Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 11
2 Current Practices Overview Modern information technologies had their beginnings at the dawn of the computer age with the application of computer technology to large problems. This process was driven, in part, by the need to solve large, complex engineering problems associated with the development of military systems. The fruits of this labor were subsequently applied to nonmilitary applications, resulting in computational techniques that are now used for modeling weather, aircraft aerodynamics, and many other types of engineering and scientific systems. One of the objectives of this study is to define how the current state of practice (i.e., operational engineering systems) might evolve as increasingly capable AEE technologies and systems are developed and deployed. The committee examined the current state of the art (i.e., AEE technologies as they exist in research and testing laboratories) for guidance in determining the future direction and capabilities of operational engineering environments. An effective design process must balance many different factors, such as customer requirements, performance, cost, safety, system integration, manufacturability, operability, reliability, and maintainability. Software relevant to AEEs, however, has been developed as a collection of individual "tools" with little or no coupling among them. Tool integration is an area of active research in academia, industry, and government, but practical, broadly applicable solutions are not yet available for operational use. This lack of interoperability inhibits the use of traditional tools in AEEs, which by their nature require a high degree of integration. Improving the interoperability of software tools has been slow because of the cost of solving this complex problem, uncertainties about the return on investment, and the psychological and social dynamics of organizations. With currently available engineering methods, many tests and analyses can be conducted using simulations instead of physical models. For example, Boeing successfully used a digital (computer-generated) mock-up of the 777 instead of building a full-scale mock-up prior to production. In addition, most certification requirements are satisfied using design analyses instead of physical tests. However, even more capable systems, such as AEEs, would improve both the accuracy of simulations, especially at the system level, and the confidence that senior managers place in those simulations. For example, Boeing uses wind-tunnel tests—not computational fluid dynamics—for final sizing of aircraft structural members. Boeing also uses physical testing as part of the certification process for the landing gear, even though the Federal Aviation Administration allows a purely analytical approach. Current attempts to implement AEE technologies often do not adequately consider cultural and social aspects of organizations, even though doing so may be critical to success. A recent National Research Council workshop on the economic and social impacts of information technology noted that information technologies rarely have consistent effects on the performance of groups or organizations, largely because outcomes are highly conditioned by the social and behavioral characteristics of the environments in which they are implemented (NRC, 1998). For example, the R&D headquarters of a global pharmaceutical firm introduced a groupware tool to facilitate the sharing of early experimental results among researchers as part of a major effort to reduce R&D cycle time (Ciborra and Patriotta, 1996). The intent was to enable researchers to capitalize quickly on successful breakthroughs and to avoid repeating others' failed trials. "Get it right the first time" was the slogan. The groupware was rarely used, however, because researchers had no incentive to put new findings into a shared database where others might use them to "get it right" first, nor did they have any incentive to disclose their failures. To stimulate use of the groupware, management announced a policy of taking contributions to the shared knowledge base into account in performance reviews. The result was a sharp increase in usage, but for the most part the contributions were neither timely nor valuable.
