What is Construction Economics?

 Construction economics investigates issues and topics associated with the construction and maintenance of the built environment by firms, industries and projects, using economic theory, concepts and analytical tools.

 Construction economics is also concerned with the macroeconomic role of the construction industry and its relationship with associated manufacturing, professional services and materials industries. 

Construction economics applies a broad range of approaches to economic aspects of the construction firms, industry, and projects. These include industry economics, industrial organization and other management studies, financial and behavioural economics, econometric analysis and modelling, legal and institutional research, and transaction cost economics.

Topics of interest in construction economics include the roles of participants and processes, productivity and value for money, environmental performance and sustainability, the delivery process and procurement, the financing, viability and competitiveness of construction firms and projects, technological and institutional development, construction statistics and measurement, international construction, regulation, and government policies affecting the industry. 

Some of the earliest construction economics publications were on developing economies, bidding strategy, input-output data, building cycles, multinational firms, market structure, firm performance, size and scope, and the role of construction in long run economic growth. Over time organizational behaviour, transaction costs, decision making under risk and uncertainty, R&D and innovation were added. Recent work has been on issues around construction statistics and data and the measurement and performance of the construction industry and construction projects. 

Over the last five decades, contributions to construction economics have come from diverse viewpoints and places. There have been contributions from economists like Patricia Hillebrandt, Paul Strassman, Graham Ive, Stephen Gruneberg, Martin Skitmore and Goran Runeson, but also from architects, quantity surveyors, sociologists and engineers like Ducio Turin, Ranko Bon, George Ofori, Jim Meikle, Graham Winch, David Gann and Lauri Koskela. Construction economics is multi-disciplinary and uses multiple models to disentangle and analyse issues associated with the construction industry in particular and the construction of the built environment more broadly.

Comparing Large and Small Construction Firms

 Output and Income for Australian construction firms 

 

Australian industry data is provided in the Australian Bureau of Statistics annual publication Australian Industry (ABS 8155), produced using a combination of the annual Economic Activity Survey and Business Activity Statement data provided by businesses to the Australian Taxation Office. The data includes all operating business entities and government owned or controlled Public Non-Financial Corporations. Australian Industry excludes the finance industry and public sector, but includes non-profits in industries like health and education and government businesses providing water, sewerage and drainage services. The selected industries included account for around two-thirds of GDP. Excluded are ANZSIC Subdivisions 62 Finance, 63 Insurance and superannuation funds, 64 Auxiliary finance and insurance services, 75 Public administration, and 76 Defence. The most recent issue is for 2018-19.

 

The analysis is based on industry value added (IVA) and industry employment. IVA is the estimate of an industry’s output and its contribution to gross domestic product (GDP), and is broadly the difference between the industry’s total income and total expenses. IVA is given in current dollars in Australian Industry. The data is presented at varying levels for industry divisions, subdivisions and classes, but unfortunately does not include the number of firms. There is, however, some firm size data. Micro firms have less than 5 employees, small firms 3-19, medium firms 20-199 and large firms more than 200 employees. 

 

Figure 1 shows large construction firms have 15% of employment, 30% of wages and salaries and 23% of output. Medium firms have 18% of employment, 27% of wages and salaries and 21% of output, and micro and small firms account for approximately 65% of employment but only 55% of output. The labour-intensive work of small firms largely explains the lack of long-run growth of productivity in construction.

 

Figure 2 shows large firms have twice the level of output and income per employee compared to small and micro firms, and medium firms nearly 50% more. There is no significant difference between micro and small firms. IVA per employee is an imperfect but useful proxy for productivity, and this shows the gap between large and medium size firms is significant. 

The relationship between firm size and IVA per employee is not surprising, large firms are typically better managed than small firms. Management is the most important determinant of the capacity and capability of construction firms, because managerial skills give a contractor greater flexibility. How firms utilise their capabilities differentiates them within a diverse, location-based production system. It is widely recognised there are differences between industries in the way that production is organized and new technology adopted, adapted and applied, but differences within industries generally get less attention. Important differences are the individual characteristics of firms such as their size, the effects of competitive dynamics, and how the adoption of new technology by one company in an industry influences the adoption of technology by other companies in that industry. For building and construction this is significant, not only because of the number of small and medium size firms, but because of the size and reach of the major firms.

 

Figures 3 and 4 show IVA and income per employee for three years respectively. The most recent 2018-19 year is representative of the industry, based on this data. Construction firms convert around a third of their income per employee into IVA per employee, however large firms have twice the income per employee. These figures identify the balance sheet effect, as firms leverage the capital on their balance sheet to maximise revenue and profits. 

