Manufacturing strategy – understanding the fitness landscape

Manufacturing strategyunderstanding the tness

landscapeIan P McCarthy

SFU Business Simon Fraser University Vancouver Canada

Keywords Dynamics Manufacturing systems Systems theory

Abstract This theoretical paper presents extends and integrates a number of systems andevolutionary concepts to demonstrate their relevance to manufacturing strategy formulationSpeci cally it concentrates on tness landscape theory as an approach for visually mapping thestrategic options a manufacturing rm could pursue It examines how this theory relates tomanufacturing competitiveness and strategy and proposes a de nition and model ofmanufacturing tness In accordance with tness landscape theory a complex systemsperspective is adopted to view manufacturing rms It is argued that manufacturing rms area speci c type of complex system plusmn a complex adaptive system plusmn and that by developing andapplying tness landscape theory it is possible to create models to better understand and visualisehow to search and select various combinations of capabilities

IntroductionIn 1997 the UK Confederation of British Industry (CBI) produced a report titledFit for the Future (Confederation of British Industry 1997) This report wastargeted at UK manufacturing and it claimed that an additional pound60 billion ayear could be added to the UK gross domestic product if manufacturingperformance was raised to US manufacturing performance levels The reportstressed the need for UK improvement programmes based on cost-cuttingef ciency gains and better product lead-times and quality As a consequence ofthe popularity of this report the concept of being ordf tordm was synonymous withbeing world-class

However what do the terms ordf tordm and ordf tnessordm mean in the context ofmanufacturing and operations management This question is the motivationand starting point of this paper It is addressed by viewing manufacturing rms as complex adaptive systems and by developing and relating tnesslandscape theory to the process of manufacturing strategy formulation Theseconcepts provide an interpretation of tness that is consistent with the CBIview of t but with a number of subtle and important differences that revolvearound rm survival and imitation

This paper also develops and extends existing work that has judged tnesslandscape theory appropriate for understanding organisational developmentand rm dynamics (Kauffman and MacReady 1995 Levinthal 1996 Reuf1997 Beinhocker 1999 Barnett and Sorenson 2002) These articles explain thevalue and relevance of tness landscape theory but avoid de ning tness and

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International Journal of Operations ampProduction ManagementVol 24 No 2 2004pp 124-150

q Emerald Group Publishing Limited0144-3577DOI 10110801443570410514858

relating it to the performance and behaviour of rms There is an implicitassumption that tness simply relates to competitiveness and effectiveness

With this introduction the contribution that this paper makes is to explore how a complex systems view can be used to understand

manufacturing strategy and competitiveness create a de nition and conceptual model of manufacturing tness that

provides a basis for better understanding the exploration of strategicoptions and

to consider the relevance of this model and tness landscape theory to thepractice of manufacturing strategy

Manufacturing and complex systems theoryFitness landscape theory has its origins in complex systems research and inparticular the study of evolutionary properties in biological systems Thusbefore introducing tness landscape theory and its relevance to manufacturingstrategy it is necessary to understand the term ordfcomplex systemordm

According to the Shorter Oxford Dictionary (Brown 1993) the wordordfsystemordm rst appeared in 1619 and is now de ned as ordfAn organised orconnected group of objects a set or assemblage of things connected associatedor interdependent so as to form a complex unityordm It is a ubiquitous term that isused to describe and consider many entities in our social physical andbiological world

Kuhn (1962) Capra (1986) and McCarthy et al (2000a) discuss and reviewseveral eras and movements of systems thinking including

the Aristotelian view (organic living and spiritual)

the Cartesian view (mechanistic and reductionism)

the Newtonian view (principles of mechanics)

the romantic view (self-organizing wholes)

the general systems science view (elements and their relationship to thewhole and open systems versus closed systems)

the cybernetic view (feedback self-balancing self-regulating andself-organisation)

the soft systems view (mental constructs) and

the complex systems view (non-linearity self-organisation andemergence)

Despite the different stance each view has a common and binding theme is thatthey are trying to understand complicated entities by

determining the system boundary components inputs and outputsrelationships and attributes and

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125

supporting the integration of views and knowledge to study the totalsystem and how it interacts with its environment

The complex systems view also known as complex systems theory (Stacey1995 Anderson 1999 Choi et al 2001 Dooley and Van de Ven 1999 Morel andRamanujam 1999) seeks to understand the interactions between the systemelements and between the system whole and its environment Theseinteractions generate non-linearity self-organisation and emergence whichare dif cult to represent and understand using a mechanistic and reductionismview For example the mechanistic and reductionism view would typicallyattempt to understand systems by reducing the whole system (eg the wholemanufacturing rm) into manageable individual elements (eg manufacturingdepartments or other sub-units) By separating and studying these individualelements of the system this view seeks to understand and formulate theoriesabout the behaviour of the whole system while the complex systems viewasserts that the whole system cannot be truly understood by reducing it intosmaller manageable units This is because non-linearity emergence andself-organisation are a product of the individual system element rules andbehaviours which are often independent of any rules that may have beenimposed on the system as a whole Thus a key reason why manufacturing rms change is because they are complex and evolving systems in uenced byalterations in their environmental (internal or external) conditions It is thisability to evolve that makes manufacturing strategy formulation necessary anddif cult (Rakotobe-Joel et al 2002)

