Exploring industry dynamics and interactions

Technological Forecasting & Social Change 80 (2013) 1147–1161

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Technological Forecasting & Social Change

Exploring industry dynamics and interactions Michèle Routley ⁎, Robert Phaal, David Probert Centre for Technology Management, Institute for Manufacturing, Department of Engineering, University of Cambridge, 17 Charles Babbage Road, Cambridge, CB3 0FS, UK

a r t i c l e

i n f o

Article history: Received 3 January 2012 Received in revised form 12 April 2012 Accepted 16 April 2012 Available online 12 May 2012 Keywords: Lifecycle Industry dynamics Technology system

a b s t r a c t Within strategic technology management and innovation, often stakeholders extrapolate past industry dynamics, trends and patterns into the future. One frequently used concept is that of ‘lifecycles’ — an analogy of a sequence of stages encountered by living organisms. Lifecycle terms – such as technology, product, industry – are frequently used interchangeably and without clear definition. Within the interdisciplinary context of technology management and forecasting, this juxtaposition of dynamics can create confusion rather than simplification. This paper explores some of the dynamics typically associated with technology-based industries, illustrated with data from the early US automotive industry. A wide range of dimensions are seen to have potential to influence the path of industry development, and technology roadmapping architecture is used to present a simplified visualisation of some of these. Stakeholders need to consider the units of analysis, causality and synchronicity of relevant different dynamics, rather than isolated lifecycles. Some graphical curves represent simple aggregation of components; other dynamics have significant impact, but incur time lags, rather than being superimposed. To optimise alignment of the important dimensions within any technology development, and for future strategy decisions, understanding these interactions is critical. © 2012 Elsevier Inc. All rights reserved.

1. Introduction Technology-based industries are considered important for economic growth [1]. There are many stakeholders who have an interest in the success of these industries, from individual entrepreneurs, firms, and industry associations through to governments seeking to optimise support mechanisms. There is a desire to reduce uncertainty and mitigate risk, to try to ensure success. The future is inherently uncertain, and often stakeholders will use trends and repetitious patterns [2] to try to predict and prepare for future events [3]. One common pattern is a ‘lifecycle’ — typically consisting of a linear sequence of phases analogous to a biological lifecycle, from birth to death. Stakeholders will try to understand their position within the analogy of a particular lifecycle, and then decide appropriate strategic actions, given this position and the predicted next lifecycle stage [4–6]. Although lifecycles are often used to assist with strategic decisions, there is considerable variance between the interpretation of different lifecycle terms, which are ill-defined [7] and often transposed [8,9], with ambiguous units of analysis [6]. Within an industrial system there are many interacting factors which may impact the lifecycle of interest [10], and yet much lifecycle analysis seems to be undertaken in isolation. There is a lack of clear understanding of how different dynamics – those forces that produce change in a system – interact within industrial systems [11]. The objective of this paper is to examine industrial dynamics as interacting components of technology-based industrial systems, exploring what is meant by different lifecycle concepts. Data from the early US automotive industry are used to illustrate the potential interactions between different factors.

⁎ Corresponding author: Tel.: +44 1223 339816; fax: +44 1223 464217. E-mail addresses: [email protected] (M. Routley), [email protected] (R. Phaal), [email protected] (D. Probert). 0040-1625/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.techfore.2012.04.015

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2. Approach This exploratory study uses a technology roadmapping (TRM) architecture [12], together with a recent framework for emerging industries, based on TRM [13], to consider the many factors of interest within a technology-based industrial system. TRM has been used at various levels as a strategic management tool for products, firms and industry sectors [14–16]. A roadmap can be used for visual communication, representing several perspectives and a large amount of information with a simplified graphical format. In this work, the roadmap architecture – depicting horizontal layers over time – has been used as a framework for considering which perspectives and industrial dynamics may be relevant to explore. The three primary layers have been characterised as value context, value capture and value creation, in line with a framework for mapping industrial emergence [13]. Each of these layers is defined and contains sub-layers (or themes) as shown in Table 1. Three of the lifecycles (amongst others) that are associated with these primary TRM layers are the industry, product and technology lifecycles, and so these are taken as a particular focus within this paper. As a visual framework, the roadmap structure allows the representation of several dynamics over a period of time, facilitating conceptual review of potential interactions. Using these layers, example data are derived from published literature on the early stage US automotive industry to explore specific interactions and possible superposition of different dynamics. Quantitative data is graphically represented, where possible, however this analysis is primarily exploratory. The automotive industry was chosen as a case study due to the availability of a significant amount of historical data relating to the industry emergence, and the usage of the automotive industry within published literature as an exemplar of many of the cycles and lifecycles indentified (e.g. [17,18]). It is acknowledged that taking this approach does not include all possible lifecycles, cycles and curves that could be considered, even within the automotive industry, nor does it take into account the wider picture of the influences other industries may have had on the industry. The intent of this paper is not an in-depth study of the automotive industry, but to use the TRM architecture to provide a pragmatic boundary for selecting relevant exemplar industry dynamics. It is also interesting to study the automotive industry, as it is an industry which is still very much ‘alive’ today, but also subject to significant new dynamics. For example, there are possibilities for a radical technology shift in the powertrain – such as a move to biofuel or electric engines – which would require significant changes in the existing industry infrastructure. This paper does not attempt to provide prescriptive definitions of the different lifecycle terms, but to raise awareness of the discrepancies of the terminologies used to date, and the need to look beyond individual lifecycles in isolation. Section 3 gives an overview of visual representation of industry dynamics and the differences seen in various lifecycle concepts, before exploring some specific lifecycles in more depth, including the range of units of analysis. Then in Section 4 we consider how the various lifecycles and curves can be combined within the TRM architecture using some early-stage US automotive industry data examples. Section 5 discusses the utility of the TRM visualisation of industry dynamics, reviewing the challenges raised in Section 3, and Section 6 contains conclusions from this work. 3. Industrial dynamics 3.1. Graphical representation There have been many publications relating to the large number of dynamics relevant to innovation systems (e.g. [18]) and technology-based industries (e.g. [19]). These range from socio-economic cycles, Kondratieff waves [20], business cycles [21], and hype curves [22] through to technology s-curves [23]. (Other reviews of lifecycles and curves are included within [24] and [25].) The various industrial dynamics which result in changes of tangible quantities, attitudes and industry structure may be represented graphically for quantification purposes or schematically, to provide visual insights [26]. Visual mapping can represent large quantities Table 1 Layers and themes within the TRM-based framework for industrial emergence. Primary layer

