(All descriptions link back to this list.)
This site is not yet set up to process charge cards. Apologies herewith for
asking that you print, fill in, and mail the following order form with a check:
Order Form Page.
(The MAC readable IBM HD format Diskette also contains an Example Decision
Model Template (Returns here), described below.)
You may assign the weights as you like (after all, it is your Decision Model),
but mathematical research has provided a good method for reducing the
subjectivity level. A complete description of the process is in the template
documentation (on the diskette), but the essentials (including a Critique [Returns here] of
the underlying process) are as follows:
The Weighting Template works with up to nine parameters to permit military
style numbering from 220.127.116.11. ... to 18.104.22.168. ... for meaningful identification
of Decision Model elements hierarchical position below the system at level 0.
Your comparison of every combination of pairings of sibling parameters converts
to a reasonably objective decimal weighting. You provide the weighting input
set for each grouping of sibling parameters within the Decision Model and place
template output results in your model (or in the Decision Template).
Divide the Decision Model into a grouping of lists, each having all the
children of one parent element paired off in turn with every other sibling
element. As an example, to build the list for three sibling attributes, perform
the following steps:
A number of different scalings could have been chosen, without substantial
improvement in validity. Some choices would improve consistency, but at the
cost of increased complexity.
Ratio scales are necessary to retain essential proportionality after
Going beyond the mathematical necessity, the assigned values are substantively
meaningful in the sense that they are mentally "natural." That is,
they can be understood easily by the decision maker and other participants in
Using the template on only two or three parameters is relatively trivial and
output is too limited when the input for By How Much is in whole numbers. That
is why you should employ direct weighting of so few with an algebraic method,
unless you input decimal numbers at places between the whole numbers for the
On the other hand, the ability to accurately weight five or more Decision Model
attributes, while maintaining perfect consistency of interrelationships,
requires a mental capability that few among us could claim. (If you can do
that, you don't need much help with the mathematical aspects of decision making
and probably can carry relatively complex models completely in your head.
Perhaps you are doing well in the stock market?)
For the rest of us, the optional weighting tool makes the process simpler and
demonstrably more repeatable by providing feedback regarding adequacy of input
inconsistency. The critical importance of the inconsistency feedback feature
will be discussed further following completion of this template input
You may use prelabeled lists of parameter pairings as paired-comparison
questionnaires for expert input. An abbreviated but universal form for
recording template assisting weighting of sibling parameters is the Short Form
Query (included in the package). It provides space to list the labels for
decision criteria by number at the top of the sheet.
Rather than separately listed pairings of the sibling parameters, it has just
the definitions of 1 to 9.9 input values and the grouping of all possible
numerical pairings for a set of nine attributes. Alongside each pairing are the
two underlined spaces to mark which of the pair is the Most Important and By
How Much, as previously explained.
The Short Form Query sheet also is useful for decision historical records (your
History File) and is supplied in Reproduction Master form to assist assembling
your documentation while you are using the spreadsheet tool for weighting.
The Inconsistency Index feature, described soon, will catch the inconsistency
of erroneous inputs quickly, helping to separate differences in beliefs from
You may provide the subjective input part of a weighting paired-comparison
using the optional Weighting Template on-line at the computer terminal with
either the questionnaire form of list or a Short Form Query. Input responses
submitted at the terminal are as data entered in the opening screen.
Figure 1 shows a typical opening screen. Input information is exactly the data
listed for each pairing in the questionnaire. A label by the pairing under
consideration identifies where you input the number for the element you chose
as Most Important. Next to it (replacing the 1, if not equal) is where you
enter the value of By How Much the selected parameter is more important than
the other member of the pair.