To further understand complex systems behaviour and its relevance tomanufacturing rms a discussion on these three related characteristics isprovided

Non-linearityThis is a system characteristic in which an input or change in the system is notproportional to the output or effect Thus effect is rarely relative to cause andwhat happens locally in one part of a system often does not apply to other partsof a system (Sterman 2002) For instance if a manager decides to addadditional resource (eg workers and machines) to a production plant theresult is not always a corresponding and linear increase in the number ofproducts manufactured As most managers know if one system parameter ischanged there are interactions between the system elements (workersequipment departments etc) that can produce an aggregate behaviour whichcould not be derived by adding up the individual element behaviours orinteractions

EmergenceThis attribute of a complex system results from the systemrsquos evolution andnon-linearity Literally emergence means ordfto dive outordm or to come out of the

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depths Thus emergence is the manifestation of new system performance dueto the collective behaviour of the elements as opposed to the individualbehaviour of each element Efforts to understand organisations in termsformalization differentiation and social adhesion cannot solely focus onindividual members of the rm (Lazarsfeld and Menzel 1961) Emergentbehaviours are typically unanticipated and sometimes novel For example if amanager decides to discipline or dismiss an employee then the unexpected andemergent result could be that the workforce goes on strike in protest and bringsthe business to a standstill The phenomenon of system emergence is consistentwith Mintzbergrsquos view of emergent strategies (Mintzberg 1978) where anunplanned and unpredicted event can materialise regardless of the plannedintention

Self-organisationVon Foerster (1960) de nes a self-organising system as the rate of increase oforder or regularity in a system This de nition is also dependent on theobserverrsquos frame of reference For example as a manufacturing rm changesover time (eg more products new technology and new working practices) amanager should accordingly update and enlarge his understanding of thesystem and its possible states and behaviours Self-organisation is also aproduct of the interactions dependency and circularity of organisationalsystems and how they address and engage with the domains in which theyoperate This leads to a range of dependent systems processes such asself-creation self-production self-maintenance and self-con guration all ofwhich are consistent with the complex systems and cybernetic view of rmsand are known as autopoiesis plusmn the process by whereby a rm produces andmaintains itself (Maturana and Varela 1980)

Before considering the concept of tness and tness landscape theory it isimportant to recognize that the complex systems view considers some systemsto have elements (ie people) which have a decision-making capability(McCarthy 2003) These elements are referred to as agents and their systemsare referred to as complex adaptive systems Agents are able to receive andprocess information according to a set of goal directed operating rules (schema)that the system may have This decision-making capability creates the internaldynamic of the system and permits system adaptation (Wooldridge andJennings 1995) Thus manufacturing rms are complex adaptive systems thatconsciously evolve and self-organise (adapt) in response to certain goals orobjectives

Introduction to tness landscape theoryThe origins of tness landscape theory are attributed to Sewall Wright (1932)who created some of the rst mathematical models of Darwinian evolution Heobserved a link between a micro property of organisms (interactions between

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127

genes) and a macro property of evolutionary dynamics (a population oforganisms can evolve multiple new ways of existing) To describe this epistasis(the effect of one variable on another) Wright proposed a tness landscapemetaphor in which a population of organisms would evolve by moving towardsa higher tness peak ie from population A to population B as shown inFigure 1

More recently tness landscape theory has been used to investigate anumber of life science problems including the structure of molecular sequences(Lewontin 1974) and mathematical models of genome evolution (Macken andPerelson 1989) One speci c model the NK modelwas devised to examine theway that epistasis controls the ordfruggednessordm of an adaptive landscape(Kauffman and Weinberger 1989 Weinberger 1991 Kauffman 1993) Withthis model N represents the number of elements in a system and K representsthe number of linkages each element has to other elements in the same systemThis formal but simple representation allows the model to be applied to othercomplex systems For example management and organisational scienceresearchers have discussed and advocated the use of tness landscape theoryfor investigating

organisational development and change (Beinhocker 1999 McKelvey1999 Reuf 1997)

the evolution of organisational structures (Levinthal 1996)

innovation networks in the aircraft industry (Frenken 2000) and

technology selection (McCarthy and Tan 2000 McCarthy 2003)

Figure 1Evolution as athree-dimensionallandscape

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128

Despite the contributions made by these works the questions of what exactly is tness and how does it relate to the studies in question are not fully addressedand in some cases are avoided

