Download presentation

Presentation is loading. Please wait.

Published byMaegan Inscoe Modified over 3 years ago

1
©Chelst & Canbolat Value-Added Decision Making Chapter 5 Structured Trade-Offs for Multiple Objective Decisions: Multi-Attribute Utility Theory Methods to assign weights to objectives and measures Methods to create a non-linear single utility function for a measure when appropriate 1 Most of the chapters tables and figures are included in the file. Instructor must decide how many and which examples to use.

2
©Chelst & Canbolat Value-Added Decision Making MAUT Process Describe Alternatives Clarify Preferences Analyze Structure TASKSSTEPS Weighted Sum Synthesize Conduct Comparative Analysis Evaluate Hybrid Alternative(s) Conduct Sensitivity Analysis Gather data for each alternative for each measure Assign weights Create a common scale for each measure Identify Measures Identify Requirements Determine Objectives Identify Alternatives TECHNIQUES Creativity & Expert Judgment Individual Analyses Swing Weight & Mid-Level Splitting 2 Chapter 5

3
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Weights and Utility Functions Decision maker(s) preferences Weights (across objectives and measures) reflect the relative value assigned to individual objectives and individual measures Utility function (Scale within a measure) Deterministic Reflects relative value (utility) of increasing or decreasing a measure Linear utility function is default relative value is strictly proportional to the measure Probabilistic Reflects attitude towards risk 3

4
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 (Maximize) Additive utility function: A weighted sum of n different utility functions takes on the following form for assumed linear additive independence between measures and objectives: 4 Chapter 5

5
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Assign weights to objectives and measures Tradeoffs 5 Direct assessment of weights SMART method – swing weights Top-Down – hierarchical Chapter 5

6
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Tradeoffs: Value and Technical 6 Chapter 5

7
©Chelst & Canbolat Value-Added Decision Making Example of Tradeoff Types : Cost and Service retail outlet 7 Technical trade-off How much will waiting time decrease by adding one more cashier? (queuing theory) How much will customer satisfaction improve if waiting time is reduced by two minutes? Value trade-off How much would a company be willing to spend to reduce waiting time by two minutes? How much more would a customer be willing to pay to reduce waiting time by two minutes? Chapter 5

8
©Chelst & Canbolat Value-Added Decision Making Difference Confusing for Experienced Managers and Decision Makers 8 Value Tradeoffs are NOT Technical Relationship Tradeoffs Experienced Managers and Designers routinely make technical relationship tradeoffs. They are less comfortable with softer issue of value tradeoffs Chapter 5

9
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Example: Light Bulb Selection Classic tradeoff: Cost vs. Performance Bill Frail has recently been promoted to a product development manager position and he will move to his new office. His new office is being repaired now. He will select light bulbs for the office. In the office, there are 10 bulb fixtures. What is the best bulb for Mr. Frail? How much value does he place on performance relative to cost? 9 Chapter 5

10
©Chelst & Canbolat Value-Added Decision Making Activity: Directly assign weights to performance and cost for bulb selection – Weights must sum to one. MeasureWeight Performance………... Cost………... Sum Total1 10 Chapter 5

11
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Data: Three Alternative Bulbs 10 lamps each with a bulb, 3000 hours a year per lamp Bulb life 60 & 75 W bulbs: 1500 hours (20 bulbs/year) Long-life 100 W bulb:3000 hours (10 bulbs/year) Electric rate: $0.10/kWh Annual Operating Cost: kWh/year*0.10 Bulb Watt Annual Operating. Cost ($) Annual Purchase Cost ($) Total Annual Cost ($) 60 1809 189 75 22510 235 100 30015 315 11 Chapter 5

12
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 No alternative is best on both measures 12 Chapter 5

13
©Chelst & Canbolat Value-Added Decision Making Determining Weights: Consider ranges Swing Weight method – Two Measures Rank the alternatives by considering the measure ranges Assign 100 points to the highest ranked measure range Assess the relative importance of swinging the next highest ranked criterion as a percentage of the highest ranked selection criterion's 100 point Swing Weight. Compute each measures relative weight by normalizing the individual Swing Weights. 13 Chapter 5

14
©Chelst & Canbolat Value-Added Decision Making Activity: Assign weights 10 bulbs Fill in the table below: look at the ranges & assign points, Calculate normalized final weight. Do not be surprised if there are significant differences from previously assigned weights that ignored ranges. MeasureLeast Preferred Value Most Preferred Value Rank Order PointsFinal Weight Performance60w100w…….….…… Annual Cost$350$150…….….…… Total …..1.00 14 Chapter 5

15
©Chelst & Canbolat Value-Added Decision Making Repeat Activity: Assign weights for 1000 bulbs (Inexpensive motel) Replace 1000 bulbs – Cost Range Changed MeasureLeast Preferred Value Most Preferred Value Rank Order PointsFinal Weight Performance60w100w….. Annual Cost$35,000$15,000….. Total…..1 15 Chapter 5 Do not be surprised if there are significant differences from previously assigned weights.

