1. Decision Making and Concept Selection
Chapter 7
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2. How can we make the right decision?
7.1 Introduction
How can we make the right decision?
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3. Concept Generation & Selection
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4. Requirements for Selecting a Design
A set of design selection criteria
A set of alternatives believed to satisfy the set of criteria
A means to evaluate the design alternatives with respect to each criterion
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5. How can we make the right decision?
7.2 Decision Making
How can we make the right decision?
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6. Evaluate & Select Concept Stage in PDP
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7. Behavioral Aspects of Decision Making
Behavioral psychology provides an understanding of the influence of risk taking in individuals and teams.
Making a decision is a stressful situation for most people because there is no way to be certain about the information about the past or the predictions of the future.
This psychological stress arises from at least two sources:
Decision makers are concerned about the material and social losses that will result from either course of action that is chosen.
They recognize that their reputations and self-esteem as competent decision makers are at stake.
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8. Challenges of Decision Making
Unconflicted adherence:
Decide to continue with current action and ignore information about risk of losses.
Unconflicted change:
Uncritically adopt whichever course of action is most strongly recommended.
Defensive avoidance:
Evade conflict by procrastinating, shifting responsibility to someone else, and remaining inattentive to corrective information.
Hypervigilance:
Search frantically for an immediate problem solution.
Vigilance:
Search painstakingly for relevant information that is assimilated in an unbiased manner and appraised carefully before a decision is made.
All of the above except the last one are defective!
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9. List of Steps to Make a Good Decision
The objectives of a decision must be established first.
The objectives are classified as to importance.
Alternative actions are developed.
The alternatives are evaluated against the objectives.
The choice of the alternative that holds the best promise of achieving all of the objectives represents the tentative decision.
The tentative decision is explored for future possible adverse consequences.
The effects of the final decision are controlled by taking other actions to prevent possible adverse consequences from becoming problems and by making sure that the actions decided on are carried out.
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10. Decision Theory
Alternative courses of action can be denoted 𝑎 1 , 𝑎 2 ,…, 𝑎 𝑛
States of nature are the environment of the decision model.
Outcome is the result of a combination of an action and a state of nature.
Objective is the statement of what the decision maker wants to achieve.
Utility is the measure of satisfaction that the decision maker associates with each outcome.
States of knowledge is the degree of certainty that can be associated with the states of nature.
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11. Decision Making Models
Decision under certainty:
Each action results in a known outcome that will occur with a probability of 1.
Decision under uncertainty:
Each state of nature has an assigned probability of occurrence.
Decision under risk:
Each action can result in two or more outcomes, but the probabilities for the states of nature are unknown.
Decision under conflict:
The states of nature are replaced by courses of action determined by an opponent who is trying to maximize his or her objective function.
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12. Decision Tree for an R&D Project
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13. Decision Tree for an R&D Project
The best place to start in this problem is at the ends of the branches and work backward.
E= 0.3(1.8) + 0.5(1.0) + 0.2(0.4) = $1.12M for the on-time project
E= 0.1(1.4) + 0.5(0.8) + 0.4(0.3) = $0.66M for the delayed project at point 3
E= 0.3(0.66) + 0.7(0) - 2 = -$1.8M for the delayed project at point 2
The calculation of the expected payoff for the on-time project at point 1 is a large negative value
E= 0.5(1.12) + 0.5(0) - 4 = -$3.44M
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14. Utility Theory Definitions
Value is an attribute of an alternative that is implied by choice.
Preference is the statement of relative value in the eyes of the decision maker.
Utility is a measure of preference order for a particular user.
Marginal utility: A key concept of utility theory is the understanding of the nature of what is gained by adding one more unit to the amount already possessed.
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15. Utility Functions
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16. Common Types of Utility Functions in Engineering Design
The most common
Typical of a high-performance situation.
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17. 7.3 Evaluation Process
What are the steps involved in concept generation and its evaluation process?
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18. Concept Generation and Evaluation
Steps that are involved in concept generation and its evaluation.
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19. Shot Buddy Concept Generation
Adapted from J. Davis, J. Decker, J. Maresco, S. McBee, S. Phillips, and R. Quinn, “JSR Design Final Report: Shot-Buddy,” unpublished, ENME 472, University of Maryland, May 2010.
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20. Design Selection Based on Absolute Criteria
Good practice to begin the evaluation process by using a series of absolute filters.