OCR for page 12
Early work by Grudin (1988) demonstrated that even a straightforward distributed tool like group scheduling may not be successful if it benefits some individuals (e.g., managers with secretaries who keep their calendars) more than others (e.g., professionals who do not have personal secretarial support). In contrast, group decision-support technology introduced in the headquarters of an international financial organization seemed to yield significant performance improvements because it equalized roles in the decision-making process (Bikson, 1996). Although not all attempts at implementation are successful, the clear trend is toward increased use of new information management and engineering design tools. In the United States, the federal government funds most R&D for computing technologies relevant to AEEs. This R&D addresses a wide spectrum of information technologies, but only up to the test bed level of implementation. Industrial R&D has focused on the evolution of existing engineering practices that are mature and low risk. To illustrate the current state of practice, the following sections summarize key aspects of several ongoing efforts to develop and implement AEEs by Ford, Boeing Commercial Airplane Group, Deneb Electronics, NASA, the U.S. Department of Defense, the National Science Foundation, the U.S. Department of Energy, and interorganizational task groups. Ford A major design challenge faced by product development teams at companies like Ford is to avoid unintentionally establishing top-level program objectives that are incompatible with each other. For example, a new product development effort might accept the challenge of meeting specific goals related to vehicle performance, retooling costs, and reliability, only to discover later that the performance and reliability goals cannot be achieved without exceeding the allowable budget for retooling costs. Goals can be adjusted at that point, but a large number of engineering changes must be made that would not have been necessary if the original program objectives had been more realistic. In the traditional vehicle design process, a top-level team meets weekly to discuss issues, disperses to conduct discipline-specific investigations of particular issues using support staff, and then reconvenes to discuss the results of the investigations. Ford's vision for the future is to have a small group meet continuously, using quick turnaround processes to investigate and resolve issues on a daily basis. This approach would greatly reduce the duration and cost of vehicle programs. Ford makes extensive use of computer-aided design (CAD), computer-aided engineering (CAE), and computer-aided manufacturing (CAM) tools. To facilitate data management and enhance overall effectiveness, Ford decided in 1995 to limit the total number of CAD, CAM, and CAE tools and to buy commercial off-the-shelf tools whenever possible to reduce its reliance on internally developed tools. Ford also decided to standardize its design processes by using one CAD tool, I-DEAS.1 The selection of I-DEAS was based as much on the capabilities of the vendor, Structural Dynamics Research Corporation, as on the particular qualities of I-DEAS as it then existed. Ford also hired Structural Dynamics as its tools integrator (to integrate I-DEAS with other tools created by Structural Dynamics and other vendors) and adopted Metaphase, another Structural Dynamics product, as its product information management tool. Ford decided to migrate from an environment with many different CAD systems to a single CAD tool over a period of five years, which the company considered a very aggressive goal. Ford's engineering organization is product-centered, and the conversion to I-DEAS is taking place on a vehicle program-by-vehicle program basis. However, some vehicle systems, such as the power train, are common to many different vehicles. This created complications when some vehicle programs (including the power train) were converted to I-DEAS while other programs using the same power train were still using old tools. Ford has partly centralized its management of engineering tools to facilitate the documentation and distribution of tools throughout the company and to eliminate marginal tools. Periodically, inventories are taken to identify new tools that have been developed in-house or purchased from outside sources. These tools are evaluated and, if not needed, they are purged. This is a difficult cultural process because people are often reluctant to give up familiar tools. Ford is increasingly using a digital mock-up to guide its entire design, engineering, and manufacturing process. In some cases, Ford has been able to assess designs and release components and systems for production without having to fabricate and test prototypes. Ford is also moving toward the use of "digital factories" to assess manufacturing processes before factories are configured for the launch of new products. CAD/CAM/CAE staff at Ford are collocated with other staff assigned to interdisciplinary product teams for design and development. Each team decides what the CAD/CAM/CAE staff will work on; central CAD/CAM/CAE management provides guidance on how tasks will be executed. For various reasons, thousands of design changes are made during the product development cycle for a new vehicle. Analytically assessing how changes individually and collectively impact total vehicle performance is difficult, and performance problems that occur infrequently may not 1 The name I-DEAS originated as an acronym for Integrated Design Engineering Analysis Software. I-DEAS is a registered trademark of Structural Dynamics Research Corporation. The committee did not conduct a comparative analysis of the engineering practices or tools used by specific organizations. The National Research Council does not endorse the use of any particular software tools or vendors.