Sub-layers (themes)

Value context: the opportunities within the industrial landscape for creating and capturing value.

Market trends and drivers Government policy Regulation and standards Industrial dynamics and competition Customers Business models and strategies Applications, products and services Support services Sales and marketing Distribution Operations Supply networks Design Development Research Management Relationships Resources (finance, skills, infrastructure)

Value capture: the mechanisms and processes used by organisations to appropriate value through delivering new products and services.

Value creation: the competences and capabilities used by organisations to generate new products and services.

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of information in relatively little space and can illustrate several dimensions simultaneously, together with interconnections [27]. While this illustration in itself does not produce a theory, it is an intermediate step between raw data and a more abstract conceptualisation. Comparing and integrating several maps may then be used to elaborate a more general theory [27]. Industrial dynamics vary in terms of timing; both in range (duration), and frequency of occurrences; and in the level of influence within a nested hierarchy [28] – integrating several dynamics into a visual representation may make some of these aspects more apparent – graphically representing a multi-level perspective [29]. While there exists no proven mechanism for predicting the future, visualisation of potential futures across the complex industrial system, can assist with strategic decision making and current understanding [26]. Mapping out details of past dynamics can assist with identifying patterns and interrelationships across a broad range of factors that need to be included in industry analysis. Reviewing previous dynamics and interactions may provide insight, if not into future directions, at least into the future possibilities and potential mechanisms for addressing these [30].

3.2. Archetypes of visual representations of dynamics Depending on their duration ‘events’ can be represented schematically as specific points or a horizontal bar along a timeline (Fig. 1 (a)), where the vertical axis may not be used explicitly. Some events have impacts on other dynamics relevant to progression of a technology-based industry. Historical examples include a particular scientific discovery, major exhibitions, a World war, or the Great Depression. All events have a temporal dimension, and the representation as a specific point, or a line, will depend on the temporal scale being used within the mapping canvas. Other factors can be shown to change over time, and the most basic representation of these changes is a linear increase or decrease, as shown in Fig. 1 (b) and (c). In complex system environments, simple linear increases and decreases are unlikely (unless represented on a logarithmic scale or over a short period of time), but they may be used where quantification is not possible, to show a general trend schematically, such as increasing public awareness of a topic; or for companies, a projected decrease in importance of a particular market sector. The changing dynamics in an industrial system typically will have more elaborate ‘curves’, rather than simple linear progressions. These curves are the patterns shown graphically by quantified variables that change over time, but which do not necessarily repeat their pattern. Typical ‘model’ curves include (see Fig. 1 (d)–(f)): exponential or asymptotic growth/decline and s-curves [26]. These ‘model’ curves are seen more in future predictions and modelling than in long-term data from complex industry systems, where often several dynamics superimpose to provide a more complex, and irregular curve shape. ‘Fluctuations’ describe the rapid, usually unpredictable, and erratic changes overlaid on any pattern observed, and generally make observed patterns, such as firm asset growth, or product sales, more ‘noisy’ than the ideal shapes presented here. When curve patterns are seen to repeat periodically, a ‘cycle’ is the term used to represent a complete series of recurring events or phenomena. In future prediction and forecasts, cycles are commonly used to indicate the probability that history will repeat itself — is the repetitiveness that provides the utility [21]. Fig. 1 (g) shows a schematic illustration of a simple sinusoidal cycle and Fig. 1 (h) a cycle with a more complex, but still repeating, pattern. Cycles range from geoclimatic cycles of about 100 years (measured by various meterological and biological measures), through Kondratieff cycles of 50–60 years in length (measured by world economy), down to business cycles of 1–10 years (measured by GNP) [21]. Often they are represented graphically in idealised, simplified formats.

Fig. 1. Schematic visualisation of events and dynamics: (a) at a single point in time and over a certain period of time; linear trends: (b) increasing and (c) decreasing; model curves: (d) exponential (e) asymptotic and (f) s-curve; cycles: (g) simple sinusoidal and (h) more complex.

