target.spec.dea: Spec target setting using DEA
In DJL: Distance Measure Based Judgment and Learning

target.spec.dea

R Documentation

Spec target setting using DEA

Description

Employs inverse DEA to estimate specifications(in/out-puts) to achieve a predetermined efficiency.

Usage

target.spec.dea(xdata, ydata, date=NULL, t=NULL, dt=NULL, dmu, et="c",
                alpha=NULL, beta=NULL, wv=NULL, rts="crs", sg="ssm", ftype="d",
                ncv=NULL, env=NULL, cv="convex", bound=TRUE, pin=TRUE)

Arguments

`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 `"crs"` Constant RTS (default) `"vrs"` Variable RTS `"irs"` Increasing RTS `"drs"` Decreasing RTS
`sg`	Employs second-stage optimization `"ssm"` Slack-sum maximization (default) `"max"` Date-sum maximization `"min"` Date-sum minimization
`ftype`	Frontier type `"d"` Dynamic frontier (default) `"s"` Static frontier
`ncv`	Non-controllable variable index(binary) for internal NDF (1 by (m+s))
`env`	Environment index for external NDF (n by 1)
`cv`	Convexity assumption `"convex"` Convexity holds (default) `"fdh"` Free disposal hull (this will override `rts`)
`bound`	Puts upper/lower bounds on alpha/beta if `TRUE`(default)
`pin`	Includes the perturbed DMU in the PPS if `TRUE`(default)

Value

`$alpha`	Estimated input(s)
`$beta`	Estimated output(s)
`$lambda`	Intensity vector
`$xslack`	Input slack
`$yslack`	Output slack

Author(s)

Dong-Joon Lim, PhD

References

Lim, Dong-Joon, "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.

Examples

# 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, D-J. (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

DJL documentation built on March 31, 2023, 9:05 p.m.