inventorize: Inventory Analytics And Cost Calculations.

Description

inventory analytics,revenue management and cost calculations for SKUs.

Author(s)

Maintainer: Haytham Omar haytham@rescaleanalytics.com

ABC

Description

Identyfing ABC category based on the pareto rule. Identyfing ABC category based on the pareto rule.A category is up to 80

Usage

ABC(data, na.rm = TRUE, plot = FALSE)

Arguments

Value

a dataframe that contains ABC categories with a bar plot of the count of items in each category.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

ABC(data.frame(SKU= seq(1:1000),demand=runif(1000,1,1000)))

CSOE

Description

Cost per stockout event

Usage

CSOE(
  quantity,
  demand,
  standerddeviation,
  leadtimeinweeks,
  cost,
  costSoe,
  holdingrate,
  na.rm = TRUE
)

Arguments

Details

Calculating K value that corresponds to the cost per stock out event, how much quantity should be put in stock as a minimum.the function solves for optimum K based on the stock out event. It should be noted that the condition(output) should be bigger than 1. other wise set K as per management.

Value

a dataframe that contains calculations of K and the minimum quantity to be put in stock .

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

CSOE(quantity=1000,demand=40000,standerddeviation=200,leadtimeinweeks=3,
cost=500,costSoe=30000,holdingrate=0.2,na.rm=TRUE)

Criticalratio

Description

Calculating critical ratio of a news vendor model under any distribution.this critical ratio maxmizes profit.

Usage

CriticalRatio(sellingprice, cost, salvage, penality, na.rm = TRUE)

Arguments

Value

the critical ratio.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

CriticalRatio(sellingprice=80,cost=60,salvage=45,penality=25,na.rm=TRUE)

EPN_singleperiod

Description

calculating expected profit for a newsvendor model.

Usage

EPN_singleperiod(quantity, mean, standerddeviation, p, c, g, b, na.rm = TRUE)

Arguments

Details

calculating expected profit for a newsvendor model. based on assumed normal distribution demand.

Value

a dataframe that contains calculations of the expected profit from a newsvendor model based on normal distribution.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

EPN_singleperiod(quantity=40149,mean= 32000,standerddeviation= 11000,p=24,c=10.9,g=7,b=0,na.rm=TRUE)

EPP_singleperiod

Description

Expected profit from a newsvendor model based on a poisson distribution.

Usage

EPP_singleperiod(quantity, lambda, p, c, g, b, na.rm = TRUE)

Arguments

Details

calculating expected profit for a newsvendor model. based on assumed poisson distribution demand.

Value

a dataframe that contains calculations of the expected profit from a newsvendor model based on poisson distribution.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

EPP_singleperiod(quantity=40149,lambda= 32000,p=24,c=10.9,g=7,b=0,na.rm=TRUE)

EUSnorm_singleperiod

Description

Calculating expected unit short based on an assumed normal distribution.

Usage

EUSnorm_singleperiod(quantity, demand, standerddeviation, na.rm = TRUE)

Arguments

Details

Calculating expected unit short based on an assumed normal distribution for a newsvendor model.

Value

a dataframe that contains Expected unit short,k and g(k).

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

EUSnorm_singleperiod(quantity=35000,demand=32000,standerddeviation=12000,na.rm=TRUE)

Hibrid_normal

Description

Hibrid Policy normal distribution service level, .

Usage

Hibrid_normal(
  demand,
  mean,
  sd,
  leadtime,
  service_level,
  Review_period,
  min = FALSE,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE
)

Arguments

Details

The Function takes a demand vector, mean of demand ,sd,lead time and requested service level to simulate and 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 order up to level is calculated based on the review period,lead time and normal distribution. Inventory is replenished if inventory position is below min or it is time for review period.

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

Hibrid_normal(demand=rpois(80,6),mean=4,sd=0.2,leadtime=5,service_level=0.95,
Review_period =9,min=30,
shortage_cost= FALSE,inventory_cost=FALSE,ordering_cost=FALSE)

Hibrid_pois

Description

Hibrid Policy Poisson distribution service level, .

Usage

Hibrid_pois(
  demand,
  leadtime,
  service_level,
  lambda,
  Review_period,
  min = FALSE,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE
)

Arguments

Details

The Function takes a demand vector, mean of demand ,sd,lead time and requested service level to simulate and 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 order up to level is calculated based on the review period,lead time and normal distribution. Inventory is replenished if inventory position is below min or it is time for review period.

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

Hibrid_pois(demand=rpois(80,6),lambda=4,leadtime=5,service_level=0.65,
Review_period =9,min=30,
shortage_cost= FALSE,inventory_cost=FALSE,ordering_cost=FALSE)

MPN_singleperiod

Description

calculating expected profit for a newsvendor model based on critical ratio.

