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A Thesis Submitted for the Degree of PhD at the University of Warwick

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Complexity in Organisations: Executive Summary

AbstractIndustrial organisations face uncertainty created by consumers, suppliers,competitors and other environmental factors. To deal with this uncertainty,managers have to coordinate the resources of the organisation to produce avariety of behavioursthat can copewith environmental change. An organisationthat does not have sufficient internal complexity to adapt to the environmentcannot survive, while, an organisation with excessivecomplexity would wasteresourcesandmight lose its ability to reactto the environment.The main objective of the research was to create a model for dealing withcomplexity and uncertainty in organisations. The initial ideas for the modeloriginated from the literature, particularly in the fields of systemsand complexitytheory. Theseinitial ideas were developedthrough a seriesof five casestudieswith four companies, namely British Airways, British Midlands International(BMI), HS Marston and the Ford Motor Company. Each case study contributedto the developmentof the model, as well as providing immediate benefits for theorganisations involved. The first three case studies were used in thedevelopment of the model, by analysing the way managersmade decisions insituations of complexity and uncertainty. , For the final two case studies, themodel was already developedand it was possible to apply it, using thesecasesasa means of validation. A summary of the case studies is presented here,highlighting their contributions to the creationandtesting of the model.The main innovation of the research was the creation and application of theComplexity-Uncertainty model, a descriptive framework that classifies genericstrategies for dealing with complexity and uncertainty in organisations. Themodel considers five generic strategies: automation, simplification, planning,control and self-organisation, and indicates when eachof thesestrategies can bemore effective according to the complexity and uncertainty of the situation.This model can be used as a learning tool to help managers in industry toconceptualisethe nature of complexity in their organisation, in relation to theuncertainty in the environment. The model shows managers the range ofstrategic options that are available under a particular situation, andhighlights thebenefits and limitations of each of these strategic options. This is intended tohelp managersmake better decisions basedon a more holistic understanding ofthe organisation, ts environment andthe strategiesavailable.


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Declaration

I, Carlos Mena hereby declare that the work presentedhere is my own unless otherwise stated, and that none ofthe content has been submitted for any other award.


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AcknowledgementsI would like to expressmy sincere gratitude to Aileen Thomson for her guidanceand support, and to Gordon Brace for his encouragementand advice throughoutthis venture. Their mentorship has been invaluable for the successof thisproject.I am indebted to Paul Jeffrey for his support as a co-supervisor and for thevaluable lessonson how to conduct research. To Beth Wishart for her help in thepresentation of my work, her efforts and valuable comments made this workmore enjoyable to read. To Linda Whicker for trusting in me throughout theresearchandto Paul Jennings, an McCarthy and Mike Hodgson for their supportin reviewing my submissions.To Maria Guadalupewho has supportedand encouragedme in the most difficultmomentsof the researchandwho hasmadethe ourney more enjoyable.

To my parentsGeorginaand Carlos Hector who have taught me to fight for whatI believe in, to enjoy the process of doing a good job and to perseverewhenthings do not go as expected. They have led me, by their example,to be a betterpersonand without them this project would not havebeenpossible.To all the people who have trusted in me by giving me the opportunity to workwith them, I would like to express my gratitude. In particular I would like tothank Bob Allen and Tony Heliwell from British Midland, SteveBarnes, MikeFoskett, Kevin Stanleyand Chris Bowles from British Airways, Peter Allen fromCranfield University, Bridget Day from HS Marston and Don Rowe, WalterFassbenderandAchim Muller from the Ford Motor Company.I should also thank the University of Warwick and the Mexican Council ofScienceand Technology (CONACYT) for providing the financial assistancehatmadethis project possible.Finally, I would like to thank all the friends I havemet in the last four yearsforthe enjoyabletime we haveshared ogether.


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Table of Contents

1 Introduction .................................................................................1.1 Background to the Research ................................................................... 1

1.2 Research problem and research questions ............................................ 2

1.3 Delimitations of scope..............................................................................1.4 Brief description of the project ............................................................... 3

1.5 Structure of the Portfolio ........................................................................ 6

1.6 Outline of this document .........................................................................

2 Literature Review ...................................................................... 112.1 Defining Complexity..............................................................................12.2 The Measurement and Classification of Complexity .......................... 13

2.3 Key concepts of Complexity Theory .................................................... 9

2.3.1 Feedback.......................................................................................... 92.3.2 Chaos............................................................................................... 92.3.3 Self-organisation..:...........................................................................12.3.4 Co-evolution .................................................................................... 52.3.5 Emergence....................................................................................... 6

2.4 Traditional approaches to dealing with complexity ........................... 272.4.1 Simplification ................................................................................... 72.4.2 Automation ...................................................................................... 82.4.3 Control 9


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2.5 Perspectives on Strategy ........................................................................32.5.1 The planning perspective.................................................................32.5.2 The Complexity Perspective o StrategyFormation........................362.5.3 Views on Complexity andStrategy.................................................9

2.6 Uncertainty ............................................................................................. 522.6.1 Defining Uncertainty ....................................................................... 522.6.2 The Measurement and Classification of Uncertainty ...................... 55

2.7 Models relating complexity and uncertainty ....................................... 612.7.1 Organic vs. Mechanistic Organisations ........................................... 612.7.2 Coping with Uncertainty..................................................................32.7.3 Order from the bottom-up................................................................4

3 Methodology ..............................................................................73.1 Research Problem and Objectives 673.2 Research Paradigm and Methodology ................................................. 68

3.3 Case Studies Selection ...............................:........................................... 71

3.4 The Research Process............................................................................23.5 Data Collection Methods .......................................................................43.6 Data Analysis Methods ..........................................................................93.7 Validity and Reliability .........................................................................2

4 The Complexity - Uncertainty Model ..................................... 844.1 The Origins of the Model ......................................................................44.2 Dimensions of the model ....................................................................... 85


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4.2.1 Complexity ..................................................................................... 54.2.2 RandomnessandUncertainty .......................................................... 7

4.2.3 The relationship betweencomplexity anduncertainty ....................894.3 Generic Strategies ..................................................................................1

4.3.1 Automation ......................................................................................34.3.2 Simplification ...................................................................................44.3.3 Control .............................................................................................54.3.4 Planning ...........................................................................................74.3.5 Self-organisation.............................................................................. 8

4.4 The Evolution of the Model ................................................................ 004.5 The Nature of the Model ..................................................................... 02

4.6 The Use of the Model ........................................................................... 044.7 Model Assumptions .............................................................................. 106

........................................Case Studies Discussion...........see. 1085.15.1.15.1.2

5.25.2.1

Performance Measurement Project...................................................

08Contributions to the Organisation.................................................. 09Contributions to the Research........................................................ 10

Aircraft Component Repair Project 114Contributions to the Organisation.................................................. 15

5.2.25.3

5.3.15.3.2

Contributions to the Research........................................................ 16Product Design Project ........................................................................ 119

Contributions to the Organisation.................................................. 20Contributions to the Research 21


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5.4 Aircraft Maintenance Project .............................................................245.4.1 Contributions to the Organisation.................................................. 255.4.2 Contributions to the Research........................................................ 26

5.5 DEALIS Project ...................................................................................305.5.1 Contributions to the Organisation.................................................. 315.5.2 Contributions to the Research........................................................ 32

5.6 Cross-CaseAnalysis.............................................................................

35

6 Conclusions .............................................................................. 1447 Further Research ....................................................................518 References................................................................................589 Appendices ...............................................................................68Appendix 1: Samples of Interview Protocol and Questionnaires ................168 .

a) British Midland Interview Protocol...........................................................68b) Interview questionnaireusedfor the CXD project....................................169

Appendix 2: Interview Summary Report (Sample)......................................171

Appendix 3. Notes from Observations (BA-TDR Project Sample) ............. 173Appendix 4. Within-case analysis tools (Samples)..:.....................................178


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List of FiguresFigure 1: Research Process .................................................................................... 4Figure 2: Structureof the Portfolio .......................................................................6Figure 3: Project Structure: Stage 1......................................................................7Figure 4: Project Structure: Stage2......................................................................7Figure 5: Project Structure: Stage 3 .................................................................... ... 8Figure 6: Structure of this document .................................................................. . 10Figure 7: Timeline of Complexity and Strategy Publications (Papers).............. 37Figure 8: Timeline of Complexity and StrategyPublications (Books) ..............