Evaluation based on judgment of functional feasibility of the design
Concepts should be placed into one of the followings:
Feasible
Not Feasible
Will Work
Evaluation based on assessment of technology readiness
Product design is not the appropriate place to do R&D.
Evaluation based on go/no-go screening of the constraints and threshold levels of engineering characteristics
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21. Measurement Scale
Rating a design parameter of several alternative designs is a measurement process.
Various scales of measurement:
Nominal Scale – data defined by named categories
Ordinal Scale – data is placed in rank order (1st, 2nd, … nth)
Interval Scale – data arranged in numerical order without a “zero” point
Ratio Scale – data arranged on an interval scale
Standard arithmetic operations are only valid for a ratio scale
Addition and subtractions are valid on an interval scale
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22. 7.4 Using Models in Evaluations
How can models help the evaluation process?
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23. Types of Models Models fall into three categories:
An iconic model is a physical model that looks like the real thing but is a scaled representation.
Analog models are models that are based on an analogy, or similarity, between different physical phenomena like using electricity flow to simulate heat flow.
Symbolic models are abstractions of the important quantifiable components of a physical system that use symbols to represent properties of the real system.
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24. Role of Models in Design
In conceptual design we use both iconic and symbolic models.
Simple mathematical models are used to help formalize a concept and to provide data, not just opinions, to use in decision evaluation tools.
A proof-of-concept prototype is typically made by the end of conceptual design.
Ideally, a succession of models, some physical, others rough sketches, are made to serve as learning tools until reaching the final proof-of-concept model.
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25. Choosing the Right Model
In conceptual design, the emphasis is on geometrical modeling using multiple hand sketches supplemented with quick physical prototypes.
In embodiment design, where major emphasis is given to establishing shape, dimensions, and tolerances, the level of detail in mathematical and physical models increases.
In detail design, more complex mathematical modeling may be conducted to optimize some product characteristic or to improve its robustness.
A proof-of process prototype will be tested using the exact materials and processes that will be used to manufacture the product.
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26. Aids to Mathematical Modeling
Dimensional analysis:
A useful tool in model building is dimensional analysis.
Scale Models:
Scale models are often used in design because they can be made more quickly and at less cost.
There are usually fewer dimensionless groups than there are physical quantities in the problem, so the groups become the real variables of the problem.
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27. Building Mathematical Model
There are four characteristics of mathematical models consisting of two classes each:
Steady state or Transient
Continuous media or Discrete events
Deterministic or Probabilistic
Lumped or Distributed
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28. Steps to Build a Mathematical Model
Determine problem statement
Define the boundaries of the model
Determine which physical laws are pertinent to the problem and identify the data that is available to support building the model
Identify assumptions
Construct the model
Perform computations and verify the model
Validate the model
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29. Sketch of Key Model Building Factors
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30. Geometric Modeling on Computer
Geometric modeling on the computer was the fastest-changing area of engineering design in the late 20th century.
An aspect of CAD modeling that has grown in importance is data associativity, the ability to share digital design data with other applications such as finite element analysis or numerical controlled machining without each application having to translate or transfer the data.
For more details on computer generation of solids and creation of features in solid models, see Computer Modeling at
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31. Finite Element Analysis
Finite element modeling is divided into three phases:
preprocessing
computation
post processing
Even before entering the first phase, a careful engineer will perform a preliminary analysis to define the problem.
Questions to ask before entering the first phase:
Is the physics of the problem known well enough?
What is an approximate solution based on simple methods of analysis?
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32. Preprocessing Phase
In the preprocessing phase the following actions are taken:
The geometry of the part is imported from the CAD model.
Determine the division of the geometry into elements, often called meshing.
Determine how the structure is loaded and supported.
Select the constitutive equation for describing the material (linear, nonlinear, etc.) that relates displacement to strain and then to stress.
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33. Computation
The FEA program renumbers the nodes in the mesh to minimize computational resources.
It generates a stiffness matrix for each element and assembles the elements together so that continuity is maintained to form the global matrix.
Then the computer solves the massive matrix equation for the displacement vector or whatever is the dependent variable in the problem.
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34. Post Processing
In a stress analysis problem, post processing takes the displacement vector and converts into strains, element by element, and then, with the appropriate constitutive equation, into a field of stress values.
A finite element solution could easily contain thousands of field values.
Increasingly, FEA software is being combined with an optimization package and used in iterative calculations to optimize a critical dimension or shape.
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35. FEA in Machine Design
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36. Simulation
Design models are created to imitate the behavior of a part or system under a particular set of conditions.