Construction has a large number of small firms bidding for work in local markets with little or no control over prices. There is a diminishing number of firms that can deliver large projects in a given region or have national operations, and there are a few dozen multinational corporations in construction. Construction economics has a wide range of views on the types of markets these firms operate in and their competitive behavour. There is, however, universal agreement that construction is an industry of projects, and firms operate in markets for projects of many different types. 

 

The relationship between firm size and contract value is therefore a fundamental reality in construction, and is also the foundation of the relationship between projects and firms. A firm is a legal entity and the typical reporting period is one year. A firm’s income is the cumulative cash flow of their portfolio of projects over a year. The focus on projects and construction management in construction research obscures the role of firms as the ongoing participants in the industry. 

 

For firms in construction markets annual revenue is the aggregated income from current work, or contracts won but not completed. Construction firms and contracts range widely in duration, size and value, but the amount of work a firm can take on must be related to the capital a firm has available. This relationship between firm size and the annual value of contracts or projects undertaken is based on the assumption that construction firms seek to maximize revenue but are constrained by their working capital. In construction the contract packages reflect the complexity of work, so there is a wide range of contract sizes. Construction contracts can, therefore, be arranged based on contract size and complexity. This is a well-known and widely agreed characteristic of the industry, with the relationship first researched in the 1980s. Competing contractors’ bids were affected by the type of project and by the value range, small firms considered both contract type and size, and large firms were more successful when bidding for large contracts. Contract size and complexity are also important because the wide range of contract sizes in the construction market is the major determinant of the number of firms. In a project-based market, defined by project size and complexity, there are many standardized projects but few companies able to undertake particularly difficult projects, those large construction firms deliver large projects and/or with a high degree of complexity.

Fewer Large Contractors in Australia

Long-run Changes in the Number and Size of Firms in the Australian Construction Industry 

There have been five Construction Industry Surveys (CIS) by the Australian Bureau of Statistics (ABS), the most recent for 2011-12.  All five surveys found the construction industry is overwhelmingly made up of small firms which contribute most of the industry's output and account for almost all of the number of enterprises. Table 1 shows the breakup between contractors in Building and Engineering and the subcontractors in Construction services (which were called trades in the earlier surveys). The 2002-03 survey used different categories of businesses (not establishments) in residential, non-residential and non-building, and trade services and is not comparable with the other surveys. In 2002-03 there were 339,982 businesses of which 269,228 were trade services and 70,753 were residential, non-residential and non-building businesses.

How the size of firms is measured in the CIS has changed twice. The three surveys in 1996-97, 1988-89, and 1984-85 divided firms into three sizes: employ less than 5, employ 5-19, and employ 20 or more. The 2011-12 survey divided firms into small 0-19, medium 20-199 and large with over 200 employees. The 2002-03 survey divided firms by income and the data cannot be compared to the other surveys however, although income was used to classify firms, the 2002-03 survey produced a similar result, finding 90% of firms were small or very small. Here the 1996-97 survey and the 2011-12 survey data is presented. The breakup of firms by size is in Table 2.

In the 1996-97 survey businesses with less than five employees accounted for 94% of all businesses and over two-thirds of all employees. Less than 1% of businesses employed 20 or more. Businesses with less than five employees accounted for slightly less than half the total income and expenses, whereas businesses with employment of 20 or more accounted for almost one-third of these. The data in Table 3 is percentages, showing the importance of the 0.62% of large firms. Their 13.6% of employees earned 32.3% of salaries and wages, generated over 28% of income and nearly 25% of gross output.

The survey in 2011-12 classified firms by the number of employees into small 0-19, medium 20-199 and large with over 200. The same data for the 2011-12 survey is in Table 4. The changes between 1996 and 2012 are revealing. The total number of firms has increased marginally from 195,000 to 210,000, but the share of small firms has increased from 94% to 98% as the number of medium and large firms fell from 12,300 to less than 5,000. There was a trend with the number of medium sized firms decreasing to less than half, while slightly increasing their share of industry employment.

In 2011-12 less than 0.1% of firms were large, employing 18.6 % of the workforce, paying 32% of wages and salaries and generating 27% of industry income and 25% of output. 

 

These are remarkably similar to the 1996-97 CIS numbers, however, the 186 large firms in 2011-12 had almost the same share of employment, income and output that 1,200 firms had in 1996-97. This was a significant increase in industry concentration. In the 1996 survey the 1,200 firms employing 20 or more had a total of 66,000 employees and accounted for 13.6% of employment and 24.4% of industry output. 

