the value creative

components in e-Business to the following ones: Efficiency; Complementarities;

Lock-In; and Novelty. These are illustrated in more detail in Figure 1. These

components can be viewed and analysed from the business plan and its validity can

be derived from the extent that it fills these requirements.

Figure 1 Sources of value creation in e-business by Amit and Zott (2001).

Although these theories presented certainly gives insight for estimating the business

plan’s prospect of success, they are engrossed to a narrow side of the prevailing

reality which is complex, fuzzy and interconnected. One can argue that a more

holistic perspective might give more robust and useable results. This is studied by

defining the context of the business plan validation and then examining system

approaches as one solution.



3 Context and Goals for This Study

3.1 General Description

TIVIT’s ecosystem creation process is based on concurrent engineering developed at

the late 80’s, especially in the aviation manufacturing, and fully exploited in the

industry at the 90’s (Shina 1991, 1994). The idea in this context is to create

ecosystems in a similar fashion as products are created in a R&D -process, especially

to do it in a fast throughput time as the concurrent engineering methodology

promises (Turino 1992). This ecosystem creation process is developed by TIVIT’s

CEO Reijo Paajanen based on his experiences gained from ICT-world, particularly

from his time at Nokia’s research and development at the 90’s.

Ecosystem model can be described as a linearly proceeding innovation process,

where one can see clear stages in the innovation development, so worth it can be

categorised as a stage-gate process (Cooper 1993). In the ecosystem creation

process innovation is understood to be at the level of companies and also at the level

of whole networks. Thus innovation inside a company, e.g. in the R&D -department

is not considered a useful or even a possible application.

Ecosystem creation process is divided into six different phases or milestones as

Paajanen describes; Figure 2 illustrates and also stipulates what are the key

elements on each phase. Each phase must be accomplished before moving the next

phase. In the figure 2 it is also defined what are the acceptance points for moving

into the next phase.



Figure 2. Ecosystem process phases (Paajanen 2009).

The main idea of this linear innovation creation model is to shorten the throughput

time of an idea to real business. The main characteristics of this model is to find for

each idea a position inside the market or to create an own market by itself, so worth

ensuring the success of the innovation idea in the long run. By going through this

innovation process model, the preliminary ideas are refined or improved so that

predictable or expected success rate of the venture will presumably be higher.

3.2 Ecosystem Definition E1 Milestone in Detail

Before commencing the ecosystem definition E1 milestone the following steps need

to be completed in the phase E0:

The idea of the new ecosystem must be clarified

The SRA (Strategic Research Agenda) validity of the idea is verified

Potential and momentum are considered and seen fit and viable

The size of opportunity is large enough

Finally the commitments to the next phase are gathered

The main goal of the phase E1 is to define the business idea to the extent that it can

proceed into realization planning in the phase E2. Tasks of the phase E1 are

described on table 1.

Ecosystem

ideaE0

Ecosystem

definitionE1

Ecosystem

realization

plan

E2Ecosystem

developmentE3

Ecosystem

pilotE4

Development for

commercial

phase

E5

- Ecosystem idea

description

-SRA validity

- References

- What will change

- Size of opportunity

- Ecosystem

def inition

- Required

technologies

- Players and roles

- Competitiveness

- Total markets

- Investments

- Business case

- Initial system spec.

-Next phase plan

-Technology plans

- Product plans

-Service plans

-Piloting plans

-Integration plans

-Program timings

-Case validity

-Commitments

-Next phase plan

-Technology dev.

-Product dev.

-Service dev.

-Integration

-Testing

-Pilot preparations

-Marketing plans

-Next phase plan

-Piloting

-Learning

documantation

-Commercialization

requirements and

change dev.plans

-Business case

summary

-Transfer plans

-Result measurement

- Next phase plan

- Core change

implementation

-Testing of changes

-Learning education

andt ransfer

-Expansion plan

-Final documentation

Inside SRA

Possible

Business

case

Ecosystem

criteria

review

Ecosystem

prototype

acceptance

Pilot

acceptance

Transfer

completed



Table 1. Ecosystem creation phase E1 tasks list (Paajanen 2009).

