There is a general consensus that since the mid-1990s the United States has enjoyed an acceleration of productivity growth from the rapid creation and widespread utilization of new information technology. Oliner and Sichel1 found that the use of information technology and the production of computers accounted for about two-thirds of the 1 percentage-point increase in annual U.S. productivity growth between the first and second halves of the 1990s. These gains in productivity are continuing despite the slowdown in growth in gross domestic product from 2000 to 2002.2 Whether productivity growth will remain high in the future is a critical question facing economic policy makers. Productivity growth is a key factor affecting the accumulation of income and wealth.
Understanding the dimensions and challenges associated with bridging design and manufacturing could generate insights into how application of information technology enhances productivity growth. Most importantly, in laying out an agenda for basic research and
development that enhances the integration of many engineering tools, that report could chart a path toward continued and perhaps even faster productivity growth in the future.
The challenge from a social science perspective is to identify the incentives and organizational structures affecting the adoption of technologies that bridge the gap between design and manufacturing. These behavioral dimensions may rise to the level of the engineering and scientific challenges that lie ahead.
Several fundamental economic questions surround the goal of bridging design and manufacturing:
x What are the costs of tool integration and how do they vary by industry?
x What are the expected benefits in terms of cost reduction and strategic advantage?
x What are the impacts for productivity growth?
x Is government-sponsored research and development necessary?
x How do organizational and management structures affect development and adoption?
The following sections address these questions and raise a number of economic issues
1 Stephen D. Oliner and Daniel E. Sichel, "The Resurgence of Growth in the Late 1990s: Is Information Technology the Story," Journal of Economic Perspectives, Vol. 14, No. 4, pp. 3-22, 2000.
2 National Research Council, New Directions in Manufacturing, Committee on New Directions in Manufacturing, The National Academies Press, Washington, D.C., 2004.
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that arise when contemplating the incentives and barriers to bridging design and manufacturing.
These issues involve questions of how firms are organized and managed and how government agencies write and manage contracts. Economic models can estimate the private and social rate of return for investments in virtual design and manufacturing tools and help understand how incentives and organizational structures affect the adoption of virtual design and manufacturing tools.
THE COST OF BRIDGING
Developing virtual computer simulation of the production process from design conception to product life and disposal would enable a firm to quickly examine multiple designs and the trade-offs between various goals, including performance, cost, and environmental impacts.
However, developing this capability in some cases may be very costly.
These capabilities can be achieved either by contracting with an outside vendor to produce the system or by developing the integrated set of design tools in-house. Both choices involve opportunity costs. While the outside vendor may deliver at a lower cost, the firm or government agency loses the ability to retain any strategic benefits from integration, such as more comprehensive knowledge of the design process and how it can be developed for new systems. Developing these capabilities within the firm involves a reallocation of engineering and software development teams that may involve adjustment costs in the form of reduced output or reduced productivity as the system is developed.
Another consideration is whether firms consider tool integration as an investment activity amortized over time or whether they treat these activities as regular production activity. As illustrated in the case of integrated design for the unmanned underwater vehicle (UUV), (see Box 3-2), the development cost of the ARL design tools was funded under a defense contract with the U.S. Navy. In this case, the integrated design tool, which is essentially a capital good because it can be reused to generate services over time, was developed as the design services were delivered. Private firms may view the cost of tool development and integration somewhat differently, placing these efforts in competition with other capital investment projects. Of course, costs are only one side of the equation.
IDENTIFYING THE EXPECTED BENEFITS
Bridging design and manufacturing requires the integration of various engineering tools developed for several key elements of the overall process as shown in Figure 3-1. This integration is an investment that involves up-front expenditures on computer resources and labor that generates a flow of expected benefits over time.
These benefits, however, may be highly uncertain due to the rapidly evolving nature of systems integration software technology. This uncertainty may induce firms to highly value the option of adopting a wait-and-see approach, holding off on investing until this uncertainty is reduced or until the expected benefits are substantially larger than expected project costs. On the other hand, early adopters of systems integration technology may be able to exercise valuable options in the future, such as the ability to apply the new methods to gain competitive advantage in the marketplace. Linking various engineering tools may enable the firm to more quickly develop products at lower cost. Identifying these real options associated with
investments to bridge design and manufacturing is a key challenge.
