The document discusses fitness landscape theory and its relevance to understanding manufacturing strategy and competitiveness. It defines manufacturing fitness as a firm's capability to survive by demonstrating adaptability and durability to the changing environment through identifying and realizing appropriate strategies that are then perceived as successful and adopted by competitors. The document also reviews key terms related to fitness from biology and translates them to a manufacturing context.

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IJOPM
24,2 Manufacturing strategy:
understanding the tness
124
landscape
Ian 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 and
evolutionary concepts, to demonstrate their relevance to manufacturing strategy formulation.
Speci cally it concentrates on tness landscape theory as an approach for visually mapping the
strategic options a manufacturing rm could pursue. It examines how this theory relates to
manufacturing competitiveness and strategy and proposes a de nition and model of
manufacturing tness. In accordance with tness landscape theory, a complex systems
perspective is adopted to view manufacturing rms. It is argued that manufacturing rms are
a speci c type of complex system ± a complex adaptive system ± and that by developing and
applying tness landscape theory it is possible to create models to better understand and visualise
how to search and select various combinations of capabilities.
Introduction
In 1997, the UK Confederation of British Industry (CBI) produced a report titled
Fit for the Future (Confederation of British Industry, 1997). This report was
targeted at UK manufacturing and it claimed that an additional £60 billion a
year could be added to the UK gross domestic product if manufacturing
performance was raised to US manufacturing performance levels. The report
stressed the need for UK improvement programmes based on cost-cutting,
ef ciency gains and better product lead-times and quality. As a consequence of
the popularity of this report, the concept of being ª tº was synonymous with
being world-class.
However, what do the terms ª tº and ª tnessº mean in the context of
manufacturing and operations management? This question is the motivation
and starting point of this paper. It is addressed by viewing manufacturing
rms as complex adaptive systems and by developing and relating tness
landscape theory to the process of manufacturing strategy formulation. These
concepts provide an interpretation of tness that is consistent with the CBI
view of t, but with a number of subtle and important differences that revolve
around rm survival and imitation.
This paper also develops and extends existing work that has judged tness
International Journal of Operations &
Production Management landscape theory appropriate for understanding organisational development
Vol. 24 No. 2, 2004
pp. 124-150
and rm dynamics (Kauffman and MacReady, 1995; Levinthal, 1996; Reuf,
q Emerald Group Publishing Limited
0144-3577
1997; Beinhocker, 1999; Barnett and Sorenson, 2002). These articles explain the
DOI 10.1108/01443570410514858 value and relevance of tness landscape theory, but avoid de ning tness and

2. relating it to the performance and behaviour of rms. There is an implicit Manufacturing
assumption that tness simply relates to competitiveness and effectiveness. strategy
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 125
provides a basis for better understanding the exploration of strategic
options; and
.
to consider the relevance of this model and tness landscape theory to the
practice of manufacturing strategy.
Manufacturing and complex systems theory
Fitness landscape theory has its origins in complex systems research and, in
particular, the study of evolutionary properties in biological systems. Thus,
before introducing tness landscape theory and its relevance to manufacturing
strategy, it is necessary to understand the term ªcomplex systemº.
According to the Shorter Oxford Dictionary (Brown, 1993), the word
ªsystemº rst appeared in 1619 and is now de ned as ªAn organised or
connected group of objects: a set or assemblage of things connected, associated,
or interdependent so as to form a complex unityº. It is a ubiquitous term that is
used to describe and consider many entities in our social, physical and
biological world.
Kuhn (1962), Capra (1986) and McCarthy et al. (2000a) discuss and review
several 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 the
whole, and open systems versus closed systems);
. the cybernetic view (feedback, self-balancing, self-regulating and
self-organisation);
. the soft systems view (mental constructs); and
.
the complex systems view (non-linearity, self-organisation and
emergence).
Despite the different stance each view has, a common and binding theme is that
they are trying to understand complicated entities by:
.
determining the system boundary, components, inputs and outputs,
relationships and attributes; and

