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ADM

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Answer 50 questions in 15 minutes.
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Match each statement with the correct term.
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1. What is the definition of inflation?
Inflation is the decrease in the buying power/value of money. It is caused by the when amount of available money changes wrt amount of product/services available
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)

2. What does TOPSIS stand for?
Technique for Order Preference by Similarity to Ideal Solution
RDTE - Investment/Acquisition - Operations and Support - Disposal
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Scaling parameters photographically scale the size of the vehicle to take full advantage of technology -(e.g. increase CL -> Can decrease S -> Decreases D -> Decreases Fuel Consumed -> etc...) This assumes that the physics of the problem remains the

3. What is the equation for present equivalent value? Define variables.
As you add n number of identical & independent distributions (IIDs) together - as n --> inf - the resulting distribution will be normal - regardless of the shape of the IIDs
is bottom- up - you look at certain technologies and see what improvements they offer
PE(i)=?Ft
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value

4. Show and explain a pareto frontier
A pareto frontier represents points of a non - dominated solution based on preferences
OEC = W1X/Xbsl + W2Nbsl/N
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order

5. What is the goal of robust design?

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6. Weaknesses of TOPSis...
(1) Identify potential technologies that may improve technical & economical feasibility (2) Establish physical compatibility rules for diff techs (3) Determine expected impact (improvements and degradations) to systems of interest Method: TRL - Techn
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Scaling parameters photographically scale the size of the vehicle to take full advantage of technology -(e.g. increase CL -> Can decrease S -> Decreases D -> Decreases Fuel Consumed -> etc...) This assumes that the physics of the problem remains the
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective

7. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
Mean and variance
To analytically answer 'How much design margin is really necessary?'
No way to tell without more information. It depends on the relation between s12+s22 and s32
X~N(0 -1)

8. What are properties of a CDF?
Range is always between zero and 1 monotonically increasing
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
OEC = W1X/Xbsl + W2Nbsl/N
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers

9. MADM
A technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Central limit theorem
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)

10. What are the four difference life cycle costs?
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
RDTE - Investment/Acquisition - Operations and Support - Disposal
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space

11. Assumptions Used in TOPSis...
Determine the design space - baseline Method: Morphological Matrix
Technology Readiness Level Ranges 1-9 - where 1 means that the basic principle have been observed and reported and 9 means the technology has had successful missions A high tech means the technology is pretty developed and should be (or is) ready for
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration

12. What are the different types of UTEs?
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
It can be continuous or discrete
Range is always between zero and 1 monotonically increasing
Active UTE (additive) - Product UTE (multiplicative)

13. What can management do to mitigate the risk associated with infusing new technologies?
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Trying to determine the metric values for any combination of design variables/ what the metrics are as a function of design variables Method: RSE: Response Surface Eqn.

14. What are the three snapshots of UTE?
Mean and variance
(1) Mission Requirements - Input: Mission metrics and requirements Output: Delta response for requirements (2) Design Variables - Input: Geometric and economic design variables Output: Delta response for design variable - (3) Technologies Input: P
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
(1) Identify potential technologies that may improve technical & economical feasibility (2) Establish physical compatibility rules for diff techs (3) Determine expected impact (improvements and degradations) to systems of interest Method: TRL - Techn

15. Name the advantages of UTE.
Range: Gives the magnitude of the spread - min and max - Variance: Indicates how spread out the data is - Skewness: Indicates if the distribution is biased - Kurtosis: Peakness
(1) Problem def - (2) Design space conception (3) Model and Simulation (4) Investigate Design Space (5) Feasible? (6) Identify Technologies (7) Evaluate Technologies (8) Select Technology
Provide for rapid trade- off capability between the three elements and search for feasible solutions - Allow graphical visualization of the combined space - Address mission requirements ambiguity and technology uncertainty.
Optimizing - finds the set of criteria that maximizes or minimizes a design criteria or several design criteria - Satisficing - finds the conditions where the constraints or requires are met but no optimization occurs.

