DFSS
benchmarking
Maps technical benchmarking data to a percentage scale of performances.

data: is the table of benchmarking data

opt: is the vector of optimization directions

best: is the vector of "best in class" values
best
Returns the best value according to a given direction for optimization
BOMConceptCost
Calculates the cost of all parts needed for different concepts

quantity: is the quantity (per concept)

costperunit: is the unit cost

conceptselection: is the concept selection table
consistencyRatio
computes the consistency ratio

matrix: is the ahp matrix

importance: is the vector containing the importance in group for the ahp

ri:

:
criticality
Determines if a combination of difficulty and importance values is critical. Returns a range from 0...4.

difficulty: is the difficulty

importance: is the importance

d_crit: is the threshold for the critical difficulty

i_crit: is the threshold for the critical importance
csi
Maps a vector from one tree to another by applying the independent scoring method. Mathematically, the independent scoring method can be expressed as a linear transformation.
gap
returns the absolute of the difference between x and y. Only existing matrix values are handled.
independentScore
Maps a vector from one tree to another by applying the independent scoring method. Mathematically, the independent scoring method can be expressed as a linear transformation.

matrix: is the QFD correlation matrix.

x: is the vector in the source tree you want to map.

sign: sets a filter for the matrix relations: all, pos(itive only), neg(ative only)

total: total sum for normalization. Use 0 in order to skip normalization.

grouphandling: group handling mode: shallow (leafs only), sums (accumulate hierarchically), levels (calculate system level and paramater level separately).
opportunity
Calculates the difference of an importance and a performance value if importance>performance>0
pairedComparison
Returns the result of a paired comparison. The result is calculated as the Eigenvector corresponding to the largest Eigenvalue of the matrix.

matrix: The matrix containing the comparisons. Must be a reciprocal matrix with 1's on the diagonal. Use mirrorInv() to create a reciprocal matrix from an upper or lower triangular matrix.
proportionalScore
Maps a vector from one tree to another by applying the proportional scoring method.

matrix: is the QFD correlation matrix.

x: is the vector in the source tree you want to map.

sign: sets a filter for the matrix relations: all, pos(itive only), neg(ative only)

total: total sum for normalization. Use 0 in order to skip normalization.

grouphandling: group handling mode: shallow (leafs only), sums (accumulate hierarchically), levels (calculate system level and paramater level separately).
pughSum
Returns the Pugh sum for a container. The Pugh sum is a measure for the number of positive and negative effects of a number of alternatives.

x: is the container for which you want to calculate the Pugh sum.

value: is the Pugh value, i.e.the value being counted. Normally, this argument should be one of 1, 0 +1 to count negative, neutral or positive relationships in the given container.

param1: is the first parameter (7.5 for positive, 5 for neutral, 2.5 for negative pugh sum)

param2: is the second parameter (10 for positive, 0 for negative pugh sum)

param3:

: is the container for which you want to calculate the Pugh sum.

: is the Pugh value, i.e.the value being counted. Normally, this argument should be one of 1, 0 +1 to count negative, neutral or positive relationships in the given container.

: is the first parameter (7.5 for positive, 5 for neutral, 2.5 for negative pugh sum)

: is the second parameter (10 for positive, 0 for negative pugh sum)

:
pughSumEx
ranking
Calculates rank numbers for a sequence according to the values in a vector. If the reverse parameter is false, the item with the highest value will get rank number 1. If the reverse parameter is true, the item with the lowest value will get rank number 1.
scaleBenchmarking
Maps technical data to a given scale of performances using a datatoperformance mapping table

data: is the table of benchmarking data

map: is the datatoperformance mapping table

scores: is the vector of performance scale values

opt: is the vector of optimization directions

limit: if true, result is limited to the scaled range

accumulate:
scaleBenchmarkingEx
Maps technical data to a given scale of performances using a datatoperformance mapping table

data: is the table of benchmarking data

map: is the datatoperformance mapping table

scores: is the vector of performance scale values

opt: is the vector of optimization directions

limit: if true, result is limited to the scaled range
scaleMax
Rescales argument x from 0..maximum(x) to the new range given by min and max

x: is the vector of values to be rescaled

min: is the new minimum

max: is the new maximum

mode: is the calculation mode: shallow: calculate in the dimension of x, levels: calculate for both the system and the parameter level of x
spectralRadius
Returns the spectral radius of the comparison matrix. The spectral radius is the largest Eigenvalue of the comparison matrix.

matrix: The matrix containing the comparisons. Must be a reciprocal matrix with 1's on the diagonal. Use mirrorInv() to create a reciprocal matrix from an upper or lower triangular matrix.
topsis
Concept selection function TOPSIS

decisionMatrix: the decision matrix with quantified performance data

importance: the vector of decision criteria weights

optimization: the vector of decision criteria optimization directions

best: the vector of bestinclass performance

worst: the vector of worst in class performance

useBestWorst: if true always use best and worse for calculation
topsisEx
Concept selection function TOPSIS

decisionMatrix: the decision matrix with quantified performance data

importance: the vector of decision criteria weights

map: is the datatoperformance mapping table

scores: is the vector of performance scale values
totalImportance
Calculates the total importance from one or more scoring methods, e.g. AHP, result of a questionnaire...

table: is the table containing the results of the different methods

total: the intended total sum for the result, 1.0 for percentage
worst
Returns the worst value according to a given direction for optimization
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