Get Started"
In planr: Tools for Supply Chain Management, Demand and Supply Planning

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
options(rmarkdown.html_vignette.check_title = FALSE)

library(tidyverse)
library(dplyr)
library(lubridate)
library(tidyverse)
library(shiny)

# for the tables
library(reactable)
library(reactablefmtr)

# for the charts
library(highcharter)

# the library planr
library(planr)

Let's present the 3 functions:

light_proj_inv() : to calculate projected inventories & coverages
proj_inv() : to calculate & analyze projected inventories vs min & max targets
drp() : to calculate a replenishment plan

Part 1 : Projected Inventories & Coverages Calculation

1.1) Data Template

Period <- c(
"1/1/2020", "2/1/2020", "3/1/2020", "4/1/2020", "5/1/2020", "6/1/2020", "7/1/2020", "8/1/2020", "9/1/2020", "10/1/2020", "11/1/2020", "12/1/2020","1/1/2021", "2/1/2021", "3/1/2021", "4/1/2021", "5/1/2021", "6/1/2021", "7/1/2021", "8/1/2021", "9/1/2021", "10/1/2021", "11/1/2021", "12/1/2021")

Demand <- c(360, 458,300,264,140,233,229,208,260,336,295,226,336,434,276,240,116,209,205,183,235,312,270,201)

Opening <- c(1310,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)

Supply <- c(0,0,0,0,0,2500,0,0,0,0,0,0,0,0,0,2000,0,0,0,0,0,0,0,0)


# assemble
my_demand_and_suppply <- data.frame(Period,
                  Demand,
                  Opening,
                  Supply)

# let's add a Product
my_demand_and_suppply$DFU <- "Product A"

# format the Period as a date
my_demand_and_suppply$Period <- as.Date(as.character(my_demand_and_suppply$Period), format = '%m/%d/%Y')


# let's have a look at it
head(my_demand_and_suppply)

It contains some basic features:

a Product: it's an item, a SKU (Storage Keeping Unit), or a SKU at a location, also called a DFU (Demand Forecast Unit)
a Period of time : for example monthly or weekly buckets
a Demand : could be some sales forecasts, expressed in units
an Opening Inventory : what we hold as available inventories at the beginning of the horizon, expressed in units
a Supply Plan : the supplies that we plan to receive, expressed in units

1.2) Calculation

Let's apply the light_proj_inv().

We are going to calculate 2 new features for each DFU:

projected inventories
projected coverages, based on the Demand Forecasts

# calculate
calculated_projection <- light_proj_inv(dataset = my_demand_and_suppply, 
                                        DFU = DFU, 
                                        Period = Period, 
                                        Demand =  Demand, 
                                        Opening = Opening, 
                                        Supply = Supply)

# see results
head(calculated_projection)

1.3) A nicer display

We will use the libraries reactable and reactablefmtr to create a nice table.

# set a working df
df1 <- calculated_projection

# keep only the needed columns
df1 <- df1 %>% select(Period,
                      Demand,
                      Calculated.Coverage.in.Periods,
                      Projected.Inventories.Qty,
                      Supply)


# create a f_colorpal field
df1 <- df1 %>% mutate(f_colorpal = case_when( Calculated.Coverage.in.Periods > 6 ~ "#FFA500",
                                              Calculated.Coverage.in.Periods > 2 ~ "#32CD32",
                                              Calculated.Coverage.in.Periods > 0 ~ "#FFFF99",
                                              TRUE ~ "#FF0000" ))



# create reactable
reactable(df1, resizable = TRUE, showPageSizeOptions = TRUE,

              striped = TRUE, highlight = TRUE, compact = TRUE,
              defaultPageSize = 20,

              columns = list(


                Demand = colDef(
                  name = "Demand (units)",

                  cell = data_bars(df1,
                                   fill_color = "#3fc1c9",
                                   text_position = "outside-end"
                  )

                ),




              Calculated.Coverage.in.Periods = colDef(
                name = "Coverage (Periods)",
                maxWidth = 90,
                cell= color_tiles(df1, color_ref = "f_colorpal")
              ),


              f_colorpal = colDef(show = FALSE), # hidden, just used for the coverages



                `Projected.Inventories.Qty`= colDef(
                  name = "Projected Inventories (units)",
                  format = colFormat(separators = TRUE, digits=0),

                  style = function(value) {
                    if (value > 0) {
                      color <- "#008000"
                    } else if (value < 0) {
                      color <- "#e00000"
                    } else {
                      color <- "#777"
                    }
                    list(color = color
                         #fontWeight = "bold"
                    )
                  }
                ),




              Supply = colDef(
                name = "Supply (units)",
                cell = data_bars(df1,
                                 fill_color = "#3CB371",
                                 text_position = "outside-end"
                                 )
                )