38Figure 9: Types of Change (Source:Stacey, 1993)............................................58Figure 10: Types of management systems (Burns & Stalker, 1961) .................. . 62Figure 11: Control & Coping Mechanisms(Allaire andFisirotu, 1989)............ 63Figure 12: Mapping the fitness landscape Clippinger, 1999)............................65Figure 13: Research Process ............................................................................... . 73Figure 14: Interviews per Case Study ................................................................. . 76Figure 15: Key documentsper Case Study.........................................................78Figure 16: Weinberg'sFramework .....................................................................84Figure 17: Dimensions of the Model ..................................................................91Figure 18: The Complexity - Uncertainty Model ...............................................92Figure 19: Automation Strategy.........................................................................94Figure 20: Simplification Strategy......................................................................95Figure 21: Control Strategy ................................................................................ . 96Figure 22: Planning Strategy .............................................................................. . 98Figure 23: Self-organisationStrategy................................................................00Figure 24: Project Evolution ..............................................................................01Figure 25: Using the Complexity - Uncertainty Model .....................................105Figure 26: GenericStrategyAnalysis - Performance MeasurementProject..... 113Figure 27: Generic StrategyAnalysis - ComponentRepair Project .................118Figure 28: Generic StrategiesAnalysis - CXD Project .....................................123Figure 29: Generic Strategy Analysis - TDR Project........................................129Figure 30: GenericStrategyAnalysis - DEALIS Project .................................134Figure 31. CrossCaseAnalysis .........................................................................42Figure 32: Complexity - Uncertainty Model ..................................................... 46


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List of TablesTable 1: Complexity of Systems(Adapted from Beer, 1967).............................15Table 2: Categorisationsof Complexity .............................................................18Table 3: Traditional vs. Complexity views of Strategy......................................45Table 4: Classification of Uncertainty (Makridakis andHeu, 1987).................. 55Table 5: Four levels of Uncertainty (Courtney, et. al., 1997)............................. 56Table 6: Types of Environment (Emery, 1967;Duncan 1972)..........................

56Table 7: Environmental dimensionsanduncertainty (Duncan, 1972)............... 57Table 8: Decision-making in different change situations ................................... . 59Table 9: Comparisonof different classifications of Uncertainty........................ 60Table 10: Conditions for defining the researchstrategy.....................................69Table 11: Case StudiesInteraction .....................................................................74Table 12: Suggestions or the use of Interviews.................................................75Table 13: Tools for within-case analysis............................................................80Table 14: Complexity of Systems.......................................................................87Table 15: Classification of Uncertainty ..............................................................88Table 16: Framework of Performance Measures............................................... 10Table 17: Areas of Opportunity for the MPC .................................................... 15Table 18. Areas of opportunity for HS Marston................................................ 21Table 19. Benefits and Limitations of TDRM ................................................... 26Table 20: ProjectsMeta-matrix ......................................................................... 36Table 21: Contributions of Industrial Projectsto the Research.........................147Table 22: Contributions of Industrial Project to the Organisations..................149


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1 IntroductionThis document is the last one of ten submissions that comprise the project"Complexity in Organisations". The objectives of this document are to presentan analysis of the evolution of the research, to demonstrate the innovationsachieved and benefits obtained by the collaborating organisations, and tosummarise he activities and conclusionsof the research.

1.1 Background to the ResearchThe subject of complexity and particularly complexity in organisations has beenwidely debated and various authors have presented radically different views.Some authors support the idea that complexity is harmful to organisations andshould be avoided (Jensen, 2000; Rommel, 1995; Shomberger, 1982,1986),others assert that complexity is inherent in organisations but that it can beplanned for and controlled (Frizelle & Woodcock, 1995; Beer, 1984). Finally,some views - supportedby the developmentof Complexity Theory - defend theposition that complexity is an essential element for the evolution andsustainability of organisations,and that it cannotbe controlled, but only managedwithin certain boundaries (McCarthy, Frizelle, & Rakotobe-Joel,2000, Stacey,Griffin & Saw,2000; Maira & Thomas, 1998;Kauffman, 1995x, 1995b).Approachesrelated to simplifying and controlling complexity were documentedby Adam Smith as early as 1776, and appear o be the most common in industrytoday. On the other hand, studiesrelated to Complexity Theory in organisationsare relatively recentand there are few casesdocumented n the literature. This isarguably one of the reasons hat the view of complexity as somethingnegative ismore prevalent in industry.


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To create a model or dealing with complexity in organisations by exploring theuse of the concepts of Complexity Theory and the approachesusedbypeople in

organisations to deal with situations where complexity is a dominant eature.This generalobjective has been broken down intro three specific objectives:

1. To identify in the literature the concepts and applications of ComplexityTheory that are relevant to organisations

2. To analysethe strategiesusedby the four selectedcompanies o managecomplexity, and to assesshow the conceptsof Complexity Theory canbe incorporated

3. To create a model of generic strategies,which can help decision makersto understandanddeal with complexity in their organisations.

1.3 Delimitations of scopeThe scope of the research initially focused on engineering and logistic areaswithin companies n the aerospaceand aviation industry. This was extendedtothe automotive industry in the final case study, becausean opportunity presentedto conduct a study in this industry and it was believed this could enrich theresearchby testing the model in a different context. In each of the case studies,the unit of analysis was a division or departmentwith a specific problem to beaddressed.

1.4 Brief description of the projectThe project was conducted from March 1998 until February 2002. During thisperiod, five cases studies were conducted with four collaborating companies:British Airways [2 projects], BMI British Midland International, HS Marston andthe Ford Motor Company. The selection of the case studies was judgemental,


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based on the complexity and accessibility to the companies. Each case studyinvolved working directly with the company on a specific project, allowing abetter understanding the organisations. During these projects, different situationswere analysed using a systemic approach, which helped to explain the processesand interactions that were taking place within the organisations and in relation totheir environment. This analysis led to the creation of the `Complexity-Uncertainty model'.Figure 1 shows the evolution of the research, indicating the main stages, thelogical flows between stages and the contributions to the objectives at each stage.Figure 1: Research Process

Analysis ofliterature onComplexityOBJECTIVE 1

help tostand...

Complexity inOrganisationsthat will help tounderstand... OBJECTIVE 3

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EFL CTand whenanalysedw` . OBSERVE

Organisations

KEY:Activity= Objective

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which will inform thedevelopment of...ACT

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OBJECTIVE 2


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The research process started with an analysis of the literature, as depicted inFigure 1. Then, the case studies were used to develop understandingby lookingat how managersdealt with complex problems and how the change process ookplace. In this cyclical process, he developing model was continuously comparedwith the actual data supporting the process of development. The first threeiterations supported the development of the model and the final two, the testingof it. This led to the creation of the Complexity - Uncertainty Model, whichrelated to all of the casestudies and could be usedto support decision-making interms of the strategies equired for managing complexity.The Complexity-Uncertainty model is a descriptive and learning model thatrelates organisational complexity to environmental uncertainty and classifies fivegeneric strategiesthat can be used in different circumstances. These strategiesare automation, simplification, planning, control and self-organisation. Thecreation of the model and the classification of the strategieswere supportedbothby the casestudies andby the literature.Each individual case study also contributed immediate benefits to thecollaborating organisations. Examples of the contributions are: a performancemeasurementsystemdeveloped for BMI (British Midlands), a simulation systemfor evaluating aircraft schedules for British Airways, the conceptual design oftwo componentsof an internet basedsupply chain managementsystemfor Ford,and a simulation model of the product development process for HS Marston.Thesecontributions are describedbriefly in this document, and full details of theprojects arepresented n the correspondingsubmissions.