OCR for page 13
show up in the relatively limited number of production-representative prototypes that can be tested. These problems eventually surface as warranty claims, which adds to the total cost of the program. Boeing Commercial Airplane Group Boeing implemented many new processes for the 777 airplane, with the goal of improving quality and reducing development cost and time. New processes included design-build teams, digital product definition of parts and tools, digital preassembly, concurrent product definition, and the use of a single CAD tool (CATIA).2 However, Boeing has not yet fully implemented concurrent product definition because subsystems with long manufacturing lead times must be designed much sooner than other subsystems. Designers of subsystems with short lead times are reluctant to finalize their designs sooner than necessary just to be compatible with the schedule of long-lead time subsystems. In a large organization like Boeing, coordinating engineering methods and practices is very difficult. In addition, because Boeing products are dispersed worldwide, Boeing encounters many cultural barriers. The 777 design process involved 4,500 engineers, about 200 design-build teams, six design partners, 3 million parts, two versions of CATIA, more than 350 Boeing-developed application programs, and more than 150,000 CATIA models. Because of the huge investment required to implement the new engineering processes used with the 777, the new processes did not reduce development costs compared to traditional methods. The 777 has demonstrated improved reliability and availability compared to previous new aircraft, but those improvements resulted from a number of factors, and it is impossible to isolate the effect of improved engineering processes. The difficulty of unambiguously identifying the economic savings and product improvements resulting from the implementation of AEE technologies is not unique to Boeing. The 737-X started out as a relatively minor design upgrade but ended up with about 90 percent new design. The 737-X design process was a modified version of the 777 process; changes were made based on lessons learned from the 777 program. For example, the digital design process used for the 777 was focused on the early steps of the product development cycle, such as requirements analysis. Because most of Boeing's costs are associated with manufacturing, the 737-X process focused more on digital manufacturing, interference management, and other activities that could improve the manufacturing process. To reduce the cycle time for new airplane development and improve its overall competitiveness, Boeing continues to work with its software vendors to improve engineering processes. Areas of current interest include the development and application of knowledge bases and virtual product and process models. Because Boeing is such a large user of CATIA, it has been able to influence the evolution of CATIA and associated tools. For example, Dassault Systèmes purchased Deneb Robotics, a software company that specializes in digital manufacturing, to improve CATIA's ability to address Boeing's manufacturing concerns. Deneb Robotics Deneb Robotics, Inc., a subsidiary of Dassault Systèmes, has distinguished itself as a provider of digital manufacturing software. Deneb products are designed for integration with major CAD programs, such as I-DEAS, CATIA, Unigraphics,3 and Pro/ENGINEER.4 A customized set of interfaces is needed for each CAD program. Creating the interface capability can be a labor-intensive job for Deneb product developers, and using the interface capability, which requires data reduction in preparation for simulation, has been a labor-intensive job for users. As products are updated, however, the interfaces are becoming more automated, and the increasing speed of computers is reducing the degree of required data reduction. Deneb offers a suite of tools that can be used to design factory layouts for maximum throughput. These tools can also be used to include manufacturing and maintenance considerations throughout the product and process development cycle. This allows system designers to avoid problems in the manufacture, assembly, and maintenance that traditional methods often do not identify until a physical prototype has been fabricated and tested. For example, one tool emulates machine tools, enabling controllers to visualize, analyze, and validate that new control programs developed to manufacture specific parts will operate as expected. Parts can be machined in a virtual environment and then evaluated to determine if they meet the accuracy specifications required by the part design. Another tool provides a three-dimensional, interactive simulation environment for visualizing and analyzing human motions required in the workplace to determine the effects of reaching, lifting, posture, cycle time, visibility, and motion for a range of body types. The resulting data can then be factored into the design of products, processes, and maintenance procedures. In addition to internally funded product development, Deneb also participates with manufacturing companies in several government-sponsored R&D projects. For example, the Defense Advanced Research Projects Agency (DARPA) is funding Deneb and Raytheon Electronic Systems to develop tools that can use models of products and manufacturing facilities to generate and execute manufacturing 2 The name CATIA originated as an acronym for Computer-Aided Three-Dimensional Interactive Application. CATIA is a registered trademark of Dassault Systèmes. 3 Unigraphics is a registered trademark of Unigraphics Solutions, Inc. 4 Pro/ENGINEER is a registered trademark of the Parametric Technology Corporation.