TASKS INCLUDES

Detailed Ecosystem

Definition

Initial pilot use cases

Required Technologies Architecture

List of proposed technologies

Players And Roles Required ecosystem players

Their role

Competitiveness Porter matrix

Value adding vs. cost savings

Total Markets Applicable markets and their size

Investments Investments for pilots

Investment for commercial phase

Business Case Summary of business potential

Initial System Spec. Spec. for realization planning

Next Phase Plan Steps to reach next milestone, tasks

and resourcing

The acceptance body in this phase is the steering group created for this ecosystem

creation process. Requirements for acceptance are the following:

SRA (Strategic Research Agenda) match

The ecosystem to be is relevant and helps to gain technology or business

leadership

Business case makes sense



Players and roles are agreed

Commitments to the next phase

The next phase plan is ready

The meeting agenda of the steering group is formulated also on the milestone

description. The agenda consist of ecosystem presentation; requirements check list;

and both discussion and decision (Paajanen 2009).

3.3 Criteria for Evaluation Different System

Approaches

Key feature of this phase is to see how viable the considered business idea is. For

this question it is investigated that what contributions would some selected system

approaches give.

Criteria for evaluating different models represented on the following chapters are

based on 1) the viability of the proposed ecosystem; 2) realisation probability of the

proposed ecosystem considering all the information so far created. Also because

ecosystem ideas differ from each other, the need for a general and easy to modify

model is essential. From these goals it is derived the following criteria for evaluating

the approaches:

1 Adaption of the model for different situations, boundaries and premises

2 Presentation of the ecosystem in question as a whole for assessment of its

viability and probability to succeed

3 Comprehensibility of the model, also to non-system-thinkers

4 Practicability and robustness of the expected results from the model

Of course these criteria are quite general by nature, but as the renowned system

thinker E. F. Wolstenholme (1984) replied to critique to his article that “In seeking to

develop a system methodology, there must be a compromise between being precise

enough to provide guidance in use and being general enough to relate to a very wide

range of fields”. The aim is to follow his footprints in evaluating different approaches

so that the result will give precise enough guidance for implementing on the real

world but still being general enough to be valid in the variety of ecosystem ideas to

come.



4 System Approaches and Applicability to Business Plan Validation

4.1 System Thinking in General

System thinking by common wide definition is a holistic way of seeing things

opposite of the reductionist thinking where whole is analysed by its parts (Jackson

2006, Jackson 2000, Ackoff 1971). The reason for abandoning the reductionist way

is the assent that in a complex and interconnected system it cannot be described by

any functional or adequate way by only looking its parts one by one.

Origins of the system thinking can be traced back to ancient Greeks, such as

Aristoteles, or to other sciences, such as philosophy or biology, but as an

independent discipline it formed after the Second World War (Jackson 2000).

Luoma (2009, 6-8) gives a good synthesis of the history of the system movement. He

sums up:

“that theories that build around the concept of a system accumulated in

the mid-20th century. ... Holism itself was nothing new, but the

institutional manifestation of it as the ‘systems movement’ was.” (Luoma

2009, 7)

Ackoff (1971) gives a good introduction on what are systems and the concepts

associated with it. First he gives a fairly good definition of a system:

“A system is a set of interrelated elements. Thus a system is an entity which

is composed of at least two elements and a relation that holds between

each of its elements and at least one other element in the set. Each of a

system's elements is connected to every other element, directly or

indirectly.” (Ackoff 1971)

He then classifies system concepts as table 2 illustrates.



Table 2. Behavioral Classification of Systems by Ackoff (1971).

Checkland (1999) writes that system thinking actually is a “process of thinking using

system ideas” meaning that the concept of a system by itself is truly too abstract for

making sense of the real world. Still he expresses that system thinking as meta-

discipline and meta-language is useful in many different fields as explanatory device

(ibid, 48). He also brings out that viewing some complex entity as a whole; it has to

have some emergent properties, at least to that observer. Emergent properties mean

that those properties are more than the sum of its parts as the author puts it (ibid,

50).

Jackson (2000) points out that actually system movement disperses to three

different branches: first is use of system thinking on other disciplines; second the

study of system in their own right; and finally system thinking for problem solving.

Jackson classifies system of methodologies into two dimensional table according the

complexity of the system, i.e. the problem situation and relations among the

participants i.e. how diversified are common values and interest. This is illustrated

in Figure 3.