0001ºPAIRED-COMPARISONS ENTERED BELOW FROM WORKSHEET PER INSTRUCTIONS, WITH º
0002ºC 1,2: 1 MOST IMPORTANT IN FIRST ADJACENT COLUMN AND "BY HOW º
0003ºC 1,3: 1 C 2,3: 1 MUCH" REPLACING 1 IN NEXT COLUMN. º
0004ºC 1,4: 1 C 2,4: 1 C 3,4: 1 º
0005ºC 1,5: 1 C 2,5: 1 C 3,5: 1 C 4,5: 1 º
0006ºC 1,6: 1 C 2,6: 1 C 3,6: 1 C 4,6: 1 º
0007ºC 1,7: 1 C 2,7: 1 C 3,7: 1 C 4,7: 1 º
0008ºC 1,8: 1 C 2,8: 1 C 3,8: 1 C 4,8: 1 º
0009:C 1,9: 1 C 2,9: 1 C 3,9: 1 C 4,9: 1 º
0010º 1 0 0 0 0 0 0 0 0 1 0 º
0011ºENTRIES BELOW ARE USED FOR WEIGHTING PROBLEMS UTILIZING MORE THAN FIVE º
0012ºC 5,6: 1 PARAMETERS OR HIERARCHY SUBELEMENTS: º
0013ºC 5,7: 1 C 6,7: 1 (C) ODDSCO, 1999 º
0014ºC 5,8: 1 C 6,8: 1 C 7,8: 1 ######º
0015ºC 5,9: 1 C 6,9: 1 C 7,9: 1 C 8,9: 1 º
0017º (PRESS F9 KEY AFTER DATA ENTRY. PARAMETER VALUES WILL APPEAR BELOW.) º
0018º #1 #2 #3 #4 #5 #6 #7 #8 #9 I.I. LIM: º
0019º 0 0 0 0 0 0 0 0 0 -1 0 º
0020º 0 0 0 0 0 0 0 0 0 SIMULT-EQU º
Following data input for all sibling pairs, the Weighting Template computes the
parameter weights. Note that input and output both take place on the initial
The procedure for weighting each grouping of parameters in your Decision Model
is to reload the blank Weighting Template and calculate the new weights with
fresh input values. Following each siblings weighting, transfer the weights
into your Decision Model to await input of the utility scores earned by the
candidate solution option for each primitive attribute.
Typing out a list of the model structure, after weighting all the parameters
with assistance of the Weighting Template, provides you solid hardcopy
documentation for your decision History File.
The Inconsistency Index
The principle ideas upon which this Weighting process are based came from:
The Analytic Hierarchy Process
Saaty, Thomas L., McGraw-Hill, 1980.
(pp. 17 to 25, 49 to 64, 167 to 197, and 249 to 263.)
Part of the AHP approach recommended by Saaty is computation of a Consistency
Index (really measures inconsistency and is so named herein) for feedback
regarding relative consistency of input to the Weighting Template.
The Inconsistency Index ("I.I." in the template) may be thought of as an
excellent figure of merit for the validity of resulting weighting.
Consistency of input means that the implied relationships in the earlier
entries correspond closely with the entered relationships for the actual
pairing, as follows:
For the answered pairings (when groupings have more than two parameters), an
implication set is formed. Every two pairings implies the relationship of
another pair. To wit, those responses input for the 1,2 and 1,3 pairings imply
the relationship for the 2,3 pairing. The actual value (and direction of
relative importance) should closely resemble that implication for consistency.
The simplest example of typical input inconsistency is for three parameters. If
the input for pair 1,2 is 1 is More Important with 9 for By How Much and input
for pair 1,3 is 1 is More Important with 7 for By How Much, then the implied
relationship for pair 2,3 is 2 is More Important with 9/7 for By How Much.
An entry of anything else for pair 2,3 is inconsistent by an amount that
increases with the quantity of the difference. Reversing the implied
relationship with the entered relationship, such as input of pair 2,3 as 3 is
More Important with 5 for By How Much, increases the inconsistency.
Usually, input assignment is not that obviously wrong, but the actual
assignments of importance should be similar to the implied relationships to
The larger the parameter set, the larger the implications set. Some will repeat
the same implied pair several times, as when inputs for pairs 1,8 and 1,9 are
followed by inputs for pairs 2,8 and 2,9 then for pairs 3,8 and 3,9 and so on,
until input for pairs 7,8 and 7,9 form the final implication. The entered value
is for the pair 8,9. Thus, you have many more places to look for the source(s)
of input inconsistency when the number of parameters approaches the maximum of
nine accommodated by the Weighting Template as supplied.
It may seem like a lot of bother to obtain consistency, but the effort
definitely improves your decision validity.
On the Weighting Template just to the right of the list of output weights at
the bottom of the screen is the computed Inconsistency Index and the associated
Inconsistency Limit (labeled "LIM"). Perfect consistency provides a computed
value of zero and inconsistency gives a higher value.
The Inconsistency Limit shown is the maximum allowed as a standard for that
number of sibling elements, to detect if you have assigned a weighting set with
too much inconsistency. Because it is increasingly difficult to be perfectly
consistent with more pairings to consider, the Inconsistency Limit rises at
larger increments as the number of parameters approaches the maximum.
