sim_min_Q_dynamic: sim_min_Q_dynamic

In inventorize: Inventory Analytics, Pricing and Markdowns

sim_min_Q_dynamic

Description

Simulating a Min,Q policy or also called S,Q policy, the min is dynamically calculated based on a forecast vector. . The Function takes a demand vector, mean of demand ,sd,lead time and requested service level to simulate an inventory system, orders are lost if inventory level is less than requested demand, also ordering is made at day t+1, metrics like item fill rate and cycle service level are calculated. the min is calculated based on a normal distribution or a poisson distribution, also min can be set manually. Q (fixed quantity) is ordered whenever inventory position reaches min

Usage

sim_min_Q_dynamic(
  demand,
  forecast,
  leadtime,
  service_level,
  initial_inventory_level = FALSE,
  Quantity,
  one_step_forecast = TRUE,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE,
  distribution = "normal",
  error_metric = "mse",
  smoothing_error = 0.2,
  metric_windows = FALSE,
  plot = FALSE,
  Backlogs = FALSE
)

Arguments

demand	A vector of demand in N time periods.
forecast	the forecast vector of equal n periods to demand.
leadtime	lead time from order to arrival (order to delivery time)
service_level	cycle service level requested
initial_inventory_level	integer,Default is False and simulation starts with min as inventory level
Quantity	integer,Fixed ordering quantity.
one_step_forecast	logical, Default is true where demand lead time is calcluated as(forecast at period t * leadtime) while if False, demand leadtime is calculated as (forecast of period t to forecast of period t+leadtime-1)
shortage_cost	numeric,Default is FALSE shortage cost per unit of sales lost
inventory_cost	numeric,Default is FALSE inventory cost per unit.
ordering_cost	numeric,Default is FALSE ordering cost for every time an order is made.
distribution	distribution to calculate safety stock based on demand distribution, current choices are 'normal' 'poisson','gamma' and negative binomial 'nbinom'
error_metric	metric is currently 'rmse' and 'mae', this calculates the error from period 1 to period t unless metric_windows is set. this contributes to the calculation of saftey stock. default is 'rmse'
smoothing_error	number between 0 and 1 to smooth the error as alpha x error[t] + (1-alpha) x error t-1, if metric_windows is used, smoothing error has to be FALSE
metric_windows	integer, for exammple if it is set to 4 rmse for t is calculated from t-1 to t-4,default is FALSE
plot	Logical, Default is False, if true a plot is generated.
Backlogs	Logical, Default is False, if true inventory level accounts for previous lost orders.

Value

a list of two date frames, the simulation and the metrics. the metrics are (1) shortage cost, (2) inventory cost which is the cost of one unit of inventory in one period,(3) which is the average inventory level per period, (4) total orders made in the simulation, (5) ordering cost if any, (6) total lost sales if any,(7) average ordering quantity across all orders,(8) ordering interval which is the average time between each order,(9) item fill rate,(10) cycle service level, (11) average saftey stock in each period,(12) the average sales in every order,(13) overall root mean square error, (14) overall mean absolute error, (14) overall mean error,(15) overall mean absolute percentage error,(16) the average flowttime which is the average time a unit spends on inventory and (17) the demand classification.

Author(s)

"haytham omar email: <[e-mail]>"

Examples

sim_min_Q_dynamic(demand = rpois(90,8),forecast = rpois(90,8),leadtime = 5,
service_level = 0.95,Quantity = 80,initial_inventory_level = 80,
 one_step_forecast=TRUE,shortage_cost = FALSE, 
                 inventory_cost = FALSE, ordering_cost = FALSE,distribution= 'normal', 
                 error_metric= 'rmse',metric_windows= FALSE,plot=TRUE)

inventorize documentation built on June 1, 2022, 1:07 a.m.