CHAPTER 3
WAYS THAT ENGINEERS USE DESIGN INFORMATION
Michael Fosmire, Purdue University
Learning Objectives
So that you can provide students with an understanding of the typical role of information in engineering design, upon reading this chapter you should be able to
•Articulate why engineers gather information and how they utilize it in the design process
•Recognize which information resources are used at different stages of the design process and what information artifacts are produced
•Recognize the main barriers to effective information use by engineers and the role of training in improving their information-seeking behaviors
INTRODUCTION
By understanding the challenges faced by practicing engineers and engineering students in effectively utilizing information to make good design decisions, you will begin to see what gaps need to be filled by instructional interventions. By gaining a deeper appreciation of the competing challenges engineers face, you will see the need to incorporate activities that build information literacy skills in students. Fundamentally, the more familiar and routine information gathering is for students, the more likely they will use those skills in their subsequent work. The observations, models, and opinions in this chapter led us to the development of the Information-Rich Engineering Design (I-RED) model introduced in Chapter 4.
MODELS OF INFORMATION GATHERING
While the library science profession has developed its own models for information gathering, the engineering profession has not neglected the question of the role of information in the design process. Industrial engineering in particular, with its focus on optimizing systems and processes, has provided an extensive body of work looking at particular techniques and information storage and retrieval systems to enhance the outputs of the design process.
Wodehouse and Ion (2010) apply the Data, Information, Knowledge, Wisdom (DIKW) model to the design process (see Table 3.1) to show the transformation of data into knowledge that takes place and the activities that go into that transformation. Briefly, data is the collection of facts and observations available to anyone. The principal activity involved is simply the location of that data. However, value is added by engineers in turning data into information—that is, in organizing it into something usable, making connections between pieces of data, and determining which data are relevant to the problem at hand. Information becomes knowledge when the information is applied to a problem. While information and knowledge are focused on the corporate body or problem under consideration, wisdom is based in the individual, who, by learning in the process of solving the problem, can apply to future problems not only specific content but also the principles and processes used.
Table 3.1Data, Information, Knowledge, Wisdom (DIKW) in the Design Context
| DIKW Stage | Activity | Design Context | Availability |
| Data | Locating | Assembling facts | Openly available |
| Information | Structuring/organizing | Facts are organized and winnowed | Internal |
| Knowledge | Applying | Information used | Internal |
| Wisdom | Reflection | Review process; self-assessment | Personal |
Data from Wodehouse & Ion, 2010
Other engineering design models include more concrete analysis of information components. These models incorporate both information inputs and outputs—that is, information gathered from external sources and that produced by the engineers in the course of the design process. Two such models are summarized in Tables 3.2 and 3.3.
Table 3.2Information Use in the Engineering Design Model of Ulrich and Eppinger
| Design Stage | Information Sourced | Information Generated |
| Planning | Market data, company reports | Briefing documents, project plan |
| Concept development | Competitor and related products, previous design schemes | Brainstorming notes, sketches, drawings, rough calculations |
| System design | Patents, previous design schemes | Sketches, drawings, mock-ups and models, cost evaluation |
| Detail design | Textbooks, catalogs, suppliers’ data | Detailed drawings and design calculations, solid and mathematical models |
| Testing | Standards, databases | Experimental data, manufacturing drawings, bills of materials, assembly instructions |
| Production | Customer feedback, retail data | Sales presentations, demonstrations, photographs, product instructions, presentation graphics |
Data from Ulrich & Eppinger, 2011.
TABLE 3.3Information Use in the Engineering Design Model of Dym and Little
| Design Stage | Sources of Information | Outputs |
| Problem definition | Client’s statement; literature on state-of-art, experts, codes, and regulations | Revised problem statement; detailed objectives, constraints, user requirements, and functions |
| Conceptual design | Competitive products | Conceptual design solutions; design specifications |
| Preliminary design | Heuristics, simple models, known physical relationships | Selected design solution; test and evaluation methods |
| Detailed design | Design codes, handbooks, local laws and regulations, suppliers’ component specs | Proposed fabrication specs; final design solution for review |
| Design communication | Feedback from customers, required deliverables | Final report to client containing fabrication specs and justification for those specs |
Data from Dym & Little, 1999.