The limit value for seven parameters is exactly the ten percent (0.1)
recommended by Saaty in his cited text because inconsistency is less important
than consistency by one order of magnitude. Allowing for some inconsistency
relates to the real world and helps people assimilate new knowledge in stages.
Working to get consistency below the computed Limit is critical to your
decision, because the level of consistency is directly related to reliability
of the information content in the model weighting and thereby directly related
to the output result upon which the decision will be based. Therefore, a
Weighting Template indication of large inconsistency suggests that you should
revise your input set for another template trial recalculation of the Decison
Model element weighting. Fortunately, the latest version of the Weighting
Template reveals the worst implied relationships when the Inconsistency Index
exceeds the computed Limit. See Figure 2 for a Weighting Template screen with deliberately
inconsistent input entries for a set of nine parameters, to show the feedback
that accompanies an Inconsistency Index greater than the adjacent Limit.
0001ºPAIRED-COMPARISONS ENTERED BELOW FROM WORKSHEET PER INSTRUCTIONS, WITH º
0002ºC 1,2: 1 2 MOST IMPORTANT IN FIRST ADJACENT COLUMN AND "BY HOW º
0003ºC 1,3: 1 3 C 2,3: 2 2 MUCH" REPLACING 1 IN NEXT COLUMN. º
0004ºC 1,4: 1 1 C 2,4: 4 2 C 3,4: 4 3 º
0005ºC 1,5: 5 2 C 2,5: 5 3 C 3,5: 5 5 C 4,5: 5 3 º
0006ºC 1,6: 6 4 C 2,6: 6 5 C 3,6: 6 7 C 4,6: 6 5 º
0007ºC 1,7: 7 3 C 2,7: 7 4 C 3,7: 7 6 C 4,7: 7 4 º
0008ºC 1,8: 8 1 C 2,8: 8 2 C 3,8: 8 3 C 4,8: 4 1 º
0009:C 1,9: 1 3 C 2,9: 2 2 C 3,9: 3 1 C 4,9: 4 3 º
0010º 1 1 1 1 1 1 1 1 1 9 1 º
0011ºENTRIES BELOW ARE USED FOR WEIGHTING PROBLEMS UTILIZING MORE THAN FIVE º
0012ºC 5,6: 6 3 PARAMETERS OR HIERARCHY SUBELEMENTS: º
0013ºC 5,7: 7 2 C 6,7: 6 8 (C) ODDSCO, 1999 º
0014ºC 5,8: 5 3 C 6,8: 6 4 C 7,8: 7 4 ######º
0015ºC 5,9: 5 5 C 6,9: 6 1 C 7,9: 7 6 C 8,9: 8 3 º
0016º [WORST IMPLICATION(S) = C 6,7:C 6,9: AND C 5,6:C 5,9:] º
0017º (PRESS F9 KEY AFTER DATA ENTRY. PARAMETER VALUES WILL APPEAR BELOW.) º
0018º #1 #2 #3 #4 #5 #6 #7 #8 #9 I.I. LIM: :
0019º 0.072 0.045 0.027 0.067 0.137 0.343 0.184 0.069 0.055 0.185 0.125 º
0020º 0.068 0.045 0.028 0.054 0.116 0.472 0.15 0.05 0.017 SIMULT-EQU º
The only difference between the consistent inputs shown in the Decision Process
Tutorial is indeed where shown in row 16. The By How Much value for comparing
criteria 6 to 7 was changed from 2 to 8. For criteria 6 to 9, the value was
changed from 7 to 1. The implied value for criteria 7 to 9 is 1/8 to 1, while
the explicit input is 6 to 1.
The inconsistency test feature helps guard against input of clerical errors
which might otherwise remain undetected. Inconsistency also may be an indicator
of some ignorance regarding one or more of the parameters involved in the study.
It can help bring about group consensus through discussion of the compromises
necessary to adjust the input By How Much values to obtain consistency for a
synthesized weighting. Attempting to reach perfect consistency with your
weighting of parameters may be misguided, however. Accuracy is more important
than consistency because you could be consistently wrong.
