Loss Function Analysis

This function performs a Quality Loss Function Analysis, based in the Taguchi Loss Function for "Nominal-the-Best" characteristics.

ss.lfa(
  lfa.data,
  lfa.ctq,
  lfa.Delta,
  lfa.Y0,
  lfa.L0,
  lfa.size = NA,
  lfa.output = "both",
  lfa.sub = "Six Sigma Project"
)

Arguments

lfa.data: Data frame with the sample to get the average loss.
lfa.ctq: Name of the field in the data frame containing the data.
lfa.Delta: Tolerance of the process.
lfa.Y0: Target of the process (see note).
lfa.L0: Cost of poor quality at tolerance limit.
lfa.size: Size of the production, batch, etc. to calculate the total loss in a group (span, batch, period, ...)
lfa.output: Type of output (see details).
lfa.sub: Subtitle for the graphic output.

Value

lfa.k: Constant k for the loss function
lfa,lf: Expression with the loss function
lfa.MSD: Mean Squared Differences from the target
lfa.avLoss: Average Loss per unit of the process
lfa.Loss: Total Loss of the process (if a size is provided)

Details

lfa.output can take the values "text", "plot" or "both".

Note

For smaller-the-better characteristics, the target should be zero (lfa.Y0 = 0). For larger-the-better characteristics, the target should be infinity (lfa.Y0 = Inf).

References

Taguchi G, Chowdhury S,Wu Y (2005) Taguchi's quality engineering handbook. John Wiley

Cano, Emilio L., Moguerza, Javier M. and Redchuk, Andres. 2012. Six Sigma with R. Statistical Engineering for Process Improvement, Use R!, vol. 36. Springer, New York. https://link.springer.com/book/10.1007/978-1-4614-3652-2/.

Author

EL Cano

Examples

ss.lfa(ss.data.bolts, "diameter", 0.5, 10, 0.001, 
    lfa.sub = "10 mm. Bolts Project", 
    lfa.size = 100000, lfa.output = "both")

#> $lfa.k
#> [1] 0.004
#> 
#> $lfa.lf
#> expression(bold(L == 0.004 %.% (Y - 10)^2))
#> 
#> $lfa.MSD
#> [1] 0.03372065
#> 
#> $lfa.avLoss
#> [1] 0.0001348826
#> 
#> $lfa.Loss
#> [1] 13.48826
#>