16
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Range specification should impact assignment of weights Different weights for different ranges as well as different decision contexts (office or hotel) Minimum Range to use when assigning weights: Difference between best and worst measures for alternatives considered. Preferable: Pick a range that is realistic for the problem and allows for the possibility of other realistic alternatives to be added The new alternatives may have values outside a too narrowly specified initial range. 16 Chapter 5

17
©Chelst & Canbolat Value-Added Decision Making Interpretation of Weights – Range Impact Assume performance rated highest and cost is 2 nd most important. Assume the range on cost was assigned 67 points relative to the 100 points for the highest ranked range Weights are 100/167 =.60 and 67/167 =.40 Assume Performance range of 40 watts (60 to 100 watts) This means that an alternative earns 0.60 utility units as the performance increases by 40 watts. = Every watt increase adds 0.015 utility to total score Now imagine the same assigned weights but a broader range from 25 to 100 watts This means an alternative earns 0.60 utility units as the performance increases by 75 watts. = Every watt increase adds ONLY 0.008 17 Chapter 5

18
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Weight Assignment Interview Question Wrong phrasing: How much weight to cost? Appropriate phrasing A: How much weight for a cost that ranges from $350 to $150 relative to a performance range of 60 to 100 watts B: How much weight for cost that ranges from $400 to $100 relative to performance range of 60 to 100 watts Answers to A and B should be different. Assume 1000 bulbs instead of just 10. C: How much weight for cost that ranges from $35000 to $15000 relative to performance range of 60 to 100 watts 18 Chapter 5

19
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Next step – Common units Single Utility Scale the Relative Values of a Measure 1. Proportional – Linear: DEFAULT assumption 2. Choose curves rough shape and evaluate points 3. Mid-level splitting 4. Direct Assessment – for category variables 19 Chapter 5

20
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Proportional Scores (SUF): Bulb Selection Assign 0 and 1 for worst and best levels of each objective, respectively SUF p (100 Watt)=1 (Best) & SUF p (60 Watt)=0 (Worst) SUF c ($150)=1(Best) & SUF c ($350)=0 (Worst) A general formula for the linear utility score The 75 Watt bulbs performance: The 75 Watt bulbs utility for cost: 20 Chapter 5

21
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Activity: Proportional Scores for Light Bulb Cost range was $150 (best) to $350 (worst) Calculate the utility score for cost measure for the 60-watt and 100-watt bulbs The 60 Watt bulbs utility for cost: SUF c ($189)= The 100 Watt bulbs utility for cost: SUF c ($315)= 21 Chapter 5

22
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 SUF : Bulb Selection – Linearity Assumptions 60 Watt Bulb75 Watt Bulb100 Watt Bulb Performance00.3751 Cost0.8050.5750.175 Linear Utility Going from 61 to 60 watts performance has the same value in utility as going from 100 to 99 watts 22 Chapter 5

23
©Chelst & Canbolat Value-Added Decision Making Calculate TOTAL Utility Score This decision maker less concerned about price and more concerned about performance. It is his lamps he must see by. Assigns 0.40 to cost and 0.60 to performance Calculate the TOTAL utility score for a 60 watt bulb U=w p SUF p (Performance)+w c SUF c (Cost) U(60 Watt)=0.60*(0.00)+0.40*(0.805)=0.322 Activity: calculate utility of 75 watt bulb __________________________________ Activity: calculate utility of 100 watt bulb __________________________________ 23 Chapter 5

24
©Chelst & Canbolat Value-Added Decision Making Calculate Total Utility U=W p SUF p (Performance)+W c SUF c (Cost) The weighted utilities U(60 Watt)= 0.60 *(0.000)+0.40*(0.805)=0.322 U(75 Watt)= 0.60 *(.375)+ 0.40 *(0.575)=0.455 24 Chapter 5 Alternative 100-Watt Bulb 75-Watt Bulb 60-Watt Bulb Utility 0.675 0.454 0.317 Maximize PerformanceMinimize Cost