16. How is inflation measured?

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17. TIES Step 3: Model and Simulation
#=2^n = 2^15
To analytically answer 'How much design margin is really necessary?'
M&S environment is needed to facilitate rapid assessments with minimal time and monetary expenditures of the alternative concepts identified in the Morphological Matrix Method: DoE
It gives the probability that a value will be met or exceeded.

18. Ratio scale
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Has a natural zero - is a cardinal scale
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Mean =0 Variance =1

19. Define fixed cost and variable cost.
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
X+Y and X-Y are normally distributed. - (X
Range is always between zero and 1 monotonically increasing
A probability density contour plot is a visualization method for Joint probability density function (a 2D representation). Their shapes (contour shapes) tell if the metric analyzed in each axis are correlated or not (Circular -> no correlation) (elli

20. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
OEC = W1X/Xbsl + W2Nbsl/N
Has a natural zero - is a cardinal scale
Sample size is 4 - the sample is the sum of the five dice.
(1) Problem def - (2) Design space conception (3) Model and Simulation (4) Investigate Design Space (5) Feasible? (6) Identify Technologies (7) Evaluate Technologies (8) Select Technology

21. Why do we use a sample?
Mean and variance
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Mean =0 Variance =1

22. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
CDF= ?_(-8)^8
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)

23. TIES Step 6: Identify Technology
(1) Identify potential technologies that may improve technical & economical feasibility (2) Establish physical compatibility rules for diff techs (3) Determine expected impact (improvements and degradations) to systems of interest Method: TRL - Techn
Provide for rapid trade- off capability between the three elements and search for feasible solutions - Allow graphical visualization of the combined space - Address mission requirements ambiguity and technology uncertainty.
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
RDTE - Investment/Acquisition - Operations and Support - Disposal

24. What are the parameters for a standard normal distribution?
Mean =0 Variance =1
Cumulative Distribution Function
Range is always between zero and 1 monotonically increasing
Central limit theorem

25. TIES Step 7: Assess Technology
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
is bottom- up - you look at certain technologies and see what improvements they offer
Active UTE (additive) - Product UTE (multiplicative)

26. TIES Step 1: Problem Definition
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
The interest i such that 0=PE(i^)
Technique for Order Preference by Similarity to Ideal Solution
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

27. 4 Measures of Dispersion
Range: Gives the magnitude of the spread - min and max - Variance: Indicates how spread out the data is - Skewness: Indicates if the distribution is biased - Kurtosis: Peakness
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
Trying to determine the metric values for any combination of design variables/ what the metrics are as a function of design variables Method: RSE: Response Surface Eqn.

28. Indirect Operating Cost
(1) Mission Requirements - Input: Mission metrics and requirements Output: Delta response for requirements (2) Design Variables - Input: Geometric and economic design variables Output: Delta response for design variable - (3) Technologies Input: P
Cumulative Distribution Function
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor

29. In what regions of the graph is UTE applicable?
X+Y and X-Y are normally distributed. - (X
Gaussian Distribution
Regions 1 to 3.
(1) Mission Requirements - Input: Mission metrics and requirements Output: Delta response for requirements (2) Design Variables - Input: Geometric and economic design variables Output: Delta response for design variable - (3) Technologies Input: P

30. What is TIM? What is the size and what value can it take?

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31. Why use uniform dist for input variables (Gap Analysis)
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
P(between B and A)=F(B)-F(A)
Allows designer to assess feasibility of design

32. How do you get the CDF from the PDF?
Technique for Order Preference by Similarity to Ideal Solution
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
F(x)=1/(s(2p)^(.5) )exp?(-(x-
CDF= ?_(-8)^8

33. What is TCM? What is the size and what value can it take?
Range: Gives the magnitude of the spread - min and max - Variance: Indicates how spread out the data is - Skewness: Indicates if the distribution is biased - Kurtosis: Peakness
Technology Compatability Matrix - For n techs - is nxn matrix - Tells whether the intersecting technologies are compatible - It only has 0s and 1s - 0 means the technologies are not compatible with each other - 1 means techs are compatible with each
Allows designer to assess feasibility of design
Active UTE (additive) - Product UTE (multiplicative)