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3.3. Lifecycles as a technology-based industry concept One specific subset of cycles is ‘lifecycles’. Within technology-intensive industries, lifecycle metaphors are often used for assisting with strategic direction [8,31] — building on the theory that the patterns of evolution observed in living organisms can be applied to concepts such as industry, products and technology (see [32], for detailed discussion). Lifecycles are time-based models, breaking down the descriptions of observations into phases or stages using the lifecycle analogy [32]. Within a lifecycle (Fig. 2), there is typically emergence, development (growth), a peak (maturity), and then a fall (decline) of some chosen unit of analysis. However the sequence of phases can be as detailed as conception, birth, childhood, adolescence, adulthood, maturity, senility and death [33]. There are lifecycles for industry [24], market [7], demand [34], organisation [35], product [36], product categories, product forms, branded products, brand names [25], technology [37], technology adoption and innovation [38] and technology maturity [33], amongst many others. 3.4. Lifecycle ‘issues’ observed 3.4.1. Limitations of the lifecycle metaphor One of the difficulties in using this type of analogy for analysis is how far the lifecycle metaphor is appropriate. Challenges have been raised against the analogy, with respect to the complex nature of the evolution of technologies, products and technology-based industries. Lifecycles are said to describe a particular evolution, and do not provide details of when this will hold, and when it will not [8]; they are described as linear and simplistic, compared with the iterative and complex reality [39,40]. Industries with strong technology and innovation drivers are by no means a homogenous group, and do not present similar dynamics over time [41], therefore not all lifecycles have the same graphical shape. In contrast to lifecycles of living organisms, ‘products’ can become ‘younger’ in the lifecycle by the addition of innovative features [42]. Despite these limitations, the lifecycle metaphor continues to be used at many levels [33,43–46]. 3.4.2. Definition of terms Many publications relating to technology-based industries (for example: [8,32,37,47]) use the terms industry lifecycle (ILC), product lifecycle (PLC), and technology lifecycle (TLC) interchangeably. Klepper [9] explains that the automobile industry is often used as an exemplar of the product lifecycle — interchanging industry and product level analysis. Primeaux [7] describes the industry lifecycle and the market or industry evolution cycle as essentially the same thing, and goes on to postulate that “all firms within an industry need not be in the same stage of the industry lifecycle” (p.318), indicating the challenges of defining what phase an industry or firm is in at any point in time. Table 2 includes some examples of ILC, PLC and TLC from the literature, indicating some of the different phases. The terms ‘industry’, ‘product’ and ‘technology’ also lack standard definition, which perhaps underlies one of the difficulties in specifying each of the lifecycles. It is also not obvious which of the many parameters available should be used as the dependent variable on the vertical axis of a lifecycle graph. The horizontal axis is clearly temporal — indicated by time itself, or progression through phases, however many different units of analysis are included on the vertical axes of lifecycle curves [6]. Table 2 includes some examples. Often an industry is purely defined in terms of products (e.g. [48]), however there is certainly a need to take product generations into account [46,49]. Also, when products have close substitutes (e.g. digital cameras for film cameras) — we have to ask if this should be considered a different ‘industry’, with its own lifecycle? [36]. If the dependent variable on a lifecycle graph is the number of competitors, then will this be considered for the old product, the new product — or both? It is acknowledged that from the very emergence of an industry, its structure (i.e. those entities involved and their organisation) is likely to change [24], making the tracking of any particular parameter through time very challenging. There can also be a problem of perspective. Even taking a systems approach, boundaries will be applied and these boundaries may not be appropriate for different actors involved in an industrial system. One person's technology (say, internal combustion engine for an automotive manufacturer) is another person's product (say, an engine manufacturer); therefore systems and subsystems also require careful definition [28].

Fig. 2. Schematic lifecycle with four indicative phases.

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Table 2 Comparison of ILC, PLC and TLC concepts from selected publications. Reference

Unit of analysis

Phases

Industry lifecycle (ILC) Ayres [32] McGahan et al. [45]

N/A Sales volume

McGahan et al. [45]

Sales volume

Twiss [50]

Industry size

Infancy, childhood, adolescence, maturity, senescence Fragmentation, shakeout, maturity, decline (traditional model) Emergence, convergence, coexistence, dominance (alternative model — emerging industry replacement) Incubation, diversity, segmentation and growth, maturity, decline

Product lifecycle (PLC) Agarwal and Bayus [48] Ayres [32] Rink and Swan [36]

A statistically determined ‘index’ (values 0–1) Investment return Sales revenue

Technology lifecycle (TLC) Ansoff [34] Ayres [32] Ford and Ryan [37]

Sales N/A Penetration of technology

Nolte [33] Popper and Buskirk [51]

Utility N/A

Invention, commercialisation, firm take-off, sales take-off Basic research, applied research, development, adolescence/expansion, maturity Introduction, growth, maturity, decline

Emergence, accelerating growth, decelerating growth, maturity, decline Infancy, childhood, adolescence, maturity, senescence Technology development, technology application, application launch, application growth, technology maturity, degraded technology Conception, birth, childhood, adolescence, adulthood, maturity, old age, senility, death Cutting edge, state of the art, advanced, mainstream, mature, decline