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1.5 Structure of the PortfolioThe ten submissions have been structured around the specific objectives of theproject, comprising three stages: an initial stage that focused on a review of theliterature on Complexity, a second one which was comprised of the five casestudies, and a final stage which synthesised the learning and presented the modeland its conclusions. This structure is depicted in Figure 2.

Figure 2: Structure of the Portfolio

STAGE ISpecific Objectivel STAGE IISpecific Objective 2 STAGE IIISpecific Objective 3The Model1: Concepts ofComplexity2: Applications ofComplexity

3: Methodology

4: PerformanceMeasurement (BMI)

5: Component Repair(British Airways)6: Product Design(HS Marston)7: Aircraft Maintenance(British Airways)8: Logistics / SCM(Ford)

9: The Model

10: Exec. Summary

The following three diagrams present a breakdown of each of the project stages,including a description of how each submission contributed to the objectives.


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Figure 3: Project Structure: Stage I

Submission 2: Applications of Complexity: A general viewPresents a review of the current applications of complexity tools and complexitythinking in different management fields.Submission 3: MethodologyPresents the research problem, research paradigm, methodology, and projectstructure.

Figure 4: Project Structure: Stage 2

GENERAL OBJECTIVESTAGE STAGE II STAGEI To analyse the strategies followed by four se/ectec IIIcompanies to manage complexity, and assess theapplicability of the conceptsof Complexity Theory.

Submission 4: Complexity in Performance Measurement - BMIA case study in collaboration with British Midlands (BMI), looking at theirperformance measurement system. The research analyses the implications ofperformance measurement and control as a strategy for managing complexity.Submission 5: Complexity in Component Repair - British AirwaysA case study focusing on the component repair process at British Airways, usingtime-compression approaches to streamline the processes. This case studyanalyses simplification as a strategy for managing complexity in organisations.


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Figure 4: Project Structure: Stage 2 (Continued)

STAGE STAGE II PSTAGEI III

Submission 6: Complexity in Product Design - HS MarstonA case study in collaboration with HS Marston, a company involved in the designand manufacture of heat exchangers for the aerospace industry. Here, a simulationmodel based on a Genetic Algorithm is used to analyse the product developmentprocess analysing its implications for innovation, collaboration and learning.

Submission 7: Complexity in Aircraft Maintenance - British AirwaysPresents a simulation model of the operation British Airways' fleet 2. Thesimulation is used to identify areas of improvement in the planning and schedulingof aircraft maintenance with a view to reducing uncertainty in flying schedules.Submission 8: Complexity in Supply Networks - Ford Motor CompanyA case study conducted for Ford Motor Company looking at the development of acomputer system to manage the aftermarket supply network. The case studycovers the analysis of the various strategies used by Ford and their businesspartners to manage complexity in the supply network.

Figure 5: Project Structure: Stage 3GENERAL OBJECTIVE

STAGE STAGE STAGE IIII II Todevelop a model of generic strategies to managecomplexity based on the casestudies.

Submission 9: The ModelPresents a model for analysing organisations in terms of complexity anduncertainty. The model is based on the learning obtained from the case studies.Submission 10: Executive SummarySynthesises the research and presents the main conclusions and innovations of theproject.


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1.6 Outline of this documentThis Executive Summary has been structured as a stand-alonedocument, aimedat providing a clear and complete understanding of the research. It includes themost important elements of the research: a literature review, the researchmethodology; the description of the model and its evolution; the discussionof thecasestudies andthe contributions to the collaborating companies.The document is divided into seven sections,the first onebeing this Introduction.Section 2 presents the Literature Review, followed by the Methodology insection 3. A description of the model is presented n section 4 and the case,studies, are described in section S. The Conclusions and areas for furtherresearcharepresented n section6 and 7 respectively.Figure 6 shows the seven chaptersof this document asboxes. The four centralchapters,Literature Review, Methodology, The Model and The Case Studiesarebroken down into their main sections, and arrows are used to indicate the keyrelationships between these sections. A bi-directional link between the Modeland the CaseStudiessectionindicates that both helped to shapeeach other in the

iterative process explained earlier. For the sake of clarity in the diagram theorder of someof the sectionshasbeenchanged.The structureof this document is different from the chronological order in whichthe research ook place. The logic of the deductive processfollowed during theresearchis represented n the structure of the portfolio, where the case studiesprecede he description of the model. Here, the literature review is followed by adescription of the model and then a review of the case studies. This allows theconcepts of the model to be defined first so that they can be used in describingthe casestudies.


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Figure 6: Structure of this document

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2 Literature ReviewThis literature review is aimed at bringing together various areasof the literature,

which have already been reviewed in Submissions 1 to 9. The reviewconcentrates on those concepts that are directly related to the Complexity-Uncertainty model.2.1 Defining ComplexityComplexity is one of the central concepts of this researchand reviewing variousdefinitions presentedn the literature is important for explaining how the conceptwill be used in this research. In this sub-section, several definitions ofcomplexity are reviewed and compared.Thesedefinitions are usedto support thecreationof the Complexity-Uncertainty model presented n section4.The term complexity is rooted in the Greek word plekts, which means"twistedor "braided". This term gave rise to the Latin word complexus originally"braided together", from which the English word complexity is derived (Gell-Mann, 1996). The Latin simplex, which originally meant "once folded", is alsoderived from the Greek plekts (Gell-Mann, 1996). For this reason the wordsimple is used as something "understood or done easily and without difficulty"(Thompson, 1996), and complex is "something difficult to understand" (Floodand Carso, 1993). However, these two definitions refer to the "understanding",leaving these erms to personal nterpretation.The Oxford English Dictionary (1989a) defines complexity as "A wholecomprehending n its compassa number of parts, (in later use) of interconnectedparts or involvedparticulars; a complexor complicated whole". This definitionrefers to the sources of complexity, in particular number of parts andinterconnectedness. In Submission 1, six main sources of complexity were


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discussed: population, connectivity, feedback, non-linearity, asymmetry andnonholonomic constraints. This means, complexity comes from the structure(population, connectivity, asymmetry), behaviour (feedback, non-linearity) andrules that regulate the system (nonholonomic constraints). Further discussionabout the sourcesof complexity canbe found in Submission 1, Section 4.1.The SantaFe Institute, one of the leading research organisations in the field ofComplexity Theory, hasproducedthe following definition:

"Complexity refers to the condition of the universe which isintegrated andyet too rich and varied for us to understand in simplecommon mechanistic or linear ways. Wecan understandmany partsof the universe in these ways but the larger and more intricatelyrelated phenomena can only be understood by principles andpatterns -not in detail. Complexity deals with the nature ofemergence,nnovation, learning and adaptation"The Santa Fe Group, 1996

The following is another definition provided by Peter Murray (1998), whoadapted an original definition by Covney and Highfield (1995) to -fit anorganisational environment: [Murray's comments n brackets]

"The study of the behaviour of macroscopic collections [likeorganisations] of such [basic but interactive] units [like people] thatare endowed hepotential to evolve over time"

CovneyandHighfield, 1995 adapted)The key elements of complexity that can be identified from thesedefinitions arethe variety and richness in structure that cannot be understood in detail only ingeneral patterns. It is also evident that complex systemshave some particularproperties, such as the ability to learn, adapt and evolve. This is, they arecapable of qualitative changeover time.


