Aggregate Learning Curve

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.

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: ------------------------------------
# # 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
## ---------------------------------------------