Both Ulrich and Eppinger (2011) and Dym and Little (1999) design models recognize that different stages of the design processes call for different information sources, and they explicitly acknowledge that the information process is not only about consuming information but the production of information as well. These models help guide the student through the transformation of data and information into knowledge for the project, with specific activities and processes (i.e., outputs) in the authentic context of engineering design. While neither set of authors spend much time discussing how to access those various kinds of information, Dym and Little (1999) observe that “the literature review [emphasis theirs] is so well documented and understood that it might seem unnecessary for us to comment on it. However, it is worth noting that the relevant literature can be both vast and greatly dependent on the stage or phase of the design” (p. 41). These models provide the structure, through the engineer’s lens, for activities that engineers and librarians, working together, can develop to build information gathering skills and, ultimately, an informed design product.
VALUE OF INFORMATION GATHERING
A variety of interview and observation studies indicate that engineers appreciate the role of information gathering in the design process. Mosberg et al.’s (2005) interview of engineers found gathering information to be the fourth most important activity out of 24 components of the design process, below only understanding the problem, understanding constraints, and communicating (all of which have information-based components). Gathering information came out ahead of analyzing, brainstorming, planning, prototyping, testing, and building, for example. Atman et al. (2007) found that, with experience, engineers make increasing numbers of information requests when solving design problems. The number of sources, kinds of requests, and time spent gathering information all increased substantially when comparing groups of first-year, senior, and professional engineers. Bursic and Atman (1997) also found a positive correlation within each group between the number and kinds of requests and the quality of the final products, although they believed that even the senior students needed substantial improvement in their use of information in the design process.
Several studies of user behaviors have attempted to quantify the impact of information on success for engineers. Tenopir and King (2004) studied the habits of university and national laboratory engineers and scientists and found that university engineers read on average twice as many articles as engineers at national laboratories. In terms of time, engineers spent about 90 hours a year, or 5 percent of their time, reading journal articles. Overall, engineers reported spending 280 hours per year reading some form of documents, more than they spent in informal discussions (104 hours) or internal meetings (136 hours). They also found that engineers who had won awards or received other recognitions of excellence read on average about twice as many articles as those who didn’t. Many corporations have gatekeepers—that is, engineers who are more familiar with information resources, including a network of professional contacts, and who are often the go-to people for help answering information needs. These gatekeepers tend to publish more than their counterparts, and their employees tend to perform better than the company average.
Engel, Robbins, and Kulp’s (2011) survey of engineering faculty at 20 different institutions found that more than three quarters reported seeking information at least weekly to prepare for student lectures, and over half reported seeking information at least weekly both for their research projects and to stay current in their field. According to this survey, engineering faculty about equally often use conferences, current journals, personal communication, and following article references as ways to stay abreast of developments in their field. They still rely on discussions with colleagues and students as significant sources of information, but they rely even more on scholarly journals and Internet resources, with monographs and conference attendance rated highly, although not quite as highly as discussions. Engel, Robbins, and Kulp (2011) found ease of access the most important factor for engineering faculty when gathering information; therefore, electronic access to current and historical journals were of primary interest, although print books were still rated more highly than e-books in importance by respondents. Kwasitsu (2003) found that practicing engineers with an advanced degree used scholarly literature more frequently than did those without, implying that the increased familiarity with those sources might make them more accessible to those engineers in the workplace.
INFORMATION HABITS OF ENGINEERS
Studies have consistently found that engineers engage in information activities for on average between 20 and 40 percent of their workday, which is more time than they spend on more traditional design activities such as prototyping and modeling (Tenopir & King, 2004). Information activities here include locating, using, producing, and communicating information in any format. Characterizing the information habits of engineers can be problematic, however, since they may take on a wide variety of roles within a project team, and there are substantial disciplinary differences between information use habits. As Tenopir and King (2004) indicate, during his or her career, an engineer may assume a variety of functions, “including research and development, design, testing, manufacturing and construction, sales, consulting, government and management, and teaching” (p. 78). They go on to state that, for example,
design engineers want original, up-to-date information, relying heavily on internal reports and test results rather than the published literature. In a consulting role they rely more on external market information about vendors and customers. When an engineer takes on an administrative role, he or she needs a wider variety of both external and internal information, including regulations, information on new technologies, personnel records, and business information. R&D information needs similarly vary with each stage of the project. (p. 79)
That said, some general principles can be drawn. As Leckie, Pettigrew, and Sylvain (1996) found, engineers, like other professionals such as health care workers and lawyers, engaged in very context-specific information-seeking behaviors and rely heavily on their previous knowledge and personal collections when approaching a problem. Overall, engineers’ information-seeking behaviors have consistently been characterized as a least effort approach. That is, engineers act in a way to minimize the work involved when searching for information, rather than to maximize the results of the search. Engineers will accept a lower quality information source if it is easier to locate, access, and/or apply to a problem, with Gerstberger and Allan (1968) finding no correlation between source quality and use. Kwasitsu (2003) found that quality, relevance, currency, and reliability of the information source ranked significantly lower than accessibility and availability, although they all were rated as important by the majority of the engineers surveyed.