3. IJOPM .
supporting the integration of views and knowledge to study the total
24,2 system and how it interacts with its environment.
The complex systems view, also known as complex systems theory (Stacey,
1995; Anderson, 1999; Choi et al., 2001; Dooley and Van de Ven, 1999; Morel and
Ramanujam, 1999), seeks to understand the interactions between the system
126 elements and between the system whole and its environment. These
interactions generate non-linearity, self-organisation and emergence, which
are dif cult to represent and understand using a mechanistic and reductionism
view. For example, the mechanistic and reductionism view would typically
attempt to understand systems by reducing the whole system (e.g. the whole
manufacturing rm) into manageable individual elements (e.g. manufacturing
departments or other sub-units). By separating and studying these individual
elements of the system, this view seeks to understand and formulate theories
about the behaviour of the whole system, while the complex systems view
asserts that the whole system cannot be truly understood by reducing it into
smaller manageable units. This is because non-linearity, emergence and
self-organisation are a product of the individual system element rules and
behaviours, which are often independent of any rules that may have been
imposed on the system as a whole. Thus, a key reason why manufacturing
rms change is because they are complex and evolving systems in uenced by
alterations in their environmental (internal or external) conditions. It is this
ability to evolve that makes manufacturing strategy formulation necessary and
dif cult (Rakotobe-Joel et al., 2002).
To further understand complex systems behaviour and its relevance to
manufacturing rms, a discussion on these three related characteristics is
provided.
Non-linearity
This is a system characteristic in which an input or change in the system is not
proportional to the output or effect. Thus, effect is rarely relative to cause, and
what happens locally in one part of a system often does not apply to other parts
of a system (Sterman, 2002). For instance, if a manager decides to add
additional resource, (e.g. workers and machines) to a production plant, the
result is not always a corresponding and linear increase in the number of
products manufactured. As most managers know, if one system parameter is
changed there are interactions between the system elements (workers,
equipment, departments, etc.) that can produce an aggregate behaviour which
could not be derived by adding up the individual element behaviours or
interactions.
Emergence
This attribute of a complex system results from the system’s evolution and
non-linearity. Literally, emergence means ªto dive outº or to come out of the

4. depths. Thus, emergence is the manifestation of new system performance due Manufacturing
to the collective behaviour of the elements, as opposed to the individual strategy
behaviour of each element. Efforts to understand organisations in terms
formalization, differentiation and social adhesion cannot solely focus on
individual members of the rm (Lazarsfeld and Menzel, 1961). Emergent
behaviours are typically unanticipated and sometimes novel. For example, if a
manager decides to discipline or dismiss an employee, then the unexpected and
127
emergent result could be that the workforce goes on strike in protest and brings
the business to a standstill. The phenomenon of system emergence is consistent
with Mintzberg’s view of emergent strategies (Mintzberg, 1978), where an
unplanned and unpredicted event can materialise regardless of the planned
intention.
Self-organisation
Von Foerster (1960) de nes a self-organising system as the rate of increase of
order or regularity in a system. This de nition is also dependent on the
observer’s frame of reference. For example, as a manufacturing rm changes
over time (e.g. more products, new technology, and new working practices) a
manager should accordingly update and enlarge his understanding of the
system and its possible states and behaviours. Self-organisation is also a
product of the interactions, dependency and circularity of organisational
systems and how they address and engage with the domains in which they
operate. This leads to a range of dependent systems processes such as
self-creation, self-production, self-maintenance and self-con guration, all of
which are consistent with the complex systems and cybernetic view of rms
and are known as autopoiesis ± the process by whereby a rm produces and
maintains itself (Maturana and Varela, 1980).
Before considering the concept of tness and tness landscape theory it is
important to recognize that the complex systems view considers some systems
to have elements (i.e. people) which have a decision-making capability
(McCarthy, 2003). These elements are referred to as agents and their systems
are referred to as complex adaptive systems. Agents are able to receive and
process information according to a set of goal directed operating rules (schema)
that the system may have. This decision-making capability creates the internal
dynamic of the system and permits system adaptation (Wooldridge and
Jennings, 1995). Thus, manufacturing rms are complex adaptive systems that
consciously evolve and self-organise (adapt) in response to certain goals or
objectives.
Introduction to tness landscape theory
The origins of tness landscape theory are attributed to Sewall Wright (1932),
who created some of the rst mathematical models of Darwinian evolution. He
observed a link between a micro property of organisms (interactions between

5. IJOPM genes) and a macro property of evolutionary dynamics (a population of
24,2 organisms can evolve multiple new ways of existing). To describe this epistasis
(the effect of one variable on another) Wright proposed a tness landscape
metaphor in which a population of organisms would evolve by moving towards
a higher tness peak, i.e. from population A to population B as shown in
Figure 1.
128 More recently, tness landscape theory has been used to investigate a
number of life science problems including the structure of molecular sequences
(Lewontin, 1974) and mathematical models of genome evolution (Macken and
Perelson, 1989). One speci c model, the NK model,was devised to examine the
way that epistasis controls the ªruggednessº of an adaptive landscape
(Kauffman and Weinberger, 1989; Weinberger, 1991; Kauffman, 1993). With
this model, N represents the number of elements in a system and K represents
the number of linkages each element has to other elements in the same system.
This formal, but simple representation allows the model to be applied to other
complex systems. For example, management and organisational science
researchers have discussed and advocated the use of tness landscape theory
for investigating:
.
organisational development and change (Beinhocker, 1999; McKelvey,
1999; 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 1.
Evolution as a
three-dimensional
landscape