Usage

MPN_singleperiod(mean, standerddeviation, p, c, g, b, na.rm = TRUE)

Arguments

Details

calculating expected profit for a newsvendor model. based on assumed normal distribution demand.

Value

a dataframe that contains calculations of the maximum expected profit from a newsvendor model based on normal distribution.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

MPN_singleperiod(mean= 32000,standerddeviation= 11000,p=24,c=10.9,g=7,b=0,na.rm=TRUE)

MPP_singleperiod

Description

Maximum profit from a newsvendor model based on a poisson distribution.

Usage

MPP_singleperiod(lambda, p, c, g, b, na.rm = TRUE)

Arguments

Details

calculating expected profit for a newsvendor model. based on assumed poisson distribution demand based on the critical ration.

Value

a dataframe that contains calculations of the maximum expected profit from a newsvendor model based on poisson distribution.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

MPP_singleperiod(lambda= 32000,p=24,
c=10.9,g=7,b=0,na.rm=TRUE)

Max_policy_dynamic

Description

Simulating a max policy or also called S policy, the Max is dynamically calculated based on a forecast vector.

Usage

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

Arguments

Details

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. and order is equal to max((Max[t]-inventory position [t-1])+ sales[t],0)

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: <haytham@rescaleanalytics.com>"

Examples

Max_policy_dynamic(demand = rnorm(90,9,2),forecast = rpois(90,9) ,
service_level = 0.7,leadtime = 10)

Multi_Competing_optimization

Description

Calculating the optimum price based on consumer choice model for products that competes with each other.

Usage

Multi_Competing_optimization(X, y, n_variables, initial_products_cost)

Arguments

Details

for multiple products that are offered , some of these products compete with each other. for example; Beef, chicken and lamb. each of them provides a certain value to consumer and are offered with different prices. this function calculates the intrinsic utility value -what is the perceived value of this product to the consumer- for competing products and optimize thee price of each product accordingly. please note that the more the products you put in the model, the more processing time it will take due to complexity of optimization problem.it is recommended to maximum of 8 products to your model.

Value

a data frame with the product names which are names of X,the intrinsic utility value,the current cost and the optimized price for each product

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

Multi_Competing_optimization(X= data.frame(Chedar_Cheese= runif(100,10,15),
Mozarella=runif(100,8,10),
Parmesan=runif(100,9,12)),y= as.numeric(rep(c(1,2,3,NA,2),20)),n_variables = 3,
initial_products_cost = c(8,6,7))

Periodic_review_normal

Description

Simulating a Periodic order up to level policy, .

Usage

Periodic_review_normal(
  demand,
  mean,
  sd,
  leadtime,
  service_level,
  Review_period,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE
)

Arguments

Details

The Function takes a demand vector, mean of demand ,sd,lead time and requested service level to simulate and 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 order up to level is calculated based on the review period,lead time and normal distribution .

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

Periodic_review_normal(demand=rpois(80,6),mean=6,sd=0.2,leadtime=5,service_level=0.95,
Review_period =9,
shortage_cost= FALSE,inventory_cost=FALSE,ordering_cost=FALSE)

Periodic_review_pois

Description

Simulating a Periodic order up to level policy, .

Usage

Periodic_review_pois(
  demand,
  lambda,
  leadtime,
  service_level,
  Review_period,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE
)

Arguments

Details

The Function takes a demand vector, mean of demand ,sd,lead time and requested service level to simulate and 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 order up to level is calculated based on the review period,lead time and Poisson distribution .

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

Periodic_review_pois(demand=rpois(80,6),lambda=6,leadtime=5,service_level=0.95,
Review_period =9,
shortage_cost= FALSE,inventory_cost=FALSE,ordering_cost=FALSE)

R_s_S

Description

Simulating a Min Max periodic policy or also called R,s,S policy,R represents the ordering/review period. .

Usage

R_s_S(
  demand,
  mean = FALSE,
  sd = FALSE,
  leadtime,
  service_level,
  initial_inventory_level = FALSE,
  min = FALSE,
  Max = FALSE,
  Min_to_max = 0.6,
  Review_period,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE,
  distribution = "normal",
  recalculate = FALSE,
  recalculate_windows = FALSE,
  plot = FALSE,
  Backlogs = TRUE
)

Arguments

Details

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. Max - inventory position is ordered whenever inventory position reaches min at the priod of review

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

R_s_S(demand = rpois(90,9),service_level = 0.97,leadtime = 10,
Review_period = 10,Backlogs=TRUE)

R_s_S_dynamic

Description

Simulating a Min Max periodic policy or also called R,s,S policy, R represents the ordering/review period, the Max is dynamically calculated based on a forecast vector. .