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2.2 TheMeasurement and Classification of ComplexitySeveral approacheshave been suggestedto quantify complexity. Variety, forexample,has beenproposed as a measure of systemic complexity, defined asthenumber of distinguishable elements n a system or, by extension, the number ofdistinguishable systemic states(Ashby, 1965). However, to consider the manydifferent dimensions of an organisation such as people, processesand products,and to account for all the distinguishable elementsand systemic statesappears obe impossible. Gell-Mann (1994) presents a classification of approachestomeasuring complexity, which is discussed in Submission 1 (pp. 19-22) and arebriefly summarisedhere:Crude complexity: This measureshe quantity of information required to describe

a system, this is, the length of the shortestpossible message Cohen, et. al.,1994; Gell-Mann, 1996). This measurehas some major drawbacks: firstly thequantity required to describea system varies dependingon the level of detailof the description, secondly the description is dependent on the amount ofinformation already available, and finally the measure overlooks the fact thatdescriptionscanbe presented n many different forms.

Algorithmic information content (AIC): This measure,considers he length of theshortest program that, using a universal computer, can generate he descriptionof the entity. This measurerecognisesthat a description can be compressed

.by a program or procedure that can generate it. This approach, however,cannot cope with random behaviour, since this kind of behaviour isincompressible. Furthermore, this measure also requires assumptionsaboutthe level of detail of the description andthe current knowledge available.


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Effective complexity: this measurefocuseson the AIC of the regularities of anentity, as opposed to its incidental features (Gell-Mann, 1996). A regularentity, such as a string consisting entirely of ones, will have very littleeffective complexity, because ts regularities can be described very briefly.An entity with high effective complexity must have intermediate AIC andobey a set of rules requiring a long description.

These three measuresprovide different approaches o quantifying complexity.However, in every casethe measurement emains context-dependent. As Casti(1997) asserts, complexity will always remain on the eye of the beholder".Another approach to measuring complexity is the use of entropic measures(Frizelle, & Woodcock, 1995; Calinescu, Efstathiou, Sivadasan, Schire &Huaccho Huatuco, 2000). This type of measurecan provide a good assessmentof complexity in a stable process,where it is possible to accountfor the differentstates of the system and to assessthe probability of each of these states.However, it seemsextremely difficult to consider all of the possible states or anorganisation and assess heir probabilities. For this reason, entropic measureswere not considered suitable for this research.An alternative to a precise figure, which is considered more useful for thisresearch, s a classification of the degreeof complexity. Authors such as Beer(1967), Senge (1992), Battram (1998), Allen (1999), Glouberman andZimmerman (2002) and Lucas (2002b) have provided a number ofclassifications,which aredescribedandcomparedhere.Beer (1967) defines three main categories of systems, simple, complex andexceedingly complex, each divided into two levels deterministic and stochastic.Table 1 showsthe six categoriesdescribedby Beer along with someexamples.


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Table 1: Complexity of Systems (Adapted from Beer, 1967)Simple Complex Exceedingly

ComplexDeterministic Few components and, Complicated Svstems that exhibit

reveals a completely components anddeterministic chaos

predictable dynamic interrelations but behaviour.behaviour deterministic E.g.E.g. Lay-out of a E.g. Weathermachine shop (routes An automatic factory Supply Chains& distances) "automation"

Stochastic Few components and Highly elaborated and Systems that are sointerrelations. richly interconnected. complicated they cannotPredictable. Unpredictable. be described in preciseE.g. E.g. and detailed fashion.SQC (Simple system Profitability of a firm E.g. (The brain, awith probabilistic nature) company, the economy)

An adaptation made to Beer's model was the introduction of ExceedinglyComplex Deterministic Systems. According to Beer (1967) such systems do notexist, however it is currently known that chaotic systems are deterministic andexhibit unpredictable behaviour (Gleick, 1987; Stacey, 1992; Wilding, 1998).

Glouberman and Zimmerman (2002) present a three-way classification ofproblems, segmenting them into simple, complicated and complex.Simple: these are problems of basic issues of technique and terminology, but

once these are mastered, the problem can be solved. Here a set of rules forsolving the problem can be formulated and replicated ensuring a high degreeof certainty of outcomes. An example of these would be following a recipe.

Complicated: this kind of problem is essentially a collection of simple problemswhich can be dealt with independently of each other. Even when each of theproblems might require many different areas of expertise, there is littleinterdependence between them. Here rules can be followed and applied tosimilar problems and there is a high degree of certainty of outcome.

Complex: this kind of problem includes both complicated and simple problems,however the problem is not reducible (Glouberman et. al., 2002; Goodwin,1994). Each complex problem is unique and experience of similar problemscannot guarantee success,hence the outcomes are inherently uncertain.


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Battram (1998), based on the work of Kauffman and Langton, presents aclassification of the behaviour of complex dynamic systems, defining fourdifferent classes:" Class I Stasis: The system reaches a steady state and all the variables

settlein a fixed value." Class II Order: The system settles down into a pattern that repeats

itself." Class III Chaos: The system presents aperiodic behaviour but continues

to have a structurein phase space." Class IV Complexity: This is a transition phase between periodic

behaviour of Class II and the aperiodic behaviour in Class III.This is order co-existing with disorder at the edgeof chaos.

Lucas (2002b) classifies complex systems in terms of their structure andbehaviour into static, dynamic, evolving and self-organising. Allen (1999) uses asimilar classification to define models of complex systems, and highlights theassumptions made in each kind of model. Senge (1992) uses the first twocategories, static and dynamic, to describe systemic complexity. Here the fourcategorieswill be briefly describedusing elements rom the threeauthors.I. Static ComplexityThis form of complexity is related to fixed systems. Here the assumption ismade that the structure of the systemdoesnot changewith time. This refers toquantitative aspects of the system such as size, diversity and multiplicity ofhierarchical levels (Senge, 1992; Lucas,2002b)II. Dynamic ComplexityThis type of complexity takes into account the time dimension. It refers to thecauses and effects in the system and their relationships, however it makes the


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assumption that change in the system is cyclical, repetitive or in some waypredictable. This excludes those aspects of the system that are one-off or

variable (Senge, 1992;Lucas,2002b)III. Evolving ComplexityThis classof complexity doesnot consider the assumptionof repeatability and isused to describesystemsthat evolve over time, this is, it dealswith open-endedchange. A common exampleof this class s the neo-Darwinian theory of NaturalSelection(Lucas, 2002b).IV. Self-Organizing ComplexityThis form of complexity combinesthe internal constraints of closed systemswiththe creative evolution of open systems (Lucas, 2002b). In this case,systemsco-evolve with their environment and must be described according to theirrelationship to the environment. Here systems are capable of emergentbehaviour, creating new structuredbasedon their interaction (Lucas, 2002).Several approaches to measuring complexity have been presented in this section.It has been shown that even when some measures of complexity are useful forspecific applications, they are difficult to use in an organisational context andremain context-dependent. For this reason categorisation has been presented asan alternative to precise measurement of complexity and four approaches tocategorising complexity have been presented. Table 2 compares these fourapproachesto categorising complexity, and draws a distinction between thedifferent definitions used by the original authors. These four approacheshavebeenused as a foundation for developing a categorisation of complexity for thisresearch,aspresentedn section4.2.1 of this document.


