A presentation of the ODDSCO products that support practitioner's of New Product Development, Integrated Product Teams (IPT), Systems Engineering, Concurrent Engineering, Requirements Management, Project Risk Management, and Continuous Improvement Processes.

ODDSCO's Product Descriptions

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Weighting Template ............... $40US

(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 ... to ... 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:

  1. Begin the page with identification data, including the name of the parent element.
  2. Set forth the names of attributes One and Two as a pair, followed by several lines of space as a visual divider.
  3. Similarly, set forth the names of attributes One and Three followed by blank space.
  4. Finally, conclude the list with the names of attributes Two and Three. For nine parameters, the list of visually separated name pairs will occupy several sheets.
  5. By each pairing of attributes, underline two areas. One area is a place to mark which parameter of the pair is Most Important with respect to fulfillment of the objectives represented by the parent element within the context of the entire system. The second underlined area is a place to mark the quantity designating By How Much that parameter is more important than the other element of the pair.
  6. The person doing the weighting is told that the scale of importance intensity ranges from 1.0 to 9.99. The assignment of 1.0 means equality of contribution to the parent, no difference in value, that either one is exactly as good as the other. Assignment of 9.99 means that the Most Important of the pair is overwhelmingly dominant, extremely more important than the other element. (The intermediate values will be defined in a few paragraphs, along with two ways to set up the pairing lists more compactly.)

    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 normalization.

    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 the decision.

    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 comparison values.

    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 description.

    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 marking styles.

    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.

    0003C 1,3:          1 C 2,3:          1  MUCH" REPLACING 1 IN NEXT COLUMN.  
    0004C 1,4:          1 C 2,4:          1 C 3,4:          1                  
    0005C 1,5:          1 C 2,5:          1 C 3,5:          1 C 4,5:          1
    0006C 1,6:          1 C 2,6:          1 C 3,6:          1 C 4,6:          1
    0007C 1,7:          1 C 2,7:          1 C 3,7:          1 C 4,7:          1
    0008C 1,8:          1 C 2,8:          1 C 3,8:          1 C 4,8:          1
    0009:C 1,9:          1 C 2,9:          1 C 3,9:          1 C 4,9:          1
    0010    1     0     0     0     0     0     0     0     0     1     0      
    0012C 5,6:          1  PARAMETERS OR HIERARCHY SUBELEMENTS:                
    0013C 5,7:          1 C 6,7:          1                   (C) ODDSCO, 1999  
    0014C 5,8:          1 C 6,8:          1 C 7,8:          1             ######
    0015C 5,9:          1 C 6,9:          1 C 7,9:          1 C 8,9:          1
    0018        #1    #2    #3    #4    #5    #6    #7    #8    #9  I.I.  LIM:  
    0019          0     0     0     0     0     0     0     0     0    -1     0
    0020          0     0     0     0     0     0     0     0     0  SIMULT-EQU

    Figure 1

    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 screen.

    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 maintain consistency.

    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.

    0003C 1,3:    1     3 C 2,3:    2     2  MUCH" REPLACING 1 IN NEXT COLUMN.  
    0004C 1,4:    1     1 C 2,4:    4     2 C 3,4:    4     3                  
    0005C 1,5:    5     2 C 2,5:    5     3 C 3,5:    5     5 C 4,5:    5     3
    0006C 1,6:    6     4 C 2,6:    6     5 C 3,6:    6     7 C 4,6:    6     5
    0007C 1,7:    7     3 C 2,7:    7     4 C 3,7:    7     6 C 4,7:    7     4
    0008C 1,8:    8     1 C 2,8:    8     2 C 3,8:    8     3 C 4,8:    4     1
    0009:C 1,9:    1     3 C 2,9:    2     2 C 3,9:    3     1 C 4,9:    4     3
    0010    1     1     1     1     1     1     1     1     1     9     1      
    0012C 5,6:    6     3  PARAMETERS OR HIERARCHY SUBELEMENTS:                
    0013C 5,7:    7     2 C 6,7:    6     8                   (C) ODDSCO, 1999  
    0014C 5,8:    5     3 C 6,8:    6     4 C 7,8:    7     4             ######
    0015C 5,9:    5     5 C 6,9:    6     1 C 7,9:    7     6 C 8,9:    8     3
    0016            [WORST IMPLICATION(S) =  C 6,7:C 6,9:  AND C 5,6:C 5,9:]    
    0018        #1    #2    #3    #4    #5    #6    #7    #8    #9  I.I.  LIM:  :
    0019      0.072 0.045 0.027 0.067 0.137 0.343 0.184 0.069 0.055 0.185 0.125
    0020      0.068 0.045 0.028 0.054 0.116 0.472  0.15  0.05  0.017 SIMULT-EQU

    Figure 2

    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 real?

    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 unlikely.

    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

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    Utility Graph/Bound Tutorials ................. $5 ea. or 5 for $20.

    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.

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    How to "Presell" Group Decisions (book)

    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.)

    Preface of Book

    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 process.

    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 justifications.

    To demonstrate the value of each procedural step to the initial skeptics, the following tests are consistently addressed:

    • Does it appear to be obviously and logically necessary to the decision process?
    • Is it understandably simple and easy to use, like a toolkit for building custom decisions?
    • Does it accommodate diverse technical criteria and subjective personal considerations, including their uncertainty and risk?
    • Does it enable audit or review by "management," clients, or independent observers with highly graphic, easily presented documentation that communicates the process as well as the decision results?

    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 decisions importance.

    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 math.

    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.

    Why This Book?

    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 problem.

    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 inflexible procedures.

    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.

    Organization of Book

    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 decision modeling.

    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 applications.

    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 involved.

    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 book.

    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 CIP/TQM concepts.

    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 book.

    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 and appendices.

    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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