6. Despite the contributions made by these works, the questions of what exactly is Manufacturing
tness and how does it relate to the studies in question are not fully addressed, strategy
and in some cases are avoided.
A review of tness
Although the term tness is used regularly in biological and evolutionary 129
publications, its de nition and use is unclear. This ambiguity has been
transferred to those management and strategy papers that discuss the
relevance and insights that tness landscape theory could offer to management
scholars. It seems that most authors assume there is a universally understood
meaning of the term and therefore do not provide a working de nition. This
problem was identi ed by Stearns (1976), who observed that the term tness
has not been de ned precisely, but that everyone seems to understand it. In an
attempt to avoid repeating this problem, this paper presents a review and
explanation of the term tness, which will be the basis for the proposed
de nition and model of manufacturing tness.
The term tness was rst used by Herbert Spencer in 1864 in the context of
ªsurvival of the ttestº and ªnatural selectionº as proposed by Darwin in his
Origin of Species four years beforehand(Gould, 1991). In a later edition of the same
book, Darwin used the two phrases interchangeably, and later, it became widely
known as ªDarwinian tnessº, which generally meant the capacity to survive and
reproduce. It was not until 1930 that Fisher (1930) related tness to an organism’s
reproduction rate, although he himself did not formally de ne tness.
To better understand the biological meaning of tness and its relevance to
manufacturing strategy and survival, Table I presents a de nition of tness
and four related terms (Endler, 1986). Each de ntion is translated into a
manufacturing context.
The de nitions in Table I show that tness is traditionally de ned as the
relative reproductive success of a system as measured by fecundity or other life
history parameters. Yet, it also indicates that tness is a measure of a system’s
ability 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 similar
systems.
Manufacturing rms do not sexually reproduce, but those that compete by
creating new strategic con gurations often inspire others to imitate their
strategy and mode of working. Thus, it is proposed that manufacturing tness is
the capability to survive by demonstrating adaptability and durability to the
changing environment. This involves identifying and realising appropriate
strategies, which in turn are perceived by competitors to be successful, who then
adopt the same strategy. This process is similar to the biological view that
considers tness to be an observable effect (i.e. the reproduction rate) and is also

7. IJOPM
Context De nition and measurement Manufacturing relevance
24,2
1. Fitness The average contribution to the A successful manufacturing strategy
breeding population by an organism, will spawn a host of imitators who
or a class of organisms, relative to seek the same bene ts
the contributions of other organisms
130 2. Rate Coef cient The rate at which the process of The rate at which manufacturing
natural selection occurs. Measured rms will successfully adopt a new
by the average contribution to the strategy
gene pool of the following
generation, by the carriers of a
genotype, or by a class of genotypes,
relative to the contributions of other
genotypes
3. Adaptedness The degree to which an organism is A form of absolute tness, that
able to live and reproduce in a given relates to the ability to survive (an
set of environments; the state of internal factor) and a rm’s
being adapted. Measured by the perceived competitiveness (an
average absolute contribution to the external factor)
breeding population by an
organisms or a class of organisms
4. Adaptability The degree to which an organism or The internal process by which
species can remain or become manufacturing rms survive in the
adapted to a wide range of long-term. It is based on
environments by physiological or self-organisation learning,
genetic means innovation and adaptation
5. Durability The probability that a carrier of an The robustness and longevity of a
allele or genotype, a class of manufacturing rm’s
genotypes, or a species will leave competitiveness
Table I. descendants after a given long period
The ve contexts of
tness Source: Adapted from Endler (1986, p. 40)
consistent with the notion of rm effectiveness. For example, Seashore and
Yuchtman (1967, p. 898) describe the effectiveness of a rm as ªits ability to
exploit its environment in the acquisition of scarce and valued resourceº.
Therefore, rms with high tness are able to adapt to survive. When faced with
dif culties, they do not just dissipate, but nd ways to overcome circumstances,
even if this means sacri cing short-term objectives. This view is supported by
Katz and Kahn (1978), who assert that the behaviour of a rm simply revolves
around the primary goal of survival, i.e. ªthe continuation of existence without
being liquidated, dissolved or discontinuedº (Kay, 1997, p. 78).
The strategic management view of tness is concerned with the balance
between environmental expectations placed on the rm (costs, delivery,
quality, innovation, customisation, etc.) with the resources and capabilities
available in the rm. This is a process of matching environmental t and
internal t (Hamel and Prahalad, 1994; Miller, 1992) and is consistent with the

