(3/20/2012)

The term systems engineering has been the subject of a great deal of speculation regarding how to define the discipline [1].  General consensus seems to attribute the development of this field, early on, as an approach to solving complex problems faced by the aerospace and defense industries [2-3].  The success of the early efforts (late 1940's) suggested approaches for the management of large-scale hardware development programs and the associated integration of highly technical skills into this management process.  

It became clear, once a need was established for the generation of a "system" to meet operational requirements, that a general approach to satisfying this need must be organized and implemented.  Subsequent to the early defense system focus, other disciplines adopted the developing principals of systems engineering, while attempting to further define the mix between technical analysis and project management. 

Most notably, in the early 1980's when less emphasis on R&D occurred in the defense sector, the financial community began to realize the importance of applying stochastic modeling in a systems context (see, for example, [4]).  This led to a new shift in the paradigm; topics such as: group interactions, survival, genetics, economics, population behavior and epidemics, and quality control, as well as, project risk assessment became popular.  Strategies for hedging risk, for example, developed with an eye towards what systems issues were relevant in projecting performance.

Commensurate with the evolving systems approach, from an analytical perspective, was the impact of advances in software development and the establishment of a related discipline, software systems engineering.  This became firmly entrenched in the early 1980's. 

Preceding this was the revolution in computer architectures from the early mainframes (mid-1960's through the mid-1970's) to time-sharing (1970's through mid-1980's) and the resulting increase in computing power -- both from a software viewpoint and the magnitude of the computational problem addressed.  Of course, with the advent of the microprocessor and subsequent exponential growth in computing power, software systems engineeriing has become a dominant mechanism for implementing systems' concepts in what was once a purely hardware-oriented systems approach..

Today, systems engineering spans a much broader category of problem-solving techniques.  The establishment of a heuristic approach to many tasks has led to new definition of these techniques applied across a wide range of disciplines.  The evolution in understanding organizational structure has led to new approaches to the management aspects of systems engineering [5] and the management process. 

Currently, there exists a dichotomy in approaches to problem-solving and project organization.  This dichotomy manifests itself in two alternative approaches:  first, one school of thought emphasizes the need to implement direct statistically-based analysis using existing and new data relevant to the task at hand; second, the alternative is to employ model-based approaches, which quantify aspects of the model using numerical inference and introduce additional parameters to characherize more "behavioral issues."  General trends seem to favor a melding of both of these approaches. [6]

In general, systems engineering, as a discipline has tended to focus on larger-scale problem areas of corporate and national interest.  With the shift in focus of problems of national interest, for example, during the past decade, there has been a resurgence of interest in economic and financial problem areas.  Since 2001, the Market, for example has been "flat," and the economy has had only very moderate growth.  Hence, it is clear that this area is one into which the discipline of systems engineering can be expected to refocus.

On this web site, a variety of topical categories are examined from a systems perspective.  Of particular interest is the application of quantitative methods to future performance of macroeconomic parameters and project risk assessment.

Computer Systems Analytics, Inc. 

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[1] Kasser, J. A Framework for Understanding Systems Engineering, The Right Requirement Ltd., 50 Crane Way, Cranfield, Bedfordshire, MK43 0HH, England, ISBN 1-4196-7315-7 (2007).

[2] Sage, A.P., Methodology for Large-Scale Systems, McGraw-Hill Book Company, NY, NY, ISBN 0-07-054438-7 (1977).

[3] Mackay, W.F. and Mackay, W.F., Jr., A Systems Engineering Approach to Highway Design, 4th Annual International Symposium of the National Council on Systems Engineering (NCOSE), San Jose, CA (1994).

[4] Luenberger, D.G., Introduction to Dynamic Systems: Theory, Models, & Applications, John Wiley & Sons, NY, NY, ISBN 0-471-02594-1 (1979).

[5] Holmes, H., Quirk: Brain Science Makes Sense of Your Peculiar Personality, Random House, NY, NY, ISBN 978-0-679-60452-5 (2011).

[6] Casselman, B. and Lahart, J., Economists Win Nobel For Focus on Real World, the Wall Street Journal, U.S. News, (October 111, 2011).

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