OCR for page 14
simulations automatically. DARPA also funded a portion of Deneb's development of technologies associated with virtual prototyping, virtual reality, ergonomic analysis, high-level architectures,5 and web browsers through multiple programs with the Electric Boat Division of General Dynamics. In addition, the Air Force funded development of Deneb's common-object request broker architecture (CORBA)6 capabilities through the Simulation, Assessment, and Validation Environment (SAVE) project with Lockheed Martin, which is now being implemented as a pilot project with both the Boeing and Lockheed Martin teams involved in the Joint Strike Fighter Program. National Aeronautics and Space Administration Like many other large research and technology organizations, the most common forms of communications used by NASA rely on viewgraphs, paper, telephones, and email. Video-conference facilities enable real-time personal interactions, and desktop computer networks enable the electronic transfer of information between compatible systems and tools. But a broad spectrum of engineering analysis tools can neither communicate electronically nor interact effectively with each other. The NASA administrator has stated that NASA must do more than update its engineering tools to keep pace with advanced scientific and engineering knowledge—it must fundamentally change its engineering culture. Accordingly, NASA is instituting the Intelligent Synthesis Environment (ISE) functional initiative to develop AEE technologies and systems. The ISE initiative is focused on integrating widely distributed science, technology, and engineering teams and enabling them to create innovative, affordable products rapidly. The ISE initiative, which is targeted at both science and engineering applications, has five elements: Rapid Synthesis and Simulation Tools Cost and Risk Management Technology Life-Cycle Integration and Validation Collaborative Engineering Environment Revolutionize Cultural Change, Training, and Education In the near term, NASA's Collaborative Engineering Environment element is trying to implement a state-of-the-art, multidisciplinary, integrated design and analysis capability to enable teaming of NASA personnel located at geographically dispersed sites. This program includes building collaborative engineering centers at each NASA Center7 and uses commercial off-the-shelf technology as much as possible. The current design for the collaborative engineering centers provides audio, video, and data conferencing using video projectors, smart-boards, video scan converters, remote control systems, scanners, and document cameras. Additional capabilities are being installed in some collaborative engineering centers. For example, specialized graphics hardware is being integrated with existing video projectors to provide an immersive environment and virtual-reality conferencing. In some cases, the utility of the collaborative engineering centers has prompted individual Centers to procure additional facilities at their own expense. For example, Kennedy Space Center is installing six collaborative engineering centers. Standardized, simplified, pre-engineered procurement has proven to be an important factor in the proliferation of these facilities because it makes it much easier for Centers to acquire additional facilities (compared to the effort it would take to design and install such facilities as separate procurements). Even so, the incorporation of AEE technologies into the daily work of NASA personnel has not yet spread broadly across Center organizations and programs. In some cases, AEE technologies seem to be spreading primarily through informal, personal contacts by midlevel managers rather than as a result of implementation plans approved by high-level Center managers. The Collaborative Engineering Environment element of the ISE functional initiative is using an evolutionary approach to deploy AEE technology and improve NASA's near-term capabilities. Plans for all five elements of the ISE initiative include R&D focused on long-term, revolutionary improvements. The five-year objectives and associated metrics proposed for each element are listed in Table 2-1. After the objectives in Table 2-1 were established, the resources allocated to the ISE functional initiative in federal budget guidelines were reduced by about one-third. ISE program managers intend to revise the ISE objectives to align them with these guidelines. The objectives will probably remain the same, but the metrics will change. In addition, ISE managers are negotiating partnerships with personnel from other NASA offices with the hope that the original objectives might still be achieved. 5 High-level architecture, which is commonly referred to by the acronym HLA, is an emerging technology for linking geographically dispersed simulations of various types to create realistic, virtual environments for highly interactive simulations. 6 CORBA is an architecture and specification for creating, distributing, and managing distributed program objects in a network. It allows programs developed by different vendors and operating at different locations to communicate in a network through an "interface broker." Object-oriented programming focuses on objects that must be manipulated rather than the logic required to manipulate them. Examples of objects include human beings (who can be identified by name and address) and structures (which can be defined in terms of properties and characteristics). 7 In this report, Center (with a capital C) refers to a NASA field Center, such as Johnson Space Center or Langley Research Center; center (with a lower case c) refers to other types of centers, such as collaborative engineering centers or NASA centers of excellence.