3.4.3. Interactions and superposition While each lifecycle concept has the potential to be useful individually – when clearly applied and defined – by using a systems approach, integrating the analysis between different roadmapping layers will provide a much richer picture. Considering the different lifecycle concepts individually, it is clear that each lifecycle, and indeed any industry dynamic curve, is a complex result of many variables and interactions [6]. It is important to realise the dependence a factor has on other developments to be able to progress, as “often, adoption of one technology depends upon complementary innovations” [52, pp. 701–702]. In many cases, lifecycle analysis undertaken at one level is then aggregated into another: Menhart and Rennhak [46] explain that “if development of sales volume is analysed, the industry lifecycle is the sum of lifecycles of product generations and single products in the respective industry”. Care should be taken over this kind of application of a system hierarchy [2]. One danger of the discrepancies observed between the differing lifecycle interpretations is seen in the ‘simple’ aggregation of several curves into a new curve, not considering adequately the units of analysis, causality or synchronicity. Table 2 shows that metrics used for quantifying lifecycles are not always obvious or consistent. Geels [29] proposes that changes in socio-technological systems can arise specifically from the alignment of co-evolutionary dynamics, and so this work uses the TRM architecture to visualise the potential interactions of a number of relevant dynamics. 4. Exploring industry dynamics: examples from the US automotive value system 4.1. Value context The top layer of the TRM structure, value context, relates to market demand (pull) dynamics. The ILC is included within this, under the industrial dynamics and competition theme (Table 1). The whole layer includes a much broader range of influences, some external to the direct industry, such as market trends and government policy, which may well impact on an industry. The concept of the industry lifecycle is somewhat ambiguous, with not much known about the nature of the lifecycle and how it affects firms in a particular industry [7]. As “industries cannot, even in principle, always be defined with precision” [53, p.151], this may provide an explanation as to why the industry lifecycle concept is ambiguous. Any particular industry evolves [24], often specialised at first, but broadening over time [54]. Van de Ven [55] uses a unit of analysis called the interorganisational or industrial community — this includes not only the private firms developing similar, complementary or substitute technologies, but also all other actors in the public and private sectors who play key roles in development of an industrial infrastructure. This type of industrial systems approach [11] allows for more holistic analysis and consideration of impacts of different actors involved, but also makes the definition of boundaries more complex — defining which actors, and other factors, will be included. Twiss [50] proposes that while concepts such as market or technology s-curves may be quantified, an ILC is more conceptual and illustrative, primarily due to the difficulties in defining an appropriate industry parameter (unit of analysis), due to the number of influencing factors. Typical units of analysis for ILCs include the number of firms competing in the industry, or the sales volume for a particular industry [46]. Both of these metrics could be expected to progress through the phases of emergence, growth, slowed growth and then decline, over a period of time. Production/turnover, employment/number of employees, value added, origin of imports, structure by subsectors, rate of growth of industry sales, growth in demand, sales [24], and the number of competitors [46] have all also been used as the dependent variable representing the ILC. The rate of progression of the ILC will depend on many intervening factors, not least the type of change a particular industry is going through. McGahan [45] describes how industries generally evolve along a single type

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Fig. 3. Number of US firms involved in the emergence of the US automobile industry, from 1898 to 1927. Adapted from [59, p.324].

of change trajectory at any one time: progressive, radical, intermediating or creative. The classic industry lifecycle can be considered an appropriate metaphor for progressive and creative change, but is not helpful for radical or intermediating change. In these latter cases, a new industry is likely to emerge and replace the original industry, indicating the importance of considering inter-industry analysis as part of a holistic perspective. In general, industries comprise firms, and so “individual firm data, of course, can be summed to obtain industry data” [7]. For emerging industries, where new firms may be created to capture the value of new technologies, firm lifecycles are also an important factor. Garnsey [56] puts forward a theory of the early growth of the firm, noting that steady, rapid growth (of assets) through maturity is unusual. For industry lifecycles where the dependent variable is the number of firms, the nature of the evolution of these firms will impact on the shape of the industry lifecycle curve. The difficulties in defining the boundaries of a particular industry [53,57], and therefore knowing which firms to include in the analysis mean that any such summation is likely to be indicative, rather than absolute. It should be assumed that aggregation of the available data will provide a representative, rather than a complete picture of the industry. Examples of industry lifecycle patterns are included in Figs. 3 and 4 — plots of the number of US automobile firms and the US automobile production units, respectively. Both graphs show an emergence, growth, maturity and decline pattern. However the time period for the production chart (Fig. 4), is much longer (~100 years, compared with ~30 years) than that in the firm chart (Fig. 3). If one only looked at the industry lifecycle using the number of US firms, the data in Fig. 3 may lead to the conclusion that the industry was close to its complete decline by the end of the 1920s, having reached maturity in 1909. Looking at the production information in Fig. 4, it can be seen that the peak of US industry production did not occur until the 1970s. These dynamics – a peak in number of firms occurring before the maturity phase of production unit ILCs – are seen within a number of industries, as there is often a shake-out

Fig. 4. Number of automobiles produced in the US (5-year running average), complied from multiple firm production data, 1899–20001. 1

http://en.wikipedia.org/wiki/U.S._Automobile_Production_Figures.

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Fig. 5. Reliability and speed contests arranged by the US automotive industry between 1895 and 1912. Adapted from [62, p.372].

phenomenon, with failures and consolidation leading to a reduction in the number of firms, prior to the realisation of full economies of scale that drive mass production [48]. In fact, a review of US firm automotive data over a longer period shows continuing activity and dynamics [58], supporting the graphic of Fig. 4. Events that were particularly important to the emergence of the automotive industry were exhibitions and competitions, held in the US and in Europe. Initially stationary engines were demonstrated at exhibitions, to sell the idea to investors [60], while competitions were held, originally to establish credibility with customers, as the industry as a whole needed legitimacy [61]. These reliability competitions evolved into opportunities for automotive companies to demonstrate individual performance aspects, such as speed, to potential customers — see Fig. 5 [62]. Interacting with the automotive industry are other market trends and drivers which may influence automotive purchases. These would include factors such as the availability of complementary infrastructure — surfaced roads and fuel provision; public transport, environmental awareness and fuel prices. Other charts exist showing the effects of competing transportation mechanisms – comparing the fraction of intercity journeys undertaken by cars, buses, railway and airways [63]. The technology adoption curve [38], is often relevant within the early stages of an industry lifecycle, while different technologies compete for dominance [64]. This is seen in the automobile industry, as initially (late 1800s onwards) the new cars were aimed at a luxury market, those who wanted to race cars, or experiment with new technology [60]. In the 1920s, there were four primary reasons for purchasing automotives: transportation service, sport, personal possession and social prestige [63]. Customers can be considered in terms of geographical location, or penetration of a particular market, as shown in Fig. 6, where the penetration of the US domestic market is shown over the emergence of the automotive industry. Once a market has reached ‘saturation’ then other drivers must be found (or created) for increasing the number of purchases, either meeting new performance needs, or expansion of the market into new geographical areas.

