Description Usage Arguments Value Author(s) References See Also Examples
View source: R/target.spec.dea.R
Employs inverse DEA
to estimate specifications(in/outputs) to achieve a predetermined efficiency.
1 2 3 
xdata 
Input(s) vector (n by m) 
ydata 
Output(s) vector (n by s) 
date 
Production date (n by 1) 
t 
A vantage point from which the RoC is captured 
dt 
Delta t i.e., specs are estimated within PPS at t+dt 
dmu 
DMU whose inputs(or outputs) are to be estimated 
et 
Efficiency target; default value ("c") retains the current efficiency 
alpha 
Perturbed input(s) of designated DMU (1 by m) 
beta 
Perturbed output(s) of designated DMU (1 by s) 
wv 
Weight vector for scalarization (1 by m or s) 
rts 
Returns to scale assumption 
sg 
Employs secondstage optimization 
ftype 
Frontier type 
ncv 
Noncontrollable variable index(binary) for internal NDF (1 by (m+s)) 
env 
Environment index for external NDF (n by 1) 
cv 
Convexity assumption 
bound 
Puts upper/lower bounds on alpha/beta if 

Estimated input(s) 

Estimated output(s) 

Intensity vector 

Input slack 

Output slack 
DongJoon Lim, PhD
Lim, DongJoon, "Inverse DEA with frontier changes for new product target setting." European Journal of Operational Research 254.2 (2016): 510~516.
Wei, Quanling, Jianzhong Zhang, and Xiangsun Zhang. "An inverse DEA model for inputs/outputs estimate." European Journal of Operational Research 121.1 (2000): 151~163.
dm.dea
Distance measure using DEA
roc.dea
RoC calculation using DEA
target.arrival.dea
Arrival target setting using DEA
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  # Reproduce Example 2 in Wei, Q. et al.(2000)
# ready
x < matrix(c(1, 1, 1), 3)
y < matrix(c(4, 8, 5, 8, 4, 5), 3)
a < matrix(1.8, 1)
w < matrix(c(0.5, 0.5), 1)
# go
target.spec.dea(x, y, dmu = 3, alpha = a, wv = w, rts = "crs")$beta
# Reproduce Table 4 in Lim, DJ. (2016)
# Load engine dataset
df < dataset.engine.2015
# Subset for forced induction systems
fis < subset(df, grepl("^.C..", df[, 8]))
# ready
# Suppose one wants to estimate Porsche 911 turbo s' engine specs
# to retain its current competitiveness with downsized 3.5 litre engine in 2018.
# What might be the minimum specs to achieve this goal
# considering the technological changes we've seen so far?
# Plus, the CEO wants to put more emphasis on the torque improvement over HP.
d < subset(fis, select = 2)
x < subset(fis, select = 4)
y < subset(fis, select = 6 : 7)
a < as.matrix(3.5)
w < matrix(c(0.3, 0.7), 1)
# go
target.spec.dea(x, y, d, 2015, 3, 262, alpha = a, wv = w, rts = "vrs", sg = "min")$beta

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