When we exercise the model by inputting a series of values to determine the behavior of the proposed design under a stated set of conditions, we are performing a simulation.
A simulation model can also be used to understand an existing system when data is not readily available.
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37. 7.5 Pugh Chart What is Pugh Chart?
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38. Pugh Chart
A particularly useful method for identifying the most promising design concepts among the alternatives generated at is the Pugh chart.
Pugh’s method compares each concept relative to a reference or datum concept and for each criterion determines whether the concept in question is better than, poorer than, or about the same as the reference concept.
Pugh Chart is a relative comparison technique.
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39. Steps of Building the Pugh Chart
Choose the criteria by which the concepts will be evaluated
Formulate the decision matrix
Clarify the design concepts
Choose the datum concept
Complete the matrix entries
Evaluate the ratings
Establish a new datum and rerun the matrix
Examine the selected concept for improvement opportunities
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40. Pugh Chart 1 for Shot-Buddy Example
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41. Pugh Chart 2 for Shot-Buddy Example
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42. 7.6 Weighted Decision Matrix
What is Weighted Decision Matrix?
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43. Weighted Decision Matrix
A decision matrix is a method of evaluating competing concepts by ranking the design criteria with weighting factors and scoring the degree to which each design concept meets the criterion.
To do this it is necessary to convert the values obtained for different design criteria into a consistent set of values.
The simplest way of dealing with design criteria expressed in a variety of ways is to use a point scale.
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44. Evaluation Scheme for Design Alternatives or Objectives
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45. Systematic Methods for Determining Weighted Factors
Direct Assignment:
This method is only recommended for design teams where there are many years of experience designing the same product line.
Objective Tree:
This method relies on some experience with the importance of the criteria in the design process.
Analytic Hierarchy Process (AHP):
This method is the least arbitrary method for determining weighting factors.
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46. Objective Tree: Design of a Crane Hook
Weighting factor for material cost O111= 0.3 x 0.6 x 1.0 = 0.18
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47. Weighted Decision Matrix: Steel Crane Hook
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48. 7.7 Analytical Hierarchy Process(AHP)
What is AHP?
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49. Analytic Hierarchy Process (AHP)
The Analytic Hierarchy Process (AHP) is a problem-solving methodology for making a choice from among a set of alternatives when the selection criteria represent multiple objectives, have a natural hierarchical structure, or consist of qualitative and quantitative measurements.
AHP was developed by Saaty.
AHP is a decision analysis tool that is used throughout a number of fields in which the selection criteria used for evaluating competing solutions that do not have exact, calculable outcomes.
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50. AHP’s Ratings for Pairwise Comparison of Selection Criteria
The ratings of even numbers (2, 4, 6, 8) are used when the decision maker needs to compromise between two positions in the table.
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51. Example: Design of Crane Hook
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52. AHP Process for Determining Criteria Weights
The process is:
Complete criteria comparison matrix [C] using 1–9 ratings described in Table 7.8.
Normalize the matrix [C] to give [NormC].
Average row values. This is the criteria weight vector {W}.
Perform a consistency check on [C] as described in Table 7.10.
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53. Consistency Check Process for AHP Comparison Matrix( C )
Calculate weighted sum vector, {Ws} = [C] × {W}
Calculate consistency vector, {Cons} = {Ws}/{W}
Estimate λ as the average of values in {Cons}
Evaluate consistency index, CI = (λ − n)/(n − 1)
Calculate consistency ratio, CR = CI/RI
If CR < 0.1 the {W} is considered to be valid; otherwise adjust [C] entries and repeat.
RI Values are given in Table 7.11.
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54. Consistency Check for {W} for Crane Hook
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55. AHP’s Ratings for Pairwise Comparison of Design Alternatives
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56. Determining Ratings for Design Alternatives with Respect to a Criterion
The process is summarized as:
Complete comparison matrix [C] using 1–9 ratings of Table 7.12 to evaluate pairs of competing design alternatives.
Normalize the matrix [NormC].
Average row values—This is the vector priority {Pi} of design alternative ratings.
Perform a consistency check on [C].
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57. Determine the Best of Design Alternatives
The process is summarized as:
Compose Final Rating Matrix [FRating].
Calculate [FRating]{W}={Alternative Value} by first taking the transpose of [FRating].
Select the alternative with the highest rating relative to others.
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58. Design Alternative Ratings for Material Cost
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59. Final Rating Matrix The best solution is the riveted plate
design option.
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