OCR for page 15
Table 2-1 Five-Year Objectives and Associated Metrics for Each Element of NASA's ISE Functional Initiative Rapid Synthesis and Simulation Tools • Objective: Develop advanced design and analysis tools. • Metrics —Reduce design and mission development time by 50 percent. —Reduce design cycle testing by 75 percent. —Reduce costs related to redesign and rework by 75 percent. Cost and Risk Management Technology • Objective: Improve cost and risk management capability. • Metrics —Develop capability to predict mission life-cycle cost to within 10 percent. —Develop capability to predict quantified mission life-cycle risks with 95 percent confidence. Life-Cycle Integration and Validation • Objective: Streamline mission life-cycle integration. • Metrics —Increase science return per mission dollar by an order of magnitude. —Develop approaches to reduce mission risks by two orders of magnitude. —Reduce mission development costs by an order of magnitude while retaining appropriate levels of science return. —Develop design processes that use trade-off analyses involving mission life-cycle cost, risk, and performance to identify and achieve realistic goals in each of these areas. Collaborative Engineering Environment • Objective: Revolutionize engineering and science practice in NASA enterprises. • Metrics —Demonstrate, in practice, reduction of mission development time to 18 months. —Reduce technology insertion time, risk, and costs by an order of magnitude. —Reduce by 80 percent the workforce required to support mission operations. Revolutionize Cultural Change, Training, and Education • Objective: Revolutionize the engineering and science culture to enhance the creative process. • Metrics —Enhance and augment practical experience of new engineering graduates by 50 percent. —Eliminate technical obsolescence of the workforce through education and training. —Remove cultural management barriers. Source: Malone, 1998. In addition to the ISE functional initiative, NASA is sponsoring the Intelligent Systems Program as a separate, though complementary, effort to develop information technologies with application to AEEs. The Intelligent Systems Program has four elements: Automated Reasoning Human-Centered Computing Intelligent Systems for Data Understanding Revolutionary Computing AEE technologies are multidisciplinary in nature, can be used in a wide variety of applications, and are relatively new. As a result, large organizations often have a difficult time keeping track of and coordinating efforts to develop or apply AEE technologies and processes. In fact, the top-level requirements for the ISE functional initiative include execution of a national program that involves partnerships between NASA and other government agencies, industry, and academia. However, in addition to the ISE initiative and the Intelligent Systems Program, many other NASA programs sponsor research and application projects involving AEE tools and systems. In some cases, these projects seem to have been initiated in response to local problems or opportunities and do not appear to be coordinated with, or to take advantage of, AEE development efforts by other NASA programs, other government agencies (see below), or industry. Kennedy Space Center is building a virtual shuttle operations model as a ground processing aid to support space station missions. Ground processing aids such as this enable first-time work to be conducted in a virtual environment instead of the real environment. This reduces the need for mock-ups and allows real work to be done by real facilities and planning work to be done by virtual facilities. Aids like the shuttle operations model can also be used to brief personnel prior to operations in the real environment. Kennedy chose to develop its shuttle operations model as an in-house program instead of using commercially available software. The model is currently being used to conduct real-time "what-if" assessments of how to move and manipulate equipment within the Space Station Processing Facility, to develop and validate procedures, and to support the development of government-supplied equipment for the shuttle and space station. Kennedy intends to enhance the system by adding capabilities for human-factors assessments, thermal management (to predict temperature changes), calculation of equipment center of gravity (to track the effect of changes in mass), calculation of distances between any two points, enhanced proximity and collision avoidance (to validate that planned operations will avoid equipment collisions), and dual-user capability (to allow simultaneous, interactive manipulation of the virtual environment by two users). Many of these capabilities already exist in similar, commercially available software. U.S. Department of Defense DARPA has funded a number of R&D projects related to AEE technologies and processes. For example, the Simulation Based Design Initiative is developing open, scalable systems to support distributed concurrent engineering using
OCR for page 16