A review of tnessAlthough the term tness is used regularly in biological and evolutionarypublications its de nition and use is unclear This ambiguity has beentransferred to those management and strategy papers that discuss therelevance and insights that tness landscape theory could offer to managementscholars It seems that most authors assume there is a universally understoodmeaning of the term and therefore do not provide a working de nition Thisproblem was identi ed by Stearns (1976) who observed that the term tnesshas not been de ned precisely but that everyone seems to understand it In anattempt to avoid repeating this problem this paper presents a review andexplanation of the term tness which will be the basis for the proposedde nition and model of manufacturing tness

The term tness was rst used by Herbert Spencer in 1864 in the context ofordfsurvival of the ttestordm and ordfnatural selectionordm as proposed by Darwin in hisOriginof Species fouryearsbeforehand(Gould1991) Ina later edition of the samebook Darwin used the two phrases interchangeably and later it became widelyknownasordfDarwinian tnessordm which generally meant thecapacity to surviveandreproduce It was not until 1930 that Fisher (1930) related tness to an organismrsquosreproduction rate although he himself did not formally de ne tness

To better understand the biological meaning of tness and its relevance tomanufacturing strategy and survival Table I presents a de nition of tnessand four related terms (Endler 1986) Each de ntion is translated into amanufacturing context

The de nitions in Table I show that tness is traditionally de ned as therelative reproductive success of a system as measured by fecundity or other lifehistory parameters Yet it also indicates that tness is a measure of a systemrsquosability to survive Thus we have two dimensions to tness

(1) survival tness which is the capability to adapt and exist and

(2) reproductive tness which is an ability to endure and produce similarsystems

Manufacturing rms do not sexually reproduce but those that compete bycreating new strategic con gurations often inspire others to imitate theirstrategy and mode of working Thus it is proposed that manufacturing tness isthe capability to survive by demonstrating adaptability and durability to thechanging environment This involves identifying and realising appropriatestrategies which in turn are perceived by competitors to be successful who thenadopt the same strategy This process is similar to the biological view thatconsiders tness to be an observable effect (ie the reproduction rate) and is also

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129

consistent with the notion of rm effectiveness For example Seashore andYuchtman (1967 p 898) describe the effectiveness of a rm as ordfits ability toexploit its environment in the acquisition of scarce and valued resourceordmTherefore rms with high tness are able to adapt to survive When faced withdif culties they do not just dissipate but nd ways to overcome circumstanceseven if this means sacri cing short-term objectives This view is supported byKatz and Kahn (1978) who assert that the behaviour of a rm simply revolvesaround the primary goal of survival ie ordfthe continuation of existence withoutbeing liquidated dissolved or discontinuedordm (Kay 1997 p 78)

The strategic management view of tness is concerned with the balancebetween environmental expectations placed on the rm (costs deliveryquality innovation customisation etc) with the resources and capabilitiesavailable in the rm This is a process of matching environmental t andinternal t (Hamel and Prahalad 1994 Miller 1992) and is consistent with the

Context De nition and measurement Manufacturing relevance

1 Fitness The average contribution to thebreeding population by an organismor a class of organisms relative tothe contributions of other organisms

A successful manufacturing strategywill spawn a host of imitators whoseek the same bene ts

2 Rate Coef cient The rate at which the process ofnatural selection occurs Measuredby the average contribution to thegene pool of the followinggeneration by the carriers of agenotype or by a class of genotypesrelative to the contributions of othergenotypes

The rate at which manufacturing rms will successfully adopt a newstrategy

3 Adaptedness The degree to which an organism isable to live and reproduce in a givenset of environments the state ofbeing adapted Measured by theaverage absolute contribution to thebreeding population by anorganisms or a class of organisms

A form of absolute tness thatrelates to the ability to survive (aninternal factor) and a rmrsquosperceived competitiveness (anexternal factor)

4 Adaptability The degree to which an organism orspecies can remain or becomeadapted to a wide range ofenvironments by physiological orgenetic means

The internal process by whichmanufacturing rms survive in thelong-term It is based onself-organisation learninginnovation and adaptation

5 Durability The probability that a carrier of anallele or genotype a class ofgenotypes or a species will leavedescendants after a given long period

The robustness and longevity of amanufacturing rmrsquoscompetitiveness

Source Adapted from Endler (1986 p 40)

Table IThe ve contexts of tness

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130

theory of congruence where each element of the rm ts with reinforces or isconsistent with other elements (Nadler and Tushman 1980) Although theseuses of the term ordf tordm were developed independently of tness landscape theorythey are consistent with the biological view of tness and the concept ofepistasis (the effect of one variable on another)