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Table 2: Categorisations of ComplexityL U U 15V, U

C' N c'.1y= --a`) u r_ uti) Cy v

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18


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2.3 Key concepts of Complexity TheoryThe central concepts of Complexity Theory were described n submissions1 and2. This section revisits those concepts that were particularly relevant in thedevelopmentof the Complexity - Uncertainty model, mainly those of feedbackand non-linearity, chaos,self-organisation, co-evolution and emergence.2.3.1 FeedbackFeedback s a processby which information generated by an action is used forthe decision-making or regulation process, to affect the next action (Stacey,1996a). Feedback s classified into positive and negative, dependingon the kindof behaviour that it promotes n the system.Negative feedbackguides the systemto a certain target. During the operation ofthe system, outcomes are compared with the target, feeding information aboutdeviations back into the decision-making process in order to reduce thesedeviations (Stacey, 1996a). Positive feedback feeds back information thatamplifies the outcomes of the system by creating a reinforcing loop. Examplesof both positive and negative feedbackare presented n Submission 1 (pp. 27-30)Positive feedback is responsible for amplifying small variations in certainvariables affecting the system, making it difficult to predict future behaviour.This concept s discussed n more detail in submission 1 (pp. 31-32)2.3.2 ChaosIn colloquial language,chaos s used to refer to disorder or confusion; however,the term is used here in a more specific way, referred to as deterministic chaos.Kaplan and Glass (1995) define it as `aperiodic bounded dynamics in adeterministic systemwith sensitivedependenceon initial conditions' and Stewart(1989) as`stochasticbehaviour occurring in a deterministic system'


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Thesedefinitions refer to systems whose behaviour is basedon rules, however,due to non-linear relationships, its behaviour never repeats tself and is extremely

sensitive to changes. Nevertheless,behaviour in chaotic systems s still boundedwithin certain limits. As Stacey (1993) asserts, chaos theory `explains thatborder area between stability and instability (bounded instability), wherepatterns are irregular and inherently unpredictable, butyet havea structure'Chaotic behaviour has been found in many different kinds of systems such asbiological, chemical, climatic, ecologic and economic (Gleick, 1987). Someresearchershave also recognisedthat chaotic behaviour is present n the businessenvironment (Parker & Stacey, 1994; Stacey, 1996a: Wilding, 1997; 1998). Inchaotic systems, causesand effects becomedistant in time and space,due to thesensitivity of the system and the non-linearity of relationships. This makes thefuture unknowable,having significant implications for organisations.Stacey(1993) has summarisedthe implications of chaos in organisationsin thefollowing points:

a) Analysis loses ts primacyb) Contingency loses ts primacy (an organisationhasto be both mechanistic

and organic)c) Long term planning becomes mpossibled) Visions become llusionse) Consensusandstrong culturesbecome dangerousf) Contradiction, conflict, dialectics andlearning becomeessentialg) Statistical relationshipsbecome doubtfulh) Probability helps only in the short termi) Long term forecastsandsimulations are impossiblej) Requisite variety loses its usefulness: changes are unique not repetitive

and small changesdo escalate o large consequences


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One of the most important areas within management where Chaos Theory hasimplications is in the field of strategy formation. Section 2.5.3.1, presents adetailed discussion n this subject.The concepts of feedback, non-linearity and chaos have contributed to thedevelopment of the Complexity - Uncertainty model because they help toexplain the limitations of two of the generic strategies, Control and Planning.These imitations arediscussedn detail in sections4.3.3 and4.3.4 respectively.2.3.3 Self-organisationThe concept of self-organisationhad a central contribution to the developmentofthe Complexity- Uncertainty model, as it consolidated as one of the genericstrategies of the model. For this reason t was decided to review this concept inmore detail than the other conceptspresented n this section. A discussion onhow self-organisationfits into the Complexity-Uncertainty model is presented nsection4.4.5The concept of self-organisation has evolved over time and has been applied indifferent fields of research(Krohn, Kuppers and Novotny, 1990). This makes itdifficult to encapsulate he concept n a single definition.Krohn, Kuppers and Novotny (1990) trace the concept of self-organisation toKant's "Critique of Judgement" (1790), referring to the capacity of parts ororgans of producing other parts, each, consequently reciprocally producingothers. Another early reference is from Farey and Clark in 1954 (Heylighen,1997a) who define a self-organising system as one which changes its basicstructureas a function of its experience and environment. Von Foerester (1960)argues that an organism organises itself independently of its environment andAshby (1960) redefines self-organisation as a process that consists of the

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organism and its environment taken together. Some other authors such asHeylighen (1997b) and Lucas (2002a) define it as a process of evolution in

which the involvement from the environment is minimal. In self-organisation,evolution is triggered by internal variation processes,usually called fluctuationsor noise (Heylighen; 1997a). For Casti (1997) self-organisation is a property ofcomplex systems hat explains how systemswithout central control tend to settledown into just a few states even when, theoretically, the number of possiblestates is almost infinite, suggesting that the interaction of the elements of thesystem createssomekind of order.During the 1960's, the subject of self-organisation was explored from differentperspectives. Heinz von Foerester (1960) looked at if from an informationtheory perspective. Herman Haken (1983) also explored in the context of lasertheory, and Ilya Prigogine in the field of thermodynamics (Prigogine, 1976).Prigogine's researchshowsthat when a thermodynamic system s drivenfar fromthis equilibrium, by pumping energy, it will reacha threshold above which it canexhibit "self-organisation", at this stage, the systems become dissipativestructures (Prigogine, 1976)According to Prigogine (1976) self-organisation is not restricted tothermodynamic systems,examplesof a similar type of order emerging from theinteraction of the elements of the system can be found in chemical, biologicaland even social systems. All these appearto have some order despite the high

complexity of the systems in which they interact. Some commonly usedexamples of self-organisation are lasers, Bernard cells, cellular autocatalysis,birds flocking, brains, ecosystemsandeconomies(Lucas,2002)


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Self-organisation is usually associated with non-linearity that results from theinteraction of positive and negative feedback cycles, where some variationsreinforce themselves,while others mitigate themselves. This interaction can leadto unpredictable patterns, which can develop very quickly until they reach astable configuration (an attractor) (Heylighen, 1997a).Lucas (2002) has identified a number of typical features of a self-organisingsystem, which arehelpful for characterising his phenomenon:" Absenceof centralised control" Fluctuations (searcheshrough options)" Multiple equilibria (possibleattractors)" Global order (emergence rom localinteractions)

" Dynamic operation (time evolution)" Symmetry breaking (loss of freedom)" Criticality (threshold effect phasechanges)0

" Redundancy (insensitive to damage) "" Adaptation (stability to external variation) "" Hierarchies (multiple self-organizedlevels)

Self-maintenance(repair & partreplacement)Dissipation (energyusage and export)Complexity (multiple parameters)

Self-organisationrelates to order creation and innovation and therefore it is veryappealingto the study and practice of management. As Stacey(1995) maintains,in order to innovate, `managers rely on self-organizing political andorganizational learning processes to produce an emerging unfolding butunpredictable uture'. Other authors such asWheatley (1994), Coleman (1999),Anderson (1999) and Clippinger (1999) have explored and supported viewsabout self-organisation n abusinessenvironment.Some authors define self-organisation in the business context in terms of itsenablers. Coleman(1999), for example,defines it as simply "a process of humanmotivation enabled by empowermentpractices", where self-organisation takes

are free to follow their


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Bonabeau& Mayer (2001) claim that self-organisation takesplace when a grouphas little supervision or top down control, and Pascale(2001) considers hat it is

a property that emergesfrom intelligence in the remote clusters of a networkwhich are able to generate novel patterns. All these definitions appear to beconsistentwith some of the characteristics of self-organising systems n naturesuch as absence of centralised control, fluctuation and emergencefrom localinteraction. However, they only provide a partial picture.A more complete definition provided by Stacey(1993) defines self-organisationin a management context as "the spontaneous formation of interest groups andcoalitions around specific issues,communication about those ssues,cooperationand the formation of consensuson and a commitment to a response to thoseissues". According to Stacey (1992), the conflict, instability and lack ofconsensusproducedby a multiplicity of cultures is what provokes the systemtoinnovate. Self-organisation can produce order in the form of `innovation andnew strategic direction out of chaos' (Stacey, 1992). Stacey (1992) also suggeststwo mechanismsthat allow self-organisation to take place: group learning andpolitical interaction.Basedon the concept of self-organisation,a number of authorshave suggestedaneed for changes n the role of managers (Anderson, 1999; Clippinger, 1999;Stacey, 1992). The call for freedom and decentralisedcontrol does not meanthat managersarenot necessaryanymore; it simply means hat no central controlis necessary Anderson, 1999). Basedon the work of Stacey (1992), Anderson(1999) and Clippinger (1999) it is possible to list a seriesof factors suggestingwhat self-organisation means(and doesnot mean) for management:


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" Managers create the environment for, establishing self-organisinglearning teams through self-selection and challenge. They alsoinfluence how widespread earning is andwhat quality it takes.