25
©Chelst & Canbolat Value-Added Decision Making Moneymark – financial service phone line Activity: Rank, assign points and calculate weights Chapter 5 25 StrategyAnnual Cost (Dollars)Waiting Time (Minutes) 4 staff 115,20011.1 5 staff 144,0001.9 6 staff 172,8000.5 ObjectiveMeasure Least Preferred Most Preferred Rank Order PointsWeight Minimize waiting time Waiting time (minutes) 120 Minimize cost Annual cost (dollars) 175,000115,000

26
©Chelst & Canbolat Value-Added Decision Making Assume 0.4 weight for cost Chapter 5 26 WaitingCostTotal Score ServersTimeUtilityDollarsUtility 411.10.075$115,2000.9970.444 51.90.842$144,0000.5170.712 60.50.958$172,8000.0370.590 Alternative 5 staff 6 staff 4 staff Utility 0.712 0.590 0.444 Waiting TimeCost Weights how much money would manager be willing to spend to reduce waiting time from 11.1 minutes to 1.9 minutes and to 0.5 minutes)

27
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Interview Process: Relative importance weights Discuss measure ranges Define goals and measures Provide measure level ranges State assumptions Condition the Responses Provide relevant information Elicit / Verify Responses Conduct interview Record response / rationale Check belief of sub-totals 27 Chapter 5

28
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Whose Values and Weights to be traded off? Decision Maker(s) represent values organization Senior executives Customer or Subject Matter Expert(s) reflect values of the ultimate customer or end user. Marketing experts or representative users Engineers who understand relationship between design parameters and performance on key measures of interest. Financial services phone line Waiting time is customer perspective Cost is company perspective 28 Chapter 5

29
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Activity – used car: large number of measures 29 Preferred ObjectiveMeasureLeastMost Rank Order Points Weight Measure Reliability Mileage130,00080,000 Dependability ratings2 circles4 circles Total Cost Purchase cost$6,500$2,500 Mpg20 mpg30 mpg Maintenance Annual$600$400 Longevity5 years3 years Aesthetics ColorDarkLight InteriorPoorExcellent ExteriorPoorExcellent Accessories A/C & Heater Neither works Both work Seating Capacity26 or more Sound SystemNoneRadio & CD Sum

30
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Other Weighting Methods Direct Tradeoffs How much is it worth in dollars to increase the value of this other measure Large Hierarchy Allocate weight to broad categories with range awareness Rank order: reliability, total cost, aesthetics, and accessories Directly assign weights to each major category Subdivide the allocation with each category Within aesthetics Rank order: color, interior and exterior Directly assign local weights to each measure Global weight = product of objective weight and local measure weight 30 Chapter 5

31
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Hierarchical Approach – Top down 31 ObjectiveMeasureLeastMostRank Weights Objective Rank Weight Measure Weight Global Reliability Mileage125,00080,000 30.20 20.450.09 Dependability2 circles4 circles 10.550.11 Total Cost Purchase cost$6,000$1,000 10.40 10.40.16 mpg20 mpg30 mpg 20.250.10 Maintenance$600$400 40.10.04 Longevity5 years3 years 20.250.10 Aesthetics ColorDarkLight 40.15 20.40.04 InteriorPoorExcellent 30.150.015 ExteriorPoorExcellent 10.450.045 Accessories A/C & Heater Neither work Both work 20.25 30.30.075 Seating26 or more 20.30.075 Sound SystemNoneRadio & CD 10.40.10 Sum 1

32
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 32 Chapter 5 Convert score on each measure to a point on a zero-to-one scale Default assumption = linearity or proportional Often reasonable assumption or approximation Construct nonlinear function Approximate shape Direct assessment Mid-level splitting (time consuming) Utility or Value Function common scale

33
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Motivate Need for Non-Linear Utility Function Home Choice: 3, 4, or 5 bedrooms Are 4 bedrooms midway in value between 3 and 5? Kitchen remodeling: range is 12 weeks to 18 weeks Are 15 weeks midway in value between 12 and 18? Waiting Time on Phone: range is 0 to 12 minutes Is 6 minutes midway in value between 0 and 12? Suggest a measure with a non-linear utility function Describe Context ______________________________ 33 Chapter 5