At this stage it is concluded that the tness of any complex adaptive systemis a measure of its ability to survive and produce offspring Ultimately the term tness is used tautologically because what exists must be t by de nition Thekey issue for managers is to recognise that manufacturing strategy formulationand competition is a complex systems issue Changes in one part of their rmcan sometimes lead to non-linear and disproportional outcomes in other areasAs will be discussed these changes also affect the shape and membership ofthe tness landscape in which they reside

The NK modelThis section will explain how the NK model can be used to better understandstrategy formulation as complex adapting system of capabilities and torecognise the epistasis between capabilities and competing strategies

To begin with the system of study is a manufacturing strategy as de ned indetail in the next section It is analysed and coded as a string of elements (N)where each element is a capability For any element i there exist a number ofpossible states which can be coded using integers 0 1 2 3 etc The totalnumber of states for a capability is described as Ai Each system (strategy) s isdescribed by the chosen states s1s2 sN and is part of an N-dimensionallandscape or design space (S) The K parameter in the NK model indicates thedegree of connectivity between the system elements (capabilities) It suggeststhat the presence of one capability may have an in uence on one or more of theother capabilities in a rmrsquos manufacturing strategy

To understand the signi cance of this design space to manufacturingstrategy formulation a seminal example is adopted and conceptually modi edfrom Kauffmanrsquos work (Kauffman 1993 McCarthy 2003) Table II shows theNK model notation and outlines its relevance to manufacturing strategyTable III provides the data for the example which has the followingparameters

N = 3 (three capabilities such as quality exibility and cost)

A = 2 (two possible states such as the presence (1) or absence(0) of a capability) and

K = N 2 1 = 2 (each capability will affect the other two capabilities inthe strategy)

With these parameters the design space is AN = 23 which provides eightpossible manufacturing strategies each of which is allocated a random tness

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131

value between 0 and 1 (see Table III) A value close to 0 indicates poor tnesswhile a value close to 1 indicates good tness In principle the tness valuescan then be plotted as heights on a multidimensional landscape where thepeaks represent high tness and the valleys represent low tness InKauffmanrsquos model the tness function f (x) is the average of the tnesscontributions fi(x) from each element i and is written as

f (x) =1

N

XN

i=1

f i(x)

As N = 3 a three-dimensional wire frame cube can be used to represent thepossible combinations and their relationship to each other (see Figure 2) Each

Notations Evolutionary biology Manufacturing strategy

N The number of elements orgenes of the evolving genotypeA gene can exist in differentforms or states

The number of capabilities that constitute thestrategy and the resulting con gurationThese could include exibility facilitylocation technology management degree ofstandardisation process structure approachto quality etc

K The amount of epistaticinteractions(interconnectedness) among theelements or genes

The amount of interconnectedness among thecapabilities This creates trade-offs oraccumulative dependencies

A The number of alleles (thealternative forms or states) thata gene may have

Number of possible states a capability mighthave For instance the quality capability couldhave four states inspection quality controlquality assurance and total qualitymanagement

C Coupledness of the genotypewith other genotype

The co-evolution of one strategy with itscompetitors

Table IINK model notation

System(strategy)

Element 1(capability X)

Element 2(capability Y)

Element 3(capability Z)

Assigned random tness value

000 Absent Absent Absent 00001 Absent Absent Present 01010 Absent Present Absent 03011 Absent Present Present 05100 Present Absent Absent 04101 Present Absent Present 07110 Present Present Absent 08111 Present Present Present 06

Table IIIManufacturing strategyas a three bit string

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corner point of the cube represents a manufacturing strategy and itshypothetical tness value Strategic change is assumed to be a process ofmoving from one strategy to another in search of an improved tness This isknown as the ordfadaptive walkordm If we arbitrarily select a point on the cube (egpoint 011) there are three ordfone-mutation neighboursordm These are points 010 111and 001 If point 011 has an immediate neighbour strategy with a higher tnessvalue then it is possible that a manufacturing rm would evolve to this tterstrategy (point 111) The arrows on the lines of Figure 2 represent either anuphill walk towards a greater tness value or a downhill walk to a smaller tness value A ordflocal peakordm is a strategy (eg point 101) from which there is no tter point to move to in the immediate neighbourhood A ordfglobal peakordm is the ttest strategy (point 110) on the entire landscape

As this is a simple example consisting of three capabilities it is relativelyeasy to visualise the space of strategic options using a wire frame cube If theexample dealt with several capabilities it then becomes harder to visualise thedesign space using a multi-dimensional cube To overcome this problem aBoolean hypercube can be used to map the strategic design space Figure 3illustrates the landscape of strategic options generated by four capabilities(cost quality exibility and delivery) The tness values shown in Figure 3 aretaken from the work of Tan (2001) who carried out an NK analysis of theManufacturing Excellence 2000 competition data in the UK