Weighting Method Criticism
The weighting process described above has made a sufficient impression on the
mathematical community to bring out detractors, which is not unusual for
anything important. The principal critic (Yu, see reference ending the Decision
Process Tutorial) has correctly pointed out that the weighting produced by
Saaty's AHP method is not the set of values which obtain from normalizing the
algebraic simultaneous equations solution to the set of input values. The
example given in his book to critique Saaty's method is the following three
parameter input set:
For pair 1,2 the input is 1 for Most Important and 9 for By How Much. For pair
1,3 the input is 1 for Most Important and 7 for By How Much. For pair 2,3 the
input is 3 for Most Important and 5 for By How Much. Saaty's method results in
output parameter weights of 1 = .7719, 2 = .0546, and 3 = .1735. The
simultaneous equation solution results in weights of 1 = .7857, 2 = .0357, and
3 = .1786. A significant difference, in the second decimal place, but is it
The implied relationship for pair 2,3 from the 1,2 and 1,3 pairings just given
is 2 for Most Important and 9/7 for By How Much. With that input for pair 2,3,
the Consistency Index becomes zero and the output weights from Saaty's method
are identical to the simultaneous equation solution weights. Therefore, because
Saaty's method provides extremely clear feedback when the input is inconsistent,
final weights that differ so extremely from the exact solution are quite
When the number of parameters is large, however, the trial and error work
required to find an input set that results in an Inconsistency Index of true
zero expands toward impracticality.
A good compromise is to adjust input values until the Inconsistency Index drops
below the Inconsistency Limit. Because the Inconsistency Index is not zero,
Saaty's method alone does not provide the exact solution for the input values.
What to do?
Combining methods provides the best overall approach. The computation of
Saaty's method provides good feedback on input consistency, while the
simultaneous equation solution provides nearly exact weights for the input set.
Both sets of weights are shown at the bottom of the Weighting Template input
screen, so you can see just what the differences are. When adjusted input
values result in an Inconsistency Index anywhere below the Inconsistency Limit,
you have sufficiently valid weights for the parameters. The weights produced by
the simultaneous equation solution should be used in the Decision Models.
Providing the suggested improvement was not without cost, however. The
spreadsheet template size is larger and takes a little bit longer to compute.
Still, the criticism has been addressed to this author's satisfaction. Output
for each of the approaches is in the Weighting Template's screen and complete
cell formulas listing is provided in the accompanying documentation.
The templates are not compiled or "protected," in keeping with a
philosophy of complete process revelation, so you may print the listings of
their contents upon obtaining the diskette for your computer.
Return to Citing Paragraph
The Example Decision Template
The calculations that you must perform to obtain the net utility score for each
option in your selection decision process are simple, as is tabulation of all
parameter weighting and scoring data for an Automobile Example. Once understood,
you easily can perform all the weighting summations with standard use of a
simple hand calculator.
It isn't sufficiently complex to require a separate program, so none is
provided. (Rather, tradeoff analysis of the work required to design an example
program when the weighted summation is but a few minutes of keyboard work on
any calculator indicates that it would hardly be worth it.)
Spreadsheets are another matter, however. The ease of building your Decision
Model with interrelated formula cells in a Decision Template suggests its use
as an extremely valuable tool for quickly performing "What if ... ?"
investigations of candidate utility score or weighting changes.
The Weighting Template can provide the remaining necessary data for your
Decision Model. The set of utility curves translates measured or estimated
performance into candidate scores for their associated primitives.
The accompanying documentation describes the construction of the model
worksheet for template assistance to the Decision process for an Automobile
selection. The description basis for this introduction assumes using Microsoft
Excel spreadsheet program, although any of the other popular programs would
serve as well (with appropriate revisons to the formulas when an Import function
is not available).
An advantage of the organization outlined is that replication of each candidate
area requires editing only the weight cell references. The default
"natural" and automatic recalculation settings are acceptable and
appropriate. The calculation order within the spreadsheet is of little
importance, because a recalculation after each data entry will update all of
the cell values quickly enough after any entry.
When you work with an extremely involved model having many candidates, it
speeds up data entry if you set the spreadsheet to await manual recalculation
until you have filled the candidate areas with their earned utility scores
before forcing the calculation of results.
Once the spreadsheet layout is well understood, you can build your own
version quickly and easily for your decision problem.
Return to Citing Paragraph
Return to Products List and Order Form Link.
A Reproduction Master of the Utility Graph is supplied free with each bound Tutorial. It is a fine-lined blank utility graph form for use with your copier to make an unlimited supply of the scoring graphs for your decision models.
Printing of the tutorials is "free" although not necessarily very well finished,
and the HTML sources can be captured and word processed to clean them up but
not without expenditure of substantial time and effort, so reproduction ready
prepackaged versions are available.
