Type: Package
Title: A Quantified Implementation of the Kraljic Matrix
Version: 0.2.1
Maintainer: Bradley Boehmke <bradleyboehmke@gmail.com>
Date: 2017-11-01
Description: Implements a quantified approach to the Kraljic Matrix (Kraljic, 1983, https://hbr.org/1983/09/purchasing-must-become-supply-management) for strategically analyzing a firm’s purchasing portfolio. It combines multi-objective decision analysis to measure purchasing characteristics and uses this information to place products and services within the Kraljic Matrix.
URL: https://github.com/koalaverse/KraljicMatrix
BugReports: https://github.com/koalaverse/KraljicMatrix/issues
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (≥ 2.10)
Imports: ggplot2, dplyr, tibble, magrittr
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-03-06 20:16:18 UTC; bradboehmke
Author: Bradley Boehmke [aut, cre], Brandon Greenwell [aut], Andrew McCarthy [aut], Robert Montgomery [ctb]
Repository: CRAN
Date/Publication: 2018-03-06 22:49:03 UTC

Pipe functions

Description

Like dplyr, KraljicMatrix also uses the pipe function, %>% to turn function composition into a series of imperative statements.

Arguments

lhs, rhs

An R object and a function to apply to it

Examples


# given the following \code{psc2} data set
psc2 <- dplyr::mutate(psc, x_SAVF_score = SAVF_score(x_attribute, 1, 5, .653),
                           y_SAVF_score = SAVF_score(y_attribute, 1, 10, .7))

# you can use the pipe operator to re-write the following:
kraljic_matrix(psc2, x_SAVF_score, y_SAVF_score)

# as
psc2 %>% kraljic_matrix(x_SAVF_score, y_SAVF_score)

Multi-attribute value function

Description

MAVF_score computes the multi-attribute value score of x and y given their respective weights

Usage

MAVF_score(x, y, x_wt, y_wt)

Arguments

x

Numeric vector of values

y

Numeric vector of values with compatible dimensions to x

x_wt

Swing weight for x

y_wt

Swing weight for y

Value

A vector of the same length as x and y with the multi-attribute value scores

See Also

MAVF_sensitivity to perform sensitivity analysis with a range of x and y swing weights

SAVF_score for computing the exponential single attribute value score

Examples


# Given the following \code{x} and \code{y} attribute values with \code{x} and
# \code{y} swing weight values of 0.65 and 0.35 respectively, we can compute
# the multi-attribute utility score:

x_attribute <- c(0.92, 0.79, 1.00, 0.39, 0.68, 0.55, 0.73, 0.76, 1.00, 0.74)
y_attribute <- c(0.52, 0.19, 0.62, 1.00, 0.55, 0.52, 0.53, 0.46, 0.61, 0.84)

MAVF_score(x_attribute, y_attribute, x_wt = .65, y_wt = .35)

Multi-attribute value function sensitivity analysis

Description

MAVF_sensitivity computes summary statistics for multi-attribute value scores of x and y given a range of swing weights for each attribute

Usage

MAVF_sensitivity(data, x, y, x_wt_min, x_wt_max, y_wt_min, y_wt_max)

Arguments

data

A data frame

x

Variable from data frame to represent x attribute values

y

Variable from data frame to represent y attribute values

x_wt_min

Lower bound anchor point for x attribute swing weight

x_wt_max

Upper bound anchor point for x attribute swing weight

y_wt_min

Lower bound anchor point for y attribute swing weight

y_wt_max

Upper bound anchor point for y attribute swing weight

Details

The sensitivity analysis performs a Monte Carlo simulation with 1000 trials for each product or service (row). Each trial randomly selects a weight from a uniform distribution between the lower and upper bound weight parameters and calculates the mult-attribute utility score. From these trials, summary statistics for each product or service (row) are calculated and reported for the final output.

Value

A data frame with added variables consisting of sensitivity analysis summary statistics for each product or service (row).

See Also

MAVF_score for computing the multi-attribute value score of x and y given their respective weights

SAVF_score for computing the exponential single attribute value score

Examples


# Given the following data frame that contains \code{x} and \code{y} attribute
# values for each product or service contract, we can compute how the range of
# swing weights for each \code{x} and \code{y} attribute influences the multi-
# attribute value score.

df <- data.frame(contract = 1:10,
                 x_attribute = c(0.92, 0.79, 1.00, 0.39, 0.68, 0.55, 0.73, 0.76, 1.00, 0.74),
                 y_attribute = c(0.52, 0.19, 0.62, 1.00, 0.55, 0.52, 0.53, 0.46, 0.61, 0.84))

MAVF_sensitivity(df, x_attribute, y_attribute, .55, .75, .25, .45)

Plot the single attribute value curve

Description

SAVF_plot plots the single attribute value curve along with the subject matter desired values for comparison