Usage

R_s_S_dynamic(
  demand,
  forecast,
  leadtime,
  Review_period,
  service_level,
  initial_inventory_level = FALSE,
  Min_to_max = 0.6,
  min = FALSE,
  one_step_forecast = TRUE,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE,
  distribution = "normal",
  error_metric = "mse",
  metric_windows = FALSE,
  smoothing_error = 0.2,
  plot = FALSE,
  Backlogs = TRUE
)

Arguments

Details

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. Max - inventory position is ordered whenever inventory position reaches min at the priod of review

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: <haytham@rescaleanalytics.com>"

Examples

R_s_S_dynamic(demand = rpois(90,9),forecast = rpois(90,9),service_level = 0.9,
             leadtime = 10,Review_period = 10,min = 70,Backlogs=TRUE)

TQpractical

Description

Identyfing Practical ordering quantity based on the economic order quantity.it is assumed that practical order quantity will be always withing 6

Usage

TQpractical(
  annualdemand,
  orderingcost,
  purchasecost,
  holdingrate,
  na.rm = TRUE
)

Arguments

Value

a dataframe that contains the economic order quantity and the practical order quantity, Tstar (optimum)and Tpractical which is always away from the optimum up to 6

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

TQpractical(annualdemand=1000,orderingcost=100,
purchasecost=72,holdingrate=0.25,na.rm=TRUE)

TRC

Description

Identyfing Total relevant cost.

Usage

TRC(annualdemand, orderingcost, purchasecost, holdingrate, na.rm = TRUE)

Arguments

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

TRC(annualdemand=2500,orderingcost=250,purchasecost=98,
holdingrate=0.25,na.rm=TRUE)

abc_dynamic

Description

Identyfing ABC category based on the pareto rule. the function can have flexibility in defining the A,B thresholds. can be done on multiple splits for example countries or stores

Usage

abc_dynamic(
  product,
  key_to_split = F,
  first_attribute,
  second_attribute = F,
  A = F,
  B = F
)

Arguments

Value

a dataframe that contains ABC categories.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

abc_dynamic(c(1:1000), rep(seq(1:10),100), runif(1000,4,10000),rnorm(1000,100,20))

dl.sigmadl

Description

claculating demand lead time,saftey stock when there is a leadtime variability.

Usage

dl.sigmadl(expected_demand, sd_demand, expected_leadtime, sd_leadtime)

Arguments

Details

calculating leadtime with leadtime variablility as delivery time diffires to long distances and reliability of mode of transport. thus demand leadtime and standard deviation during lead time takes into consideration the lead time variability.

Value

a dataframe that contains calculations of the expected demand lead time and the expected saftey stock during leadtime. It is noted that saftey stock here is more than normal due to leadtime variability.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

dl.sigmadl(expected_demand=100,sd_demand=22,expected_leadtime=12,sd_leadtime=3)

elasticity

Description

calculating elasticity of price change.

Usage

elasticity(salesP1, salesP2, priceP1, priceP2)

Arguments

Details

This function is helpful to determine the elasticity of a product with effect to price change, the figure could be negative as the change is price is negative. it translates as for each unit percentage decrease in price , this much is ecpected precentage of increase of sales. condition must be that Price in period one was more than proce in period 2 and sales in period two was more than sales in period 1.

Value

the elasticity ratio in unit sales, the -ve number represents the increase in sales for each decrease of unit currency.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: "<haytham@rescaleanalytics.com>"

Examples

elasticity(salesP1=50,salesP2=100,priceP1=6,priceP2=4)

eoq

Description

economic order quantity.

Usage

eoq(annualdemand, orderingcost, purchasecost, holdingrate, na.rm = TRUE)

Arguments

Value

the eoq,cycle stock time in years and cycle stock time in weeks.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

eoq(annualdemand=5000,orderingcost=400,purchasecost=140,holdingrate=0.2,na.rm=TRUE)

eoqsenstivity

Description

the rate of increase of total relevant cost compared to the EOQ.

Usage

eoqsenstivity(quantity, quantityoptimal, na.rm = TRUE)

Arguments

Value

the rate of increase of total relevant cost compared to the EOQ.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

eoqsenstivity(quantity=5400,quantityoptimal=6000,na.rm=TRUE)

Hybrid

Description

Simulating a Min Max periodic policy, diffirent from R,s,S because here order is made in case the Inventory position reaches min or the ordering period comes . 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. Max - inventory position is ordered whenever inventory position reaches min or at the period of review

Usage

hybrid_policy(
  demand,
  mean = FALSE,
  sd = FALSE,
  leadtime,
  service_level,
  initial_inventory_level = FALSE,
  min = FALSE,
  Max = FALSE,
  Min_to_max = 0.6,
  Review_period,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE,
  distribution = "normal",
  recalculate = FALSE,
  recalculate_windows = FALSE,
  plot = FALSE,
  Backlogs = FALSE
)

Arguments

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

hybrid_policy(demand = rpois(90,8),leadtime = 6,Review_period = 10,service_level = 0.8)

hybrid_policy_dynamic

Description

Simulating a Min Max periodic policy, diffirent from R,s,S because here order is made in case the Inventory position reaches min or the ordering period comes the Max is dynamically calculated based on a forecast vector. .