Thus traditionally, colleagues and personal collections have provided the lower barrier to locating information, and engineers will use their personal collections preferentially even though they might be of limited scope. However, gathering information from colleagues is not without drawbacks, as the time spent locating an appropriate colleague, the intellectual and social effort involved in interacting, lack of specificity of answers, poor memory of their subjects, and inappropriate information have been described as challenges (Tenopir & King, 2004). Furthermore, some engineers are intimidated by admitting to a colleague their ignorance on a subject. Although colleagues and personal collections traditionally have been preferred, recently, Googling has become a first-resort method of locating information for engineers as well (Allard, Levine, & Tenopir, 2009; Hirsh & Dinkelacker, 2004).
Hertzum and Pejtersen (2000) investigated the social aspects of information seeking and found that the search for documents and people is frequently intertwined. Since technical documents are static, when more context is desired, engineers go to the human source of the information, especially to explain how results can be appropriately applied to a problem or to interpret the information implicit or missing from the document. By consulting a trusted expert, engineers also frequently gather feedback on their own ideas. Conversely, technical documents contain specific facts and figures, and since memories fade with time, having access to those pieces of data provides a level of assurance of the accuracy of the information. Often, the process is iterative, with engineers finding people who know where the useful documents are and what they contain, and documents in turn providing pointers to experts who can expand on a particular topic. As a rule of thumb, the more complex, uncertain, or ambiguous the task, the more likely an engineer will search out a personal contact instead of a documentary resource. With the growth of the Web, including videos, tutorials, and forums, richer information can be made available without contacting colleagues directly, so the balance of personal and documentary information gathering is changing as well.
Ellis and Haugan (1997) explained different information habits based on the type of problem faced. They classified problems as incremental, radical, or fundamental. Incremental projects primarily involved conversations with colleagues to understand the context for minor improvements to a product. Radical projects involved major redesign of a product or service. In these cases, collegial interactions are supplemented with environmental scanning of current technologies or principles, mainly through reading review articles and conference proceedings. Fundamental projects are those in which a company moves into a completely new area. Since there will be little in-house expertise in this kind of project, engineers typically begin with a literature review before consulting others. This kind of activity requires the most in-depth information seeking and is most likely to include consultation with corporate librarians and use of formal library materials.
In terms of the actual kinds of textual resources accessed by engineers, corporate intranets that contain internal reports and data dominate the usage. Journals and conference proceedings, patents, marketing data, regulations, standards, external technical reports, and product information also are common information sources. Depending on the role of a particular engineer or the field he or she is working in, the distribution of sources varies significantly. Research and development engineers, for example, have a profile of information use similar to scientists, while production engineers or marketing specialists will have utilitarian information needs.
Jeffryes and Lafferty (2012) surveyed returning co-op students, largely mechanical engineers, as a proxy for entry-level engineers and found that, in their internships, 75 percent used standards, 60 percent used books, over 50 percent used technical reports, 33 percent used journal articles, and 20 percent used patents, and the vast majority learned how to locate all those information sources except books during their college career.
Generally speaking, engineers dislike searching for information in the typical indexes that librarians love. Rather, most engineers locate information through recommendations from colleagues or citations from other papers, or as a result of their own current awareness browsing of technical or trade journals, blogs, and so forth. Tenopir and King (2004) found that about half of journal articles read by engineers in their study were located through browsing, with another third coming as suggestions from colleagues. Only 10 percent of papers read were located through conscious searching. Again, as Internet search engines have substantially decreased the barrier to searching, information habits are changing.