8. theory of congruence, where each element of the rm ts with, reinforces, or is Manufacturing
consistent with, other elements (Nadler and Tushman, 1980). Although these strategy
uses of the term ª tº were developed independently of tness landscape theory,
they are consistent with the biological view of tness and the concept of
epistasis (the effect of one variable on another).
At this stage, it is concluded that the tness of any complex adaptive system
is a measure of its ability to survive and produce offspring. Ultimately, the term
131
tness is used tautologically, because what exists must be t by de nition. The
key issue for managers is to recognise that manufacturing strategy formulation
and competition is a complex systems issue. Changes in one part of their rm
can sometimes lead to non-linear and disproportional outcomes in other areas.
As will be discussed, these changes also affect the shape and membership of
the tness landscape in which they reside.
The NK model
This section will explain how the NK model can be used to better understand
strategy formulation as complex adapting system of capabilities and to
recognise the epistasis between capabilities and competing strategies.
To begin with, the system of study is a manufacturing strategy as de ned in
detail 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 of
possible states which can be coded using integers 0, 1, 2, 3, etc. The total
number of states for a capability is described as Ai. Each system (strategy) s is
described by the chosen states s1 s2 . . .sN and is part of an N-dimensional
landscape or design space (S). The K parameter in the NK model indicates the
degree of connectivity between the system elements (capabilities). It suggests
that the presence of one capability may have an in uence on one or more of the
other capabilities in a rm’s manufacturing strategy.
To understand the signi cance of this design space to manufacturing
strategy formulation, a seminal example is adopted and conceptually modi ed
from Kauffman’s work (Kauffman, 1993, McCarthy, 2003). Table II shows the
NK model notation and outlines its relevance to manufacturing strategy.
Table III provides the data for the example, which has the following
parameters:
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= N2 1= 2 (each capability will affect the other two capabilities in
the strategy).
With these parameters the design space is AN = 23 , which provides eight
possible manufacturing strategies, each of which is allocated a random tness

9. IJOPM
Notations Evolutionary biology Manufacturing strategy
24,2
N The number of elements or The number of capabilities that constitute the
genes of the evolving genotype. strategy and the resulting con guration.
A gene can exist in different These could include: exibility, facility
forms or states location, technology management, degree of
132 standardisation, process structure, approach
to quality, etc.
K The amount of epistatic The amount of interconnectedness among the
interactions capabilities. This creates trade-offs or
(interconnectedness) among the accumulative dependencies
elements or genes
A The number of alleles (the Number of possible states a capability might
alternative forms or states) that have. For instance, the quality capability could
a gene may have. have four states: inspection, quality control,
quality assurance, and total quality
management
Table II. C Coupledness of the genotype The co-evolution of one strategy with its
NK model notation with other genotype competitors
value between 0 and 1 (see Table III). A value close to 0 indicates poor tness,
while a value close to 1 indicates good tness. In principle, the tness values
can then be plotted as heights on a multidimensional landscape, where the
peaks represent high tness and the valleys represent low tness. In
Kauffman’s model, the tness function f (x), is the average of the tness
contributions, fi (x), from each element i, and is written as:
1X N
f (x) = f i (x)
N i= 1
As N = 3, a three-dimensional wire frame cube can be used to represent the
possible combinations and their relationship to each other (see Figure 2). Each
System Element 1 Element 2 Element 3 Assigned random
(strategy) (capability X) (capability Y) (capability Z) tness value
000 Absent Absent Absent 0.0
001 Absent Absent Present 0.1
010 Absent Present Absent 0.3
011 Absent Present Present 0.5
100 Present Absent Absent 0.4
Table III. 101 Present Absent Present 0.7
Manufacturing strategy 110 Present Present Absent 0.8
as a three bit string 111 Present Present Present 0.6

10. Manufacturing
strategy
133
Figure 2.
A tness landscape:
N = 3 and K = 2
corner point of the cube represents a manufacturing strategy and its
hypothetical tness value. Strategic change is assumed to be a process of
moving from one strategy to another in search of an improved tness. This is
known as the ªadaptive walkº. If we arbitrarily select a point on the cube (e.g.
point 011), there are three ªone-mutation neighboursº. These are points 010, 111
and 001. If point 011 has an immediate neighbour strategy with a higher tness
value then it is possible that a manufacturing rm would evolve to this tter
strategy (point 111). The arrows on the lines of Figure 2 represent either an
uphill walk towards a greater tness value, or a downhill walk to a smaller
tness value. A ªlocal peakº is a strategy (e.g. point 101) from which there is no
tter point to move to in the immediate neighbourhood. A ªglobal peakº is the
ttest strategy (point 110) on the entire landscape.
As this is a simple example consisting of three capabilities, it is relatively
easy to visualise the space of strategic options using a wire frame cube. If the
example dealt with several capabilities, it then becomes harder to visualise the
design space using a multi-dimensional cube. To overcome this problem a
Boolean hypercube can be used to map the strategic design space. Figure 3
illustrates the landscape of strategic options generated by four capabilities
(cost, quality, exibility and delivery). The tness values shown in Figure 3 are
taken from the work of Tan (2001), who carried out an NK analysis of the
Manufacturing Excellence 2000 competition data in the UK.
As with the Figure 2 example, Figure 3 uses a binary notation to represent
the presence (1) or absence (0) of a capability. For example, strategy 0011
indicates that the capabilities exibility and delivery are present, while the
capabilities cost and quality are absent. The base strategy 0000 is at the top of
the diagram, while the maximum strategy 1111 is at the bottom of the diagram.

11. IJOPM
24,2
134
Figure 3.
A Boolean hypercube of
four manufacturing
capabilities
As a manufacturing rm’s strategy aggregates additional capabilities, it
descends into the lower parts of the diagram. The assigned tness value for the
various combinations of capabilities is represented by the bracketed gure.
Lines are used to connect two immediate neighbours and the direction of the
arrowhead indicates an increase in tness. The dotted lines represent the route
from 0000 to 1111 that has the greatest gain in tness with each move. The
dashed lines with double arrows indicate two neighbouring strategies with the
same tness. When all the arrowheads are directed to a single strategy, this is
considered an optimal strategy (either local or global). In Figure 3, there are two
optimal points, 1101 and 1111, both with tness values of 0.67.
The K and C parameters
As mentioned in the previous section, the K parameter is an indicator of a
system’s (a strategy’s) connectivity. It represents the epistatic interactions
between each system element (capability) and can range from K = 0 to
K = N 2 1. The former being the least complex system, where each element is
independent from all other elements; and the latter being the most complex
system; where each element is connected in some way to all other elements. For
K = 0, the resultant landscape is relatively simple and smooth, except for one
single global peak. This suggests that one single strategy dominates the

12. competitive landscape (see Figure 4). As K increases from 0 towards its Manufacturing
maximum of N 2 1, the tness landscape changes to an increasingly rugged, strategy
uncorrelated, and multi-peaked landscape (see Figure 5). This level of
connectivity indicates frustration in the system, because it can lead to many
local tness maxima on the landscape. If the NK model is applied to the process
of manufacturing strategy formulation it is assumed that the contribution of 135
any capability to the overall tness of a manufacturing strategy depends on the
status of that capability and its in uence on the status of the other capabilities
in the strategy.
Figure 4.
Fitness landscape for
K= 0
Figure 5.
Fitness landscape for
K= N2 1

13. IJOPM Kauffman’s NK model was originally a xed structure model, in that the
24,2 system under study was not be in uenced by factors outside of its system
boundary. In other words, it was a closed system in a static environment. In
practice, this assumption is simplistic and invalid for complex systems.
Therefore, Kauffman introduced a C parameter, to indicate coupledness
between the system and other systems in the environment. Coupledness means
136 that any system will not just depend on internal factors, but also the behaviour
and performance of the systems in the same environment. This notion is central
to competition, because if the tness of one rm’s manufacturing strategy is
increased, it is almost certain to affect the tness of other rms’ manufacturing
strategies.
In summary, manufacturing rms are complex adaptive systems that aim to
consciously evolve by seeking new strategic con gurations. Fitness landscape
theory and the NK model offer an approach by which to map, quantify and
visualise manufacturing strategy formulation as a search process that takes
place within a design space of strategic possibilities, whose elements are
different combinations of manufacturing capabilities.
A de nition and model of manufacturing tness
At this point, the paper has discussed the concept of manufacturing rms as
complex adaptive systems. It has introduced tness landscape theory and the
NK model, provided a review of the term tness and brie y examined the
relevance of the NK model to manufacturing strategy. The following sections of
this paper develop these discussions by providing a de nition and model of
manufacturing tness. Whilst not presenting a systematic review as such
(Tran eld et al., 2003), a relatively comprehensive review of manufacturing
strategy is offered. A theory of evolution is then presented to help understand
how manufacturing strategies and their capabilities evolve according to
ªvariation, selection, retentionº and ªstruggleº. This theory provides the basis
for the proposed de nition and model of manufacturing tness.
The anatomy of a manufacturing strategy
The previous sections view manufacturing strategy as a system of connected
capabilities. Before providing a de nition of manufacturing tness, it is
important to con rm and justify this view.
Skinner (1969) proposed manufacturing strategy as a process to help rms
de ne the manufacturing capabilities needed to support their corporate
strategy. He argued that an appropriate manufacturing strategy could provide
a competitive advantage in terms of cost, delivery, quality, innovation,
exibility, etc. Since Skinner’s article, numerous other terms have been
proposed by operations management researchers for describing capabilities.
These include competitive priorities (Hayes and Wheelwright, 1984; Boyer,

14. 1998), order winner and quali ers (Hill, 1994), and competitive capabilities Manufacturing
(Roth and Miller, 1992). strategy
The eld of strategic management has also made important contributions to
the concept of rm capabilities, speci cally through work dealing with the
distinctive competences (Selznick, 1957) and resource-based perspectives
(Penrose, 1959; Barney, 1991; Peteraf, 1993). To relate this and recent work to
137
the anatomy of a manufacturing strategy and tness landscape theory, this
paper adopts and develops the dynamic capabilities view (Teece et al., 1997) by
de ning the following terms:
.
Resources are the basic constituents of a manufacturing rm. They are
the tangible assets such as labour and capital, and the intangible and tacit
assets such as knowledge and experience.
.
Routines are the norms, rules, procedures, conventions, and technologies
around which manufacturing rms are constructed and through which
they operate (Levitt and March, 1988, p. 320).
.
Core competencies are created by developing and combining resources
and 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) that
provide competitive advantage in terms of cost, delivery, quality,
innovation, etc. (Skinner, 1969; Stalk et al., 1992).
.
Dynamic capabilities provide a manufacturing rm with the ability to
integrate, build, and recon gure resources, routines and competencies
that will create new capabilities and a competitive advantage (Teece and
Pisano, 1994; Teece et al., 1997; Eisenhardt and Martin, 2000).
.
Con gurations are the resultant form or type of manufacturing rm.
They are de ned by the collection of resources, routines and resulting
competencies and capabilities (Miller, 1996).
With these de nitions, capabilities are considered the basic elements of a
manufacturing strategy, while a dynamic capability is the collective activity
through which a manufacturing rm systematically generates and modi es its
resources and routines to improve tness (see Figure 6). Dynamic capabilities
enable strategic choice and permit manufacturing rms to move from one
position on the tness landscape to another by re-deploying resources
(Lefebvre and Lefebvre, 1998). This process of resource deployment is achieved
by the rm’s routines, which connect, manage and co-ordinate the resources in
a particular fashion. The importance of routines to manufacturing rms is such
that Tran eld and Smith (1998) outline how strategic regeneration and
performance improvement are underpinned by the routines found in a
manufacturing rm. Thus, if competitive manufacturing rms inspire others to
imitate their strategy and mode of working, then this is a process of

15. IJOPM
24,2
138
Figure 6.
The anatomy of a
manufacturing strategy

16. organisational learning and evolution where routines become ªtransmitted Manufacturing
through socialisation, education, imitation, professionalisation, staff strategy
movement, mergers, and acquisitionsº (March, 1999, p. 76).
The notion of interconnectedness (the K parameter) can be found in
manufacturing strategy. For instance, Skinner (1974) argued that it would be
dif cult for a manufacturing rm to perform well if it adopted all capabilities,
and that the rms should focus on a selection of capabilities only. This view
139
implied that some form of trade-off or negative connectivity between
capabilities was unavoidable (Corbett and Vanwassenhove, 1993; Mapes et al.,
1997), while others argue that capabilities are positively connected and that
certain capabilities must be in place before another can be adopted. Hence,
capabilities can often reinforce each other, creating a strategy that is a
sequential, cumulative and dependent system (Ferdows and De Meyer, 1990).
Understanding and managing this connectivity is dif cult, because strategy
formulation attempts to serve an unpredictable environment and the process
often leads to emergent strategies (Mintzberg, 1978). Also, a major constraint
for strategy formulation is the inherent and incorrect assumption that the
strategic options available on the known landscape are xed. This assumption
is false, because the size and shape of the landscape, along with the de ning
environment, is continuously changing. This creates new and unexplored
niches for rms to discover or create. It is these territories that the rm should
explore to ensure that maximum bene ts are gained (Hamel and Prahalad,
1989).
Variation, selection, retention and struggle
These four processes underpin the evolution of a population of organisations
(Campbell, 1969; Pfeffer, 1982; Aldrich, 1999). Though they will be presented
and discussed individually, it is important to note that they act simultaneously
and are coupled to each other.
Using these evolutionary concepts, this paper proposes Figure 7 as a model
of manufacturing tness. The model assumes that manufacturing strategy
formulation involves populations of manufacturing con gurations responding
to and creating manufacturing systems around speci c socio-technical
con gurations. It is important to note that the population concept asserts
that for the con gurations under study to follow an evolutionary pattern, they
must exist in populations. That is, they must be a group of similar entities,
which co-exist on a particular area of the landscape (Allaby, 1999). A
population could be an industry or market sector, but is ultimately a collection
of con gurations grouped because they compete in and serve a common
environment. Thus, the boundaries of a population can often exceed that of a
single sector, and the criterion for membership is simply that a rm faces
similar evolutionary and competitive forces to other rms in the population
(McCarthy et al., 2000b).

17. IJOPM
24,2
140
Figure 7.
Model of manufacturing
tness

18. The following sections describe Figure 7 by explaining variation, selection, Manufacturing
retention and struggle. strategy
Variation
This process is consistent with the concept of dynamic capabilities, as it
involves changing resources, routines, competencies and capabilities to create a 141
new strategy and a resulting con guration. Variations can be either intentional
(planned) or blind (unplanned). They are intentional when decision makers in
the rm deliberately seek new strategies and ways of competing. For instance,
rms may have formal programs of experimentation and imitation such as
benchmarking, internal change agents, research and development, the hiring of
external consultants and innovation incentives for employees. Such programs
are intentionally created to promote innovative activities that could change the
current con guration of a rm. Blind variation occurs when environmental or
selection pressures govern the process of change. This includes trial and error
learning, serendipity, mistakes, misunderstanding, surprises, idle curiosity and
so forth. It can also take the form of new knowledge or experience introduced
into the rm by newly recruited employees.
Selection
This process eliminates certain variations. It is a ltering function that removes
ineffective strategies and their routines, competencies and capabilities. The
selection forces can be internal or external. For example, external selection
occurs when customers request a certain management practice or an approach
to quality, or when industry norms and regulations demand certain
performance standards. Internal selection refers to intra-organisational forces
such as policy, group behaviours and culture. Such forces not only select
variations, but also create a positive reinforcement of old innovations and
practices. The result is that manufacturing rms can sometimes carry on doing
what they know best, and maintain their existing strategy rather than
exploring the landscape for alternatives.
Retention
Once variations have been selected, the process of retention preserves and
duplicates the strategy. The strategy and its elements are replicated and
repeated, in a fashion that is consistent with the concept of tness and the
ability to reproduce. For example, the JIT practices that existed in the US
supermarket industry in the 1950s were positively selected by Japanese
automotive rms, who then demonstrated the competitive value of this
approach to other manufacturers, and this led to further selection and retention
of JIT con gurations across a wide range of industries. The retention process
allows rms to capture value from existing routines that have proved or are
perceived to be successful (Miner, 1994).

19. IJOPM Retention can occur at two levels, the organisational and the population
24,2 level. Organisational retention occurs through the industrialisation and
documentation of successful routines, and by existing personnel transferring
knowledge about the routines to new personnel. Population level retention
takes place by spreading new routines from one manufacturing rm to another.
This can happen through personal contacts, or through observers, such as
142 academics or consultants publishing successful new technologies or
management practices. Retention is the process that promotes capabilities
and routines that are perceived to be bene cial, because rms unlike biological
systems, have the capacity to observe and imitate successful rms.
Struggle
Struggle occurs because the resources on offer to manufacturing rms are not
unlimited. This process governs the other three evolutionary processes by
fuelling or limiting their potential. For example, during the industrial
revolution, raw material and energy were key resources, while the present need
is for knowledge-based resources such as skilled workers, research partners
and value adding suppliers. In new industries, the leading rms have ample
gain and enjoy fast growth. As competition and volume in the industry grows,
the resources become more limited, and failure rates increase.
In summary, Figure 7 helps represent how manufacturing rms evolve
strategies and con gurations to serve different environments or niches. It
shows that variation, selection and struggle govern survival tness and that
selection, retention, and struggle govern reproductive tness. To a degree, this
is consistent with aspects of the institutional view of strategic evolution
(Meyer, 1977; Scott and Meyer, 1994; Tran eld and Smith, 2002) which states
that variations are introduced primarily by mimetic in uences; selection is due
to business conformity (regulative and normative) and retention occurs
through the diffusion of common understanding. Figure 7 is the basis for the
following de nition of manufacturing tness:
The capability to survive in one or more populations, and imitate and/or innovate
combinations of capabilities, which will satisfy corporate objectives and market needs, and be
desirable to competing rms.
Conclusions
So what is the signi cance of tness landscape theory and the NK model to the
process of manufacturing strategy formulation? To address this question, this
concluding section reviews the implications and relevance of these concepts
under three headings. Central to each is the view that manufacturing strategy
formulation is a combinatorial system design problem. It involves identifying
the elements of the strategy and recognising that the connectivity between the