              ), # close columns lits

              columnGroups = list(
                colGroup(name = "Projected Inventories", columns = c("Calculated.Coverage.in.Periods",
                                                                     "Projected.Inventories.Qty"))


              )

    ) # close reactable

1.4) A little chart

# set a working df
df1 <- calculated_projection

# keep only the needed columns
df1 <- df1 %>% select(Period,
                      Projected.Inventories.Qty)


# create a value.index
df1$Value.Index <- if_else(df1$Projected.Inventories.Qty < 0, "Shortage", "Stock")


# spread
df1 <- df1 %>% spread(Value.Index, Projected.Inventories.Qty)


#----------------------------------------------------
# Chart

    u <- highchart() %>% 
      hc_title(text = "Projected Inventories") %>%
      hc_subtitle(text = "in units") %>% 
      hc_add_theme(hc_theme_google()) %>%

      hc_xAxis(categories = df1$Period) %>% 

      hc_add_series(name = "Stock", 
                    color = "#32CD32",
                    #dataLabels = list(align = "center", enabled = TRUE),
                    data = df1$Stock) %>% 

      hc_add_series(name = "Shortage", 
                    color = "#dc3220",
                    #dataLabels = list(align = "center", enabled = TRUE),
                    data = df1$Shortage) %>% 

      hc_chart(type = "column") %>% 
      hc_plotOptions(series = list(stacking = "normal"))

    u

Part 2 : Calculation & Analysis

Now, let's consider some parameters such as : - a target of minimum stock level - a target of maximum stock level

And then: - calculate the projected inventories and coverages - analyze those values vs those defined targets

First, let's add some parameters to our initial database.

2.1) Data Template

Define min & max coverages, through 2 parameters: - Min.Cov - Max.Cov

Expressed in number of periods of coverages. The periods can be in monthly buckets, weekly buckets, etc...

my_data_with_parameters <- my_demand_and_suppply

my_data_with_parameters$Min.Cov <- 2
my_data_with_parameters$Max.Cov <- 4

head(my_data_with_parameters)

2.2) Calculation

Let's apply the proj_inv() function

df1 <- proj_inv(data = my_data_with_parameters, 
                DFU = DFU, 
                Period = Period, 
                Demand =  Demand, 
                Opening = Opening, 
                Supply = Supply,
                Min.Cov = Min.Cov, 
                Max.Cov = Max.Cov)

# see results
calculated_projection_and_analysis <- df1

head(calculated_projection_and_analysis)

2.3) A nicer display

First, let's create a function status_PI.Index()

# create a function status.PI.Index
status_PI.Index <- function(color = "#aaa", width = "0.55rem", height = width) {
  span(style = list(
    display = "inline-block",
    marginRight = "0.5rem",
    width = width,
    height = height,
    backgroundColor = color,
    borderRadius = "50%"
  ))
}

And now let's create a reactable:

# set a working df
df1 <- calculated_projection_and_analysis


# remove not needed column
df1 <- df1[ , -which(names(df1) %in% c("DFU"))]


# create a f_colorpal field
df1 <- df1 %>% mutate(f_colorpal = case_when( Calculated.Coverage.in.Periods > 6 ~ "#FFA500", 
                                              Calculated.Coverage.in.Periods > 2 ~ "#32CD32",
                                              Calculated.Coverage.in.Periods > 0 ~ "#FFFF99",
                                              TRUE ~ "#FF0000" ))



#-------------------------
# Create Table



reactable(df1, resizable = TRUE, showPageSizeOptions = TRUE, 

              striped = TRUE, highlight = TRUE, compact = TRUE, 
              defaultPageSize = 20,

              columns = list(


                Demand = colDef(
                  name = "Demand (units)",

                  cell = data_bars(df1, 
                                   #round_edges = TRUE
                                   #value <- format(value, big.mark = ","),
                                   #number_fmt = big.mark = ",",
                                   fill_color = "#3fc1c9",
                                   #fill_opacity = 0.8, 
                                   text_position = "outside-end"
                  )

                ),



                Calculated.Coverage.in.Periods = colDef(
                  name = "Coverage (Periods)",
                  maxWidth = 90,

                  cell= color_tiles(df1, color_ref = "f_colorpal")
                ),


                f_colorpal = colDef(show = FALSE), # hidden, just used for the coverages



                `Projected.Inventories.Qty`= colDef(
                  name = "Projected Inventories (units)",
                  format = colFormat(separators = TRUE, digits=0),

                  style = function(value) {
                    if (value > 0) {
                      color <- "#008000"
                    } else if (value < 0) {
                      color <- "#e00000"
                    } else {
                      color <- "#777"
                    }
                    list(color = color
                         #fontWeight = "bold"
                    )
                  }
                ),