Usage

SAVF_plot(desired_x, desired_v, x_low, x_high, rho)

Arguments

desired_x

Elicited input x value(s)

desired_v

Elicited value score related to elicited input value(s)

x_low

Lower bound anchor point (can be different than min(x))

x_high

Upper bound anchor point (can be different than max(x))

rho

Exponential constant for the value function

Value

A plot that visualizes the single attribute value curve along with the subject matter desired values for comparison

See Also

SAVF_plot_rho_error for plotting the rho squared error terms

SAVF_score for computing the exponential single attribute value score

Examples


# Given the single attribute x is bounded between 1 and 5 and the subject matter experts
# prefer x values of 3, 4, & 5 provide a utility score of .75, .90 & 1.0 respectively,
# the preferred rho is 0.54. We can visualize this value function:

SAVF_plot(desired_x = c(3, 4, 5),
          desired_v = c(.75, .9, 1),
          x_low = 1,
          x_high = 5,
          rho = 0.54)

Plot the rho squared error terms

Description

SAVF_plot_rho_error plots the squared error terms for the rho search space to illustrate the preferred rho that minimizes the squared error between subject matter desired values and exponentially fitted scores

Usage

SAVF_plot_rho_error(desired_x, desired_v, x_low, x_high, rho_low = 0,
  rho_high = 1)

Arguments

desired_x

Elicited input x value(s)

desired_v

Elicited value score related to elicited input value(s)

x_low

Lower bound anchor point (can be different than min(x))

x_high

Upper bound anchor point (can be different than max(x))

rho_low

Lower bound of the exponential constant search space for a best fit value function

rho_high

Upper bound of the exponential constant search space for a best fit value function

Value

A plot that visualizes the squared error terms for the rho search space

See Also

SAVF_preferred_rho for identifying the preferred rho value

SAVF_score for computing the exponential single attribute value score

Examples


# Given the single attribute x is bounded between 1 and 5 and the subject matter experts
# prefer x values of 3, 4, & 5 provide a utility score of .75, .90 & 1.0 respectively, we
# can visualize the error terms for rho values between 0-1:

SAVF_plot_rho_error(desired_x = c(3, 4, 5),
                    desired_v = c(.75, .9, 1),
                    x_low = 1,
                    x_high = 5,
                    rho_low = 0,
                    rho_high = 1)

Identify preferred rho

Description

SAVF_preferred_rho computes the preferred rho that minimizes the squared error between subject matter input desired values and exponentially fitted scores

Usage

SAVF_preferred_rho(desired_x, desired_v, x_low, x_high, rho_low = 0,
  rho_high = 1)

Arguments

desired_x

Elicited input x value(s)

desired_v

Elicited value score related to elicited input value(s)

x_low

Lower bound anchor point (can be different than min(x))

x_high

Upper bound anchor point (can be different than max(x))

rho_low

Lower bound of the exponential constant search space for a best fit value function

rho_high

Upper bound of the exponential constant search space for a best fit value function

Value

A single element vector that represents the rho value that best fits the exponential utility function to the desired inputs

See Also

SAVF_plot_rho_error for plotting the rho squared error terms

SAVF_score for computing the exponential single attribute value score

Examples


# Given the single attribute x is bounded between 1 and 5 and the subject matter experts
# prefer x values of 3, 4, & 5 provide a utility score of .75, .90 & 1.0 respectively, we
# can search for a rho value between 0-1 that provides the best fit utility function:

SAVF_preferred_rho(desired_x = c(3, 4, 5),
                   desired_v = c(.75, .9, 1),
                   x_low = 1,
                   x_high = 5,
                   rho_low = 0,
                   rho_high = 1)

Single attribute value function

Description

SAVF_score computes the exponential single attribute value score of x

Usage

SAVF_score(x, x_low, x_high, rho)

Arguments

x

Numeric vector of values to score

x_low

Lower bound anchor point (can be different than min(x))

x_high

Upper bound anchor point (can be different than max(x))

rho

Exponential constant for the value function

Value

A vector of the same length as x with the exponential single attribute value scores

See Also

SAVF_plot for plotting single attribute scores

SAVF_preferred_rho for identifying the preferred rho

Examples


# The single attribute x is bounded between 1 and 5 and follows an exponential
# utility curve with rho = .653

x <- runif(10, 1, 5)
x
## [1] 2.964853 1.963182 1.223949 1.562025 4.381467 2.286030 3.071066
## [8] 4.470875 3.920913 4.314907

SAVF_score(x, x_low = 1, x_high = 5, rho = .653)
## [1] 0.7800556 0.5038275 0.1468234 0.3315217 0.9605856 0.6131944 0.8001003
## [8] 0.9673124 0.9189685 0.9553165

Plotting the Pareto Optimal Frontier

Description

The frontier geom is used to overlay the efficient frontier on a scatterplot.