virtual prototypes, virtual environments, and shared product models. Department of Defense laboratories and contractors are also investigating simulation-based acquisition to provide government and industry personnel with collaborative simulation technology integrated across the entire acquisition process. Specific goals are listed below: Substantially reduce the time, resources, and risk associated with acquisition. Increase the quality, military worth, and supportability of fielded systems while reducing life-cycle costs. Enable integrated product and process development (IPPD) across the entire acquisition life cycle. The Joint Simulation Based Acquisition Task Force used quality function deployment (QFD)8 to create a prioritized list of 34 actions for advancing simulation-based acquisition. The 10 highest priority items are listed below (MSOSA, 1998): 1. Implement appropriate collaborative environments. 2. Define, adopt, and develop relevant standard data interchange formats for the simulation-based acquisition architecture. 3. Establish a concept of operations for using distributed product descriptions throughout the acquisition life cycle. 4. Establish a process for populating and managing an on-line repository for use by the Department of Defense and industry. 5. Define and develop "reference" systems and a technical architecture for implementing a collaborative environment. 6. Implement technical mechanisms to protect proprietary and classified information. 7. Identify and provide core funding support for simulation-based acquisition. 8. Establish consistent multilevel modeling and simulation frameworks. 9. Establish a process for verification, validation, accreditation, and certification for determining authorities for models, simulations, and data. 10. Establish service/agency ownership authority for models, simulations, tools, and data in the simulation-based acquisition systems architecture. Simulation-based acquisition is being developed and prototyped by facilities such as the Navy's Acquisition Center of Excellence. Also on behalf of the Navy, the Electric Boat Division of General Dynamics has assembled a team of hardware, software, and modeling companies to develop a system that includes virtual environments and anthropomorphic simulations for the design of new submarines. Similarly, the Air Force is exploring the use of AEE technologies for the Joint Strike Fighter Program being conducted by Lockheed Martin and Boeing. The Fast Track Virtual Manufacturing System and the SAVE project are being used to evaluate cost, schedule, and risk factors of alternative approaches to manufacturing specific items. These projects include feature-based design, integrated analysis, feature-based machining, assembly simulation, and process-flow simulation. The Joint Strike Fighter Program projects that these tools and processes could reduce total life-cycle costs for the joint strike fighter by 2 to 3 percent, which could result in savings on the order of $3 billion. Both the Navy and Air Force programs use commercial software packages. National Science Foundation The National Science Foundation (NSF) has funded a great deal of U.S. computer science research related to AEEs. Since 1990, NSF has funded half a dozen projects to explore collaboratory technology. A National Research Council study in 1993 coined the term "collaboratory" by merging the words "collaboration" and "laboratory." That study defined a collaboratory as a ". . . center without walls," in which the nation's researchers can perform their research without regard to geographical location—interacting with colleagues, accessing instrumentation, sharing data and computational resources, and accessing information in digital libraries (NRC, 1993). The same report suggested that . . . the fusion of computers and electronic communications has the potential to dramatically enhance the output and productivity of U.S. researchers. A major step toward realizing that potential can come from combining the interests of the scientific community at large with those of the computer science and engineering community to create integrated, tool-oriented computing and communications systems to support scientific collaboration (NRC, 1993). NSF is currently sponsoring cross-disciplinary research as part of its knowledge and distributed intelligence initiative. This is an ambitious effort that . . . aims to achieve, across the scientific and engineering communities, the next generation of human capability to generate, model, and represent complex and cross-disciplinary scientific data . . .; to transform this information into knowledge by combining and analyzing it in new ways; to deepen the understanding of learning and intelligence in natural and artificial systems; to explore the cognitive, ethical, educational, legal, and social implications of new types of learning, knowledge, and interactivity; and to collaborate in sharing knowledge and working together interactively (NSF, 1999). 8 QFD is a formal process of mapping system components and characteristics against program goals.
OCR for page 17
Table 2-2 Implementations of Collaborative Environments for Various Scientific and Engineering Purposes Project Field Internet Address (as of January 1999) Remote Experiment Environment fusion www.FusionScience.ORG/collab/REE/ DCEEa physics www-itg.lbl.gov/DCEEpage/DCEE_Overview.html DOE2000 Projects physics www-unix.mcs.anl.gov/DOE2000/ ACSIb physics www.llnl.gov/asci/ SPARCc space physics www.crew.umich.edu/UARC/ BioMOOd biology bioinfo.weizmann.ac.il:8888/ Microscopic Digital Anatomy biology www-ncmir.ucsd.edu/CMDA/ InterMed medicine smi-web.stanford.edu/projects/intermed-web/ Diesel Collaboratory combustion www-collab.ca.sandia.gov/Diesel/ui.new/ EMSL Collaboratorye environment www.emsl.pnl.gov:2080/docs/collab/ a DCEE = Distributed, Collaboratory Experiment Environments b ACSI = Accelerated Strategic Computing Initiative c SPARC = Space Physics and Aeronomy Research Collaboratory d MOO = MUD, object oriented, where MUD = multiple user dimension e EMSL = Environmental Molecular Science Laboratory (of the Pacific Northwest Laboratory) The anticipated payoffs of research into knowledge and distributed intelligence include higher scientific productivity; improved