Return to Products List and Order Form Link.
The new edition hardbound target price is about $60. The Preface of the
upcoming edition is presented below so you can determine your level of
interest. If you would like to receive an e-mail notification when the book is
available, please submit an email to the provided address. (Your e-mail
information will not be sold or otherwise disseminated to others. It will be
used only for new product notification.)
What, exactly, does "preselling" group decisions mean and how does
that affect you?
Even when the subject of a decision seems trivial, it may be of greater
importance than you realize. Your professional reputation may rest on the
quality of your most recent decisions, regardless of how competently you made
the last critical one.
Most decisions involve one or more persons in addition to the decision maker,
even when they "just happen" over time. Many decisions also require
agreement with or approval of some part of the process by someone who may be a
spouse, boss, customer, or peer. Whenever other people become involved, whether
or not they ever meet to discuss any aspect of the decision, it is a group
Although the title of this and other books may imply otherwise, groups do
not decide anything. A so-called "group decision" is the revealed
consensus of separate individual decisions. The consensus result is
indistinguishable from an individual's decision except for potentially
higher quality because more brains are applied to solving the problem. You
cannot abdicate responsibility to think for yourself, however, if you are to
assist attaining that potential.
This should give sole proprieters/small businesspersons no small encouragement,
because they lack management support staff for determinining the value of
options and always must "go it alone" when making tough decisions. It
is seldom true that you are completely alone, at least while gathering
information. Still, if a decision-making process isn't simple and effective for
use when you are working alone, it may as well not exist. Therefore, throughout
this book I will address you as if you were making the decision alone or acting
as a decision consultant to a person or group charged with responsibility for
the final results.
You already know that persuading others to give open approval can be difficult
and frustrating, especially if the subject at hand has deficiencies as well as
benefits. You also know that the presentation often becomes more important than
the information, particularly with a multi-faceted problem. As more people
become involved in complex decisions, it becomes increasingly difficult to
"sell" the result. If you reduce complexity by subdividing the
decision into several simpler considerations, however, you can gain agreement
with each separate, simpler, and understandable portion of the decision.
Obtaining consensual approval of each separate part is much, much easier than
when complex concepts are combined into a less clearly definable form.
Further, when each separate consideration sells itself, you can gain acceptance
of the decision process result without need for a "hard sell"
approach. Gaining separate approval of all the pertinent aspects of the
decision is, in its final effect, equivalent to obtaining prior acceptance of
the outcome. Thus, early justification of relevant decision criteria
effectively "presells" the result to every individual who influences
or must approve it, before the outcome is known. Trust developed in the
rational decision process, further reinforced at each step, replaces the usual
need for later convincing.
A decision process facilitating such preselling does not require your
salesmanship for success. Rather than focusing on selling as manipulating
emotions to obtain the key agreements, this book presents a logical procedure
with separately verifiable steps which develop well-documented justifications
for your associate's acceptance of the process and approval of the result.
The procedure grew from my applying the System Engineering approach to
developing a better decision making process. Therefore,the highly structured
and systematic but customizable method is not "new" for any separate
step, except in final arrangement. It is an improvement, made up of
steps adapted from older and contemporary decision process descriptions to
simultaneously satisfy multiple process objectives.
Success with subsets of one to several of the recommended procedural steps is
documented in the cited references. In that respect, each step has been tested
thoroughly and may be applied with confidence. You will find that irrelevant,
time-wasting objections become less frequent and their disposition easy.
The "information explosion" is not a myth, as our bulging mailboxes
reveal. Computers have greatly accelerated the flow of data with which you must
deal and this will continue to get worse as the world becomes ever more
complicated. The recommended decision method necessarily organizes a normally
chaotic process. So, by helping to reduce pressure and assist in gaining
consensus, applying this approach should save you and everyone involved in
decision making significant amounts of both time and money.
The recommended approach addresses two seemingly conflicting needs. On the one
hand, you want a tailored solution for each decision. On the other hand, you
know it is easiest to work with standard tools for maximum mental comfort and
productive efficiency, as long as their effectiveness is based on being simple
rather than merely simplistic.
Accordingly, the method employs a standardized process to build decision models
with a similar format. You tailor the detailed content specifically to the
decision at hand. Managerial information is presented in a structured framework
that assists conflict resolution, knowledge gathering, discovery of
relationships, and decision making within the problem solving arena. The
primary benefit is that you quickly will discover how to easily develop custom
versions for making convincing decisions within your own areas of interest. The
approach is not, "Do it this way!" but simply, "Here's
how to do it your way."