 

In 2012 there were 186 firms employing 200 or more with 177,000 employees, accounting for 18.6% of employment and 25.5% of IVA. These long-run changes in industry structure can not only be the result of business failures, which are common with SMEs but less so for large firms. Instead, there has been a long wave of mergers and acquisitions reducing the number of large firms and increasing industry concentration. 

A stylized representation of construction industry firms by market type is in table 8, showing how concentrated markets can be the outcome of either firm size or specialization. Figure 5 relates market type to contract size. As a firm gets larger it takes on bigger projects and compete with fewer other firms. How construction economists sought to reconcile theoretical and conceptual models of construction firms with the messy reality of the construction industry is discussed in the next section.

Construction and Advanced Technologies

US Survey Data and the Construction Industry 

 

The previous post was on the United States Census Bureau Annual Business Survey (ABS). In 2018 the ABS included a technology module with three questions about the extent of technology use between 2015 and 2017: the availability of information in digital format (digitization), expenditure on cloud computing services, and use of a range of advanced business technologies. The first results were released in a working paper from the National Bureau of Economic Research in January. There were 583.000 responses to the survey, and two thirds of the firms employed under 10 people and were less than 20 years old.

 

The survey links technologies across firm size and age categories, as well as the co-presence patterns for the technologies at the firm level. It also identifies which technologies are in the early stages of diffusion as indicated by the rates of testing versus the rates of actual use of technologies by firms. The survey shows construction is not significantly lagging other industries in the US in digitization and use of cloud services, however it is doing less testing and development of advanced business technologies.

 

The main finding of the survey was “Despite increasingly widespread discussion in the press of machine learning, robotics, automated vehicles, natural language processing, machine vision, voice recognition and other advanced technologies, we find that their adoption rates are relatively low. Furthermore, adoption is quite skewed, with heaviest concentration among a small subset of older and larger firms. We also find that technology adoption exhibits a hierarchical pattern, with the most sophisticated technologies being present most often only when more-basic applications are as well.” 

 

 

Size and number of firms in US construction

 

The structure of an industry is the number of firms categorized by size, typically the number of employees. Firms are classified as small, medium or large, with the numbers used varying by country and industry, as the tables below show. Data on firms (often called enterprises in the statistics) is presented using the International Standard Industrial Classification. Section F in ISIC includes the complete construction of buildings (division 41), the complete construction of civil engineering works (division 42), and specialized construction activities or special trades, if carried out only as a part of the construction process (division 43). Also included is repair of buildings and engineering works. Although there are national variants on the Standard Industrial Classification format SIC codes therefore represent industries, and firms are classified (or often self-classify) to industries on the basis of common characteristics in products, services, production processes and logistics.

 

In the US the Census Bureau collects data on industries and enterprises, the latest data for 2012. The website has this notice: “Due to limited resources and competing priorities of critical programs within the Census Bureau, the Enterprise Statistics Program has been suspended.” Reflecting the scale of the American economy, the size range of firms is much greater than the EU and the largest firms much larger. Over 95 percent of US firms are small, in this case with less than 100 employees, and have on average five or six employees. However, there were 212 firms with 1,000 or more employees that had a total 630,000 employees, of which nearly 160,000 were employed by the nine largest firms. 

 

Table 1. US Construction 2012

Enterprise employment size	Number of enterprises	Sales or revenue $1,000,000	Annual payroll $1,000,000	Number of paid employees
All enterprises	581,601	1,349,346	260,606	5,006,131
Less than 100 employees	576,272	812,924	154,461	3,336,286
100 - 499 employees	4,788	226,818	46,899	817,823
500 - 999 employees	na	82,320	14,787	222,481
1,000 - 2,499 employees	141	79,475	14,968	211,141
2,500 - 4,999 employees	45	62,749	10,516	145,875
5,000 - 9,999 employees	17	38,072	7,497	113,133
10,000 employees or more	9	46,988	11,476	159,392

Source: US Census Bureau 2012, table 2; na is not available due to sampling issues. 

 

The data, which emphasises the number of firms, is deceptive because of the very large number of small firms the entire industry is often characterized as unconcentrated. Viewing the construction industry as predominantly made up of small firms supports the view of the industry as fragmented with the characteristics of perfect competition. That description is too broad, some segments are much less fragmented than others. Competition among large contractors and among specialty supplier firms is oligopolistic, while small contractors are closer to perfect competition. There are few significant barriers to entry to the construction industry for small firms, so labour-intensive subcontractors and small contractors can be assumed to operate under perfect competition. There are relatively few contractors capable of managing large projects, and the barriers to entry at this level in the form of prequalification are significant, based on track record, financial capacity and technical capability.