Usage

hybrid_policy_dynamic(
  demand,
  forecast,
  leadtime,
  Review_period,
  service_level,
  initial_inventory_level = FALSE,
  Min_to_max = 0.6,
  min = FALSE,
  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

Details

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. Max - inventory position is ordered whenever inventory position reaches min or at the period of review

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: <haytham@rescaleanalytics.com>"

Examples

hybrid_policy_dynamic(demand = rpois(90,9),forecast = rpois(90,9),service_level = 0.9,
leadtime = 10,Review_period = 10,min = 50)

inventorymetricsCIS

Description

calculating inventory metrics based on cost per item short.

Usage

inventorymetricsCIS(
  CIS,
  demand,
  standerddeviation,
  quantity,
  leadtime,
  cost,
  holdingrate,
  na.rm = TRUE
)

Arguments

Details

after cost per item short is explicitly calculated, item fill rate, cost per stock out event and cycle service level are implicitly calculated.

Value

a dataframe that contains demand leadtime,sigmadl(standard deviation in leadtime),saftey factor k determined based on cost per itemshort,unit normal loss function,expected units to be short,cycle service level, fill rate,implied cost per stockout event, saftey stock and suggested reorder point.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

inventorymetricsCIS(CIS= 90, demand= 35000,standerddeviation=9000,
quantity= 9000,leadtime=3 ,cost=90,holdingrate=0.15,na.rm =TRUE)

inventorymetricsCSL

Description

calculating inventory metrics based on CYCLE SERVICE LEVEL.

Usage

inventorymetricsCSL(
  csl,
  demand,
  standerddeviation,
  quantity,
  leadtime,
  cost,
  holdingrate,
  na.rm = TRUE
)

Arguments

Details

cycle service level is the desired no of times demand is compleltey fulfiiled from cycle stock,after cycle service level is explicitly calculated, cost per item short, cost per stock out event and item fill rate are implicitly calculated.

Value

a dataframe that contains demand leadtime, sigmadl(standard deviation in leadtime), saftey factor k determined based on item fillrate provided, unit normal loss function, expected units to be short, cycle service level, fill rate,implied cost per stockout event, saftey stock and suggested reorder point.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

inventorymetricsCSL(csl=0.95,demand=20000,standerddeviation=1200,
quantity=4500,leadtime=3,cost=100,holdingrate=0.15,na.rm=TRUE)

inventorymetricsIFR

Description

calculating inventory metrics based on item fillrate.

Usage

inventorymetricsIFR(
  fillrate,
  demand,
  standerddeviation,
  quantity,
  leadtime,
  cost,
  holdingrate,
  na.rm = TRUE
)

Arguments

Details

item fill rate is the percentage of demand that is fullfilled directly from the cycle stock,after item fill rate is explicitly calculated, cost per item short, cost per stock out event and cycle service level are implicitly calculated.

Value

a dataframe that contains demand leadtime, sigmadl(standard deviation in leadtime), saftey factor k determined based on item fillrate provided, unit normal loss function expected units to be short,cycle service level, fill rate,implied cost per stockout event, saftey stock and suggested reorder point.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

inventorymetricsIFR(fillrate= 0.90, demand= 35000,standerddeviation=9000,
quantity= 5000,leadtime=3 ,cost=50,holdingrate=0.15,na.rm=TRUE)

linear_elasticity

Description

calculating elasticity of a linear price response function This function is helpful to determine if your product is elastic or not based on a linear price response function. if product demand is not linear to price, try using the single product optimization function instead. The price elasticity of demand which is often shortened to demand elasticity is defined to be the percentage change in quantity demanded, q, divided by the percentage change in price, p. When Elasticity bigger 1, we say the good is price elastic.In this case, percentQ bigger percentP, and so, for a 1 percent change in price, there is a greater than 1 percent change in quantity demanded.In this case, management should decrease price to have a higher revenue. When Elasticity smaller 1, we say the good is price inelastic.In this case, percentQ smaller percentP, and so, for a 1 percent change in price, there is a less than 1 percent change in quantity demanded.In this case, management should increase price to have a higher revenue. When Elasticity equal 1, we say the good is price unit elastic.In this case, percentQ equal percentP , and so, for a 1percent change in price, there is also an 1percent change in quantity demanded. This is the optimal price which means it maximizes revenue.

Usage

linear_elasticity(prices, Sales, present_price, cost_of_product, plot = FALSE)

Arguments

Value

the elasticity at the present price , the price for optimum revenue and thee price for optimum cost.

