Posted by Estimating Support Labor for a Production Program
Analysis Track
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Abstract:
Common methods for estimating support labor are; Percent to Touch Labor, Fixed / Variable, Semi-Variable, and Improvement Curves. The problem with improvement curves is that rate variation may require an adjustment. The problem with the other methods is that they do not significantly address variation due to program maturity. Variation in support labor due to program maturity is usually different than the variation in touch labor due to program maturity. In other words, support labor does not follow the same improvement slope as touch labor.
Support Labor costs to a production program are a function of two things:
1. Experience (or Maturity)
– As a program matures, you typically need less support
2. Production Quantity (or Rate)
– Higher production rates require more support (but typically a lower proportion)
– Lower production rates require less support (but typically a higher proportion)
This model uses Experience and an adjusted formula for Production Quantity as the two predictor variables to predict the dependent variable which is the support labor hours per year.
• Experience is a number which is as a combination of years and cum quantity.
• Production Quantity is adjusted to get a number that represents the “Degree of Difficulty” in producing that quantity.
Regression analysis was performed of support labor hours against the two predictor variables and achieved high correlation. The resultant formula looks like this:
Support Hours = a – b · (Year · Cum Quantity ^0.5 + c · Qty ^ (1 / Year ^ 0.5)
This formula is used in estimating similar production programs with adjustments for programmatic differences.
The methodology is applicable to almost any industry, from aerospace to zippers. This paper describes how to set up your historical data, perform regression analysis, and calibrate the model to create an estimate for your production program.
Author:
Jeffrey Platten
Jeff Platten is a Systems Project Engineer with Northrop Grumman Corp.
He earned a BS in Statistics from the University of Minnesota – IT.
He has 15 years experience as an Industrial Engineer, with emphasis in
time study and standards development and was certified in several courses of Methods Time Measurement.
Another 18 years experience as an Estimator, Affordability Analyst and a
Systems Project Engineer.
He is a SCEA Certified Cost Estimator / Analyst and is also a
Project Management Professional from the Project Management Institute.
He recently completed Six Sigma Black Belt training from UCLA.