Remember that when anything goes wrong, your audience of critics will multiply
and someone will say, "I told you so!" Their potential carping is
defused when criticism is invited early on and they develop a "face"
involvement in the outcome.
This book presents an easy step-by-step approach to subdividing your decision
and influencing acceptance of the final result by justifying each decision
process step. It also includes what you need to know to "sell" the
To demonstrate the value of each procedural step to the initial skeptics, the
following tests are consistently addressed:
Applying the process recommended in this book, you will learn how to employ
proven techniques which convincingly meet all four tests for every step. You
will learn a comprehensive and completely integrated system of step-by-step
manual procedures and simple arithmetic operations that help you to quickly
organize, control, document, and gain approvals in every stage of complex,
many-faceted decisions. You will learn to include costs, benefits, and
probabilities with confidence during evaluation of alternate systems or
strategies in a real-world environment of uncertainty.
Anything you learn which improves your individual ability to make good
decisions will correspondingly make you a more valuable member of any group
involved in decision making. There is a better way, because you can take
this process and make it yours.
For the small business operator, it can mean the crucial difference between
success and failure. A large firm can recover from errors but a single small
business decision may catastrophically affect the product or service, profits,
and the people involved daily in the survival of the firm. except that many
downsized big company profit centers are like small businesses in terms of
The potential for major improvement in your decision making involves four areas.
First, personal decisions become easier, yet more logical and effective because
you can better encompass and define the problem and required information.
Second, decision outcomes improve through your better identification of and
evaluation of alternatives. Third, increased knowledge will give you courage to
implement the best decision despite lingering uncertainty. Fourth, as the best
test of the methodology, your increased understanding and ability to use the
process is transferrable to others for improved decision making throughout your
organization or social group.
Making competent decisions with a systematic procedure gives others increased
confidence in your ability. You can develop a solid reputation for reducing
uncertainty and producing accurate judgments. You will not only know but
be able to explain exactly why a particular option is best. You will be able to
tell when others are making systematic decisions (or just putting up the facade
of an analysis process) from a brief review of their supporting arguments. You
can train others to employ the simple, systematic, effective, easily
communicated method, such that they are more likely to arrive at results
similar to yours. If you must delegate some of the decision process work,
which is common in even a group of your peers, you should be glad to have such
a method available.
If you anticipate a process full of complex formulas, relax! In recognition
that the language of science and math is not the language of small business or
most executives, this is neither an academically oriented pipedream nor a
quantitative methods text that requires charts and specialized jargon. Instead,
this book features practical, proven methods while excluding arcane theories.
You can do everything necessary without having to know or learn complicated
Requiring only knowledge of how to add, subtract, multiply and divide, the
basic recipe for concocting a good decision is easily grasped and practical to
use by nearly anyone with pencil and paper, anywhere. All of the numeric
operations are explained in detail. An electronic calculator is recommended for
maintaining accuracy, however. If you have access to a personal computer, as
well, the spreadsheet template approach is fully described.
Now, if you will invest a little time and mental effort, you can improve the
financial and physical well-being of your business or your personal career
through learning how to apply a decision making process that is readily taught
to others. You will be pleased to discover how powerful, yet simple the method
can be to use, except for your possibly feeling that some of it is so
"obvious" it should have been apparent to you long ago.
Perhaps this book will only reinforce your existing knowledge. Perhaps you
already use a similar approach to assessing benefits, costs, and implications
of incremental variations on a theme to converge on the optimum solution. If
so, you are hereby congratulated for developing an unusual breadth of interests.
Even old hands should find something new within this book, however, because the
"same old stuff" is presented in context of application.
Through 1981, I worked as a Staff Systems Engineer for a defense corporation to
develop a design concept study for a new weapon system design. Difficulties
encountered in evaluating technical application alternatives and selecting the
best possible final system configuration to propose for prototype development
made me aware of common problems with decision-making processes.
Experience provided a "feel" for the tasks and certainly helped me to
evaluate the interactive technical design alternatives to my own satisfaction.
It did not, however, build convincing arguments that would be required to
"sell" the solution to project management.
Acceptance of results comes with any "ownership" of the method.
Necessity to explain the conclusions to non-technical persons pushed me to seek
a simple, practical approach to decision making that nearly anyone could use.