" Managers influence the learning and political processes in theorganisation

" Managerscommit to guiding the evolution of behaviours" Managersare responsiblefor maintaining the boundaries." Managersdo not engineersolutions, workers do" Managers ntervene indirectly by shaping the environment" It means that effective behaviour emerges from the interaction of

independentagentsnot from standardsandplans definedby managers" It doesnot mean letting people do whatever they want to do .9 It doesnot imply that managersare passive" It means hat managershaveno control over self-organising networks

Self-organisation presents an alternative way of achieving order, which applies,not only to natural systemsbut also to managementsystems. This is importantfor the development of the model becauseit presents a way of dealing withuncertainty that doesnot relate to traditional ways of simplification and control.2.3.4 Co-evolutionCo-evolution is the result of the interaction of systemswhere the actions of onesystem affect another. Kauffman (1993) asserts hat co-evolution is similar toadaptation,but in this case, he system never reachesequilibrium and continuesto develop, striving for progress n terms of profitability, growth or sustainability.

From an ecology perspective, co-evolution has been defined as `theinterdependent evolution of "species" that interact "ecologically". Theinteractions may be antagonistic (consumer-resource) or cooperative(mutualism). Because each "species" in the coevolved pair is an important


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component of the environment of the other, changes in one select adaptiveresponsesn the other, and vice versa (Ricklefs, 1990).Co-evolution can be found in a range of systems from an ecosystem,wherespecieschange as they interact with one another, through to strategies n a gamewhere in theory the players are co-evolving with eachother (Holland, 1998).In a co-evolutionary process,according to Kauffman (1993), the parameters ormeasuring the success of the system are continuously changing, so that thefitness landscape is deformed, altering the fitness value of peaks and valleys.This is one of the reasons for the prediction of future states being virtuallyimpossible, making emergent properties evermore important.

A more detailed description of the concepts of co-evolution and fitnesslandscapess presented n Submission 1 (pp. 40 - 43).2.3.5 EmergenceThis conceptof Emergencewas introduced in Submission 1 (pp. 36 -38), wherea discussion,along with a number of examplesfrom different fields of research,is presented. Emergence efers to thoseproperties of a systemwhich arebeyondthe properties of any of its components (O'Connor et. al., 1997). These areknown as emergent properties - they emerge from the system when it isoperating (O'Connor et. al., 1997). Emergence embodies the idea that "thewhole is greater han the sum of its parts".The concept of emergencechallenges he idea that systems canbe fully analysedby breaking them into its individual components. Emergent properties cannotbepredicted and can only be understood using a holistic approach (Lucas, 2003).

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According to Lucas (2003), `this is a feature of open-ended evolution - noveltyappearsoutsideour current experienceor that of thesystem'.Holland (1998) points out that when agents co-evolve and self-organise theycreate new and emergentpatternswhich would be `next to impossible' topredict'even if all the initial strategies and the individual learning procedures wereknown from the outset.

The concept emergence s important for this researchbecauseit explains thespontaneousnnovation that results from self-organisation and co-evolution. Asit will be discussed later, this innovation is critical for organisations in anuncertain environment.

2.4 Traditional approaches to dealing with complexityIn organisations,managershave always had to deal with complexity. Usuallystrategieshave been focused on eliminating and controlling complexity. Forexample, Handfield (1995) and Gregory and Rawling (1997) have developedclassifications of the main strategies for dealing with complexity in processesidentifying strategies such as those of control, simplification and automation.For this research, heseapproacheshave been called `traditional', since they arenot related to Complexity Theory. A number of these traditional approacheshave been identified and are discussed n detail in Submission 9 (pp. 15-36). Inthe following subsections, these strategies will be described based on thediscussionpresentedn Submission9.2.4.1 SimplificationSimplification is defined by Gregory and Rawling (1997) as strategy concernedwith removal of the sources of complexity and waste in organisations.

N


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as Adam Smith (1776) and Frederick Taylor (1911), among others, whosupportedapproachessuch as the division of labour and time & motion studies.Theseapproachesendedto focus on finding the simplest and most efficient wayof performing individual tasks. More recent approaches o simplification havebroadened he scope rom individual tasks to complete processes.There are many management approaches centred on the elimination ofcomplexity, such as Value Engineering (DeMarle & Shilito, 1992), BusinessProcessRe-engineering(BPR) (Hammer & Champy, 1993), Time Compression(Stalk and Hout, 1990; Gregory, et. al, 1997), Lean Production (Womack &Jones,1996)andJust-in-Time (Shingo, 1989).The benefits of simplification have been researchedand discussedby a numberof authors (Rommel, et. al. 1995, Shomberger,1986,1982; Peters& Waterman,1982). Shomberger(1986,1982) identifies simplicity as one of the key successfactors of Japanesendustry during the 70's and 80's. Similarly, Rommel (et. al.,1995) highlights simplicity as the key for the successof the Germanmachinerymanufacturing industry during the 1990's.Simplification hasbeenproved to have many benefits but also limitations. Theselimitations reside on the fact that it assumeshat processescan be isolated, fullyunderstood and centrally designed. However, most processeshave more statesand interactions than those that can be analysed, and this causes a risk ofoversimplification.2.4.2 AutomationAutomation is the substitution of human physical and mental work by the workof machines (Cox, et. al., 1992). This strategy can be effective for simple andrepetitive activities, however, for complex processes,automationmight prove tobe very difficult to implement. Moreover, in an uncertain environment in which

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technologies are changing and demand is volatile, the investment required toautomatea systemmight be difficult to justify (Talavage& Hannam, 1992).Automation has been applied to manufacturing processeswith technologiessuchas Computer Numerically Controlled Machines (CNC), Robots and FlexibleManufacturing Systems (FMS). In most service organisations the main enablerof automation is Information Technology (IT), taking advantageof the ability ofcomputers o transmit dataand perform calculations.The main limitation of automation is that it does not have the flexibility andability to learn and adapt that humans have. Replacing people with machinescreates systemswith less internal complexity, which might not be able to copewith changesn the environment.According to Shomberger (1986), the main advantagethat equipment has overpeople is to decreasevariability: uniform motions, uniform cycle times, anduniform quality. This reduction in variability can be effective only if theorganisation needs to standardise ts processesand products, that is, if there isexcesscomplexity in the system. However, if there were no excesscomplexityin the organisation, automating processeswould only decrease he ability of theorganisation to adapt to changes n its environment. According to Rommel (et.al. 1995), trying to automatea process n an uncertain environment can lead to anunstableprocesscausingconsiderabledisruption to the system as a whole.2.4.3 ControlControl has been defined in many different ways. Henry Fayol (1949) defined itas a critical stage n the managementprocess hat helps to ensure hat everythingoccurs in conformity with policy and practice. The Cybernetics/Systemsapproachdefines it as a processto maintain a system within certain limits usingfeedback (Flood, 1999). Buchanan and Huczynski (1991) follow an

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organisational behaviour perspective and define management control, as "theprocess through which plans are implemented and objectives are achieved bysetting standards, measuring performance comparing actual performance andthen deciding necessarycorrective action and eedback"Buchanan and Huczynski (1991) argue that the concept of control inorganisationshas many different connotations. It can mean predictability, orderand stability, which they believe to be a positive connotation since it providespeople with a degreeof order and predictability in their lives. However, controlcan also mean coercion, domination, exploitation and manipulation, and fromthis perspective,the absenceof control means freedom, individuality, discretionresponsibility and autonomy.Galbraith (1973) defines three forms of control, which he associateswith themechanistic model (Bums & Stalker, 1961). These orms are:a. Rules, Programs, Procedures: this approach specifies the necessary

behaviours in advance of their execution. This is the simplest form ofcoordinationbetween nterdependentsubtasks.

b. Hierarchy: managerial roles are used to deal with situations that have notbeen encounteredbefore and therefore there are no roles to deal with them.Managers handle the information collection and decision making tasksrequiredby uncertainty.

c. Target or Goal Setting: brings the points of decision making down to thepoint of action where information originates, increasing the amounts ofdiscretion by employeesat lower levels of the organisation. Targetsor goalsare usedto coordinateinterdependentsubtaskswhile allowing discretion at alocal subtask evel.