As with the Figure 2 example Figure 3 uses a binary notation to representthe presence (1) or absence (0) of a capability For example strategy 0011indicates that the capabilities exibility and delivery are present while thecapabilities cost and quality are absent The base strategy 0000 is at the top ofthe diagram while the maximum strategy 1111 is at the bottom of the diagram

Figure 2A tness landscape

N = 3 and K = 2

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133

As a manufacturing rmrsquos strategy aggregates additional capabilities itdescends into the lower parts of the diagram The assigned tness value for thevarious combinations of capabilities is represented by the bracketed gure

Lines are used to connect two immediate neighbours and the direction of thearrowhead indicates an increase in tness The dotted lines represent the routefrom 0000 to 1111 that has the greatest gain in tness with each move Thedashed lines with double arrows indicate two neighbouring strategies with thesame tness When all the arrowheads are directed to a single strategy this isconsidered an optimal strategy (either local or global) In Figure 3 there are twooptimal points 1101 and 1111 both with tness values of 067

The K and C parametersAs mentioned in the previous section the K parameter is an indicator of asystemrsquos (a strategyrsquos) connectivity It represents the epistatic interactionsbetween each system element (capability) and can range from K = 0 toK = N 2 1 The former being the least complex system where each element isindependent from all other elements and the latter being the most complexsystem where each element is connected in some way to all other elements ForK = 0 the resultant landscape is relatively simple and smooth except for onesingle global peak This suggests that one single strategy dominates the

Figure 3A Boolean hypercube offour manufacturingcapabilities

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competitive landscape (see Figure 4) As K increases from 0 towards itsmaximum of N 2 1 the tness landscape changes to an increasingly ruggeduncorrelated and multi-peaked landscape (see Figure 5) This level ofconnectivity indicates frustration in the system because it can lead to manylocal tness maxima on the landscape If the NK model is applied to the processof manufacturing strategy formulation it is assumed that the contribution ofany capability to the overall tness of a manufacturing strategy depends on thestatus of that capability and its in uence on the status of the other capabilitiesin the strategy

Figure 5Fitness landscape for

K = N 2 1

Figure 4Fitness landscape for

K = 0

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135

Kauffmanrsquos NK model was originally a xed structure model in that thesystem under study was not be in uenced by factors outside of its systemboundary In other words it was a closed system in a static environment Inpractice this assumption is simplistic and invalid for complex systemsTherefore Kauffman introduced a C parameter to indicate couplednessbetween the system and other systems in the environment Coupledness meansthat any system will not just depend on internal factors but also the behaviourand performance of the systems in the same environment This notion is centralto competition because if the tness of one rmrsquos manufacturing strategy isincreased it is almost certain to affect the tness of other rmsrsquo manufacturingstrategies

In summary manufacturing rms are complex adaptive systems that aim toconsciously evolve by seeking new strategic con gurations Fitness landscapetheory and the NK model offer an approach by which to map quantify andvisualise manufacturing strategy formulation as a search process that takesplace within a design space of strategic possibilities whose elements aredifferent combinations of manufacturing capabilities

A de nition and model of manufacturing tnessAt this point the paper has discussed the concept of manufacturing rms ascomplex adaptive systems It has introduced tness landscape theory and theNK model provided a review of the term tness and brie y examined therelevance of the NK model to manufacturing strategy The following sections ofthis paper develop these discussions by providing a de nition and model ofmanufacturing tness Whilst not presenting a systematic review as such(Tran eld et al 2003) a relatively comprehensive review of manufacturingstrategy is offered A theory of evolution is then presented to help understandhow manufacturing strategies and their capabilities evolve according toordfvariation selection retentionordm and ordfstruggleordm This theory provides the basisfor the proposed de nition and model of manufacturing tness

The anatomy of a manufacturing strategyThe previous sections view manufacturing strategy as a system of connectedcapabilities Before providing a de nition of manufacturing tness it isimportant to con rm and justify this view

Skinner (1969) proposed manufacturing strategy as a process to help rmsde ne the manufacturing capabilities needed to support their corporatestrategy He argued that an appropriate manufacturing strategy could providea competitive advantage in terms of cost delivery quality innovation exibility etc Since Skinnerrsquos article numerous other terms have beenproposed by operations management researchers for describing capabilitiesThese include competitive priorities (Hayes and Wheelwright 1984 Boyer

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1998) order winner and quali ers (Hill 1994) and competitive capabilities(Roth and Miller 1992)

The eld of strategic management has also made important contributions tothe concept of rm capabilities speci cally through work dealing with thedistinctive competences (Selznick 1957) and resource-based perspectives(Penrose 1959 Barney 1991 Peteraf 1993) To relate this and recent work tothe anatomy of a manufacturing strategy and tness landscape theory thispaper adopts and develops the dynamic capabilities view (Teece et al 1997) byde ning the following terms

Resources are the basic constituents of a manufacturing rm They arethe tangible assets such as labour and capital and the intangible and tacitassets such as knowledge and experience

Routines are the norms rules procedures conventions and technologiesaround which manufacturing rms are constructed and through whichthey operate (Levitt and March 1988 p 320)

Core competencies are created by developing and combining resourcesand routines They in uence performance and de ne and differentiate a rm from its competitors (Prahalad and Hamel 1990)

Capabilities are a collection of competencies (core or otherwise) thatprovide competitive advantage in terms of cost delivery qualityinnovation etc (Skinner 1969 Stalk et al 1992)

Dynamic capabilities provide a manufacturing rm with the ability tointegrate build and recon gure resources routines and competenciesthat will create new capabilities and a competitive advantage (Teece andPisano 1994 Teece et al 1997 Eisenhardt and Martin 2000)

Con gurations are the resultant form or type of manufacturing rmThey are de ned by the collection of resources routines and resultingcompetencies and capabilities (Miller 1996)

With these de nitions capabilities are considered the basic elements of amanufacturing strategy while a dynamic capability is the collective activitythrough which a manufacturing rm systematically generates and modi es itsresources and routines to improve tness (see Figure 6) Dynamic capabilitiesenable strategic choice and permit manufacturing rms to move from oneposition on the tness landscape to another by re-deploying resources(Lefebvre and Lefebvre 1998) This process of resource deployment is achievedby the rmrsquos routines which connect manage and co-ordinate the resources ina particular fashion The importance of routines to manufacturing rms is suchthat Tran eld and Smith (1998) outline how strategic regeneration andperformance improvement are underpinned by the routines found in amanufacturing rm Thus if competitive manufacturing rms inspire others toimitate their strategy and mode of working then this is a process of

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137

Figure 6The anatomy of amanufacturing strategy

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138

organisational learning and evolution where routines become ordftransmittedthrough socialisation education imitation professionalisation staffmovement mergers and acquisitionsordm (March 1999 p 76)

The notion of interconnectedness (the K parameter) can be found inmanufacturing strategy For instance Skinner (1974) argued that it would bedif cult for a manufacturing rm to perform well if it adopted all capabilitiesand that the rms should focus on a selection of capabilities only This viewimplied that some form of trade-off or negative connectivity betweencapabilities was unavoidable (Corbett and Vanwassenhove 1993 Mapes et al1997) while others argue that capabilities are positively connected and thatcertain capabilities must be in place before another can be adopted Hencecapabilities can often reinforce each other creating a strategy that is asequential cumulative and dependent system (Ferdows and De Meyer 1990)Understanding and managing this connectivity is dif cult because strategyformulation attempts to serve an unpredictable environment and the processoften leads to emergent strategies (Mintzberg 1978) Also a major constraintfor strategy formulation is the inherent and incorrect assumption that thestrategic options available on the known landscape are xed This assumptionis false because the size and shape of the landscape along with the de ningenvironment is continuously changing This creates new and unexploredniches for rms to discover or create It is these territories that the rm shouldexplore to ensure that maximum bene ts are gained (Hamel and Prahalad1989)

Variation selection retention and struggleThese four processes underpin the evolution of a population of organisations(Campbell 1969 Pfeffer 1982 Aldrich 1999) Though they will be presentedand discussed individually it is important to note that they act simultaneouslyand are coupled to each other

Using these evolutionary concepts this paper proposes Figure 7 as a modelof manufacturing tness The model assumes that manufacturing strategyformulation involves populations of manufacturing con gurations respondingto and creating manufacturing systems around speci c socio-technicalcon gurations It is important to note that the population concept assertsthat for the con gurations under study to follow an evolutionary pattern theymust exist in populations That is they must be a group of similar entitieswhich co-exist on a particular area of the landscape (Allaby 1999) Apopulation could be an industry or market sector but is ultimately a collectionof con gurations grouped because they compete in and serve a commonenvironment Thus the boundaries of a population can often exceed that of asingle sector and the criterion for membership is simply that a rm facessimilar evolutionary and competitive forces to other rms in the population(McCarthy et al 2000b)

Manufacturingstrategy

139

Figure 7Model of manufacturing tness

IJOPM242

140

The following sections describe Figure 7 by explaining variation selectionretention and struggle

VariationThis process is consistent with the concept of dynamic capabilities as itinvolves changing resources routines competencies and capabilities to create anew strategy and a resulting con guration Variations can be either intentional(planned) or blind (unplanned) They are intentional when decision makers inthe rm deliberately seek new strategies and ways of competing For instance rms may have formal programs of experimentation and imitation such asbenchmarking internal change agents research and development the hiring ofexternal consultants and innovation incentives for employees Such programsare intentionally created to promote innovative activities that could change thecurrent con guration of a rm Blind variation occurs when environmental orselection pressures govern the process of change This includes trial and errorlearning serendipity mistakes misunderstanding surprises idle curiosity andso forth It can also take the form of new knowledge or experience introducedinto the rm by newly recruited employees