                Supply = colDef(
                  name = "Supply (units)",
                  cell = data_bars(df1, 

                                   #round_edges = TRUE
                                   #value <- format(value, big.mark = ","),
                                   #number_fmt = big.mark = ",",
                                   fill_color = "#3CB371",
                                   #fill_opacity = 0.8, 
                                   text_position = "outside-end"
                  )
                  #format = colFormat(separators = TRUE, digits=0)
                  #number_fmt = big.mark = ","
                ),



                PI.Index = colDef(
                  name = "Analysis",

                  cell = function(value) {
                    color <- switch(
                      value,
                      TBC = "hsl(154, 3%, 50%)",
                      OverStock = "hsl(214, 45%, 50%)",
                      OK = "hsl(154, 64%, 50%)",
                      Alert = "hsl(30, 97%, 70%)",
                      Shortage = "hsl(3, 69%, 50%)"
                    )
                    PI.Index <- status_PI.Index(color = color)
                    tagList(PI.Index, value)
                  }),



                `Safety.Stocks`= colDef(
                  name = "Safety Stocks (units)",
                  format = colFormat(separators = TRUE, digits=0)
                ),

                `Maximum.Stocks`= colDef(
                  name = "Maximum Stocks (units)",
                  format = colFormat(separators = TRUE, digits=0)
                ),

                `Opening`= colDef(
                  name = "Opening Inventories (units)",
                  format = colFormat(separators = TRUE, digits=0)
                ),


                `Min.Cov`= colDef(name = "Min Stocks Coverage (Periods)"),

                `Max.Cov`= colDef(name = "Maximum Stocks Coverage (Periods)"),


                # ratios
                `Ratio.PI.vs.min`= colDef(name = "Ratio PI vs min"),

                `Ratio.PI.vs.Max`= colDef(name = "Ratio PI vs Max")




              ), # close columns lits

              columnGroups = list(
                colGroup(name = "Projected Inventories", columns = c("Calculated.Coverage.in.Periods", 
                                                                     "Projected.Inventories.Qty")),

                colGroup(name = "Stocks Levels Parameters", columns = c("Min.Cov", 
                                                                        "Max.Cov",
                                                                        "Safety.Stocks",
                                                                        "Maximum.Stocks")),

                colGroup(name = "Analysis Features", columns = c("PI.Index", 
                                                                        "Ratio.PI.vs.min",
                                                                        "Ratio.PI.vs.Max"))

              )

    ) # close reactable

Compared to the previous table, we have here some additional information available: the calculated fields [Analysis Features] - based on safety & maximum stocks targets - useful for a mass analysis (Cockpit / Supply Risks Alarm), but perhaps too detailed for a focus on a SKU

We also can notice that the minimum and maximum stocks coverages, initially expressed in Periods (of coverage) are converted in units. It's quite useful to chart the projected inventories vs those 2 thresholds for example.

2.4) A little chart

# set a working df
df1 <- calculated_projection_and_analysis



# Chart
p <- highchart() %>% 
      hc_add_series(name = "Max", color = "crimson", data = df1$Maximum.Stocks) %>% 
      hc_add_series(name = "min", color = "lightblue", data = df1$Safety.Stocks) %>% 
      hc_add_series(name = "Projected Inventories", color = "gold", data = df1$Projected.Inventories.Qty) %>% 

      hc_title(text = "Projected Inventories") %>%
      hc_subtitle(text = "in units") %>% 
      hc_xAxis(categories = df1$Period) %>% 
      #hc_yAxis(title = list(text = "Sales (units)")) %>% 
      hc_add_theme(hc_theme_google())

    p

We can visualize the periods when we are in Alert & OverStock, comparing to the minimum and Maximum stocks levels.

Part 3) Replenishment Plan

3.1) Data Template

Let's now add a few parameters to the initial database "my_demand_and_suppply"

df1 <- my_demand_and_suppply

df1$SSCov <- 2
df1$DRPCovDur <- 3
df1$MOQ <- 1
df1$FH <- c("Frozen", "Frozen", "Frozen", "Frozen","Frozen","Frozen","Free","Free","Free","Free","Free","Free","Free","Free","Free","Free","Free","Free","Free","Free","Free","Free","Free","Free")


# get Results
my_drp_template <- df1

head(my_drp_template)

3.2) Calculation

Apply drp()

# set a working df
df1 <- my_drp_template

# calculate drp
demo_drp <- drp(data = df1,
           DFU = DFU,
           Period = Period,
           Demand =  Demand,
           Opening = Opening,
           Supply = Supply,
           SSCov = SSCov,
           DRPCovDur = DRPCovDur,
           MOQ = MOQ,
           FH = FH
)


glimpse(demo_drp)