Fig. 6. US automotive industry shipments 1898–1925. Adapted from [59, p.334].

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Although most of the original automotive inventions were implemented in Europe [63], before World War I there was a 45% import tariff on European automobiles into the USA, making European cars comparatively expensive [58]. Coupled with the fact that the US roads were generally unsurfaced at the time of the emergence of the automobile industry, therefore requiring different product characteristics to their European counterparts, the US automotive industry took on its own development path [59]. Other government policies, such as purchase of military vehicles, or making use of automotive factories for munition production during war time, have also influenced the development of the automotive industry [60]. Using a common timeline to compare several dynamics relevant to the automotive industry value context (Fig. 7), it is possible to investigate interactions and hypothesise about the causality of the different shapes seen. For example, once the races for reliability had peaked, the races for speed really took off. Around the same time, strong growth can been seen in the volume of automobiles being produced. Production volumes regress around the time of the Great Depression, when customers would not only have had difficulty raising finance for cars, but also in affording fuel for them. Fig. 7 only shows some of the relevant value context dynamics for the US automotive industry — many others, from the sub-layers in Table 1, should be considered before any conclusions about causality or synchronicity can be drawn.

4.2. Value capture The middle layer of the TRM structure encompasses the systems used by organisations to capture value within an industry. Typically this is demonstrated by the business models, applications, products and services chosen by particular organisations. The value capture dynamics will be directly influenced by an organisation's own logistics, business processes and networks. Often industries are defined by the end market they serve, and these markets are provided for by “products that are close substitutes for each other” [53, p.151]. Therefore relevant product lifecycles will impact the shape of industry lifecycles. Researchers have identified many different PLC patterns, however in Rink and Swan's review (1979), the bell curve with four stages (introduction, growth, maturity and decline) was seen to be the most widely accepted. They defined the PLC as “the unit sales curve for some product, extending from the time it is first placed on the market until it is removed” [36, p.219]. Using sales revenue as the unit of analysis can be misleading when comparing PLCs, for example, between companies, due to the accounting practices that can influence these figures, and so production units may be preferred [59]. Also, as economies of scale provide reductions in manufacturing costs, these can be passed on to customers in terms of price reductions, which impacts the shape of a PLC defined in terms of sales revenue [60]. The overall shape of both revenue and production volume curves is similar, but not identical, for the Ford Model T, shown in Fig. 8. Kotler [25] focused particularly on PLCs, from a marketing perspective, describing several variations: product ‘categories’; product ‘forms’ (which exhibit the ‘standard’ product lifecycles more faithfully); ‘branded products’; and ‘brand names’. Rink and Swan [36] point out that in empirical studies, there is confusion between what they term the product ‘class’, ‘form’ and ‘brand’. These are different levels of aggregation used in PLC studies, however often it is not clear at which level analysis has been undertaken. There appears to be some agreement across different studies that product form is the most appropriate level for PLC analysis [25,36]. Product form is understood to be a finer classification of product class (substitute products for the same customer need), i.e. ‘family cars’,

Fig. 7. A schematic combination of some value context factors relating to the early years of the US automotive industry (with data from [59]; [62]; Wikipedia2). Note various vertical scales. 2 http://en.wikipedia.org/wiki/U.S._Automobile_Production_Figures

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Fig. 8. Ford Model T: annualised revenue and production, from 1908 to 1927. Adapted from [60, p.213].

‘sports cars’, etc. rather than the more generic ‘cars’ or ‘trucks’. This still leaves the difficulty of specific definitions of product forms and what, in particular, can be considered a ‘new’ product. Products are produced by firms, and as seen in Fig. 3, the number of firms involved in the US automotive industry reached a peak and then fell, indicating that some firms either failed or moved out of the industry. For emerging industries, often new companies are set up to exploit the opportunities the new industry offers, but some of these will fail for a variety of reasons [56]. It has been noted that the failure rate of automotive companies correlates with the periods of greatest technological change [59]. Even Henry Ford, founder of one of the largest family-owned companies in the world, set up two automotive companies prior to the successful formation of the Ford Motor Company; disagreements with investors over appropriate business models were the main reason Henry Ford moved on from his original companies [60]. Ford wanted to ensure the products were of high standard, but had a longer-term business model to reduce costs. Races sponsored by the press were the mainstay of marketing at the start of the automotive industry (Fig. 5). Ford himself used races to build his own credibility, with his Grosse Point MI win in 1901 convincing investors to support his new automobile company [60]. As discussed within Section 4.1, the automobile industry initially targeted a luxury market for their very high value vehicles [60]. Ford successfully implemented mass manufacturing techniques – the moving assembly line – to drive down costs, and to make cars more affordable for the general market. Fig. 9 shows the production volumes of the Ford company between 1903 and 1930 — before and after the introduction of the moving assembly line (in 1913). Fig. 10 shows the reduction in unit price of the Ford Model T over a similar period. Indeed, Ford also doubled the wages of production staff, which affected sales of the Model T, as workers could now purchase one, with less than four months' pay [65], thus opening up a new market segment. One of the key factors in the cost reductions seen within the Ford manufacturing facility was the reduction of product options, thus streamlining the manufacturing process. Typically, over a period of time, a company will expand its product offering, building product generations from different products, with many manufacturing businesses in recent times expanding into service offerings as well

Fig. 9. Ford Motor Company automobile production figures between 1903 and 19303. 3

http://en.wikipedia.org/wiki/U.S._Automobile_Production_Figures.