 

 

Technology testing and diffusion

 

The testing-versus-use rates across different technologies are used to assess which technologies are in earlier phase of diffusion, that is, where testing is high relative to use. From the survey data the Construction industry is neither a leader nor a laggard in the availability of information in digital format. Manufacturing, Information and Professional Services are the industries with the highest rate of adoption of digitization, with firm size the primary correlate of adoption. For expenditure on cloud computing services Construction is lagging, with use rates below the average and well behind Professional Services. Overall, cloud services purchases have much lower diffusion rates compared to those for digital information. On these two questions of digitization and cloud usage Construction is comparable to the Agriculture, Retail and Transport industries on the extent of adoption, which is significantly lower than the rate in Information, Professional Services and Health Care industries.

 

Where Construction is well behind is in the testing and use of a range of advanced business technologies. The butterfly chart below shows sectoral diffusion rates for all business technologies considered together. Manufacturing leads with about 15% of firms indicating use of at least one business technology, followed by Health Care (14%), Information (12%), Education (11%) and Professional Services (10%). The lowest diffusion rates for the technologies are in Construction, Agriculture, Mining and Utilities, Management and Administrative, and Finance, Insurance and Real Estate sectors. 

 

 

Figure 1: Extensive and Intensive Margin Measures of Use and Testing Rates for Business Technologies by Sector 

 

Across all AI-related technologies, the aggregate adoption rate for all firms in the economy was 6.6% meaning that approximately 1 in 16 firms in the US were utilizing some form of AI in the workplace. The AI adoption rate varies greatly by firm size. Adoption rates (defined as usage or testing) increase from 5.3% for the group of firms with the smallest number of employees to 62.5% for firms with 10,000+ employees. Scale appears to be a primary correlate of AI usage, and its use by large firms means the employment-weighted adoption rates (estimates of the fraction of workers employed by firms using the technologies for advanced business technologies) are five times higher than the firm rates (i.e. because large firms are using AI the number of employees working with AI is five time greater than the number of firms using AI). There is increasing concentration of both employment and advanced technology adoption in fewer, larger firms.

 

The analysis finds “In general, the business technologies explored in the module’s third question are more prevalent in larger and older firms. This skewness in technology prevalence suggests that these technologies may have a disproportionate economic impact despite their generally low adoption rates’ and “This may potentially have far-reaching implications on topics such as inequality, competition and the rise of “superstar” firms, especially if AI is shown to have widespread productivity benefits. If only a select group of firms are able to fully realize the benefits of AI, we can expect further divergence for the “frontier” and most productive set of firms.”

 

From table 1, in the US in 2012 there were 9 construction firms with 10,000 employees and 17 with 5-10,000 employees, employing nearly 280,000 people between them (out of 580,000 firms and 5 million employees). Although there will be small, young firms experimenting with AI and other technologies, the data suggests some of these large firms will be investing in advanced technologies like AI, robotics and augmented reality at a scale the rest of the industry cannot. This has already been seen with the use of BIM, which is spreading to smaller firms in the industry a decade after many larger firms began the process of implementation. Another example is the way some large contractors are already running their own platforms for procurement and project management, which their suppliers and subcontractors have to use. These are closed, internal platforms. However, there are also open platforms developed by digital systems integrators such as Project Frog. 

 

It seems clear that digital platforms providing building design, component and module specification, fabrication, logistics and delivery will become widely used. Platforms provide outsourced business processes, usually cheaply because they are standardized, and are available to large and small firms. Also, platforms use forms of AI to monitor and manage the data they produce, the function of intelligent machines. Examples are Linkedin (matching jobs and people), Skype (simultaneous translation of video calls), AWS and other cloud-computing providers, and marketing, legal and accounting software systems. Such cheap, outsourced, cloud-based business processes can lower fixed costs and thus firm size, because firms can focus on their core competency and purchases services as necessary as they scale, leading to more entry and more innovation. 