Note

this is the third version of the inventorize package, all the functions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: "<haytham@rescaleanalytics.com>"

Examples

linear_elasticity(prices=c(5,10,8,5,14),Sales= c(450,400,420,450,360),
present_price=15,cost_of_product=40)

periodic_policy

Description

Simulating a periodic policy, different from R,s,S because here order is made at the ordering time without a min(reordering quantity)

Usage

periodic_policy(
  demand,
  mean = FALSE,
  sd = FALSE,
  leadtime,
  service_level,
  initial_inventory_level = FALSE,
  Max = FALSE,
  Review_period,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE,
  distribution = "normal",
  recalculate = FALSE,
  recalculate_windows = FALSE,
  plot = FALSE,
  Backlogs = TRUE
)

Arguments

Details

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. Max - inventory position is ordered at the period of review

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

periodic_policy(demand = rpois(90,9),service_level = 0.9,
leadtime = 10,Review_period = 10,recalculate = TRUE,Backlogs=TRUE)

periodic_policy_dynamic

Description

Simulating a periodic policy, different from R,s,S because here order is made at the ordering time without a min(reordering quantity) the Max is dynamically calculated based on a forecast vector. .

Usage

periodic_policy_dynamic(
  demand,
  forecast,
  leadtime,
  Review_period,
  service_level,
  initial_inventory_level = FALSE,
  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

Details

The Function takes a demand vector, forecast vector 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. Max - inventory position is ordered at the period of review

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: <haytham@rescaleanalytics.com>"

Examples

periodic_policy_dynamic(demand = rpois(90,9),forecast = rpois(90,9),
service_level = 0.9,leadtime = 10,Review_period = 10)

possible_markdowns

Description

a markdown model This is a markdown model proposed in Walker, John. "A model for determining price markdowns of seasonal merchandise." Journal of Product & Brand Management (1999), the idea that it is possible for seasonal merchandise to forecast how much for a specific product can be left at the end of the season. based on the sales rate in the periods of the selling season. for example, if a seasonal shirt initial buying quantity is 500, during the the first two weeks we sold 100 and the season for this shirt is 6 weeks, then it is possible to forecast for a one time shot product how much is expected to be left with at the end of the season (at the end of the 6 weeks), the function applies the algorithm in walker (1999), the returning value is a classification of the item if it is a slow moving or a regular item. also the possible markdowns that can be applied. (only markdowns where there is a economic viability) and this can be a dynamic markdown process where the process can be repeated every week, preferably when the product changes its status from Regular to slow moving. if the markdown recommendation is for example 0.9 then it means that the new price is 90

Usage

possible_markdowns(
  begining_inventory,
  weeks,
  current_week,
  inventory_at_week,
  expected_at_season_end,
  plot = TRUE
)

Arguments

Value

a dataframe that contains all tthe possible economically viable markdowns.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

possible_markdowns(begining_inventory=1000,weeks=16,
current_week=2,inventory_at_week=825,expected_at_season_end=150,plot=TRUE)

productmix

Description

Identyfing ABC category based on the pareto rule for both demand and selling price,a mix of nine categories are produced. Identyfing ABC category based on the pareto rule.A category is up to 80

Usage

productmix(SKUs, sales, revenue, na.rm = TRUE, plot = FALSE)

Arguments

Value

a dataframe that contains ABC categories with a bar plot of the count of items in each category.

Note

this is the first version of the inventorize package, all the fucntions are common knowlege for supply chain without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

productmix(SKUs=c(1:100),sales=runif(100,1,1000),revenue = rnorm(100,200,10),na.rm=TRUE)

productmix_storelevel

Description

Identyfing ABC category based on the pareto rule for both demand and selling price,a mix of nine categories are produced. Identyfing ABC category based on the pareto rule.A category is up to 80 in this fuction the data is splitted by store and a product mix is made on each store individually.

Usage

productmix_storelevel(
  SKUs,
  sales,
  revenue,
  storeofsku,
  na.rm = TRUE,
  plot = FALSE
)

Arguments

Value

a dataframe that contains ABC categories by store with a bar plot of the count of items in each category.

Note

this is the first version of the inventorize package, all the functions are common knowledge for supply chain without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

productmix_storelevel(c(1:1000),sales = runif(1000,4,10000),
revenue  = rnorm(1000,100,20),storeofsku = rep(seq(1:10),100))

profit_max

Description

maxmizing profit based on chage in price and elasticity.

Usage

profit_max(cost, salesP1, salesP2, priceP1, priceP2, na.rm = TRUE)

Arguments

Details

This function is helpful to determine the elasticity of a product with effect to price change, the figure could be negative as the change is price is negative. it translates as for one currency unit change in price, this much is ecpected in units in increase of sales. condition must be that Price in period one was more than price in period 2 and sales in period two was more than sales in period 1. a proposed price is given to period 3 which is future period to maxmize profit. it is advisable that elasticity to be calibrated by testing it on several periods. this function does not take into account advertising and campaigns,i.e external factors. yet it's a good indicator of best pricing per SKU.