3.3) A nicer display

# set a working df
df1 <- demo_drp

# keep only the needed columns
df1 <- df1 %>% select(Period,
                      Demand,
                      DRP.Calculated.Coverage.in.Periods,
                      DRP.Projected.Inventories.Qty,
                      DRP.plan)


# replace missing values by zero
df1$DRP.plan[is.na(df1$DRP.plan)] <- 0
df1$DRP.Projected.Inventories.Qty[is.na(df1$DRP.Projected.Inventories.Qty)] <- 0

# create a f_colorpal field
df1 <- df1 %>% mutate(f_colorpal = case_when( DRP.Calculated.Coverage.in.Periods > 8 ~ "#FFA500",
                                              DRP.Calculated.Coverage.in.Periods > 2 ~ "#32CD32",
                                              DRP.Calculated.Coverage.in.Periods > 0 ~ "#FFFF99",
                                              TRUE ~ "#FF0000" ))



# create reactable
reactable(df1, resizable = TRUE, showPageSizeOptions = TRUE,

              striped = TRUE, highlight = TRUE, compact = TRUE,
              defaultPageSize = 20,

              columns = list(


                Demand = colDef(
                  name = "Demand (units)",

                  cell = data_bars(df1,
                                   fill_color = "#3fc1c9",
                                   text_position = "outside-end"
                  )

                ),




              DRP.Calculated.Coverage.in.Periods = colDef(
                name = "Coverage (Periods)",
                maxWidth = 90,
                cell= color_tiles(df1, color_ref = "f_colorpal")
              ),


              f_colorpal = colDef(show = FALSE), # hidden, just used for the coverages



                `DRP.Projected.Inventories.Qty`= colDef(
                  name = "Projected Inventories (units)",
                  format = colFormat(separators = TRUE, digits=0),

                  style = function(value) {
                    if (value > 0) {
                      color <- "#008000"
                    } else if (value < 0) {
                      color <- "#e00000"
                    } else {
                      color <- "#777"
                    }
                    list(color = color
                         #fontWeight = "bold"
                    )
                  }
                ),




              DRP.plan = colDef(
                name = "Replenishment (units)",
                cell = data_bars(df1,
                                 fill_color = "#3CB371",
                                 text_position = "outside-end"
                                 )
                )





              ), # close columns lits

              columnGroups = list(
                colGroup(name = "Projected Inventories", columns = c("DRP.Calculated.Coverage.in.Periods",
                                                                     "DRP.Projected.Inventories.Qty"))


              )

    ) # close reactable

3.4) A little chart

# set a working df
df1 <- demo_drp



# Chart
p <- highchart() %>% 
      hc_add_series(name = "Max", color = "crimson", data = df1$Maximum.Stocks) %>% 
      hc_add_series(name = "min", color = "lightblue", data = df1$Safety.Stocks) %>% 
      hc_add_series(name = "Projected Inventories", color = "gold", data = df1$DRP.Projected.Inventories.Qty) %>% 

      hc_title(text = "(DRP) Projected Inventories") %>%
      hc_subtitle(text = "in units") %>% 
      hc_xAxis(categories = df1$Period) %>% 
      #hc_yAxis(title = list(text = "Sales (units)")) %>% 
      hc_add_theme(hc_theme_google())

    p

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planr
Tools for Supply Chain Management, Demand and Supply Planning

Get Started"
In planr: Tools for Supply Chain Management, Demand and Supply Planning

Part 1 : Projected Inventories & Coverages Calculation

1.1) Data Template

1.2) Calculation

1.3) A nicer display

1.4) A little chart

Part 2 : Calculation & Analysis

2.1) Data Template

2.2) Calculation

2.3) A nicer display

2.4) A little chart

Part 3) Replenishment Plan

3.1) Data Template

3.2) Calculation

3.3) A nicer display

3.4) A little chart

Try the planr package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

planr Tools for Supply Chain Management, Demand and Supply Planning

Get Started" In planr: Tools for Supply Chain Management, Demand and Supply Planning

Part 1 : Projected Inventories & Coverages Calculation

1.1) Data Template

1.2) Calculation

1.3) A nicer display

1.4) A little chart

Part 2 : Calculation & Analysis

2.1) Data Template

2.2) Calculation

2.3) A nicer display

2.4) A little chart

Part 3) Replenishment Plan

3.1) Data Template

3.2) Calculation

3.3) A nicer display

3.4) A little chart

Try the planr package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

planr
Tools for Supply Chain Management, Demand and Supply Planning

Get Started"
In planr: Tools for Supply Chain Management, Demand and Supply Planning