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Misuse of Statistics

S Y S T E M   E N G I N E E R I N G   C O U R S E

Challenger Disaster Disclaimer National Security Space Programs Mythical Man–Month Resolving Engineer–Manager Conflicts Chief Scientist Analytic & Gaming Sims Complex Systems

These pages contain a sampling of the information presented in the six-hour course, Introduction to System Engineering.   The course, in its present form, contains four one-hour and three half-hour modules.   Longer versions of the course, with problem sets and more detail, are available. The course avoids detailed calculations requiring advanced mathematics, physics, or engineering knowledge.   For this reason the course can be taken by both engineers and managers.   Despite the relatively low level of mathematics, it is likely that anyone with less than a decade of enginering experience will find the material too difficult.

For those of you who've read the pages on songs and monkeys, you realize (if you hadn't already) that "common sense" or intuition can often lead you astray.

C O U R S E   A P P R O A C H

This course examines three classic engineering case studies, which take one hour each:     —   Richard Feynman's Report On The Challenger Disaster     —   Acquisition Of National Security Space Programs     —   The Mythical Man-Month by Frederick P. Brooks Most engineers have probably heard of Feynman's report; many are probably familiar with Brooks.   The material in these documents is often interpreted as highlighting failures of management.   This is NOT the intent of this course. A major course objective is to help engineers and managers act as a team, rather than as adversaries.   This is particularly important when some external force (such as an ambitious CEO, company VP, or overzealous General) attempts to dictate unrealistic schedules or shortcut testing.   Only by acting together can engineers and managers resist these pressures. The following 30 – minute modules cover this material:

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Narrowing The Gap Between Engineers and Managers

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The Role of the Chief Scientist which shows how to build quality and realism checks into a contract, without busting the budget;

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Differences Between Analytic and Gaming Simulations DoD is fond of the latter, much more so than the former; the latter are overused and often misused; the former underused.

The final session is on

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Introduction to Complex Systems - very few large, complex systems have been successful; standard approaches (such as waterfall development) usually fail; evolutionary approaches MUST be used, and even then only highly automated genetic approaches have a chance of covering enough of the test space to succeed.

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E X A M P L E   C O U R S E   S C H E D U L E

TIME

ITEM

0800 – 0810

Definition of System Engineering

0810 – 0930

Richard Feynman's Report On The Challenger Disaster

0930 – 1000

Acquisition of National Security Space Programs

1000 – 1030

Break

1030 – 1100

Acquisition of National Security Space Programs (cont.)

1100 – 1130

The Mythical Man–Month

1130 – 1230

Lunch

1230 – 1300

The Mythical Man–Month (cont.)

1300 – 1330

Resolving Engineer–Manager Conflicts

1330 – 1400

The Role of The Chief Scientist

1400 – 1430

Break

1430 – 1500

Analytic Vs. Gaming Simulations

1500 – 1600

Introduction to Complex Systems

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DEFINITION 1

DEFINITION 2

DEFINITION 3

Systems Engineering is an interdisciplinary field of engineering focusing on how complex engineering projects should be designed and managed over their life cycles. Issues such as reliability, logistics, coordination of different teams (requirement management) and different disciplines become more difficult when dealing with large, complex projects. Systems engineering deals with work-processes and tools to manage risks in such projects, and it overlaps with both technical and human-centered disciplines such as control engineering, industrial engineering, organizational studies, and project management.

Systems Engineering is a transdisciplinary and integrative approach to enable the successful realization, use, and retirement of engineered systems, using systems principles and concepts, and scientific, technological, and management methods.

The design of a complex interrelation of many elements (a system) to maximize an agreed-upon measure of system performance, taking into consideration all of the elements related in any way to the system, including utilization of worker power as well as the characteristics of each of the system's components. Also known as system engineering. Postwar growth in the field was spurred by advances in electronic systems and by the development of computers and information theory.   Systems engineering usually involves incorporating new technology into complex, man-made systems, in which a change in one part affects many others.   One tool used by systems engineers is the flowchart, which shows the system in graphic form, with geometric figures representing various subsystems and arrows representing their interactions. Other tools include mathematical models, probability theory, statistical analysis, and computer simulations.

(1)

http://en.wikipedia.org/wiki/Systems_engineering

(2)

https://www.incose.org/systems-engineering

(3)

http://encyclopedia2.thefreedictionary.com/Systems+Engineering

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What do these definitions have in common?   Rather than try to create a succinct definition of System Engineering, I will simply list its important characteristics.

Complex engineering projects     — Designed     — Managed     — Life cycles Interdisciplinary     — Processes     — Tools     — Manage risks Overlaps     — Control engineering     — Industrial engineering     — Organizational studies     — Project management Complex systems     — Commercial telephone     — Military     — Life cycles     — Subsystem interactions Planning and Development

Successful systems     — Operations     — Cost & Schedule     — Training & Support     — Performance     — Test     — Manufacturing     — Disposal Interdisciplinary     — Customer needs     — Required functionality     — Requirements     — Design synthesis     — System validation Tools     — Flow chart     — Mathematical models     — Probability theory     — Statistical analysis     — Computer simulation     — Information theory

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