abilities to analyze complex problems; enhancements in science and engineering education through the development of improved learning tools, technologies, and environments; and a better understanding of the legal, ethical, and societal implications of increased capabilities to gather and access information. U.S. Department of Energy The U.S. Department of Energy has made a major commitment to developing the technology needed to create a virtual laboratory system encompassing the scientific resources of U.S. national laboratories. Virtual laboratories would enable greater participation by scientists around the world in achieving the science and technology objectives of the Department of Energy. A major step in this effort was the Distributed, Collaboratory Experiment Environments Program, which included several research projects related to AEEs. For example, Lawrence Livermore National Laboratory, Oak Ridge National Laboratory, the Princeton Plasma Physics Laboratory, and General Atomics have developed a computer environment that allows scientists at remote locations to conduct research using the D-IIID tokamak fusion facility. R&D on fusion energy is an archetype of research that must be carried out at a few large central facilities, and systems that facilitate the involvement of remote users increase the efficient use of these facilities. In a separate effort, the University of Wisconsin-Milwaukee demonstrated remote operation of a synchrotron radiation beam-line at the Advanced Light Source located at Lawrence Berkeley National Laboratory. In addition, Pacific Northwest National Laboratory developed a test bed for research in environmental and molecular sciences that allows remote operation of unique instruments. The DOE2000 program, which replaced and expanded the Distributed, Collaboratory Experiment Environments Program, is focused on industrial collaboration. The most recent Department of Energy initiative, which is being conducted in collaboration with NSF, is the Scientific Simulation Initiative. The initiative is applying the high-performance computing capability developed under the Accelerated Strategic Computing Initiative to nondefense purposes.9 The Scientific Simulation Initiative will include R&D on human-centered computing technology to improve interactions associated with problem definition and visualization of results. Additional information on collaborative efforts, including some of those mentioned above, is available via the Internet as indicated in Table 2-2. Interorganizational Studies Industry and government have sponsored a number of studies related to AEE technologies and systems. For example, the interim report of the President's Information Technology Advisory Committee (formed in 1997 to conduct an independent assessment of information technology 9 The Accelerated Strategic Computing Initiative is part of the Department of Energy's Science-Based Stockpile Stewardship Program (see www.llnl.gov/asci/). The purpose of the Accelerated Strategic Computing Initiative is to create leading-edge computational modeling and simulation capabilities to facilitate a shift from nuclear test-based methods to computational-based methods for maintaining the safety, reliability, and performance of the U.S. nuclear weapons stockpile.
OCR for page 18
in the United States) identified four high-priority research areas for information technology: software, scalable information infrastructure, high-end computing, and socioeconomic and workforce impacts (PITAC, 1998). The report states that special emphasis should be placed on component-based software design and production techniques and on techniques for designing and testing reliable, fault-tolerant systems. The advisory committee also determined that significant research will be necessary to understand the behavior of flexible, scalable systems serving diverse customers, especially in complex applications that involve large numbers of users, users demanding high reliability and low latency,10 or mobile users requiring rapid reconfiguration of networks. Extremely fast computing systems, with both rapid calculation and rapid data movement, are essential for many applications, such as improved weather and climate forecasting, advanced manufacturing design, and the development of new pharmaceuticals. The President's Information Technology Advisory Committee also concluded that the government was under-investing in long-term research on information technologies. In response, the President's fiscal year 2000 budget proposes to increase research on information technology by 28 percent ($366 million). The increase would fund the Information Technology for the Twenty-First Century (IT2) initiative, which would build on existing federal research programs such as the Next-Generation Internet Program and the Accelerated Strategic Computing Initiative. Agencies to be involved in the IT2 initiative include NSF, the Department of Defense (including DARPA), the Department of Energy, NASA, the National Institutes of Health, and the National Oceanic and Atmospheric Administration. As currently planned, about 60 percent of the funding will be used to support university-based research. IT2 research will develop advanced software, networks, supercomputers, and communications technology. In addition, the IT2 initiative will examine economic, social, training, and educational issues associated with the development and use of advanced information technologies (NCO, 1999). The Next-Generation Manufacturing Project, which was sponsored by the Department of Energy, the Department of Defense, the National Institute of Standards and Technology, and