Type of a System Behavior of a System Outcome of Behavior

State-MaintainingVariable but

determined (reactive)Fixed

Goal-SeekingVariable and chosen

(responsive)Fixed

Multi-Goal-Seeking and

PurposiveVariable and chosen Variable but determined

Purposeful Variable and chosen Variable and chosen



Figure 3. Classification of System methodology by Jackson (2000).

System approach and system thinking are as concepts commonly mixed together,

but in this study system approach is considered, likewise Luoma (2009) puts it, as

well-defined guideline to react to real-world problem situation whereas system

thinking is a wider concept of mental activity itself.

Jackson (2006) reminds us that many of the system approaches, also described on

the following chapters, studies the system only in one perspective. He encourages

us:

“…being systemic is also coming to mean being able to look at problem

situations and knowing how to manage them from a variety of points of

view and using different systems approaches in combination.” (ibid, 651)

4.2 Discussion about Appliance of System Concepts

in This Work

In this work, it is essential to see what kind of tools there is for decision making

when addressing the E1 phase and valuating the business proposition. We must

keep in mind that that the purpose of this study is to find models to represent the

Increasing divergence of values/interest

Unitary Pluralist Conflictual/Coersive

S

i

m

p

l

e

ORSoft OR /

System

Emancipatory System

Approaches

C

o

m

p

l

e

x

Design of

Complex

Adaptive

System

?

I

n

c

r

e

a

s

i

n

g

c

o

m

p

l

e

x

i

t

y



future ecosystem in question and see how viable it will be. This of course is quite

hard to accomplish when considering that all ideas differ from each other (initial

situation, external and internal circumstances etc.) and what works for one idea

doesn’t necessarily work for another, but it is believed that the holistic perspective

has value by itself, because seeing the big picture will help to avoid the pitfall of

falling in love with details.

The following system approaches or models can provide only the structure for

thinking, as Luoma (2009, 31) presents, but the precondition is that they are

consciously applied. When considering business ideas as systems we must always

put boundaries on our description about the system in question. This limitation of

intellectual ability must always be considered and kept in mind when doing the final

evaluation of the ecosystem.

4.3 Models and Methodologies from System

Approaches

4.3.1 Introduction

On the following chapters a short introduction is given to selected system

approaches on the perspective of this study. These introductions are not in any way

detailed reports on all of the twists and subtleties of those approaches but sufficient

accounts on what are their roots and main ideas. At the end of each model is

described the ways how it can contribute to evaluation of the ecosystem. The

classification of system approaches used on the following chapters is mainly based

on Jackson’s (2000) view.

4.3.2 Contingency Theory

Contingency theory falls in the category of what Jackson (2000, 108) calls

“organizations-as-systems”, meaning theories that represent or model organisations

as system with an analogy to either mechanical or organismic functions. It derives

its theoretical ideas from sociology, management and organisation disciplines

(Jackson 2000).

Contingency theory, which has an analogy to organismic view, is based on the

concept of interdependent subsystems. Each of them has a function to perform

within the context of the whole. Organisation and environment are deeply



interconnected, in state of mutual influence and interdependence. Goals for

subsystems must be flexible if environment is uncertain. (Thompson, McEwan

1958). The paradigm of this theory can be encapsulated on the following procedure,

derived from Jackson (2000):

First: no universal organisational model exists. Second: contextual factors determine

the nature of structure according to constraints. As Jackson (2000, 110) writes

“these constraints are assumed to have force because organizations must achieve

certain levels of performance in order to survive”. If organisation structure doesn’t

adjust, then the opportunities are lost, cost will rise and the maintenance of the

organisation is threatened. Third: some structure are better than others, nature on

the situation has the primary influence which structure will succeed. Empirical data

has established a correlation between organisational structure and the nature of

demands placed on it by technology, environment, humans and size (Jackson 2000).

The key or critical subsystems are not generally agreed on in the literature, but

Jackson (2000) points out four subsystems that he considers to be significant: “the

goal, human, technical and managerial subsystems” (ibid, 110).

The contribution to ecosystem creation process is that using the paradigm of

contingency theory, each subsystem has a functional imperative which it must meet

if the whole (ecosystem) is to be viable and efficient. The business proposition is

validated in the context of proposed ecosystem. Thus the first step is to identify the

essential subsystems of the ecosystem-to-be and then conceptualise the functional

imperatives for those subsystems. The subsystems represented above are a good

starting point for the analysis. From the analysis that how well are those subsystems

considered on the business proposition, the proposition itself can be evaluated.

