Construction as a technological system of production: A life cycle approach

 Innovation and industry evolution

The stages in the life cycle of an industry typically start with first applications of a new invention by technology leaders, followed by development and refinement of products and services, before becoming a mature industry with well-understood products and practices. Mature industries are past the early growth phase, their culture of technology has stabilised and the shape of industrial structure and processes has emerged. In many cases these industries are oligopolistic, with a few specialised firms dominating market niches in the supply chain. Consolidation leads to concentration. 

The new technology that starts a cycle of industry development can be a general purpose technology (GPT) that becomes the basis of a new system of industrial production. The key feature of a GPT is ‘pervasiveness’, how it is used by other sectors in the economy and leads to ‘complementary investments and technical change in the user sections’ (Helpman and Trajtenberg 1998: 86).  The examples originally used by David (1990), and broadly followed since, were steam, electricity and information technology.  Lipsey, Carlaw and Bekar  (2005) include two organizational GPTs in their list of two dozen since 9000BCE: mass production and the factory system; and lean production and the Toyota system. It is widely believed AI is a new GPT.

Thinking about the construction industry and the production of the built environment as an evolving ‘system of production’ provides a new perspective on the context and direction of innovation and its evolution since the first industrial revolution. Hughes’ (1987) life cycle model had seven phases: invention, development, innovation, transfer, growth, competition, and consolidation. Within those seven phases of the life-cycle are two interior cycles that divide an industry’s evolution into two stages: Cycle 1 is invention, development, innovation, and transfer, Cycle 2 is growth, competition, and consolidation.

Cycle 2 focuses on innovation in production and organization, when mature technological systems emerge and construction materials like cement, concrete and glass, and components like building management systems, interior walls, plumbing fixtures, lifts and elevators have become oligopolistic industries in a mature supply chain. A mature industry produces a specific culture of technology, embodied in the firms and social institutions of the system of production, and creates the tendency for an industry to develop along defined technological trajectories unless or until deflected or disrupted by a powerful external force.

A diverse cluster of industries with deep layers of specialised firms in a dense network of producers, suppliers and materials is a ‘technological system’ (Hughes 1987: 47). Electricity grids and railways have networks, telecommunications and air traffic use interconnected nodes, postal systems use existing networks, some are geographically large, some are local, some are narrow, some broad.  

Construction innovation has been narrowly focused because construction is a mature technological system, but this is changing. With a technological trajectory based on AI and associated emerging production technologies, the commercial contracting part of the industry will adopt these technologies as they become viable. The organization and structure of the industry will then change in response to changes in relative costs as the economies of scale of digitized production technologies are realized.

AI as a new GPT may be the start of a new life cycle in building and construction technology, and may be as disruptive as steam power was in the nineteenth century to the master builders and craftsmen of the day. The organization of construction is currently centred on project managers and incremental innovation, but a transformed industry would be focused on integrators who combine site preparation with production and assembly of digitally designed and fabricated components and modules.

 

 

David, P. A. 1990, The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox‟, American Economic Review, Vol. 80, pp. 355 - 361.

Helpman. E. and Trajtenberg, J. 1998. Diffusion of General Purpose Technologies, in Helpman, E. (ed.), General Purpose Technologies and Economic Growth, Cambridge: MIT Press. 85-119.

Hughes, T. P. 1987. The evolution of large technological systems, in The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology, W. E. Bijker, T. P. Hughes, and T. J. Pinch (eds.), Cambridge, Mass.: MIT Press.

Hughes, T. P. 1989. American Genesis: A Century of Invention and Technological Enthusiasm 1870-1970, Chicago: University of Chicago Press. 

Lipsey, R. G., Carlaw, K. I. and Bekar, C. T. 2005. Economic Transformations: General Purpose Technologies and Long-term Economic Growth, Oxford: Oxford University Press.

Democratizing Our Data

A Significant Contribution to an Important Issue

 

In Democratizing Our Data, Julia Lane argues that good data are essential for democracy. She believes that public policy choices can only be made intelligently when the people making the decisions have accurate and objective statistical information to inform them of the choices they face and the results of choices they make.

“We must rethink ways to democratize data. There are successful models to follow and new legislation that can help effect change. The private sector's Data Revolution—where new types of data are collected and new measurements created by the private sector to build machine learning and artificial intelligence algorithms—can be mirrored by a public sector Data Revolution, one that is characterized by attention to counting all who should be counted, measuring what should be measured, and protecting privacy and confidentiality. Just as US private sector companies—Google, Amazon, Microsoft, Apple, and Facebook—have led the world in the use of data for profit, the US can show the world how to produce data for the public good.”

Lane’s book really only covers the US. It is very focused on the institutional problems there in chapter 3, and features a couple of good case studies on developing useful data sets from disparate sources in chapter 4. While the problems collecting and managing data for national statistics in the US is unique, broader issues around extent and quality are not. Chapter 2 addresses those issues, and looks at why measurement is difficult, and why it is hard for agencies to innovate (no incentive) and develop (no funding) new measures.  Its very much an insiders account. I thought it a big improvement on recent books on GDP etc that tend to highlight the problems, it’s a good read (and quick, at 120 pages).

There is discussion on new data sources, and how the private sector finds ways to use it. However, because public data requires confidentiality agencies need new tools and skills to be able to use it. That is chapter 5, and chapter 6 proposes a new organizational model. Lane makes a compelling argument for building a new public data system in order to safeguard privacy and improve the US government's ability to implement policy initiatives.

I suspect National  Statistical Agencies everywhere are under pressure. Lane emphasises the increasing costs and diminishing returns for surveys, the traditional source of data. However, bureaucratic inertia and vested interests, lack of funding for pilot projects, and privacy and confidentiality issues combine to make developing new sources and products difficult. How difficult in different countries I don’t know. I’d like to think most would have something based on administrative data by now, but am probably being way too optimistic.

As the cover says “A Manifesto”. For those who care about data and the statistics used for policy decisions on the economy, health, education, transport, community and social assistance and so on, this book is a must read.