Value

the elasticity ratio in unit sales, the -ve number represents the increase in sales for each decrease of unit currency.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

profit_max(cost=2,salesP1=50,salesP2=100,priceP1=6,priceP2=4)

profit_max_withfixedcost

Description

maxmizing profit based on chage in price and elasticity taking into consideration fixed and variable costs.

Usage

profit_max_withfixedcost(
  fixed_cost,
  variable_cost,
  salesP1,
  salesP2,
  priceP1,
  priceP2
)

Arguments

Details

This function is helpful to determine the elasticity of a product with effect to price change, the figure could be negative as the change is price is negative. it translates as for one currency unit change in price, this much is ecpected in units in increase of sales. condition must be that Price in period one was more than price in period 2 and sales in period two was more than sales in period 1. a proposed price is given to period 3 which is future period to maxmize profit. it is advisable that elasticity to be calibrated by testing it on several periods. this function does not take into account advertising and campaigns,i.e external factors. yet it's a good indicator of best pricing per SKU.

Value

the elasticity ratio in unit sales, the -ve number represents the increase in sales for each decrease of unit currency.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

profit_max_withfixedcost(fixed_cost=200,variable_cost=20,salesP1=50,salesP2=100,priceP1=6,priceP2=4)

reorderpoint

Description

Calculating saftey stock based on the cycle service level.

Usage

reorderpoint(
  dailydemand,
  dailystandarddeviation,
  leadtimein_days,
  csl,
  distribution = "normal"
)

Arguments

Details

Calculating re-order point based on demand variability without lead time variability in an assumed normal distribution. cycle service level is provided to calculate saftey stock accordingly.

Value

a dataframe that contains demand lead time,sigmadl,safteyfactor and re_order point.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

reorderpoint(dailydemand=50,dailystandarddeviation=5,leadtimein_days=6,csl=0.90)

reorderpoint_leadtime_variability

Description

Calculating saftey stock based on the cycle service level.

Usage

reorderpoint_leadtime_variability(
  dailydemand,
  dailystandarddeviation,
  leadtimein_days,
  sd_leadtime_days,
  csl,
  distribution = "nbinom"
)

Arguments

Details

Calculating re-order point based on demand variability and lead time variability in an assumed normal distribution. cycle service level is provided to calculate saftey stock accordingly.

Value

a dataframe that contains demand lead time,sigmadl,safteyfactor and re_order point.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

reorderpoint_leadtime_variability(dailydemand=50,dailystandarddeviation=5,
leadtimein_days=6,sd_leadtime_days=2,csl=0.90)

revenue_max

Description

maxmizing revenue based on chage in price and elasticity.

Usage

revenue_max(salesP1, salesP2, priceP1, priceP2, na.rm = TRUE)

Arguments

Details

#' This function is helpful to determine the elasticity of a product with effect to price change, the figure could be negative as the change is price is negative. it translates as for each unit percentage decrease in price , this much is ecpected precentage of increase of sales. condition must be that Price in period one was more than proce in period 2 and sales in period two was more than sales in period 1. a proposed optimum price is given to period 3 which is future period to maxmize revenue.

Value

the elasticity ratio in unit sales, the -ve number represents the increase in sales for each decrease of unit currency.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

revenue_max(salesP1=50,salesP2=100,priceP1=6,priceP2=4)

saftey_stock_normal

Description

Calculating saftey stock based on the cycle service level.

Usage

saftey_stock_normal(
  annualdemand,
  annualstandarddeviation,
  leadtimeinweeks,
  csl,
  na.rm = TRUE
)

Arguments

Details

Calculating saftey stock based on the cycle service level in an assumed normal distribution.

Value

a dataframe that contains calculations of K the cost per item short metric noting that condition must me less than 1.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

saftey_stock_normal(annualdemand=8000,annualstandarddeviation=600,
leadtimeinweeks=4,csl=0.92,na.rm=TRUE)

safteystock_CIS_normal

Description

Calculating K value that reduces cost per item short.

Usage

safteystock_CIS_normal(
  quantity,
  demand,
  standerddeviation,
  leadtimeinweeks,
  cost,
  Citemshort,
  holdingrate,
  na.rm = TRUE
)

Arguments

Details

Calculating K value that reduces cost per item short inventory metric based on an assumed normal distribution.

Value

a dataframe that contains calculations of K the cost per item short metric noting that condition must me less than 1.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

safteystock_CIS_normal(quantity=3000,demand=50000,standerddeviation=4000,
leadtimeinweeks=4,cost=90,Citemshort=15,holdingrate=0.15,na.rm=TRUE)

safteystock_CSL_normal

Description

calculating saftey stock based on cycle service level rate.