4.3.3 System Engineering and System Analysis

System analysis studies complex problems of choice under uncertainty. It was

created during the 1940’s and 1950’s for military operations planning, and the

greatest developer of this approach was and still is the RAND Corporation, which is a

non-profit think tank in the USA. It was soon used outside the military area as a tool

for solving complex socio-technical problems. Quade (1963) defines system analysis

as (quoted from Jackson (2000, 130)):

Analysis to suggest a course of action by systematically examining the

costs, effectiveness and risks of alternative policies or strategies - and



designing additional ones if those examined are found wanting (Quade

1963, 122)

System analysis consists of seven major steps which are divided to three sections:

Formulation; Research; Evaluation and Presentation (Miser ja Quade 1985).

System engineering is wider methodology than system analysis by itself; it consists

of the following phases: System analysis; System design; Implementation and

Operation (Jenkins 1969). According to Jenkins (1969) the process goes the

following course, derived from Jackson (2000). In system analysis, important

subsystem are defined and analysed as well as their interactions. The definition of

the wider system and its objectives leads to specification of the objectives of the

system being studied. In system design phase the future environment is forecasted.

After that the model is then simulated in quantitative method for finding the optimal

design in different operational conditions. The optimal design is then chosen. In the

method, there is also the implementation and operational phase, where the design is

carried out to the real world.

The whole ecosystem creation process has similar phases as the system engineering.

So worth the implementation of the phases in Jenkins method can naturally be done.

As for the ecosystem evaluation the methodology can be applied on the following

way. The problem to be answered is for instance “how the ecosystem will drive to

succeed?” or it can be particular to the business idea in question such as “how idea X

will generate market share of YY% of the market ZZ?” From this problem disposition

the subsystems of the ecosystem and their interactions are described. Then the

whole ecosystem goals are specified. After that the environment where the

ecosystem will exist is forecasted. Forecasting can be done using different methods,

such as scenario analysis or Porter matrix etc. Implementation and operational

phase is only planned for instance on the process task “Initial System Spec”. The key

question for getting usable and valid results for evaluating the ecosystem idea is the

quantitative analysis derived from the forecasting phase. This can be challenging if

mathematical model cannot be created from the forecast. Of course the process of

going through this method creates useable information about the business idea

itself, and further the level and quality of analysis by the participators also tells what

the probability of success is in general.



4.3.4 System Dynamics

System dynamics defines systems as “’feedback process’ demonstrating a specific

and orderly structure”, as Jackson (2000) captures. The father of this approach is Jay

Forrester, an MIT professor, who formulated this method at the 50’s. He describes

modelling process (quoted from Jackson (2000, 140)):

To model the dynamic behaviour of a system, four hierarchies of structure

should be recognized: closed boundary around the system; feedback loops

as the basic structural elements within the boundary; level variables

representing accumulations within the feedback loops; rate variables

representing activity within the feedback loops. (Forrester 1969, 12)

The stages of the system dynamics methodology by Forrester (1969) is clearly

divided to human analysis and then computing done by automated machines. The

stages where human mind is needed are defining the problem, identifying the

factors involving the problem and recognising the feedback loops related to

essential indicator. Also human decision is needed when deciding finally what

actions are to be taken for improving the behaviour of the system. Jackson (2000,

142) quotes:

The human is best able to perceive the pressures, fears, goals, habits,

prejudices, delays, resistance to change, dedication, good will, greed, and

other human characteristics that control the individual facets of our social

systems. (Forrester 1971, 15)

The process of modelling the system in this approach starts by establishing the

boundary of the system in focus and essentially the elements which are interacting

inside it. The cornerstone of this approach is to describe the interaction by feedback

loops (Jackson 2000). Forrester (1958) condense the idea “Feedback theory explains

how decisions, delays, and predictions can produce either good control or dramatically

unsTable operation” (Forrester 1958, 39).