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Galbraith (1973) recognises that when uncertainty increases,the hierarchy isoverloadedand theseapproacheso control are not sufficient. He definesanotherfour strategiesfor situations of increasing uncertainty: creating slack resources,creating self-contained tasks, investing in vertical information systemsand thecreation of lateral relations. Thesestrategies or managing increasinguncertaintysuggestedby Galbraith in 1973, appear o be aligned to someof the conceptsofComplexity Theory, such as nterconnectednessandthe use of slack resources.Drucker (1974) suggests that complexity in organisations limits the ability tocontrol it. The main reasons that he provides as evidence for this are thedifficulties in measuring human systems, the multiplicity of objectives, causesand effects in organisations, the value-setting character of control mechanismsand the uncertainty of responseshe control systems can provoke. Along similarlines, Lawler (1976) identified three human problems created by control inorganisations. Firstly, misplaced controls lead to rigid bureaucratic behaviour,where peoplebehave n order to satisfy the controls and not necessarily o benefitthe organisation. Secondly, controls promote distortions in the measurementprocess and thirdly, controls may be seen as a threat and therefore resisted bypeoplein the organisation.Staceybelieves that control in organisations is paradoxical since it is requiredboth to maintain the system in equilibrium and to allow it to be flexible andinnovative (Stacey, 1993). He classifiescontrol into threemain approaches,eachof which canbe used n different situations.a) Planning and Monitoring form of control: This form of control,

suitable for situations of closed change, is based on negative feedbackintended to bring the system to stability. This approachis constrainedbyorganisational intention and is effective only in the short-term. In the long-

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term, using this approach is only a `fantasy defence to protect managersagainst he anxiety that uncertainty and ambiguity generate' (Stacey,1993).

b) Ideological form of control: This form of control, maintained bypolitical and learning feedback oops. Control by intuition and udgement isbased on the mental models that managers are using and their learningprocess. Here, managersuse visions, missions, values and ideologies tomaintain the system under control. However, when uncertainty is high, thepossibility of applying power diminishes and anxiety takes over, for thisreasonStacey(1993) believes that this form of control is suitable for closedand contained changesituations only.

c) Self-organising forms of control: This form of control relies on bothpositive and negative feedback, and it is suitable for situations of highuncertainty, this is, situations of open-ended change. In this form of control,`people interact spontaneously forming a system that is self-organising andthat their behaviour is amplified leading to overt and covert politicalactions, unconscious processes, organisational defences and the questioningof shared mental models' (Stacey, 1993). Self-organising can be consideredas a form of control firstly because, in the same way as the other two formsof control, it uses feedback connections between discovery, choice andaction, and secondly because it provides boundaries around the behaviour ofthe system (Stacey 1992,1993).

According to Stacey(1993), in the self-organising form of control the role of topmanagers s different from that in stable situations. Here managersdo not planor create ideologies, but they are responsible for influencing the learning andpolitical processesn the organisation. In this case,managersdo not have centralcontrol over choices and outcomesbut can determine how learning takes placeandis disseminatedwithin the organisation.Most definitions and approaches o control are restricted to the planning andmonitoring, and ideological forms of control. However, as Staceyclearly statestheseapproachesare only effective in situations of closed or contained change.This clearly sets he limits to theseforms of control.

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2.5 Perspectives on StrategyStrategyformation hasbeen a widely debatedsubject for at least 40 years. Overthis period, several schools of thought have emerged, representing different

perspectives on strategy. Several authors have classified and compared thedifferent schools (Segal-Horn, 1998; Mintzberg, 1998; Van der Heijden, 1996;Whittington, 1993; Taylor, 1987). Two such classifications, developed byMintzberg (1998) and Taylor (1987), are presented n Submission 9 (pp. 27-28).The schools range from the mechanistic -analytical processesbasedon planning

and monitoring- to the unstructured mental processes searching for innovationandadaptation.According to Mintzberg (1998) the more prescriptive and analytical approacheswere in vogue in the 1960's and 70's, while during the 1980's and 90'sdescriptive approachesbecamemore popular. Segal-Horn (1998) holds a similar

view, stating that there has been a gradual evolution of strategic managementaway from rational planning and towards an emergent and incrementalview.2.5.1 The planning perspectivePlanning is a controversial subject that has been defined and interpreted in manydifferent ways. Mintzberg (1981) argues hat there is a lack of clarity about themeaning of planning and he explores and criticises different approachestodefining it. He classifies definitions of planning into the following categories:" Planning as future thinking: this is simply taking the future into

consideration. However, Mintzberg argues that all decision-making dealswith the future and hence this definition makes the two termsindistinguishable.

" Planning as integrated decision-making: this refers to a consciousattempt tointegrate decisions across different areas. Mintzberg believes that this

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definition lacks specificity because t could also include the entrepreneurialprocessof visioning anddecision-making as a form of planning.

" Planning as formalised procedure and articulated result: refers to asystematic, explicit, recoverable thought process', helping to analyseinformation and feeding it to decision making processes. However,Mintzberg arguesthat this falls short of an operational definition because tdefines intentions and not actions.

" Planning as programming: here planning is not usedto develop the intendedstrategybut to elaborateon the consequences f an intended strategy alreadyconceived.

Mintzberg (1981) argues hat the last two definitions (i.e. Formalised proceduresand programming) are the ones that reflect what planners in organisationsactually do. He then concludesthat planning takesa role, not at the centreof thestrategy process,but at either side; first feeding the information necessaryfordecision making and then codifying, elaborating and converting intended

strategy. Mintzberg (1994a, 1994b, 1994c) has been critical of the concept ofstrategic planning, arguing that it is more formalised thinking about strategythanstrategic thinking. However, Mintzberg also acceptsthat planning is necessaryin certain situations to coordinatehuman and other resources.The view that planning does not develop strategy is shared by a number of

authors and, in fact it is one of the arguments presentedby some proponents ofComplexity theory in organisationsas s described n the following section.There are also several authorswho actively support planning as a key element ofstrategy formation (Ansoff, 1991; Porter, 1996; Gaddis, 1997). Ansoff (1991)directly contradicts Mintzberg, by stating that his definition of planning isdifferent from that used in practice and arguing that there is observableevidencethat planning works. Porter (1996) accepts hat there are limitations to planning,however he argues hat it is essential hat a company try to `extend ts uniqueness

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while strengthening the fit among its activities'. Gaddis (1997) alsoacknowledgesthe limitations of planning but asserts hat managersand boardsstill needto demonstratepurposefulness n their plans and the best way of doingthis is through gaining a better understanding of their organisation and itsenvironment.The suitability of different approaches o planning depends on the scale andscopeof the plan as well as on the complexity and uncertainty of the situation.For example,mechanistic approacheso planning might be suitable for relativelystableandprogressivesituations,but ineffective in situations of high uncertainty.Other approaches,such as scenarioplanning, are bound to be more effective insituations of increasinguncertainty.Planning is outward looking, as it tries to understandthe environment and guidethe organisation towards a desired future. However, planning has its limitationsas, even when some repetitive patterns are predictable inside and outside theorganisation, forecasting discontinuities, such as technological innovation orprice increases, is virtually impossible (Mintzberg, 1994a). This does notinvalidate the need for planning; according to de Geus (1997), "the real purposeof effectiveplanning is not to makeplans but to change the... mental modelsthat... decision makerscarry in their heads". This view considersplanning as alearning process ather than an aid to control.Planning is one of the strategies in the Complexity-Uncertainty model. Themodel indicates how planning can support organisations in dealing withcomplexity and uncertainty, and it acknowledges ts limitations. Section 4.3.4describesn more detail how this strategyfits into the model.