Usage

safteystock_CSL_normal(
  rate,
  quantity,
  demand,
  standerddeviation,
  leadtime,
  na.rm = TRUE
)

Arguments

Details

calculating saftey stock and expected unit short based on the cycle service identified assuming a normal distribution.

Value

a dataframe that contains calculations of the expected profit from a newsvendor model based on normal distribution.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

safteystock_CSL_normal(rate=0.95,quantity=30000,demand=28000,standerddeviation=5000,8,na.rm=TRUE)

safteystock_IFR_normal

Description

Calculating K value corresponding to item fill rate.

Usage

safteystock_IFR_normal(
  rate,
  quantity,
  demand,
  standerddeviation,
  leadtime,
  na.rm = TRUE
)

Arguments

Details

Calculating K value that corresponds to the desired item fill rate.

Value

a dataframe that contains calculations of K the item fill rate metric.cycle service level and expected unit short.

Note

this is the first version of the inventorize package, all the fucntions are basic knowlege for supply chain without any contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <h.omar5942@gmail.com>"

Examples

safteystock_IFR_normal(rate=0.97,quantity=9000,demand=100000,
standerddeviation=5000,leadtime=4,na.rm=TRUE)

sim_Base_normal

Description

Simulating a Base Stock policy.

Usage

sim_base_normal(
  demand,
  mean,
  sd,
  leadtime,
  service_level,
  Base = FALSE,
  ordering_delay = FALSE,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE
)

Arguments

Details

The Function takes a demand vector, mean of demand ,sd,lead time and requested service level to simulate and 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 based on a normal distribution. the base is calculated automatically based on the mean demand and standard deviaiton. every period the order is exactly as the sales.

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

sim_base_normal(demand=rpois(80,6),mean=6,sd=0.2,leadtime=5,service_level=0.95,Base = 50,
shortage_cost= 1,inventory_cost=1,ordering_cost=1,ordering_delay=FALSE)

sim_base_pois

Description

Simulating a Min,max policy or aslo called s,S policy, .

Usage

sim_base_pois(
  demand,
  lambda,
  leadtime,
  service_level,
  Base = FALSE,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_delay = FALSE,
  ordering_cost = FALSE
)

Arguments

Details

The Function takes a demand vector, mean of demand ,sd,lead time and requested service level to simulate and 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 based on poisson distribution..

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

sim_base_pois(demand = rpois(50,8),lambda = 4,leadtime = 4,shortage_cost = 20,ordering_delay=FALSE,
Base  = FALSE,service_level = 0.70,inventory_cost = 50,ordering_cost=50)

sim_base_stock_policy

Description

Simulating a base stock policy where order is made every period equal to the demand sold and having a Base stock enough for leadtime and saftey stock. 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. demand and base adjustment (if any) is ordered every period.

Usage

sim_base_stock_policy(
  demand,
  mean = FALSE,
  sd = FALSE,
  leadtime,
  service_level,
  Base = FALSE,
  ordering_delay = FALSE,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE,
  distribution = "normal",
  recalculate = FALSE,
  recalculate_windows = FALSE,
  plot = FALSE
)

Arguments

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

sim_base_stock_policy(demand = rpois(90,8),leadtime = 6,service_level = 0.95,recalculate = 5)

sim_min_Q

Description

Simulating a Min,Q policy or also called S,Q policy, . 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(
  demand,
  mean = FALSE,
  sd = FALSE,
  leadtime,
  service_level,
  initial_inventory_level = FALSE,
  min = FALSE,
  Quantity,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE,
  distribution = "normal",
  recalculate = FALSE,
  recalculate_windows = FALSE,
  plot = FALSE,
  Backlogs = FALSE
)

Arguments

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

sim_min_Q(demand = rpois(90,7),leadtime = 5,service_level = 0.95,Quantity = 80,
recalculate = TRUE,distribution = 'normal',recalculate_windows = 5,Backlogs=FALSE)

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

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: <haytham@rescaleanalytics.com>"

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)

sim_min_Q_normal

Description

Simulating a Min,Q policy or also calleD S,Q policy, .

Usage

sim_min_Q_normal(
  demand,
  mean,
  sd,
  leadtime,
  service_level,
  Quantity,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE
)

Arguments

Details

The Function takes a demand vector, mean of demand ,sd,lead time and requested service level to simulate and 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.

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

sim_min_Q_normal(demand = rpois(50,8),mean = 5,sd=1,
service_level = 0.9,leadtime = 4,
shortage_cost = 5, Quantity = 12,inventory_cost = 1,ordering_cost = 50)

sim_min_Q_pois

Description

Simulating a Min,Q policy or also calleD S,Q policy, .

Usage

sim_min_Q_pois(
  demand,
  lambda,
  leadtime,
  service_level,
  Quantity,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE
)

Arguments

Details

The Function takes a demand vector, mean of demand ,sd,lead time and requested service level to simulate and 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.