34
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Activity: Direct Assessment - Bedrooms SUF(5) =1 SUF(3) = 0 SUF(4) = ? Which change produces a greater value improvement? If Change 1 – Improve from 3 to 4 SUF(4) > 0.5 If Change 2 – Improve from 4 to 5 SUF(4) < 0.5 Which is greater for you _________________ Specify your SUF(4) = _________ 34 Chapter 5

35
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 0 1 Measure Level Common Units of Utility Decreasing Rate of Value Constant Rate of Value Combination Increasing Rate of Value Conversion To Common Units Single-Measure Utility Function (SUF) There is no right or wrong SUF for a measure. Shape of SUF depends on the context and personal preferences. Preferences should be captured well enough to understand and analyze the current situation. 35 Chapter 5

36
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Management Targets for Measures lead to Non-Linear Utility Function **Over-Emphasis on achieving a specific Target leads to an extremely non-linear utility function. Steep curve near target and until target is achieved Relatively flat curve past target Utility Measure 1 0 Target Shallower SUF allows for more tradeoff opportunities 36 Chapter 5

37
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Key: SELECT Shape of Curve Decreasing rate of value - Concave Small increments in the measure add significant value. Less and less value added as approaching most preferred level. (e.g. each additional bedroom) Increasing rate of value - Convex small increments from least preferred level add little value. As level improves each additional fixed increment has even greater value. Largest incremental value occurs as measure approaches most preferred value. (e.g. NBA Draft 24 th to 23 rd and 2 nd to 1 st ) Combination – S shaped Small increases from least preferred value or as approach most preferred add significant value. (e.g. acceleration for normal car driver) 37 Chapter 5

38
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 How Much Non-Linear Detail? Office space 1500 sq feet adequate Could make do with 1000 sq feet Could use extra space up to 2000 sq feet 38 Chapter 5

39
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Mid-Level splitting Specify Utility and Find Value Mid-level Splitting: continuous measures Divide utility range between 0 and 1 into equal intervals 1.Determine measure level with 0.5 utility 2.Determine measure level with 0.25 utility 3.Determine measure level with 0.75 utility 39 Chapter 5

40
©Chelst & Canbolat Value-Added Decision Making Activity: Mid-Level Splitting - Time Waiting on hold UtilityMinutesSpecify 012 Step 10.5X X = 10 012 Step 20.25YY=Y= 0.5XSpecified in step 1 0.5XSpecified in step 1 Step 30.75ZZ = 10 40 Chapter 5

41
©Chelst & Canbolat Value-Added Decision Making Nancy Chicila of MONEYMARK – interview Range from 0 to 12 minutes – Find X 0.5 41 Let M = (Best level + Worst level)/2 = 6 = midpoint of total range U(0) = 1 and U(12) = 0 Ask which change produces a greater value improvement. Change 1: Improve from 12 to 6 min Change 2: Improve from 6 to 0 If, for example, the answer is that Change 2 has a greater impact, this implies U(6) U(12) 0.5 Because U(0) = 1 and U(12) = 0 then U(6) < 0.5 and the time value X 0.5 < 6 minutes. In Nancys opinion, unless the waiting decreases to less than 5 minutes, the utility score does not reach 0.5. She sets 4 minutes as the 0.5 level. X 0.5 = 4. Chapter 5

42
©Chelst & Canbolat Value-Added Decision Making Nancy Chicila of MONEYMARK – interview Find X 0.25 & X 0.25 42 In this example, X 0.5 = 4 Calculate midpoint (12 + 4)/2 = 8 Change 1: Is there greater value in improving from 12 to 8? Change 2: Or is there greater value in improving from 8 to 4? Nancy preferred Change 2. X 0.25 < 8 minutes, and she set X 0.25 = 7. Chapter 5 Calculate midpoint (4 0)/2 = 2 Change 1: Is there greater value in improving from 4 to 2? Change 2: Is there greater value in improving from 2 to 0? Nancy viewed change 2 as more significant, since it eliminated all waiting time. She then sets the midpoint at 1.5 min, which means that X 0.75 = 1.5.

