forecasting.R

gistfile1.R

library(tidyverse)

# produce data

pharmacy_sub <- pharmacy %>% 
  filter(Site1 == "Site C", NSVCode == "Drug A")

daily_data <- phaRmacyForecasting:::make_tsibble(pharmacy_sub, frequency = "Daily")

daily_data <- daily_data %>% 
  mutate(inventory = 10000 - cumsum(quantity))

# produce forecast

for(day in 365 : nrow(daily_data)){
  
  
  daily_forecast <- phaRmacyForecasting:::forecast_series(daily_data %>% 
                                                            slice(1 : day), 28, 
                                                          frequency = "Daily")
  
  actual_forecast <- daily_forecast %>% 
    filter(.model == "SNAIVE") %>% 
    mutate(mean_demand = cumsum(.mean),
           sd_demand = rnorm(n()))
  
  step <- drug_quantity(forecast = actual_forecast, # simulated forecast for now
                distribution = lead_time_dis,
                min_stock = 100,
                max_stock = 800,
                p_min = 0.01,
                p_max = 0.05,
                inv_i = daily_data$inventory[day],
                delta_pref = 14)
  
  print(step$Q_i)
  
  print("####")
  
  print(step$Delta_i)
}