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

sim_min_Q_pois(demand = rpois(50,8),lambda = 4,leadtime = 4,shortage_cost =30,
Quantity = 12,service_level = 0.70,
inventory_cost = 50,ordering_cost=FALSE)

sim_min_max

Description

Simulating a min max policy or also called s,S policy, . 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. Max - inventory position is ordered whenever inventory position reaches min

Usage

sim_min_max(
  demand,
  mean = FALSE,
  sd = FALSE,
  leadtime,
  service_level,
  initial_inventory_level = FALSE,
  min = FALSE,
  Max = FALSE,
  Max_to_min = 1.3,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE,
  distribution = "normal",
  recalculate = FALSE,
  recalculate_windows = FALSE,
  plot = FALSE,
  Backlogs = FALSE
)

Arguments

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

sim_min_max(demand = rpois(80,6),leadtime = 4,service_level = 0.95,recalculate = TRUE)

sim_min_max_dynamic

Description

Simulating a min max policy or also called s,S policy, the Max 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. Max - inventory position is ordered whenever inventory position reaches min

Usage

sim_min_max_dynamic(
  demand,
  forecast,
  leadtime,
  service_level,
  initial_inventory_level = FALSE,
  Max_to_min = 1.5,
  Max = FALSE,
  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

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: <haytham@rescaleanalytics.com>"

Examples

sim_min_max_dynamic(demand = rpois(90,6),forecast = rpois(90,6),
leadtime = 6,service_level = 0.95,one_step_forecast  = FALSE,Max = 80,
distribution = 'normal',error_metric = 'mae',Backlogs=TRUE)

sim_minmax_normal

Description

Simulating a Min,max policy or aslo called s,S policy, .

Usage

sim_minmax_normal(
  demand,
  mean,
  sd,
  leadtime,
  service_level,
  Max,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE
)

Arguments

Details

The Function takes a demand vector, mean of demand ,sd,lead time and requested service level to simulate and 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.

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

sim_minmax_normal(demand=rpois(80,6),mean=6,sd=0.2,leadtime=5,service_level=0.95,Max=25,
shortage_cost= FALSE,inventory_cost=FALSE,ordering_cost=FALSE)

sim_minmax_pois

Description

Simulating a Min,max policy or aslo called s,S policy, .

Usage

sim_minmax_pois(
  demand,
  lambda,
  leadtime,
  service_level,
  Max,
  shortage_cost = FALSE,
  inventory_cost = FALSE,
  ordering_cost = FALSE
)

Arguments

Details

The Function takes a demand vector, mean of demand ,sd,lead time and requested service level to simulate and 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 poisson distribution.

Value

a list of two date frames, the simulation and the metrics.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

sim_minmax_pois(demand = rpois(50,8),lambda = 4,leadtime = 4,shortage_cost = 20,
Max = 32,service_level = 0.70,inventory_cost = 50,ordering_cost=50)

single_product_optimization

Description

Calculating the optimum price based on linear and logit models for a single product.

Usage

single_product_optimization(
  x,
  y,
  service_product_name,
  degree_poly = 3,
  current_price,
  plot = FALSE
)

Arguments

Details

calculate the optimized price based on the price response function. the price response function is measured twice, one with linear model and one time with a logit model. a simulation is then made with each price response function to define the maximum revenue for each. finally, a suggestion of which model to choose and the optimum price to use for this product. it is preferable to de-seasonalize the sales data before fitting if the sales are affected by spikes and declines due to regular events as holidays and weekends.

Value

a list of the squared error of th logit model, the squared error of the linear model, the best model for this product, the optimum price for both the linear and the logit model, the current price,the a,b,c parameters of th logit model,the linear model paremeters , data simulated at different price points and th expected revenue and the fitting results of both the logit and linear model.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

single_product_optimization(x= c(5,8,10,12),y=c(25,21,23,15),
service_product_name = "Movie",current_price = 8.5,plot=TRUE)

total.logistics.cost

Description

calculating total logistics cost .

Usage

total.logistics.cost(
  quantity,
  expected_annual_demand,
  sd_annual_demand,
  expected_leadtimeindays,
  sd_leadtime,
  costperunit,
  transportcost,
  holdingrate,
  ordering_cost,
  csl
)

Arguments

Details

calculating total logistics cost based on a normal distribution.

Value

a dataframe that contains calculations of the total logistics cost in detail.

Note

this is the second version of the inventorize package, all the fucntions are without any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.

Author(s)

"haytham omar email: <haytham@rescaleanalytics.com>"

Examples

total.logistics.cost(quantity=32,expected_annual_demand=1550,
sd_annual_demand=110,expected_leadtimeindays=64,sd_leadtime=8,
costperunit=107,transportcost=22,holdingrate=0.15,ordering_cost=500,csl=0.95)