BARRIERS TO INFORMATION USE
As mentioned in the previous section, engineers tend to take a least effort approach to information gathering. Several factors can contribute to increasing the effort of searching, including the fiscal and psychological cost, accessibility of resources, lack of familiarity with appropriate sources, inappropriate formats, irrelevance, and lack of high-quality material.
Cost
Costs come in different forms, with monetary costs actually influencing engineers’ behaviors least. Rather, time is the most important cost, including the time it takes to search, acquire, and process the material. Additionally, the mental cost—that is, devoting one’s attention to the process of finding information—is another important component.
Accessibility
Does an information source exist and is it available to be accessed? Again, there can be many levels of accessibility. In the past, a physical journal might have been located in a locked library after hours. Now, the information might exist, but it could be behind a subscription wall (and although the monetary cost might not be a barrier, the process of acquiring access could be). An information source might exist but be buried in a poorly constructed knowledge management system, so therefore inaccessible to the end user. Gerstberger and Allan (1968) found that the more experience an engineer had with a particular information source, the more accessible he or she found it to be.
Familiarity
Lack of familiarity with a resource type or information system also leads to nonuse. In line with the principle of least effort, if a search system is unfamiliar, it will take much more effort to use effectively. Similarly, if an engineer has not used patents, standards, or technical documents before, or has not heard of a particular collection of documents, these are not in that engineer’s toolbox of sources and thus will be neglected in the search for appropriate information.
Format
An information source might contain appropriate content to solve a problem, but it might not be in a format usable by the engineer. For example, the treatment of the topic might be at a level inappropriate for the background of the reader. Alternatively, the method of encoding the information (textual, graphic, or electronic) might not allow for easy importing into a project. Data files might be in a different format than that used by a project’s software programs, or perhaps the project team needs a drawing, when only a written description is available. Engineers determine whether it is worth their time and effort to convert information into a usable format.
Relevance/Information Overload
When conducting searches, engineers struggle with sifting through an overwhelming number of results, most of which are not relevant to their search. Engineers often consult with colleagues to locate relevant information, whether internal or externally produced documents, as well as for assistance with extracting the appropriate information from those documents and with the context of the application of that information.
Quality
Engineers desire high-quality information, and although quality doesn’t rank as the most important factor, it does rank highly in their search process. The difficulty is locating high-quality information and determining which information is of high quality. Particularly since engineers tend to have little patience with searching specialized databases, including, frequently, corporate intranets, they may only be looking at the open Web, excluding many high-quality sources from their searches. Furthermore, engineers at smaller firms often do not have ready access to subscription material such as journals, further limiting their ready access to high-quality materials.
SUMMARY
The previous discussion indicates that the information-seeking behavior of engineers is quite complex but that, overall, the more advanced and accomplished an engineer, the more information the engineer seeks and uses in his or her professional career. While engineers prefer finding information from their personal collection and from their colleagues, they increasingly rely on Internet search engines. When they need accurate facts and figures, they do consult the written record, whether internally or externally produced. Information habits center around the concept of minimizing effort, rather than maximizing the value of information retrieved.
Increasing the effectiveness of engineers’ information-seeking habits, then, requires a combination of training, to increase the familiarity and accessibility of resources, and improvement of knowledge management systems, to increase accessibility of previously located resources. Learning about different document types (e.g., technical reports, patents, journal articles), as well as search systems, will enable engineering students to conduct, in terms of time and effort, a lower cost search for information. Students need to be trained to extract information efficiently from different resources—for example, to read a scientific paper effectively and to become familiar with sources that provide information in a variety of formats (e.g., tabular, graphical, textual) —so that the information is not only available but usable in the context of the problem at hand. Finally, in order for engineers to develop their own personal knowledge bases, training in knowledge management tools and the habits of using them are critical so that information doesn’t become forgotten or lost to the system. Since engineers almost exclusively resort first to their personal collection of information, the better their knowledge management system, the more effective they will be in their careers.
All of these information literacy principles— locating, accessing, using, and learning from information—need to be instilled in engineering students so that they can thrive in their increasingly competitive knowledge-based society. In order to achieve this goal, we have developed an information-integrated model of engineering design, which is introduced in the following chapter.
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