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Fig. 10. Ford Model T price in US dollars between 1908 and 1926. Adapted from [60, p.213].

[44]. At an industry level, the number of makes and models available will be influenced by the number of companies in the industry, the rate of technological innovation within the industry, and the general economic conditions, amongst other factors [66]. One would expect to see very different dynamics across the many different individual manufacturing companies in the automotive industry, particularly in the early phases, which taken together would aggregate into the industry overview picture. Ford's strong ideas about products, wanting to ensure cost-effective, reliable product performance for customers, without model proliferation, meant that Ford focused on a single product line for many years, was slower than others to adopt technological changes and offer extended credit terms [60]. These led to Ford losing market share, having being the industry leader up until the mid-1920s. As an industry emerges, with increasing customers, the number and level of support services would also be expected to increase, particularly with the current trend of servitisation [44]. Mapping the number of service centres or the distribution networks would provide a visualisation of the dynamics within support services. Value capture is usefully analysed from the perspective of a single company, and so the Ford Motor Company has been taken as an example within this paper. Comparing some of the different dynamics within the value capture layer (Fig. 11), we can see the build up of assets, through the setting up of subsidiaries and the moving assembly line. Production clearly increases with the automation of

Fig. 11. A combination of some of the value capture factors relating to Ford Motor Company in the early years of the US automotive industry (with data from [60]; Wikipedia4). Note various vertical scales. 4

http://en.wikipedia.org/wiki/U.S._Automobile_Production_Figures.

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manufacturing, and the unit price of the Model T can be seen to decrease. As well as the drive towards cost reduction, automation within the US automotive industry as a whole was important due to the shortage of skilled labour between 1870 and 1940 [60]. This provided a capital intensive trajectory of change within the production technologies. Ford's determination to focus on single product lines appears to be reflected in the strong correlation between the shape of the total production curve and the curve for Model T production. As with Fig. 7, there is a need for caution before drawing conclusions about causality as the data represented is far from complete, however it shows that several data themes can be clearly represented in a simplified integrated visual format, to allow review and investigation. 4.3. Value creation The final TRM layer examines how organisations can capture value from their R&D, resources and relationships. Relevant to this, and underpinning organisations' products and services, there are technology lifecycles. Ford and Ryan [37] describe the TLC (for a major technology application) as the “equivalent to the product life cycle for an entire industry for a generic product”. They say that it should include “the proportion of the total use of the technology accounted for by the originator's technology sales and the product or, more accurately, the production life cycle of the original manufacturer” [37, p.119]. Their perspective is one of how the technology penetrates into the market, and they differentiate between major and minor technologies. There is a challenge associated with plotting TLCs; of the units provided in Table 2 – penetration, sales and utility – sales is the most quantifiable, however once a technology is embedded within a saleable entity, the measure is of a product, rather than of a technology. Others have looked at technology maturity development [33], or patent and publication levels [40,67] as units of analysis for particular technologies. Murmann and Frenken [28] extend the basic product lifecycle view to a nested hierarchy of technology cycles involving system, sub-system and component-level technology cycles. Schmoch [40] describes double-boom cycles: the idea of non-linear, recursive models replacing the former discourse on linear models for technology cycles. Design encompasses how products and services use available technologies to fulfil customer needs. This is therefore clearly related both to technology trends and lifecycles (which may be external to the industry, but used within particular subsystems), and to customer trends (Section 4.1). Nakicenovic [62] plots the trends in the US market for the reduction in the use of horses and buggies, while the volume of car production was increasing. Therefore car design had to be able to replace the functions previously performed by horses (single person traffic, and carriage pulling), and buggies (multiple person traffic). Design will be affected by an organisation's operational and manufacturing facilities. Early automobile manufacturers came from other transport industries: bicycle and carriage manufacturers. Initially they produced designs based on their previous products — hence Ford's ‘quadricycle’ and the more generic ‘horseless carriage’ terminology [60]. For an industry such as automotive, there are many different technologies used within any single product, and thus many different designs, developments and research programmes to consider, even within a single company. There are therefore likely to be hundreds of TLCs associated with the industry. A simplistic example is the engine technology. In the early days of the automotive industry, it was not clear which engine would succeed as the dominant design choice. Steam, electric and gasoline (internal combustion) engines were all contenders, with different advantages and disadvantages. While in 1900 steam engines had 40% share of the US market (electric had 38% and gasoline had 22%), by 1905, the gasoline engine had become the dominant design [60]. The primary drivers appear to have been speed performance and overall price — press reports included speed competitions and price comparisons of the new technologies with the costs of buying and maintaining a horse and carriage. However there will also have been the influence of complementary technologies, such as the development of reliable electric ignition systems. Given the long-term dominance of the internal combustion engine, the TLC is therefore likely to resemble Fig. 4 — the US automotive production volumes [6]. However the internal combustion engine technology has many uses outside automotive, including other forms of transport such as aircraft and boats. Initially companies were able to improve the performance of their engines by applying them to stationary motor applications, or other vehicles, such as boats. This provided not only technical development, but also a source of income for further automotive research. Market desire for particular performance characteristics is likely to change over an industry lifecycle. For example, due to environmental and legislative pressures, coupled with high fuel prices, much research has been directed towards reducing fuel consumption and improving the fuel efficiency of automotive vehicles, as shown by the technology s-curve in Fig. 12. S-curves often represent the performance of a particular characteristic for a technology. Industry progression typically sees a change in technology when a new technology is able to outperform the old, on a particular performance characteristic, demanded by the market [68]. Several publications look at the change in innovation over an industry's development, and often data from Abernathy et al. [58] are used to show the shift in focus from automotive product innovations to process innovations. More recently work has been published on the subsequent transfer into service innovations [44], which links with the business models, products and support services provided by a company, mentioned in Section 4.2. We may therefore expect a greater number of shorter product lifecycles at the start of an industry lifecycle, settling to longer, more stable products as the industry as a whole matures. Companies' internal management and resources not only affect their own performance, but can also impact the industry as a whole. When Ford first introduced the moving assembly line, initially this increased employee turnover. Addressing this issue, Ford doubled wages both to reduce staff turnover and continue reduction of production time. Typical dynamics relating to a firm's specific management dimension which can be tracked are firm growth curves – assets, revenues and number of staff – or indeed the firm lifecycle itself. Other dynamics and interactions can be plotted within the value creation layer — perhaps tracking the numbers of patents against the government grants offered for a particular area of research. At present there are national and multinational programmes to reduce