NSF, involved representatives of more than 100 organizations from industry, government, and academia. The project issued a report in 1997 that recommended how manufacturers, working individually and in partnership with government, industry, and the academic community, can improve their competitiveness. Table 2-3 lists the imperatives for success described in the report (NGM, 1997). Table 2-3 Imperatives from the Next-Generation Manufacturing Project Workforce/workplace flexibility, which is provided by a new set of practices, policies, processes, and culture that enables the employee to feel a sense of security and ownership, while enabling a company to capitalize on the creativity, commitment, and discretionary effort of its employees and, at the same time, maintain the flexibility to continually adjust the size and skills of the workforce Knowledge supply chains, which radically improve the supply and dissemination of knowledge throughout manufacturing organizations by applying concepts of supply-chain management to the relationships between industry, universities, schools, and associations Rapid product and process realization, which enables all stakeholders to participate concurrently in the design, development, and manufacturing process Management of innovation, which includes both initial creativity and the successful implementation of new concepts Management of change, which applies deliberate change to the current state of an organization to achieve a more competitive future state Next-generation manufacturing processes and equipment, which are facilitated by a growing knowledge base of the science of manufacturing and used to rapidly adapt to specific production needs Pervasive modeling and simulation, which enable virtual production and allow production decisions to be made on the basis of modeling and simulation methods rather than on build-and-test methods Adaptive, responsive information systems, which can be reshaped dynamically into new systems by adding new elements, replacing others, and changing how modules are connected to redirect data flows through the total system Collaboration among extended enterprises, which are formed by the seamless integration of a group of companies, suppliers, educational organizations, and government agencies to create a timely and cost-effective service or product Integration of enterprises to connect and combine people, processes, systems, and technologies and ensure that the right information is available at the right location, with the right resources, at the right time Source: NGM, 1997 Observations on the Current State of the Art Based on its selective review of current AEE activities, the committee made the following general observations: The challenge of tool and system integration is ubiquitous. Proliferation of information and information management problems are intrinsic to AEEs and will create difficulties both in the near and far term. Cultural, management, and economic issues often impede AEE implementation. 10 Latency refers to the time delays that occur in real-time interactions between remote locations. Low latency (i.e., small time delays) helps increase the fidelity of simulations.
OCR for page 19
Education and training are significant factors in terms of the time and cost required to realize the benefits of AEEs. References Bikson, T. 1996. Groupware at the World Bank. Pp. 145-184 in Groupware and Teamwork, C. Ciborra (ed.). New York: John Wiley & Sons. Ciborra, C., and G. Patriotta. 1996. Groupware and Teamwork in New Product Development: The Case of a Consumer Goods Multinational. Pp. 121-143 in Groupware and Teamwork, C. Ciborra (ed.). New York: John Wiley & Sons. Grudin, J. 1988. Why CSCW Applications Fail: Problems in the Design and Evaluation of Organizational Interfaces. Pp. 85-93 in Proceedings of the CSCW Conference, 1988. New York: Association for Computing Machinery/Special Interest Group on Computer-Human Interaction and Special Interest Group on Office Information Systems. Malone, J. 1998. NASA's Intelligent Synthesis Environment Functional Initiative. Briefing by John Malone, NASA Langley Research Center, to the Committee on Advanced Engineering Environments, Hampton, Virginia, October 23, 1998. MSOSA (Modeling and Simulation Operational Support Activity). 1998. Simulation Based Acquisition Roadmap Coordinating Draft—December 8, 1998. Report of The Joint Simulation Based Acquisition Task Force. Available on line: www.msosa.dmso.mil/sba/documents.asp. January 20, 1999. NCO (National Coordination Office for Computing, Information, and Technology). 1999. Information Technology for the Twenty-First Century: A Bold Investment in America's Future. Working Draft of January 24, 1999 . Available on line: www.ccic.gov/. February 17, 1999. NGM (Next-Generation Manufacturing Project). 1997. Next-Generation Manufacturing Report. Bethlehem, Pa.: Agility Forum. NRC (National Research Council). 1993. National Collaboratories: Applying Information Technology for Scientific Research. Washington, D.C.: National Academy Press. NRC. 1998. Fostering Research on the Economic and Social Impacts of Information Technology. Washington, D.C.: National Academy Press. NSF (National Science Foundation). 1999. Knowledge and Distributed Intelligence. Directorate for Education and Human Resources. Available on line: www.ehr.nsf.gov/kdi/. January 4, 1999. PITAC (President's Information Technology Advisory Committee). 1998. Interim Report to the President. National Coordination Office for Computing, Information, and Communications. Available on line: www.ccic.gov/ac/interim/. January 4, 1999.
Representative terms from entire chapter: