DiscussWk3

library(tidyverse)

Given the below data, simulate the total cost, total revenue, and total profit for each day and for a 90-day season (via problem 3.5.17 in the text pg71):

nm_code <- c(1:4)
produce <- c("oats", "peas", "beans", "barley")
w_price <- c(1.05, 3.17, 1.99, 0.95)                 # per pound wholesale $
r_price <- c(1.29, 3.76, 2.23, 1.65)                 # per pound retail $
D_min <- c(rep(0,4))                                 # zero vector min demand
D_max <- c(10, 8, 14, 11)                            # upper-range demand 
t_season <- 90                                       # number of days to run

my_df <- cbind(w_price, r_price, D_max) %>% 
  as.data.frame() 
rownames(my_df) <- produce

my_df

##        w_price r_price D_max
## oats      1.05    1.29    10
## peas      3.17    3.76     8
## beans     1.99    2.23    14
## barley    0.95    1.65    11

Below I’ve created a function that generates a single day’s business and captures all of day’s output in single format - a business day.

D_gen <- function(i, d, w, r, nms){
  day <- i
  dmnd_qty <- round(runif(length(d))*d, digits = 0)
  cost <- dmnd_qty * w
  rev <- dmnd_qty * r
  prof <- dmnd_qty * (r-w)

  # below is the raw data frame containing cost, rev, & profit 
  # PER produce item.  Detail not shown. 
  biz_day_det <- cbind(nms, day, dmnd_qty, cost, rev, prof) %>% 
    as.data.frame()
  
  biz_day <- c(day=day, 
               daily_cost = sum(biz_day_det$cost),
               daily_rev = sum(biz_day_det$rev), 
               daily_profit = sum(biz_day_det$prof)) %>% 
    t() %>% 
    as.data.frame() 
  
  return(biz_day)
}

Here’s an example of it running on day’s 1 through 4 - the results are randomly generated and driven by the specified ranges:

##   day daily_cost daily_rev daily_profit
## 1   1      23.00     28.55         5.55
## 2   2      28.79     37.38         8.59
## 3   3      44.00     52.63         8.63
## 4   4      29.74     34.63         4.89

To capture the information for a season, I’ve created the below function that re-runs the above function for a given number of days, sums the results, and produces the season totals among cost, revenue, and profit.

season_run <- function(season_t){
  a_season <- NULL #empty season
  for(i in 1:season_t){
    a_season <- rbind(a_season, 
                      D_gen(i, D_max, w_price, r_price, nm_code)) %>% 
      as.data.frame()
    
  }
  season_results<- c(cost = sum(a_season$daily_cost), 
                   revenue = sum(a_season$daily_rev), 
                   profit = sum(a_season$daily_profit))
  return(season_results)
}

Below, please find 10 different 90-day season runs - i.e. each row represents the total season’s cost, revenue, and profit:

##          cost revenue profit
##  [1,] 3410.88 4256.15 845.27
##  [2,] 3478.20 4319.31 841.11
##  [3,] 3513.92 4351.77 837.85
##  [4,] 3537.85 4383.23 845.38
##  [5,] 3305.83 4111.16 805.33
##  [6,] 3154.14 3936.89 782.75
##  [7,] 3757.03 4626.99 869.96
##  [8,] 3372.81 4198.38 825.57
##  [9,] 3349.09 4184.70 835.61
## [10,] 3289.34 4116.93 827.59

DiscussWk3_604

jbrnbrg

September 12, 2017