20. elements and the coupledness between competing strategies will in uence the Manufacturing
topology of the tness landscape. strategy
The Red Queen effect
The complex adaptive systems view asserts that manufacturing strategy is a
consciously evolving system of resources, routines, competencies and
capabilities, which co-evolves with similar competing strategies. Thus, any 143
improvement in one manufacturing rm’s tness will provide a selective
advantage over that rm’s competitors. Thus, a tness increase by one
manufacturing rm will lead to a relative tness decrease in other competing
rms. The result is that competing rms take steps to improve their strategy
and maintain their relative tness. This process is central to the population
concept and was termed the ªRed Queen effectº by the evolutionary biologist
Van Valen (1973). The Red Queen refers to a character from Lewis Carroll’s
Through the Looking Glass, in which Alice comments, that although she is
running, she does not appear to be moving. The Red Queen in the novel
responds that in a fast-moving world ªit takes all the running you can do, to
keep in the same place.º Thus, the Red Queen metaphor represents the
co-evolutionary process where t manufacturing rms will increase selection
pressures, and those competing rms that survive by adapting and enduring
will be tter, which in turn creates a self-reinforcing loop of competition.
For leaders of manufacturing rms, traditional strategic management
theory and practice advocate avoiding the Red Queen effect by nding niche or
monopolistic positions on the tness landscape. However, isolation from
competition 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 of
development. This results in a trade-off in which those rms occupying safe
places on the tness landscape eventually suffer over time as they fall behind
those who remain in the race.
Appropriate system variety
The ability to create new manufacturing strategies and resulting
con gurations is related to a manufacturing rm’s ability to understand and
manage its system of routines and resources. Fitness landscape theory and
dynamic capability theory state that systems must recon gure themselves to
respond to the challenges and opportunities posed by the environment. This
capability to create strategic variations is dependent on the system having a
variety that matches the array of changes an environment may create (Ashby’s
law of requisite variety, Ashby (1970, p. 105)).
In terms of innovation strategies, this notion is well known and has
developed into principles such as the law of excess diversity (Allen, 2001) and
the rule of organisation slack (Nohria and Gulati, 1996). Both these principles
assert that the long-term survival of any system designed to innovate requires
more internal variety than appears requisite at any time. Appropriate system

21. IJOPM variety facilitates exploratory behaviour (Bourgeois, 1981; Sharfman et al.,
24,2 1988) and is a necessary attribute for tness and a dynamic capability.
The implication of system variety for leaders of manufacturing rms is that
they should recognise the connection and trade-off between system ef ciency
and system adaptability. Any effort to reduce system diversity and increase
system standardisation could restrict the potential for innovation. This is
144 because the evolutionary process of variation (especially blind variation)
requires excess system diversity to fuel evolutionary adaptation (David and
Rothwell, 1996). This ability to create blind variations is linked to the talent of
producing innovative strategies. This claim is supported by a study of
successful rms by Collins and Porras (1997, p. 141), who concluded:
In examining the history of visionary companies, we were struck by how often they made
some of their best moves not by detailed strategic planning, but rather by experimentation,
trial and error, opportunism and quite literally accident. What looks in hindsight like a
brilliant strategy was often the residual result of opportunistic experimentation and
purposeful accidents.
Understanding and exploring the landscape
Understanding 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 strategies,
strategy A and strategy B. The route from strategy A to strategy B is
represented by a dashed line. This route initially requires a downhill journey
that is often accompanied by a reduction in rm performance, which related to
the learning curve challenge and organisational disruption associated with the
change. With this reduction in performance, a rm often stops the strategic
change and returns to its original position on the landscape. Thus, for a
manufacturing rm to successfully explore and achieve new strategies, it must
recognise that:
.
this often involves the removal of one or more of the capabilities and
de ning routines and resources that dictate its current strategy and
position on the landscape;
. even though the landscape is posited as being static, when any rm
moves or makes a change, the topology of the landscape and associated
performance will also change.
Exploration of the landscape is a search activity and there are two basic search
strategies. The rst is a local search that enables manufacturing rms to build

22. Manufacturing
strategy
145
Figure 8.
A route or adaptive walk
between strategies
upon their current capabilities. It involves investigating those manufacturing
strategies in the immediate vicinity (the one-mutation neighbour strategies).
The second search strategy is a long distance search, i.e. looking for strategies
beyond the local area. This involves a relatively signi cant recon guration of
the 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 in
reality (Cyert and March, 1963; Nelson and Winter, 1982), because the longer
distance, 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 cant
recon guration. Studies, practice and history show that a rms’ current
strategic con guration frequently constrains a rm’s dynamic capability to
remain focused on those resources and routines which are current and familiar
to the rm.
Manufacturing strategy formulation can also involve multiple and constant
searches, as suggested by Beinhocker (1999). This approach has direct
relevance to strategy formulation as a process of organisational
resource-investment choices or options (Bowman and Hurry, 1993). However,
the capability to have options requires appropriate system variety.
Summary
This paper has reviewed, developed and synthesized a range of literature to
present a de nition and a conceptual model of manufacturing tness. It is
based on survival tness; the capability to adapt and exist, and reproductive
tness; the ability to endure and produce similar systems. These two

23. IJOPM dimensions of tness are governed by the evolutionary forces ± variation,
24,2 selection, retention and struggle.
The de nition and model offer a starting point for further research on how
factors such as landscape topology, population and rm dynamics, the type
and number of searches, and the associated costs and time to search would
affect manufacturing strategy formulation and the propositions and ideas
146 presented. To progress this work it is necessary to conduct empirical studies
that measure manufacturing tness as part of a longitudinal assessment of the
changes within and between the manufacturing rms in a de ned population.
This type of work would provide a quantitative analysis of the claim that rms
occupying a global peak on a K = 0 landscape gain bene ts from this
monopolistic position, but at the expense of maintaining and developing a
dynamic capability.
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