34. If you have a two values on a CDF what is the probability of getting a value between them?
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Has a natural zero - is a cardinal scale
P(between B and A)=F(B)-F(A)

35. What two variables are necessary to define a normal distribution?
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Mean and variance
Active UTE (additive) - Product UTE (multiplicative)
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

36. Strengths of TOPSis...
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
(1) Identify potential technologies that may improve technical & economical feasibility (2) Establish physical compatibility rules for diff techs (3) Determine expected impact (improvements and degradations) to systems of interest Method: TRL - Techn
Mean =0 Variance =1

37. TIES Step 4: Investigate Design Space
M&S environment is needed to facilitate rapid assessments with minimal time and monetary expenditures of the alternative concepts identified in the Morphological Matrix Method: DoE
X~N(0 -1)
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Trying to determine the metric values for any combination of design variables/ what the metrics are as a function of design variables Method: RSE: Response Surface Eqn.

38. What is the normal distribution that results from adding x+y and x[sub]y?
PE(i)=?Ft
X+Y and X-Y are normally distributed. - (X
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order

39. What is probability density contour plot
OEC = W1X/Xbsl + W2Nbsl/N
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
A probability density contour plot is a visualization method for Joint probability density function (a 2D representation). Their shapes (contour shapes) tell if the metric analyzed in each axis are correlated or not (Circular -> no correlation) (elli

40. What is the difference between price and cost?
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Range is always between zero and 1 monotonically increasing
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)

41. Write down a formula for a normal distribution
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
As you add n number of identical & independent distributions (IIDs) together - as n --> inf - the resulting distribution will be normal - regardless of the shape of the IIDs
F(x)=1/(s(2p)^(.5) )exp?(-(x-
CDF= ?_(-8)^8

42. What is TRL? Range? What does a high TRL mean?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Regions 1 to 3.
Technology Readiness Level Ranges 1-9 - where 1 means that the basic principle have been observed and reported and 9 means the technology has had successful missions A high tech means the technology is pretty developed and should be (or is) ready for
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.

43. TIES
Select final tech comb. For any multi attribute - constraint - or criteria problem - the selection of the 'best' family of alternatives is inherently subjective. Various selection techniques are used to provide decision maker with extensive info. Met
is bottom- up - you look at certain technologies and see what improvements they offer
A probability density contour plot is a visualization method for Joint probability density function (a 2D representation). Their shapes (contour shapes) tell if the metric analyzed in each axis are correlated or not (Circular -> no correlation) (elli
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration

44. 8 Steps in TIES
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Active UTE (additive) - Product UTE (multiplicative)
(1) Problem def - (2) Design space conception (3) Model and Simulation (4) Investigate Design Space (5) Feasible? (6) Identify Technologies (7) Evaluate Technologies (8) Select Technology
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.

45. $/RPM Equation
Allows designer to assess feasibility of design
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Sample size is 4 - the sample is the sum of the five dice.

46. Direct Operating Costs
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Has a natural zero - is a cardinal scale
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
OEC = W1X/Xbsl + W2Nbsl/N

47. What can be done about uncertainty in requirement?
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
A probability density contour plot is a visualization method for Joint probability density function (a 2D representation). Their shapes (contour shapes) tell if the metric analyzed in each axis are correlated or not (Circular -> no correlation) (elli
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.

48. What is the definition of ROI?
Gaussian Distribution
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
The interest i such that 0=PE(i^)
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor

49. Does TIES use MADM or MODM? Why?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
It gives the probability that a value will be met or exceeded.

50. MODM
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Technology Compatability Matrix - For n techs - is nxn matrix - Tells whether the intersecting technologies are compatible - It only has 0s and 1s - 0 means the technologies are not compatible with each other - 1 means techs are compatible with each
PE(i)=?Ft
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.