SelectionThis process eliminates certain variations It is a ltering function that removesineffective strategies and their routines competencies and capabilities Theselection forces can be internal or external For example external selectionoccurs when customers request a certain management practice or an approachto quality or when industry norms and regulations demand certainperformance standards Internal selection refers to intra-organisational forcessuch as policy group behaviours and culture Such forces not only selectvariations but also create a positive reinforcement of old innovations andpractices The result is that manufacturing rms can sometimes carry on doingwhat they know best and maintain their existing strategy rather thanexploring the landscape for alternatives

RetentionOnce variations have been selected the process of retention preserves andduplicates the strategy The strategy and its elements are replicated andrepeated in a fashion that is consistent with the concept of tness and theability to reproduce For example the JIT practices that existed in the USsupermarket industry in the 1950s were positively selected by Japaneseautomotive rms who then demonstrated the competitive value of thisapproach to other manufacturers and this led to further selection and retentionof JIT con gurations across a wide range of industries The retention processallows rms to capture value from existing routines that have proved or areperceived to be successful (Miner 1994)

Manufacturingstrategy

141

Retention can occur at two levels the organisational and the populationlevel Organisational retention occurs through the industrialisation anddocumentation of successful routines and by existing personnel transferringknowledge about the routines to new personnel Population level retentiontakes place by spreading new routines from one manufacturing rm to anotherThis can happen through personal contacts or through observers such asacademics or consultants publishing successful new technologies ormanagement practices Retention is the process that promotes capabilitiesand routines that are perceived to be bene cial because rms unlike biologicalsystems have the capacity to observe and imitate successful rms

StruggleStruggle occurs because the resources on offer to manufacturing rms are notunlimited This process governs the other three evolutionary processes byfuelling or limiting their potential For example during the industrialrevolution raw material and energy were key resources while the present needis for knowledge-based resources such as skilled workers research partnersand value adding suppliers In new industries the leading rms have amplegain and enjoy fast growth As competition and volume in the industry growsthe resources become more limited and failure rates increase

In summary Figure 7 helps represent how manufacturing rms evolvestrategies and con gurations to serve different environments or niches Itshows that variation selection and struggle govern survival tness and thatselection retention and struggle govern reproductive tness To a degree thisis consistent with aspects of the institutional view of strategic evolution(Meyer 1977 Scott and Meyer 1994 Tran eld and Smith 2002) which statesthat variations are introduced primarily by mimetic in uences selection is dueto business conformity (regulative and normative) and retention occursthrough the diffusion of common understanding Figure 7 is the basis for thefollowing de nition of manufacturing tness

The capability to survive in one or more populations and imitate andor innovatecombinations of capabilities which will satisfy corporate objectives and market needs and bedesirable to competing rms

ConclusionsSo what is the signi cance of tness landscape theory and the NK model to theprocess of manufacturing strategy formulation To address this question thisconcluding section reviews the implications and relevance of these conceptsunder three headings Central to each is the view that manufacturing strategyformulation is a combinatorial system design problem It involves identifyingthe elements of the strategy and recognising that the connectivity between the

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elements and the coupledness between competing strategies will in uence thetopology of the tness landscape

The Red Queen effectThe complex adaptive systems view asserts that manufacturing strategy is aconsciously evolving system of resources routines competencies andcapabilities which co-evolves with similar competing strategies Thus anyimprovement in one manufacturing rmrsquos tness will provide a selectiveadvantage over that rmrsquos competitors Thus a tness increase by onemanufacturing rm will lead to a relative tness decrease in other competing rms The result is that competing rms take steps to improve their strategyand maintain their relative tness This process is central to the populationconcept and was termed the ordfRed Queen effectordm by the evolutionary biologistVan Valen (1973) The Red Queen refers to a character from Lewis CarrollrsquosThrough the Looking Glass in which Alice comments that although she isrunning she does not appear to be moving The Red Queen in the novelresponds that in a fast-moving world ordfit takes all the running you can do tokeep in the same placeordm Thus the Red Queen metaphor represents theco-evolutionary process where t manufacturing rms will increase selectionpressures and those competing rms that survive by adapting and enduringwill be tter which in turn creates a self-reinforcing loop of competition

For leaders of manufacturing rms traditional strategic managementtheory and practice advocate avoiding the Red Queen effect by nding niche ormonopolistic positions on the tness landscape However isolation fromcompetition tends to be temporary and as reported by Barnett and Sorenson(2002) it has a less-obvious downside in that it deprives a rm of the engine ofdevelopment This results in a trade-off in which those rms occupying safeplaces on the tness landscape eventually suffer over time as they fall behindthose who remain in the race