43
©Chelst & Canbolat Value-Added Decision Making Nancys Mid-level splitting for waiting on hold 43 Utility Waiting Time (Minutes) 1 0 0.12. Level:Utility:70.25 Utility Waiting Time (Minutes) 1 0 0.12. Level:Utility:1.50.75 Chapter 5

44
©Chelst & Canbolat Value-Added Decision Making Comparison of linear and non-linear utility increases difference between 6 and 5 servers 44 Utility of time ServersTimeLinearNon-Linear 411.10.080.03 51.90.840.70 60.50.960.91 Chapter 5

45
©Chelst & Canbolat Value-Added Decision Making Rank ordering unchanged but total score gap between 1 st and 2 nd narrowed 45 Alternative 5 staff 6 staff 4 staff Utility 0.712 0.590 0.444 Waiting TimeCost Non-linear Utility for Waiting Time Alternative 5 staff 6 staff 4 staff Utility 0.626 0.558 0.419 Waiting TimeCost Linear Utility for Waiting Time Chapter 5

46
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Two ways of representing Uncertainty Include Probabilistic data for a specific measure in the Data Matrix for various alternatives Three-Point Estimate Discrete Distribution - approximations Continuous Distribution SEPARATE Risk Measure Separate Measure – label Low Risk (Most Preferred), Medium, and High Risk (Least preferred)

47
©Chelst & Canbolat Value-Added Decision Making Inclusion of Uncertainty in MAUT: Bulbs random number of hours of operation affects operating cost Bulb Annual Operating Cost Probability $2750.4 $3000.3 $3500.3

48
©Chelst & Canbolat Value-Added Decision Making Separate Risk Measure Less risky vs more risky alternative Supplier Choice – More information or Less Used Car – More variability by brand in reliability Worker – Current employee vs. new employee for MGT Activity – Example and Context ____________

49
©Chelst & Canbolat Value-Added Decision Making Word description of risk level of suppliers Word description? Low risk/uncertainty Medium risk/uncertainty High risk/uncertainty

50
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Uncertainty: Impact and Implementations in LDW Uncertainty range may lead to changes in rankings depending upon which values actually occurred (example kitchen remodeler) Logical Decisions Allows for explicit incorporation of values and their probabilities Linear utility function rankings will be based on expected value of each uncertain variable Utility (Expected value) = Expected value of utility Non-linear utility Cannot simply use expected values 50

51
©Chelst & Canbolat Value-Added Decision Making Uncertainty within measures: Cost & Delay Input uncertainty into data for measure 51 Chapter 5 Measure Build RiteQuality BuildCost Conscious Total labor cost $34,000$26,000$25,000 Total material cost $20,000$12,000$10,000 Cost overrun history 0% (p=0.33) 2% (p=0.34) 7% (p=0.33) 2% (p=0.33) 5% (p=0.34) 9% (p=0.33) 6% (p=0.33) 9% (p=0.34) 15% (p=0.33) Duration kitchen unavailable 13 weeks10 weeks9 weeks Weeks of delay On time (p=0.33), 1 week late (p=0.34) 2 weeks late (p=0.33) 1 week late(p=0.33) 2 weeks late(p=0.34) 3 weeks late (p=0.33) 2 weeks late (p=0.33) 3 weeks late (p=0.34) 4 weeks late (p=0.33) Cleanliness scale CleanMessyDirty Follow-up and resolution scale AdequateHighly responsiveAdequate Creativity scale Highly creativeCreativeMundane Brand & store reputation scale Top of line2 nd Best Brand Percent use of subcontractors 25%40%65% Fit and finish scale ExcellentGood Years in business 12 (Good)8 (OK)22 (Excellent) Quality of references scale ExcellentGoodOK

52
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Figure 5.15: Stacked bar results for kitchen remodeling Alternative Build Rite Quality Build Cost Conscious Utility 0.651 0.630 0.462 Max. QualityMin. CostMin. Hassle

53
©Chelst & Canbolat Value-Added Decision Making Kitchen remodeler: uncertainty for cost & delay scores overlap in top 2 alternatives 53 Alternative Build Rite Quality Build Cost Conscious Utility 0.651 0.630 0.462 Chapter 5

54
©Chelst & Canbolat Value-Added Decision Making Group Decision Making: Practical 54 Influence diagrams and goals hierarchy provide structured group communication approach consensus Decomposition in objectives, measures, weights and utilities allows for multiple inputs and perspectives Separates data collection and expert judgment from weighting process Rationales for weights Understanding of core differences Logical Decisions allows the analyst to simultaneously incorporate separate weights for multiple decision makers Often even though weights differ, rank orderings may not differ Chapter 5

55
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Arrows impossibility theorem for consistent group decisions Non-additive utility functions Motivation Formula 55 Additional Concepts