As a conclusion it can be said that system dynamics can be used to describe

situations where relations between elements of system are complex and dynamic by

nature (Sterman 2000). In ecosystem validation this approach can be used by

indentifying the “system actors” as Wolstenholme (1990) stresses in his

methodology called “system enquire” for system dynamics. In his views “the

emphasis on promoting holistic understanding rather than piecemeal solutions” (ibid,



1). He also expands the traditional approach by emphasising the persons who are

the system actors:

The intention is to broaden the understanding of each person and, by

sharing their perceptions, to make them aware of the system as a whole

and their role within it; that is, to provide a holistic appreciation.

(Wolstenholme 1990, 4-5)

Wolstenholme’s process is divided into two separate phases, the qualitative and the

quantitative system dynamics. The former is the one where the cause and effect

diagrams are created. These consist of two components, the “process structure” and

the “information structure”, by which the resource and information flows are

indentified, respectively. The quantitative phase consist of computer or similar

modelling in traditional system dynamics way. The phases are summarised in Table

3.

Table 3. A subject summary from Jackson (2000, 144) describing the Wolstenholme method (1990).

So the main input of the system dynamics to validation process is to model the

interactions between the variables of the system proposed by the business plan. It is

also important to analyse all the building blocks there and how the system can be

optimised. By itself, this approach can only give good descriptive analysis of the

ecosystem behaviour. But the creation of the model and its validation is hard to

accomplish because the need for real data, which is not necessarily available. A

Qualitative System Dynamics Quantitative Systems Dynamics

(Diagram construction and analysis phase) (Simulation phase)

Stage 1 Stage 2

To create and examine feedback loop

structure of systems using resource flows,

represented by level and rate variables and

information flows, represented by auxilary

variables.

To examine the quantitative

behavior of all system variables

over time.

To design alternative system

structures and control

strategies based on (i) intuitive

ideas (ii) control theory

algorithms, in terms of non-

optimizing robust policy design.

To provide a qualitative assessment of the

relationship between system processes

(including delays), information,

organizational boundaries and strategy.

To examine the validity and

sensitivity of system behavior to

changes in (i) information

structure (ii) strategies (iii)

delays/uncertainties

To estimate system behavior and to

postulate strategy design changes to

improve bahavior.

To optimize the behavior of

specific system variables.



solution to this might be that the data is gathered from similar cases and used in the

computational simulation. Of course adequate prudence should be taken when

analysing the results from this kind of data.

4.3.5 Organisational Cybernetics

Cybernetics is by the pioneer of field Norbert Wiener’s (1948) definition “the

science of control and communication”. In the heart of the cybernetics approach is

the concept of variety, that is, consequence of the probabilistic nature of the outside

nature. An English psychiatrist and also a pioneer of the cybernetics William Ross

Ashby provided this concept and defined it as the number of possible states that the

system is capable of exhibiting (Ashby 1956, 1958). Ashby describes:

Cybernetics offers the hope of providing effective methods for the study,

and control, of systems that are intrinsically extremely complex (Ashby

1956, 5-6).

The presumption that cybernetics has, is that the world outside is a complex,

dynamic system which cannot be deterministically described. To this problem

Ashby introduces “law of requisite variety”, which says that only variety can destroy

variety (Ashby 1958). Another pioneer of the field developed this to more widely

extend, to the “variety engineering”, which describes process of balancing variety

either by reducing or by increasing variety which ever suites the specific situation

best (Beer, The Heart of Enterprise 1979). In cybernetics, as also in system

dynamics, feedback loops are important. The negative loops are called “deviation-

counteracting process” and the positive “deviation-amplifying process” (Maruyama

1963).

From this basic cybernetic framework came Organisational cybernetics which was

mainly the result of one persistent scientist, a British theorist, consultant and

professor called Stafford Beer. His work focused on developing cybernetics to

contain the concepts of other system thinking fields and to derive the cybernetic

laws without referring to the mechanical and biological assumptions where they

were originally developed. His work also added to the cybernetic thinking the

observing system, therefore making the theory considering the complexity of

observer-dependent notion of variety (Jackson 2000). He created the Viable System

Model (VSM), which is a model of any viable system - the five subsystems defined

are the necessity for any viable entity (Beer 1972). He proves it to be perfectly

general by deriving it from the cybernetic first principles (Beer 1979).



Jackson (2000, 157) gives a good synthesis about the VSM according to Beer’s work

(Beer 1972, 1979, 1981, 1985). The basis of a viable system is that it is capable of

responding to the environmental changes even if they happen unexpectedly. The

system has to possess the property of requisite variety with the environment it is in.

