ss.lfa {SixSigma}

Loss Function Analysis

Description

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

Usage

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.

Details

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

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)

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).

Author(s)

EL Cano

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.

See Also

ss.lf, ss.data.bolts.

Examples

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