56
©Chelst & Canbolat Value-Added Decision Making Arrows Impossibility Theorem: Consistent group aggregation of preferences 56 SME 1SME 2SME 3Average A1322 B2132 C3212 YesNoResult A > B21 B > C21 A > C12A < C Arrow and average of preferences Arrow and majority rule vote Chapter 5

57
©Chelst & Canbolat Value-Added Decision Making Non-linear additive utility function 57 Multiplicative form U= k 1 u 1 (x 1 ) + k 2 u 2 (x 2 ) + (1- k 1 - k 2 ) u 1 (x 1 ) u 2 (x 2 ) Craftsmanship ( k 1 + k 2 < 1) Gap &Misalignment measures Bad on either undermines craftsmanship Competitiveness ( k 1 + k 2 > 1) Pricing & Styling measures Excellent on either makes products competitive Chapter 5

58
©Chelst & Canbolat Value-Added Decision Making Craftsmanship (k 1 + k 2 < 1) Gaps and Misalignment measures 58 U= 0.2u 1 (x 1 ) + 0.2u 2 (x 2 ) + (0.6)u 1 (x 1 ) u 2 (x 2 ) Poor on either undermines craftsmanship Chapter 5 Weights 0.2 0.6 AlternativeGapMisalignmentProductTotal 1Excellent and poor 1 0 00.2 2Very good and weak 0.9 0.1.09.25 3Both very good0.9 0.810.85 4Both good0.75 0.560.64 5Both OK0.5 0.250.35

59
©Chelst & Canbolat Value-Added Decision Making Competitiveness (k 1 + k 2 > 1) Pricing & Styling measures 59 U= 0.8u 1 (x 1 ) + 0.8u 2 (x 2 ) + (-0.6) u 1 (x 1 ) u 2 (x 2 ) Excellence on either product competitive Chapter 5 Weights 0.8 -0.6 AlternativePriceStylingProductTotal 1 Excellent and poor1000.8 2 Very good and weak0.90.1.09.75 3Both very good0.9 0.810.95 4Both good0.75 0.560.86 5Both OK0.5 0.250.5

60
©Chelst & Canbolat Value-Added Decision Making MAUT Process Describe Alternatives Clarify Preferences Analyze Structure TASKSSTEPS Weighted Sum Synthesize Conduct Comparative Analysis Evaluate Hybrid Alternative(s) Conduct Sensitivity Analysis Gather data for each alternative for each measure Assign weights Create a common scale for each measure Identify Measures Identify Requirements Determine Objectives Identify Alternatives TECHNIQUES Creativity & Expert Judgment Individual Analyses Swing Weight & Mid-Level Splitting 60 Chapter 5 Next Chapter

61
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 5.8 Used car scores 5.17, 5.19 and 5.20 Nuclear emergency management 5.21 and 5.23 Coating Process 61 Additional Figures from text

62
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Figure 5.8: Ranking of the alternatives for the used car example Alternative Chevrolet Cavalier Honda Civic Ford Ranger Mazda Miata Utility 0.613 0.470 0.440 0.411 Total Cost Aesthetics AccessoriesReliability

63
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Figure 5.17: Goals hierarchy for nuclear emergency management case

64
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Figure 5.19: Ranking nuclear emergency management strategies-base case scenario

65
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Figure 5.20: Ranking nuclear emergency management strategies-worst case scenario CostsOther cancersPolitical cost Soc. psych negativeSoc. psych positiveThyroid cancer Strategy 4 Strategy 3 Strategy 2 Strategy 1 Strategy 0 0.781 0.762 0.636 0.431 0.043 Utility Alternative

66
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Figure 5.21: Goals hierarchy for coating process selection

67
©Chelst & Canbolat Value-Added Decision Making 9/19/2011 Figure 5.23: Stacked bar ranking for the coating processes Alternative Selective Spray Sil-Gel Potting Coat and Extract Utility 0.702 0.650 0.596 Minimize Cost Minimize Development Time Maximize ReliabilityMaximize Performance

Similar presentations

Presentation is loading. Please wait....

OK

Part 3 Probabilistic Decision Models

Part 3 Probabilistic Decision Models

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on bmc remedy help Ppt on second law of thermodynamics evolution Full ppt on electron beam machining set Ppt on eddy current inspection Ppt on project management tools Ppt on nuclear family and joint family definition Ppt on types of research methods and designs Ppt on oxidation and reduction reactions Ppt on digital marketing Ppt on standing order act test