Moreover, I wished to discover a comprehensive process that everyone should
use. That effort failed. All of the process descriptions I encountered fell
short in one or more objectives. I shifted to process improvement. Perhaps a
satisfactory process would result if I took the best from all I had learned and
could discover, modifying or extending as necessary.
Few can boast of inventing a truly new method and I won't claim to be among
them. My perspective was gained by standing on the shoulders of the giants who
first invented or successfully described the separate useful concepts and
methods from which the recommended procedure evolved. I approached its
synthesis and development exactly as I would address any systems engineering
Becoming convinced that I had assembled a simple, yet complete and reliable
decision making procedure occupied the major part of a decade. I wanted to make
sure that the total process hadn't already been presented elsewhere by someone
in a similar or possibly better form. Verification required thoroughly
investigating anything that seemed vaguely related to making decisions. Those
sources contain the theories and principles of other's mathematical and
heuristic approaches to decision making.
I could not and evidently cannot ever think as does a theoretical
mathematician, especially with proofs of advanced theorems, but that was not my
role. My task was to learn enough to verify the practical soundness of the
approaches invented by others and to synthesize the appropriate parts into
useful tools. The verification and validation are now complete. I found many
descriptions of some of the process steps, but nothing that was
comparable or better.
Having begun as a novice in the field, I felt compelled to pass on the essence
of my decision making research. My intent throughout was to add to the general
body of business, government, and education system decision making knowledge at
the intelligent reader or "paraprofessional" level as my initial
offering to posterity. You now hold the fruit of that lengthy part-time self-
training: a single introductory level book which emphasizes fundamental
principles and associated philosophical concepts over underlying theory and
For the interested, it should help to narrow the gap between the haves (those
with training and on-the-job experience in some of the subjects) and the have-
nots (those who must guess at meaning of the "buzzwords"). I have
tried to point the way, to empower with relevant knowledge in plain language
unburdened by scholarly trappings, to provide an "exemplar" for their
problem solving process. I have made the learning process fairly easy, by
presenting process steps and the appendices of associated material as separate,
easily digested chunks of related information.
If the typical small business owner/operator can become more enabled mentally
and use the recommended process to make better decisions or if larger businesses
can use it as an effective part of their Continuous Improvement Process/Total
Quality Management (CIP/TQM) endeavors, I have provided adequate value in
exchange for the reader's effort and success is mine. If you find the process
useful, teach others.
Chapter One introduces decision making, presenting characteristics and
drawbacks of historical and other present approaches followed by descriptions
of the steps involved in various decision processes and a listing of desirable
features for such a system. With that background in place, Chapter Two sets
forth an overview of the steps involved in the process I recommend.
The next interrelated group of chapters explores each separate step of the
recommended decision making process in detail, with emphasis on obtaining the
appropriate approvals as part of the "preselling" activity. Each
concludes with a Checklist to assist formal decision design reviews and audits.
Chapter Three provides direction in discovering and describing the decision
purpose such that all the future effort is goal-directed. Chapter Four
describes using an idealized system conception to develop a complete
hierarchical, branch-structured decision model. Chapter Five reveals how to
design performance scoring or utility graphs. Chapter Six shows you how to
assign relative importance values or weighting to the sets of decision
parameters. Chapter Seven presents how to input raw data for evaluating
alternatives, then generate and interpret the decision process results.
To conclude the procedural information, Chapter Eight covers how to make
additional helpful use of the various graphical decision bases generated during
the earlier decision-making process steps. These process elements are employed
during the decision model design, development and progress reviews, decision
results summary presentations, and final decision documentation.
Chapter Nine details the available supporting tools I developed specifically
for assisting the recommended process with popular personal computers, the
spreadsheet templates or worksheets for assisting parameter weighting and
Chapter Ten illustrates the process used for selecting an automobile as the
common and understandable personal choice or business problem excerpted in the
preceding chapters to portray the simple decision model concepts, then closes
with personal applications.
Chapter Eleven describes applying the process to administrative personnel
problems of screening and rating potential employees, developing of individual
employee performance reviews, and, in keeping with today's uncertain world, it
shows how to defensibly "stack" or rank employees for potential
reductions in force. It closes with additional non-financial business
Chapter Twelve briefly describes many example applications for government and
education decision making, along with some suggested decision parameters for
each category of decision model.
Chapter Thirteen generally describes how to incorporate financial analysis
items in your decision models. This common business problem includes the
application of both tangible and intangible aspects and extensive discussion of
the difficulties of forecasting.