 

Table 2. Dimensions of Development

Dimensions	Construction and the fourth industrial revolution: Possible developments
Production of components and materials	Platforms integrate design and production with full visualisation of voice-controlled 3D models of buildings, components and location. Selection of components and modules from online design libraries, both open-source and private.  Developments in digital fabrication, design software and molecular engineering allow a range of new production technologies to spread through the industry. Economies of scale for on-site versus off-site production will determine where and what components are produced and how.
Mechanization and automation of tasks	Site workers have exoskeletons and smart helmets available.  Many on-site tasks can done by teams of robots and/or machinery and equipment, operated remotely with some autonomy. Assemblers can be designed and fabricated to install components and modules, which can be designed to be handled by assemblers.
Organization of projects	Cloud based platforms integrate delivery of the physical project with its digital model, with real-time data and monitoring of activities and tasks.  Standardized, outsourced cloud-based business processes are used, so contractors focus on integration of site work, site production and component assembly.

 

 

In the various forms that advanced technologies take on their way to the construction site, they will become central to many of the tasks and activities involved. In this, building and construction may no different from other industries and activities, however the development path in construction will be distinct and different from the path taken in other industries. This path dependence can vary not just from industry to industry, but from firm to firm as well. Because the construction industry’s technological system of production is so wide and deep this will affect a large number of firms and people, and through them the wider economy and society. Invention and innovation based around BIM, digital twins, cloud computing, digital fabrication and advanced manufacturing technology, will fundamentally affect the production system through economies of scale and scope. 

 

 

Advanced Technologies Adoption And Use By U.S. Firms: Evidence From The Annual Business Survey, by  Nikolas Zolas, Zachary Kroff, Erik Brynjolfsson, Kristina McElheran, David N. Beede, Cathy Buffington, Nathan Goldschlag, Lucia Foster and Emin Dinlersoz. 2020. National Bureau of Economic Research, Cambridge, MA Working Paper 28290 http://www.nber.org/papers/w28290

Corporate Strategy in Construction

 And how construction is different

Every so often a book comes along that reminds me of the fact that building and construction is different from other industries, and highlights some interesting points about those differences. Walter Kiechel’s Lords of Strategy was one of those experiences.

The strategists of the title were the men who founded the firms and created the modern management consultant industry. These men (and with few exception the characters in the book are all men, many of them with engineering degrees) and their ideas have fundamentally changed the world we live and work in through their effect on the corporate world, and in particular their effect on the largest global corporations that dominate the modern industrial and post-industrial economic landscape.

So this book is interesting for several reasons. The first is the story it tells about how management consulting developed from the 1960s and the manner in which it became a central player in business development. The second is that it is really a history of ideas, or from another perspective the intellectual history of an idea. The third is the way the interaction over time between the consultants with their ideas and business facing its challenges changed both.

On the face of it the story of how management consulting developed and how it became a central player in business development is not a promising topic. But this turns out to be wrong, mainly because Kiechel knows so much about his subject after a career spent observing managers of major US corporations. He gives an inside account of the birth and evolution of strategy as the dominant business paradigm of the second half of the twentieth century.

The founder of the Boston Consulting Group (BCG), Bruce Henderson, was the originator of business strategy as we know it today. His first insight was the importance of the experience curve, or the decline of unit costs as production volumes increase (also called the learning curve effect). No other idea has had such a large impact on corporate consciousness, despite its weak empirical support: “As the 1960s unfolded, fattish, complacent American companies found themselves confronted with competition from unexpected quarters – foreign manufacturers, smaller upstart enterprises in their own backyard. What was going on? What to do? The BCG had the answer to both questions in the form of the experience curve” (p. 31)

The large American industrial companies that turned to BCG were of course doomed to decline from their peak in the early 1970s. However BCG gave them the tools to fight back, mainly in the form of detailed data on their costs, capital structure, customers, competitors and market shares, data these firms had never had or needed before. This data was then integrated by BCG into a corporate strategy based on gaining market share (to drive down costs by increasing production), not maximising short-term profits. Radical stuff.

The best-known concept to come out of BCG is the growth-share matrix. This ubiquitous diagram of cash cows, dogs, stars and question marks pulled together all the elements of strategy BCG thought essential. As a single, conceptual device it was a thing of beauty that captured with brutal honesty a corporation’s situation and the decisions to be made. It also made BCG a load of money because analysing a company’s product portfolio was a lot more lucrative than drawing experience curves.

In 1973 BCG’s best salesman left to form his own company. Bill Bain wondered what happened for BCG clients after the consultant’s report was handed in, with all the insights and data it contained. Did the clients make more profits? Did anyone know? Bain and Company did not take on projects for clients, like BCG, but had an ongoing relationship, paid monthly, with only one client from an industry, or more exactly one client from a competitive set. With this approach Bain stole a march on its competitors by taking on implementation, the nuts and bolts of managing a strategy.