The goal of the system defines the balance of varieties to be achieved. Beer (1981)

defines variety engineering to both management and operations, and to which he

gives strategies to confront them successfully. According to Jackson (2000) these

strategies, presented on Table 4, have the following tasks:

First, the organization should have the best possible model of the

environment relevant to its purposes. Second, the organization’s structure

and information flows should reflect the nature of that environment so

that the organization can be responsive. Third, the variety balance

achieved between organization and environment must be matched by an

appropriate variety balance between managers and operations within the

organization. (ibid, 158)

Table 4. Strategies of variety engineering by Beer (1981), derived from Jackson (2000, 157-158).

The VSM is made of five distinct elements, which can be labelled implementation,

coordination, development and policy. In a viable system, not only these elements

must be present, but also the information flows between them must be adequately

taken into consideration (Beer 1972, 1981). The parts are described in more detail

in Figure 4.

Reducing external variety Amplify own variety

Structural (e.g. functionalisation,

delegation)Structural (e.g. Integrated teamwork)

Planning (e.g. setting priorities)Augmentation (e.g. recruit experts, employ

consultants)

Operational (e.g. management by

exception)

Informational (e.g. management

information system)



Figure 4. Viable system model according to Beer (1972). Picture source is Green (2007).

The key property of the model is the recursion involved. This means that the

structure of the model is replicated in each of its parts. The first task, when using

this model, is to define the level of recursion, that is, how many fabrics or layers the

system possesses. Each of the layers must present the VSM model; otherwise the

whole system is not viable (Beer 1985).

When adopting this model for analysis, the procedure is divided into two phases

(Jackson 2000, Beer 1985):

1. System identification (arriving at an identity for the system and working out

appropriate levels of recursion)

2. System diagnosis (reflecting on the cybernetic principles that should be

obeyed at each level of recursion)

More detailed process is described in Table 5.



Table 5. VSM adoption process modified from Jackson (2000).

The most common threats to viability can be derived from this analysis according to

Jackson (2000):

Levels of recursion are not considered or badly organised, which leads to

mismanagement at each level of operation.

Additional or irrelevant parts, that the model doesn’t require, which leads to

ineffective total system.



Systems 2, 3, 4, or 5 are autopoietic, meaning that they are by themselves

independent of the whole system. The whole system in general is

autopoietic, but the parts should not be by themselves.

Key elements described in the model are absent or working poorly.

System 5 must represent the wider system, as Beer (1984) describes “the

essential qualities of the whole system” (quoted from Jackson (2000)).

Described information flows and the present communication channels do

not correspond to each other.

This model is quite easily adapted for the validation of the business proposition.

First of all, the ecosystem is modelled according to the instructions on Table 5 and

eventually portrayed it as in Figure 4. The most common threats for the viability of

the system are then checked. If the process is completed and all the information

gathered and modelled, it can be said quite confidently is the proposition viable or

not, at least according to the data now available.

4.4 Evaluation of the Models

The evaluation of the models is done as described in chapter 3.3. Results are shown

in Table 6. Each criterion is graded by the following scale: excellent; good; adequate;

or poor.



Table 6. Results according to the criteria derived.



5 Conclusions and Considerations From the results we can see that the VSM -model seems promising for evaluating the

business proposition for the ecosystem creation process phase E1. Other

approaches seem to have their own benefits for validation as well. However, the

results are based on a literature review and not actual applications. Thus, the results

should be considered indicative, not conclusive. Nevertheless, it seems that the use

of these approaches in different phases of the analysis is by no means impossible,

rather commendable if the case in question gives a possibility to it. Combination of

these different approaches is also possibility, but it must be always considered case-

by-case. There is an opportunity for future research to explore these suggestions by

actual case-studies in the context when they become available.

As a conclusion we can say that system approaches appear to be feasible and

promising tools for validating business plans. They are complementary to the more

traditional business analysis tools. Different approaches are useful in different

situations (Jackson and Keys 1984). The application of integrative perspectives on

systems thinking, such as critical system thinking (Jackson 2006) and systems

intelligence (Hämäläinen and Saarinen 2006), is an area for future research in the

context of business plan evaluation.



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