Appropriate system varietyThe ability to create new manufacturing strategies and resultingcon gurations is related to a manufacturing rmrsquos ability to understand andmanage its system of routines and resources Fitness landscape theory anddynamic capability theory state that systems must recon gure themselves torespond to the challenges and opportunities posed by the environment Thiscapability to create strategic variations is dependent on the system having avariety that matches the array of changes an environment may create (Ashbyrsquoslaw of requisite variety Ashby (1970 p 105))

In terms of innovation strategies this notion is well known and hasdeveloped into principles such as the law of excess diversity (Allen 2001) andthe rule of organisation slack (Nohria and Gulati 1996) Both these principlesassert that the long-term survival of any system designed to innovate requiresmore internal variety than appears requisite at any time Appropriate system

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143

variety facilitates exploratory behaviour (Bourgeois 1981 Sharfman et al1988) and is a necessary attribute for tness and a dynamic capability

The implication of system variety for leaders of manufacturing rms is thatthey should recognise the connection and trade-off between system ef ciencyand system adaptability Any effort to reduce system diversity and increasesystem standardisation could restrict the potential for innovation This isbecause the evolutionary process of variation (especially blind variation)requires excess system diversity to fuel evolutionary adaptation (David andRothwell 1996) This ability to create blind variations is linked to the talent ofproducing innovative strategies This claim is supported by a study ofsuccessful rms by Collins and Porras (1997 p 141) who concluded

In examining the history of visionary companies we were struck by how often they madesome of their best moves not by detailed strategic planning but rather by experimentationtrial and error opportunism and quite literally accident What looks in hindsight like abrilliant strategy was often the residual result of opportunistic experimentation andpurposeful accidents

Understanding and exploring the landscapeUnderstanding the topology of a tness landscape can help the manufacturing rms address the three questions that underpin the strategy process

(1) What is our current position on the landscape (Strategic analysis)

(2) Where should we be on the landscape (Strategic choice)

(3) How will we get there (Implementation)

Figure 8 shows a highly rugged landscape with two manufacturing strategiesstrategy A and strategy B The route from strategy A to strategy B isrepresented by a dashed line This route initially requires a downhill journeythat is often accompanied by a reduction in rm performance which related tothe learning curve challenge and organisational disruption associated with thechange With this reduction in performance a rm often stops the strategicchange and returns to its original position on the landscape Thus for amanufacturing rm to successfully explore and achieve new strategies it mustrecognise that

this often involves the removal of one or more of the capabilities andde ning routines and resources that dictate its current strategy andposition on the landscape

even though the landscape is posited as being static when any rmmoves or makes a change the topology of the landscape and associatedperformance will also change

Exploration of the landscape is a search activity and there are two basic searchstrategies The rst is a local search that enables manufacturing rms to build

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upon their current capabilities It involves investigating those manufacturingstrategies in the immediate vicinity (the one-mutation neighbour strategies)The second search strategy is a long distance search ie looking for strategiesbeyond the local area This involves a relatively signi cant recon guration ofthe strategy and is likely to arise due to previous failure-induced searches(Tushman and Romanelli 1985) or because of the innovative nature of the rm(Nelson and Winter 1982) However long distance searches rarely occur inreality (Cyert and March 1963 Nelson and Winter 1982) because the longerdistance the less time ef cient and less cost ef cient the search becomes Also rms that already have a relatively t strategy are unlikely to risk a signi cantrecon guration Studies practice and history show that a rmsrsquo currentstrategic con guration frequently constrains a rmrsquos dynamic capability toremain focused on those resources and routines which are current and familiarto the rm

Manufacturing strategy formulation can also involve multiple and constantsearches as suggested by Beinhocker (1999) This approach has directrelevance to strategy formulation as a process of organisationalresource-investment choices or options (Bowman and Hurry 1993) Howeverthe capability to have options requires appropriate system variety

SummaryThis paper has reviewed developed and synthesized a range of literature topresent a de nition and a conceptual model of manufacturing tness It isbased on survival tness the capability to adapt and exist and reproductive tness the ability to endure and produce similar systems These two

Figure 8A route or adaptive walk

between strategies

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145

dimensions of tness are governed by the evolutionary forces plusmn variationselection retention and struggle

The de nition and model offer a starting point for further research on howfactors such as landscape topology population and rm dynamics the typeand number of searches and the associated costs and time to search wouldaffect manufacturing strategy formulation and the propositions and ideaspresented To progress this work it is necessary to conduct empirical studiesthat measure manufacturing tness as part of a longitudinal assessment of thechanges within and between the manufacturing rms in a de ned populationThis type of work would provide a quantitative analysis of the claim that rmsoccupying a global peak on a K = 0 landscape gain bene ts from thismonopolistic position but at the expense of maintaining and developing adynamic capability

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