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Fig. 12. Average fuel economy in miles per gallon with data from the US Department of Transportation5. 5

http://www.bts.gov/publications/national_transportation_statistics/html/table_04_09.html

the environmental impact of cars — leading to an increase in research in these areas. The greater the complexity of a product/ technological system, the greater the intrusion of non-technical factors in the product's evolution. In particular, Taminiau [69] shows the interaction between the automotive and oil and gas industries, concluding that more research is needed to understand exactly how policy triggers interact with technology solutions. 5. Taking a systems perspective 5.1. Clarity of representation To explore industry dynamics, using US automotive industry examples, for clarity and ease of comprehension, we have separated the three primary TRM layers, and have not shown all possible dynamics within any one layer. Reviewing some of the themes within each layer, clearly shows the potential for interactions between the different dynamics. It is important not only to view each of these layers in isolation, but also to consider the system as a whole — often described as nested hierarchies [28]. However care is required when collating several different industry dynamics within one graphic. In the strategic business area lifecycle approach described by Ansoff [34], the demand (market), demand-technology (demand for products/services based on a particular technology) and product lifecycles can all be super-imposed, using sales as the unit of analysis against time. Here the consistency of unit of analysis allows direct aggregation, but it must be remembered that Ansoff is referring to strategic business areas within a particular organisation, and presumably the same accounting practices are used. Differences can be seen between different nations for car manufacturing industry [39] and most ILC analysis is undertaken at national levels [24]. This is increasingly inappropriate in today's global marketplace, and with multinational organisations. Some dynamics are easier to define and measure than others, and when lifecycles are being compared and consolidated, care must be taken that the unit of analysis is consistent, both in the definition of the boundaries of the level of analysis, and in the measurements made. It is quite clear from US automotive industry data that choosing number of firms or production volumes gives very different ILCs. 5.2. Interactions Obviously taking each of the major themes in isolation removes much of the system perspective [70], although it shows some of the interactions within each layer. In fact it has been impossible to keep the layers separate, mentioning the effect of competition (value context) on business strategy (value capture), and operations (value capture) affected by skill shortages (value creation). Within technology-based industries, technology is not the key success factor in itself, market conditions are equally important [54], therefore the success of the internal combustion engine (value creation) as the dominant engine technology, had as much to do with cost, as performance (value context). Useful qualitative information can be gained, by comparing the dynamics within the industrial innovation system, across many different layers [29]. Klepper [9] notes that the US automotive production figures are affected by environmental circumstances such as the Great Depression and Wars — shown within Fig. 7. A number of developments occurred later in the history of the industry that were not predicted by the PLC, including the challenge by foreign products. Therefore visibility of international influences needs to be included, unless PLCs can only be considered valid within one country, which seems unrealistic within today's global marketplace [71].

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Within the different lifecycles, several factors will accelerate or retard the rate at which an industry, product or technology proceeds. Ansoff [34, p.42] suggests that “the duration of industry lifecycles has been shrinking” due to improvements in management and the effectiveness of firms. Rycroft [47] also states that the pace of technological change is increasing, and this has the effect of shortening PLCs. Both firm-level and environmental-level factors are important at each phase [64]. The PLC is influenced by: exogenous and endogenous technical change; the rate at which firms imitate each other; the difference in the segmentation of customers; variations in initial efficiencies of firms and random factors that set the industry along a specific evolutionary path [45]. 5.3. Nested hierarchies One difficulty with the TRM visualisation of dynamics, is that ILC, PLC and TLC do not form a distinctive ‘nested’ hierarchy, as often a TLC is longer than several PLCs making use of the technology [33]. Taking the automotive industry as a clear example — internal combustion engine technology has seen many different products over the years, both at firm and industry level. So the term ‘nested hierarchy’ may not be appropriate, but the TRM framework provides an opportunity to map different dynamics across the same visual space and timeline, without requiring superposition of a single metric on quantified scales. Fig. 13 is a conceptual schematic representation of some historical and potential future automotive industry dynamics, allowing visualisation of some of the lifecycle aspects discussed in this paper. It again illustrates the difference seen in ILCs, using different units of analysis. The hypothesised change in product lifecycles can be seen as the industry matures, with products lasting longer, perhaps with their lifetime being extended by incorporation of new technology features — the technology s-curves underpinning PLCs. When the PLCs use ‘sales’ as a unit of analysis, it can be seen that the aggregation of the PLCs follows the ‘sales’ ILC in the top layer. Fig. 13 also demonstrates the fact that with a dominant design based on a particular engine technology, the TLC can encompass many PLCs. The TLCs shown are representative of the three engine technologies from the emergence of the automotive industry, indicating that although three were competing at the start, one became dominant. Due to value context forces, electric engines have seen resurgence on a couple of occasions, and could become a replacement technology, perhaps being seen as a new industry, if their performance characteristic improves over that of the internal combustion engine (shown by the final technology s-curve at a higher level). In this case the sales ILC at the top represents internal combustion engine automotives – hence a decline is shown in industry sales when the electric engine technology sales in the bottom layer overtakes the internal combustion engine – once the s-curve fuel performance parameter has improved sufficiently. This would be the transition to a new industry, triggered by a technology disruption [45]. Any graphic with curves from all of the sub-themes listed in Table 1 would be overcrowded and difficult to decipher, particularly over the 100+ year lifetime of the automotive industry, and so Fig. 13 is a very simplified schematic, for illustrative purposes only. In reality, to use the TRM framework for specific analysis, one would identify a system of interest, and a particular timeframe, then select the themes and sub-themes which are likely to have greatest relevance. However a simple, schematic chart, such as that in Fig. 13 can