Another issue is whether there are substantial degrees of freedom in the design process or whether regulatory or industry standards lock companies into a rather narrowly confined design set. If so, firms in certain industries may have little incentive to develop a sophisticated design process that utilizes computer simulation. These constraints may contribute to a reluctance of firms to standardize and integrate design tools because the returns from such
efforts are diminished.
Where the returns are high, development of integrated tool sets is already taking place.
The aerospace and automotive industries invest heavily in design tools, both commercial and homegrown, because of the increased speed and accuracy of the resulting designs, the ability to design manufacturing systems faster, and the huge cost of a mistake. Here cost avoidance, such as redoing a factory, being late to market, or having high warranty costs, is the issue.
IMPACTS ON PRODUCTIVITY GROWTH
Scholars are just beginning to understand how information technology affects productivity growth. Feldstein3 observes that U.S. productivity gains made possible by information
technology have been concentrated in white-collar jobs, including management, sales,
purchasing, design, accounting, and other nonproduction activities that collectively account for most jobs, even in manufacturing. In contrast, Europe and Japan have not witnessed a similar surge in productivity. Feldstein speculates that incentives, work rules, and other institutional constraints may have slowed the adoption and flexible, innovative application of information technology in Europe and Japan. Well-defined case studies are needed in order to examine how incentives affect the creation and adoption of technologies that lead to a more integrated design process.
STRATEGIC ISSUES
Technologies that bridge design and manufacturing will no doubt require highly trained engineers who possess knowledge from several different areas. Attracting people to this emerging field will be an important challenge, especially in light of declining enrollments among domestic students in engineering schools across the nation. This diminished supply of domestic engineers to develop bridging technologies could have national security implications. In recent years, an influx of foreign students has filled this shortage, but with this development comes the risk that these professionals will transfer this knowledge to their home countries. This erosion of the manufacturing base could have a negative impact on innovation and on national
competitiveness.
UNDERSTANDING THE ROLE OF GOVERNMENT
Bridging the gap between design and manufacturing may be too complex and costly for any lone firm to accomplish. The benefits of integration may be difficult to identify, much less quantify. Moreover, if the methods and technologies for bridging design and manufacturing are shared or intended to be made widely available in an open environment, firms may be unwilling to invest because they cannot benefit from these investments in the marketplace. The
responsibility for the development and advocacy of such an approach comes to rest on a central entity, such as government or an industry trade association, that is willing to develop institutional arrangements to foster this development. Of course, this assumes that the development track is known.
INSTITUTIONAL STRUCTURES
Firms deal with complex problems that often defy highly integrated centralized control.
Instead, firms often establish certain overall design parameters and then work on specific
3 Martin S. Feldstein "Why Is Productivity Growing Faster?" Journal of Policy Modeling, Vol. 25, No. 4, pp. 445-451, 2003.
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optimization problems, such as selection of materials, control systems, and manufacturing methods. This is the strategy that has influenced how firms have developed their organizational structures for product design. The premise of this report suggests that current organizational structures need to be made more efficient.
The design process to date almost invariably involves sequential optimization within units.
For example, materials selection is not fully integrated into the design process. The shape of a product may restrict material choices. Multiattribute optimization that involves many design choices, constraints, materials, and choices may be possible given advances in computer technology. Another fundamental problem is the choice of the objective function. What is the goal? Minimize cost given certain performance constraints? What is the utility function of the design process in terms of trade-offs between cost, fatigue life, flexibility, and other attributes?
There are management science tools available to optimize systems with many process and product choices given constraints, technical parameters, and multiple objectives. Ultimately the objective function involves an articulation of the functionality of the product. After all of these individual and often uncoordinated decisions are made, the overall system still may not be optimized.
The gains from more integrated optimization strategies need to be identified and quantified in order to encourage firms to rethink their organizational structures.
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