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2.5.2 The Complexity Perspective to Strategy FormationComplexity Theory has presented the strategic management field with a newperspectivewhich has attracted increasing attention since the early 1990's. TheComplexity perspective inclines towards the emergent and incremental view ofstrategy formation, similar to other contemporary schools, but its core conceptshave their origins in scientific discoveries from the study of Complex AdaptiveSystems in nature. This section presents a critical review of the Complexity

Theory perspective of strategy formation, contrasting and comparing the viewsprovided by different authors.During the 1980's and early 1990's a number of books popularisedthe scientificdiscoveries of Chaos (Gleick, 1987) and Complexity (Waldrop, 1993; Lewin,1993; Cohen & Stewart, 1994, Casti, 1995). These publications stimulatedresearchersand practitioners to think about how these theories could be appliedto organisations. As a result, the last ten yearshave seen a surge n managementliterature related to Complexity Theory and its application to organisations,particularly the subject of strategyformation.Figures 6 and 7 show the sequence of publications in the application ofComplexity to Strategy formation; Figure 6 focuses on academic papers andFigure 7 on books published. Thesediagramsshow clearly that Complexity andits role in strategy formation started to attract attention in the late 1980's andearly 1990's andthat this attentionhascontinuedto grow sincethen.


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Figure 7: Timeline of Complexity and Strategy Publications (Papers)

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Figure 8: Timeline of Complexity and Strategy Publications (Books)

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2.5.3 Views on Complexity and StrategyThe available literature in complexity and strategyreveals a number of views ofand alternatives paths to the application of the concepts of complexity. Thissectionanalyses hesedifferent views, breaking them down into four categories:1) Consistent: thoseviews that are sharedby the majority of the authors n the

field, forming a generally accepted view of the role of Complexity instrategyformation.

2) Complementary: those views that, although they might not have beenwidely discussed by a range of authors, are aligned to the concepts ofComplexity and complement existing views.

3) Conflicting: those views where some of the ideas proposed createconflictbut where the essenceof the conceptsof Complexity is retained.

4) Contradictory: those views which radically oppose to the application ofComplexity theory in the field of strategyand oppose ts core concepts.

2.53.1 Consistent viewsIn this category,the views of most authors in the field coincide. This consensusis centred on a series of propositions, based on the concepts of Chaos andComplexity, which challengetraditional views of strategy. The term "traditionalview" is a generalisation of a widely acceptedview on strategy,but which doesnot necessarily cover all of the different schools of thought and approaches ostrategy formation. The generalisation is used here to contrast views aboutComplexity with the more conventional interpretationsof strategy.Thesepropositions of Complexity theory applied to strategy canbe expressedasdichotomies or paradoxes,contrasting the Complexity view with the traditionalview. Six of theseparadoxescanbe consideredas common amongmost authors:


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a) Order vs. ChaosTraditional perspectives on strategy assume a system in a state of stability andclosed change, where the past is understandable and the future is predictable(Nonaka, 1988; Stacey, 1992,1993; Beinhocker, 1997,1999). Such an orderedsystem can be reduced and analysed by decomposition. In contrast, thecomplexity perspectivesuggests hat orderedsystemsare rare and short lived andthat most organisations are systems in a state of bounded instability (Stacey,1992,1993). This view of organisationspresumes hat the systemis in constantflux with periods of temporary stability, and it rejects mechanistic andpositivistic approacheso understandinghuman behaviour and acknowledges hecomplexity and diversity of experience(Nonaka, 1988, Levy, 1994; Dubinskas,1994; Wilding, 1997)

b) Equilibrium vs.Far from EquilibriumIkujiro Nonaka (1988), one of the first authors to recognise the importance ofChaostheory in strategy formation, arguesthat traditional management heoriesplace emphasis on maintaining order and equilibrium by applying controlmechanisms at a strategicmanagement evel. He claims that models focusing onthe roles of ambiguity and chaos should be developed. This view is sharedby anumber of other authors such as Stacey(1992,1993), Beinhocker, (1997,1999),Pascale 1999,2001), andBrown & Eisenhardt(1997).The complexity view suggests hat for a systemto be creative and able to renewitself, it needs o be at a statefar from equilibrium (Stacey, 1992; Nonaka, 1988).In this state, also termedthe Edgeof Chaos(Kauffman, 1995),the organisationisable to innovate and adaptto change(Beinhocker, 1997;Brown et. al. 1997).

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c) Linear vs.Non-linear Causal RelationshipsTraditional views assumethat relationships in organisations are mainly linear,

and hence t is possible to extrapolatecurrent patterns into the future, making itpredictable. The Complexity view suggests that the main reason for theunknowable character of organisations is that they are non-linear feedbacksystemsand exhibit chaos.Chaosrefers to types of behaviour which are an `intricate mixture of order anddisorder, regularity and irregularity, but are neverthelessrecognisable as broadcategories of behaviour' (Parker & Stacey, 1994). In a chaotic system, causesand effects are distant in time and spaceand the systemexhibits synergy. Hence,the analysis of such a systemby decomposition is not possible (Parker & Stacey,1994; Stacey, 1996a). Furthermore,chaotic systemsarehighly sensitiveto initialconditions so minor variations in current conditions can produce largefluctuations in the future. In Non-linear feedback systems,the long-term futureis inherently unpredictable and reductionist or causaltools tend to be ineffective(Peitgen, Jurgens & Saupe, 1992, Parker & Stacey, 1994; Dubinskas, 1994;Stacey, 1996a, Wilding, 1997). Dubinskas (1994), using an ethnographicexample of a manufacturing automation project, concludesthat `no mechanisticcausal model can adequately account for the complexity, indeterminacy andunpredictability of the project's outcomes'.d) Predictability vs. UncertaintyTraditional views on strategymake the assumptionthat the future is predictableand that long-term forecastscanhave sufficient accuracyto be able to commit toa focusedplan. However, feedbackand non-linearity make the future difficult topredict and in certain aspects unknowable (Parker & Stacey, 1994; Stacey,

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1996a, Wilding, 1997). Levy (1994), using a simulation of a supply chain,concluded that in chaotic systems, whilst it might be possible to produce short-term predictions, long-term forecasting and planning is almost impossible andthat dramatic changecan occur unexpectedly.Stacey(1992) arguesthat conventional managementapproachesare not suitablein a continuously changing environment. He states that when the future isknowable, it is possible to analyse problems and identify solutions using asystematic and formalised process. Even when the future is unknown it ispossible to conduct research to gather information and perform analysis.However, when the future is unknowable it is not possible to apply analytic toolsin the creation of strategy. This calls for a different approach to strategyformation which emphasises, innovation, learning, diversity and adaptation,rather than planning and control.e) Control vs.Self-organisationThe traditional approachto planning and control is grounded in the principle ofnegative feedback; this means intervening in the system in order to reduce oreliminate variations from a predetermined target. This is usually done as acentralised process,where top managerscreate plans and targets and then applycontrol mechanisms n an attempt to achievetheir plans. An alternative proposedby the complexity approach is control through self-organisation. In self-organisation, the systemis maintained within boundaries,through a combinationof positive and negative feedback (Stacey, 1992; 1993). Self-organisation isbased on decentralisednetworks of individuals who empower themselves andcreate new patterns and mental models through learning and political dialogue

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(Nonaka, 1988; Stacey, 1992,1993; Pascale 1999,2001; Anderson, 1999;Bonabeau& Meyer, 2001; Coleman, 1999).Self-organisation, according to Nonaka (1988), creates order through physicaland mental patterns. This meansthe creation of information and mental modelsfor interpreting information. This is done by teams with three characteristics:autonomy, multidisciplinary and challenging goals (Nonaka, 1988).In self-organisation `people interact spontaneously orming a systemthat is self-organising and that their behaviour is amplified leading to overt and covertpolitical actions, unconscious processes, organisational defences and thequestioning of sharedmental models' (Stacey, 1993). Self-organisation can beconsidered controlled behaviour becau

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