learningCurve (version 1.1.2)

agg_curve: Aggregate Learning Curve

Description

Computes the approximate aggregate cumulative learning curve formula by calculating the sum of all contributing hours from all departments for all production units 1 through n.

Usage

agg_curve(t, r, n, na.rm = FALSE)

Arguments

t

vector of hours (or costs) for the first unit from departments 1 through m

r

vector of historical learning rates for departments 1 through m

n

total units to be produced across all departments

na.rm

Should NA values be removed?

Examples

# NOT RUN {
# }
# NOT RUN {
# A project is expected to get underway soon to produce 300
# widgets. Three departments will be involved. Historically,
# these departments have had learning curves of 85%, 87%, and
# 80% respectively. The first unit hours for these departments
# have been estimated at 70, 45, and 25 respectively. What is 
# the total predicted hours required for the entire effort?

t <- c(70, 45, 25)
r <- c(.85, .87, .8)

agg_curve(t = t, r = r, n = 300)
## [1] 11000.96
# }
# NOT RUN {
# }