Chapter Fourteen addresses Project Risk Analysis with a brief discussion of
assessing and quantifying the common technical, schedule and cost risk aspects
of projects for decision modeling.
Chapter Fifteen is a scenario of the recommended method's use by salespeople
working with customers who desire to effectively compare competitor's complex,
high-value products with theirs and build potential for benefits to everyone
Chapter Sixteen discusses implementation issues for application of and training
in the recommended process. It verifies that the presented process fits easily
within traditional businesses of any size, whether following the Management by
Objectives (MBO) approach or other management styles discussed in Appendix D.
It further shows application to those having adopted Kaizen, the
Japanese business philosophy of continuous quality improvement. It concludes
with some thoughts on decision process improvement.
An added benefit is discussion, in separate appendices, of closely related
subjects. With context information adding to your knowledge, you can truly
understand the processes and better evaluate where and how to apply your new
skills to solve decision problems encountered by all types of businesses.
Appendix A is an overview of Concurrent/Systems Engineering, which describes
the current extremely thorough military approach as the principal underlying
process for dealing with complexity. Its fundamental concepts reveal the
extremely strong relationship system engineering has to all the rest of this
Appendix B discusses the Knowledge Support arena, including Executive
Information Systems (as evolved from Decision Support Systems) so you can
evaluate how the method described in this book fits within the overall concepts
of decision making applied at all levels within all types of businesses. It
describes decision support subsystems, such as artificial intelligence, neural
networks, expert systems, operations research, statistical analysis, and
various forms of mathematical modeling as part of modern management science.
The recommended process easily could fit into this category if it were not so
simple to use. (To that end, this book lacks the esoteric math worshipped by
traditional scholars in the field.)
Appendix C addresses Management in general, including the prevalent approaches
in today's medium to large businesses, then illuminates the Continuous
Improvement Process and the closely related Total Quality Management approach.
It describes management tasks and styles, Management by Objectives, along with
planning, control and organization. It concludes that section with a brief
description of the small business perspective. It then discusses Quality
Circles and Work Function Teams, management functions, and Quality Function
Deployment. It concludes with discussion of concepts application to small
businesses. Individual and group decision making certainly is encompassed by
Those first three appendices are instructive in applications of both analytical
and evaluative techniques described in the main body of this book, to give you
ideas regarding when to best use them, which brings us to the treatment
of the source material.
Appendix D explains applicable fuzzy logic concepts and shows how their
inclusion in the scoring step establishes the previously missing link to
mathematics that allows including risk assessments and other qualitative
criteria in the decision model.
Appendix E lists the references from which came many of the ideas and concepts
came. I have deliberately avoided necessity to quote anyone because this is not
a collegiate project. Arrangement is alphabetically by title in each decision
making subject area. The annotation by each reference is my personal viewpoint
regarding the interpretation, content utility, and conclusions to be drawn.
Appendix F is the alphabetical list of individual authors for the references
cited in Appendix E. It adds no information to Appendix E, but is gathered in
the more traditional form. The reference title is the link to the entry of
full information in Appendix E.
Appendix G is a thorough glossary containing information useful to decision
making gathered from many wide-ranging sources, including the references cited
in Appendix E. The definitions for each term are as I used them within this
Appendix I is the index to the explanatory use of applicable terms defined in
Appendix G. Only locations of terms that illuminate their meaning in context
are listed, rather than mere results of exhaustive word search in the chapters
Finally, boldface type is employed for selected terms from the lexicon
used in this book, beginning in Chapter One and continuing through Appendix G.
Boldfacing of a key term's first occurrence in each chapter and in the
appendices reinforces its recognition as a specifically used word. All such
boldfaced terms are listed in the Index and defined in the glossary, as used
herein, to minimize misunderstanding. Some "buzzwords" and other
terms of general importance to business decision making are listed in the
glossary without their boldfacing in the text. The many other concepts that I
wished to give due emphasis are italicized in the text.
In any case, you remain faced with the risk of making an error of the first
kind (accepting a bad idea) or of the second kind (rejecting a good idea). Even
if you think it all a facade of simplicity, now, you should learn quite a lot
from this book of which you can make profitable use. Enjoy the trip.
A gentle disclaimer:
Despite my obvious confidence in the process, I don't guarantee results because
too many external factors are involved in any business success. Besides, it is
not my application of knowledge or skill that such a guarantee would have to
cover, but yours.
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