Bain’s slogan (its value proposition) could have been “We don’t sell advice by the hour; we sell profits at a discount.” The keys to profits were costs and processes, and Bain developed the ideas of best practice and benchmarking, and measuring the results by a company’s share price growth. If you have wondered where the modern obsession with measurable results originated, Bain and Company’s work in the 1970s and 80s would be the place to start looking.

The third global consulting firm is McKinsey and Company. The oldest of the three it was founded by an accountant in 1926, and in the 1950s and 60s had focused on helping large companies shift from a functional to the new divisional model of organisation. However in the 1970s it was in the doldrums and falling behind its competitors. The book tells the story of how Fred Gluck went from being a rocket scientist for Bell Labs to the founder of McKinsey’s strategy practice, and in the process turned it into a firm of “strategy buffs”. Indeed, by the mid-1980s McKinsey had arguably become the strategy firm, and had put the full weight of its prestige and reach behind the strategy revolution started by Bruce Henderson.

The next character introduced is Michael Porter: “who would eventually become the most famous business-school professor of all time. To get there, though, he would have to fight off academic elders who wanted to deny him a job, and then thoroughly disrupt both the curriculum and the pedagogy of the Harvard Business School.” (p. 117)

When Porter’s book Competitive Strategy came out in 1980 it may have built on the consultants’ previous work, but it also laid out the possible choices of strategy (three) more clearly than anything done before, and put strategy at the centre of both business management and business school teaching. The book is now past its sixtieth printing and business education has never been the same since. The story of Porter’s experience at Harvard is an engrossing example of overcoming entrenched conservatism in academia.

One of the criticisms of Porter’s ideas is the lack of a human element, his corporations do not appear to have people working in them. In 1982 Tom Peters and Robert Waterman’s book In Search of Excellence put that right, and went to the top of the best-seller lists. Peters had left McKinsey a few months earlier and Waterman left after the book’s success. They emphasised the centrality of people to a company’s success, although this turned out to something of a short-term victory against the prevailing management view.

Excellence also turned out to be difficult, within five years half of the 43 companies on Peters and Waterman’s list were in trouble. The wave of similar books that followed, with their stories of success, all found the same problem. Success is transitory, performance is impermanent, but corporations, especially large corporations, go on. More precisely, the human factors like norms and behaviours that make up what we call corporate culture persist. This turned the focus of attention onto the implementation of strategy, which is neither as sexy nor as stimulating as solving problems for a new client. What it did do was build client relationships and make Porter’s value chain the closest thing we have to a universal concept in business strategy.

Over the final chapters Keichel surveys a number of the key features of the contemporary corporate landscape. These include financial engineering, leverage and buyouts, the arrival and departure of core competencies and capabilities, and the impact of technological development and the internet. This is all interesting stuff, although these chapters lack the focus of the earlier part of the book, and unlike the original ideas in the strategy revolution none of these ideas are secrets because they have been extensively promoted by their authors. What they do is highlight just how influential in forming the contemporary corporate landscape the original ideas of the strategy merchants have been.

That is one of the genuinely important insights this book gives us. Many of the conventional wisdoms heard from chief executives are recycled and retreaded ideas from the early days of the strategy revolution. For better or worse, these ideas have been instrumental in the development of many modern management methods, and in doing so have directly affected the lives of millions.

Construction is different, strategy is difficult

This post started by observing how the book shows up differences between the building and construction industry and manufacturing, and later banking and finance, industries where the strategy consultants were most influential. The most significant difference is the limits to strategy in an industry based on tendering and low-price competitive bidding. For the great majority of projects price is the only strategy. There are contractors that specialise in a particular type of work, Westfield in shopping malls for example, and some have become geographically diversified through takeovers, but these are the exceptions that prove the rule that strategy is not an important factor for most contractors.

The second key difference is the lack of a meaningful experience curve effect. Costs are not closely related to volume or market share, so Bruce Henderson’s idea would not have much impact on a construction firm. Closely allied to this is the lack of opportunity and/or unwillingness of most clients to invest in developing long-term relationships with contractors and other industry suppliers.

That said, construction is a more diverse industry than most, in terms of types of projects and characteristics of firms. It may be that the technological or organisational conditions for a strategy revolution in construction have not yet been put in place, or perhaps it will require new entrants to apply new ideas about processes and production and bring innovative strategies with them into the industry.

Walter Kiechel III, The Lords of Strategy: The secret history of the new corporate world, Boston: Harvard Business Press, 2010.