Usage

geom_frontier(mapping = NULL, data = NULL, position = "identity",
  direction = "vh", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE,
  ...)

stat_frontier(mapping = NULL, data = NULL, geom = "step",
  position = "identity", direction = "vh", na.rm = FALSE,
  show.legend = NA, inherit.aes = TRUE, quadrant = "top.right", ...)

Arguments

mapping

Set of aesthetic mappings created by aes or aes_. If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer.

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

direction

Direction of stairs: 'vh' for vertical then horizontal, or 'hv' for horizontal then vertical.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

Logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders.

...

Other arguments passed on to layer. These are often aesthetics, used to set an aesthetic to a fixed value, like color = "red" or size = 3. They may also be parameters to the paired geom/stat.

geom

Use to override the default connection between geom_frontier and stat_frontier.

quadrant

See get_frontier.

Examples


## Not run: 

# default will find the efficient front in top right quadrant
ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  geom_frontier()

# change the direction of the steps
ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  geom_frontier(direction = 'hv')

# use quadrant parameter to change how you define the efficient frontier
ggplot(airquality, aes(Ozone, Temp)) +
  geom_point() +
  geom_frontier(quadrant = 'top.left')

ggplot(airquality, aes(Ozone, Temp)) +
  geom_point() +
  geom_frontier(quadrant = 'bottom.right')

## End(Not run)

Compute the Pareto Optimal Frontier

Description

Extract the points that make up the Pareto frontier from a set of data.

Usage

get_frontier(data, x, y, quadrant = c("top.right", "bottom.right",
  "bottom.left", "top.left"), decreasing = TRUE)

Arguments

data

A data frame.

x

A numeric vector.

y

A numeric vector.

quadrant

Chararacter string specifying which quadrant the frontier should appear in. Default is "top.right".

decreasing

Logical value indicating whether the data returned is in decreasing or ascending order (ordered by x and then y). Default is decreasing order.

Value

A data frame containing the data points that make up the efficient frontier.

See Also

geom_frontier for plotting the Pareto front

Examples


# default will find the Pareto optimal observations in top right quadrant
get_frontier(mtcars, mpg, wt)

# the output can be in descending or ascending order
get_frontier(mtcars, mpg, wt, decreasing = FALSE)

# use quadrant parameter to change how you define the efficient frontier
get_frontier(airquality, Ozone, Temp, quadrant = 'top.left')

get_frontier(airquality, Ozone, Temp, quadrant = 'bottom.right')

Kraljic matrix plotting function

Description

kraljic_matrix plots each product or service in the Kraljic purchasing matrix based on the attribute value score of x and y

Usage

kraljic_matrix(data, x, y)

Arguments

data

A data frame

x

Numeric vector of values

y

Numeric vector of values with compatible dimensions to x

Value

A Kraljic purchasing matrix plot

See Also

SAVF_score for computing the exponential single attribute value score for x and y

Examples


# Given the following \code{x} and \code{y} attribute values we can plot each
# product or service in the purchasing matrix:

# to add a new variable while preserving existing data
library(dplyr)

psc2 <- psc %>%
  mutate(x_SAVF_score = SAVF_score(x_attribute, 1, 5, .653),
         y_SAVF_score = SAVF_score(y_attribute, 1, 10, .7))

kraljic_matrix(psc2, x_SAVF_score, y_SAVF_score)

Kraljic quadrant assignment function

Description

kraljic_quadrant assigns the Kraljic purchasing matrix quadrant based on the attribute value score of x and y

Usage

kraljic_quadrant(x, y)

Arguments

x

Numeric vector of values

y

Numeric vector of values with compatible dimensions to x

Value

A vector of the same length as x and y with the relevant Kraljic quadrant name

See Also

SAVF_score for computing the exponential single attribute value score for x and y

Examples


# Given the following \code{x} and \code{y} attribute values  we can determine
# which quadrant each product or service falls in:

# to add a new variable while preserving existing data
library(dplyr)

psc2 <- psc %>%
  mutate(x_SAVF_score = SAVF_score(x_attribute, 1, 5, .653),
         y_SAVF_score = SAVF_score(y_attribute, 1, 10, .7))

psc2 %>%
  mutate(quadrant = kraljic_quadrant(x_SAVF_score, y_SAVF_score))

Product and service contracts

Description

A dataset containing a single value score for the x attribute (i.e. supply risk) and y attribute (i.e. profit impact) of 200 product and service contracts (PSC). The variables are as follows:

Usage

psc

Format

A tibble with 200 rows and 3 variables:

PSC

Contract identifier for each product and service

x_attribute

x attribute score, from 1 (worst) to 5 (best) in .01 increments

y_attribute

y attribute score, from 1 (worst) to 10 (best) in .01 increments

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