A. Wayne Wymore Quotes

In the minds of many writers systems engineering is synonomous with component selection and interface design; that is, the systems engineer does not design hardware but decides what types of existing hardware shall be coupled and how they shall be coupled. Complete agreement that this function is the essence of systems engineering will not be found here, for, besides the very important function of systems engineering in systems analysis, there is the role played by systems engineering in providing boundary conditions for hardware design.

Every author has several motivations for writing, and authors of technical books always have, as one motivation, the personal need to understand; that is, they write because they want to learn, or to understand a phenomenon, or to think through a set of ideas.

Over 80 million people have participated in Cub Scout Pinewood Derbies. Pinewood is a case study of the design of a Cub Scout Pinewood Derby for one particular scout pack. The system helps manage the entire race from initial entry through final results. Many alternatives for race format, scoring, and judging are presented.

Only if mathematical rigor is adhered to, can systems problems be dealt with effectively, and so it is that the systems engineer must, at least, develop an appreciation for mathematical rigor if not also considerable mathematical competence.

If all the theories pertinent to systems engineering could be discussed within a common framework by means of a standard set of nomenclature and definitions, many separate courses might not be required.

All that can be done pedagogically is to show the student how some phenomena have been modeled, let him model some phenomena under supervision, and then hope he will be successful on his own—or know enough to secure assistance.

[The word system is often defined in a way] that seems the most appropriate for the purpose of any given discussion.

[In the year 1957] I have just returned from an exciting meeting of the American Society for Engineering Education where I heard a paper on the new discipline of systems engineering. It is no longer sufficient for engineers merely to design boxes such as computers with the expectation that they would become components of larger, more complex systems. That is wasteful because frequently the box component is a bad fit in the system and has to be redesigned or worse, can lead to system failure. We must learn how to design large-scale, complex systems from the top down so that the specification for each component is derivable from the requirements for the overall system. We must also take a much larger view of systems. We must design the man-machine interfaces and even the system-society interfaces. Systems engineers must be trained for the design of large-scale, complex, man-machine-social systems.

The problem of the design of a system must be stated strictly in terms of its requirements, not in terms of a solution or a class of solutions.

After earning the PhD degree and acquiring some relatively extensive experience in digital computers… It was time to leave the University. The result of an extensive search for the right job was a family move to Arlington Heights, Illinois, where it was a short commute to the Research Laboratories of the Pure Oil Company at Crystal Lake. I was given the title of Mathematical and Computer Consultant. The Labs were set in a beautiful campus, the professional personnel were eager to learn what I had to teach and to include me in many interesting projects where my knowledge and skills could be put to good use. I was encouraged to initiate my own program of research. I went to work with enthusiasm. The corporate headquarters of Pure Oil were located in down town Chicago. Pure Oil had been trying to install an IBM 705 computer system for all their accounting needs including calculation of all data necessary for the management of exploration, drilling, refining and distribution of oil products and even royalties to shareholders in oil wells. Typical for those early days, the programming team was in deep difficulties and needed help; they lacked adequate resources and suitable training. The Executive Vice President of Pure Oil, when he heard that there was a computer expert already on the payroll at the Crystal Lake lab, ended our family blissful dream and I was reassigned to the down town office.

In the folklore of systems engineering, there are sayings, “If it isn't testable, it isn't a requirement,” and, “If it is not quantifiable, it is not testable.” I had always tended to subscribe to the particle of wisdom in these sayings. One of the many lessons that I learned from the CATIE experience, however, was that systems engineering had to be able to deal directly and purposively with what are usually considered to be strictly qualitative (unquantifiable) aspect of systems, such as quality of life, for example. The agricultural practices of these small farmers in Central America is such a large part of their lives that to change any part of their agricultural practices might be seen as diminishing their quality of life - even with respect to the color of the beans that the new practices might require. But even if we cannot deal directly with quality of life or user friendliness or enmity/sympathy, we can identify quantifiable figures of merit relating to these qualitative aspects and if we find enough figures of merit and combine them suitably, we might eventually obtain agreement that we have captured the essence of the qualitative aspect, at least as far as this particular set of stakeholders is concerned.

During this period I was able to carry out only one project of real interest to me. Pure Oil was a fully integrated oil company in the sense that they engaged in exploration for oil, construction of oil wells, production of crude oil, transportation to refineries, distribution of refinery products to company owned storage facilities and to gas stations. We developed a linear programming model of the whole network hoping to discover the optimum allocation to and from connected nodes in order to meet required deliveries at minimum cost. Then we collected all the data needed by our model and ran the model. The computations took several days and the results were disappointing: The optimum allocations were not significantly different from their current practices. Is it possible that we had discovered a system of human beings in which everyone knew the payoff functions and constraints, and, over time, had evolved behavior patterns that enabled them to achieve near optimum performance? Or was it possible that our model was not detailed enough as it was based so closely on current practice that no new behavior could emerge? Or was our data incorrect? This was disappointing but great experience.

[The process of system design is]... consisting of the development of a sequence of mathematical models of systems, each one more detailed than the last.