Fig. 13. A conceptual visualisation of simplified key automotive industry lifecycles within a dynamic systems framework.

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be used for initial discussion and communication purposes, to explore which industry system dynamics might be relevant, by plotting conceptual curves and dynamics, and therefore establishing what real data is worth gathering. 6. Conclusions The long-standing criticisms of lifecycle models are still valid, including that they are linear and simplistic, compared with the iterative and complex reality [39,40]. However, “theories and models are always simplifications. If they were as complex as reality, they would not be useful” [72, p.21]. For this reason, the system view is an important one, and an understanding of the interactions of different relevant influencing dynamics within any particular technology-based industry is critical. It is important for those involved in technology planning and development to consider which of the many influencing dynamics are the most important within their industry, and what unit of analysis can best be used to plot these. It may be appropriate, in some cases, to track multiple metrics for the same dynamic, as this may provide a much richer picture. The TRM architecture provides a useful, structured canvas for investigation, allowing visualisation of several dynamics and lifecycles at once; a tool for investigating causality and synchronicity. In many industrial systems, rather than straightforward superposition of dynamics, there may be a time lag before effects are seen. Mapping can bring an awareness of this, and through extrapolating past dynamics into the future, can provide insight into potential future interactions and strategic implications. In line with good practice for roadmapping, the graphic should have relevant, but simplified information to be visually communicable [12]. Further work would be useful to investigate the interactions within technology-based industries to see what lessons can be learned from the past. In particular, it would be possible to explore the automotive industry historical analysis in more depth to investigate whether or not lessons can be learned from the internal combustion engine technology emergence for the emergence of new engine technologies today. Work could be undertaken to investigate the need for synchronicity between the different dynamics within an industry, and what happens within technology-based industries when particular dynamics are out of alignment. Acknowledgements Insights and information provided by Dr Eoin O'Sullivan and Dr Simon Ford have provided valuable contributions behind this paper. The research for this paper was conducted within ongoing work on the Emerging Industries Programme, financially supported by the UK Engineering and Physical Sciences Research Council (EPSRC). References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32]

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Nakicenovic, The automobile road to technological change: diffusion of the automobile as a process of technological substitution, Technol. Forecast. Soc. Change 29 (1986) 309–340. [64] F.F. Suarez, Battles for technological dominance: an integrative framework, Res. Policy 33 (2004) 271–286. [65] G.N. Georgano, Cars: Early and Vintage, 1886–1930, Grange-Universal, London, 1990. [66] G. Rosegger, R.N. Baird, Entry and exit of makes in the automobile industry, 1895–1960: an international comparison, Int. J. Manag. Sci. 15 (2) (1987) 93–102. [67] Z. Gilriches, Patent statistics as economic indicators: a survey, J. Econ. Lit. 28 (1990) 1661–1707. [68] C.M. Christensen, The Inovator's Dilemma: When New Technologies Cause Great Firms to Fail, Harvard Business School Press, 1997. [69] Y. Taminiau, Beyond known uncertainties: interventions at the fuel–engine interface, Res. Policy 35 (2006) 247–265. [70] S. Jacobsson, A. Bergek, Transforming the energy sector: the evolution of technological systems in renewable energy technology, Ind. Corp. Change 13 (5) (2004) 815–849. [71] J.W. Spencer, T.P. Murtha, S.A. Lenway, How governments matter to new industry creation, Acad. Manag. Rev. 30 (2) (2005) 321–337. [72] N. Siggelkow, Persuasion with case studies, Acad. Manag. J. 50 (1) (2007) 20–24. Michèle Routley is a Research Associate at the Centre for Technology Management, University of Cambridge, UK. Current research interests include global technology management and sourcing, and the emergence of technology-based industries. Michèle has an MSci in Physics with Electronics, a PhD in Microelectronics, an MBA in Technology Management and several years of consultancy experience in manufacturing and innovation support. Robert Phaal is a Principal Research Associate at the Centre for Technology Management, University of Cambridge, UK. He conducts research in the area of strategic technology management, with a particular interest in the areas of technology roadmapping and evaluation, emergence of technology-based industry and the development of practical management tools. Rob has a mechanical engineering background, with a PhD in computational mechanics, and industrial experience in technical consulting, contract research and software development. David Probert is Reader in Technology Management, at the Centre for Technology Management, University of Cambridge, UK. Current research interests include technology and innovation strategy, technology management processes, technology intelligence, industry and technology evolution, software sourcing and industrial sustainability. David is currently one of the five co-investigators for the EPSRC Innovative Manufacturing Research Centre, based at the Institute for Manufacturing.