To show how to use the proj_git() function from the planr package.
proj_git stands for “Projection Goods In Transit”.
The idea is to calculate, considering a Transit Time, the projection of the inventories in transit.
More details on the planr package on : https://github.com/nguyennico/planr
Let’s use the demo dataset demo_in_transit from the planr package.
It contains 6 variables :
DFU, a location and an item
Period, a date in weekly bucket format
ETA.Current
some quantities currently in transit displayed at their ETA date in units
ETA stands for Estimated Time of Arrival
ETA.Next
ETD.Next
some quantities to be shipped, not yet in transit, displayed at their ETD date in units
ETD stands for Estimated Time of Departure
TLT, the Transit Lead Time, expressed in weeks,
There are 2 types of in transit : the current in transit and the next one, not yet shipped.
Note that the difference between ETD and ETA is the Transit Time.
The idea is to use this dataset to project the Goods In Transit.
We can apply on this dataset the proj_git() function, it will calculate the Proj.GIT which gathers the current and next In Transit quantities.
data("demo_in_transit")
head(demo_in_transit)## # A tibble: 6 × 6
## DFU Period ETA.Current ETA.Next ETD.Next TLT
## <chr> <date> <dbl> <dbl> <dbl> <dbl>
## 1 Entity 1_Product A 2024-02-18 0 0 0 8
## 2 Entity 1_Product A 2024-02-25 1008 0 500 8
## 3 Entity 1_Product A 2024-03-03 0 0 0 8
## 4 Entity 1_Product A 2024-03-10 252 0 0 8
## 5 Entity 1_Product A 2024-03-17 0 0 0 8
## 6 Entity 1_Product A 2024-03-24 0 0 800 8Let’s get a summary of the different DFU : - Total quantity In Transit over the horizon - related Transit Lead Times
We have 3 different couples Entity x Product, with 3 different Transit Times :
a long one : 8 weeks, for Entity 1_Product A
a medium one : 4 weeks, for Entity 2_Product B
a short one : 2 weeks, Entity 3_Product C
Also, for each couple, the Total quantity In Transit over the horizon is similar.
# keep only unique variables
df1 <- demo_in_transit |> group_by(DFU) |>
summarise(ETA.Total = sum(ETA.Current) + sum(ETA.Next),
TLT = mean(TLT)
)
df1## # A tibble: 3 × 3
## DFU ETA.Total TLT
## <chr> <dbl> <dbl>
## 1 Entity 1_Product A 3160 8
## 2 Entity 2_Product B 3160 4
## 3 Entity 3_Product C 3160 2Using the proj_git() function, we can calculate the Projected In Transit for all the different couples Entity x Product contained into the dataset.
# apply proj_git()
Calculated_GIT_DB <- planr::proj_git(dataset = demo_in_transit,
DFU,
Period,
ETA.Current,
ETA.Next,
ETD.Next,
TLT)
glimpse(Calculated_GIT_DB)## Rows: 447
## Columns: 11
## $ DFU <chr> "Entity 1_Product A", "Entity 1_Product A", "Entity 1…
## $ Period <date> 2024-02-18, 2024-02-25, 2024-03-03, 2024-03-10, 2024…
## $ ETA.Current <dbl> 0, 1008, 0, 252, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ ETA.Next <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 500, 0, 0, 0, 800, 0, 0, 0…
## $ ETD.Next <dbl> 0, 500, 0, 0, 0, 800, 0, 0, 0, 600, 0, 0, 0, 0, 0, 0,…
## $ TLT <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,…
## $ Current.GIT <dbl> 1260, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ ETD.Current <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ Proj.Current.GIT <dbl> 1260, 252, 252, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Proj.Future.GIT <dbl> 0, 500, 500, 500, 500, 1300, 1300, 1300, 1300, 1400, …
## $ Proj.GIT <dbl> 1260, 752, 752, 500, 500, 1300, 1300, 1300, 1300, 140…We got 5 new variables:
Current.GIT : the total inventories currently in transit
ETD.Current : when the current inventories in transit were shipped (estimated Departure Time)
Proj.Current.GIT : projected inventories of the current in transit
Proj.Future.GIT : projected inventories of the future in transit
Proj.GIT : total projected inventories, current and future in transit
Those variables will be useful to display the projection of the inventories in transit.
For our 3 different DFUs, let’s visualize the projection of the In Transit inventories.
There are 2 components to consider :
Current in transit
Future in transit
We observe (as expected!) that the longer the transit time, the higher the projected in transit.
# set a working df
df1 <- Calculated_GIT_DB
# filter
df1 <- filter(df1, df1$DFU == "Entity 1_Product A")
# make the horizon shorter
df1 <- filter(df1, df1$Period <= (min(df1$Period) + 120) )
#--------------------
# Chart
u <- highchart() |>
hc_title(text = "Projected In Transit") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$Period) |>
hc_add_series(name = "Current",
color = "steelblue",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$Proj.Current.GIT) |>
hc_add_series(name = "Future",
color = "gold",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$Proj.Future.GIT) |>
hc_chart(type = "column") |>
hc_plotOptions(series = list(stacking = "normal"))
u # set a working df
df1 <- Calculated_GIT_DB
# filter
df1 <- filter(df1, df1$DFU == "Entity 2_Product B")
# make the horizon shorter
df1 <- filter(df1, df1$Period <= (min(df1$Period) + 120) )
#--------------------
# Chart
u <- highchart() |>
hc_title(text = "Projected In Transit") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$Period) |>
hc_add_series(name = "Current",
color = "steelblue",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$Proj.Current.GIT) |>
hc_add_series(name = "Future",
color = "gold",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$Proj.Future.GIT) |>
hc_chart(type = "column") |>
hc_plotOptions(series = list(stacking = "normal"))
u # set a working df
df1 <- Calculated_GIT_DB
# filter
df1 <- filter(df1, df1$DFU == "Entity 3_Product C")
# make the horizon shorter
df1 <- filter(df1, df1$Period <= (min(df1$Period) + 120) )
#--------------------
# Chart
u <- highchart() |>
hc_title(text = "Projected In Transit") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$Period) |>
hc_add_series(name = "Current",
color = "steelblue",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$Proj.Current.GIT) |>
hc_add_series(name = "Future",
color = "gold",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$Proj.Future.GIT) |>
hc_chart(type = "column") |>
hc_plotOptions(series = list(stacking = "normal"))
u # set a working df
df1 <- Calculated_GIT_DB
# filter : make the horizon shorter
df1 <- filter(df1, df1$Period <= (min(df1$Period) + 120) )
# keep only needed variables
df1 <- df1 |> select(DFU, Period, Proj.GIT)
# spread
df1 <- df1 |> spread(DFU, Proj.GIT)
#--------------------
# Chart
p <- highchart() |>
hc_add_series(name = "8 weeks",
color = "gold",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$`Entity 1_Product A`) |>
hc_add_series(name = "4 weeks",
color = "steelblue",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$`Entity 2_Product B`) |>
hc_add_series(name = "2 weeks",
color = "mediumseagreen",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$`Entity 3_Product C`) |>
hc_title(text = "Comparison Projected In Transit") |>
hc_subtitle(text = "in units") |>
hc_xAxis(categories = df1$Calendar.Month.abb) |>
hc_add_theme(hc_theme_google())
# display chart
pObviously, the longer the Transit Lead Time, the bigger are the projected inventories in transit.
This calculation makes also easy the projection of the Total Inventories of a supply network.
For example if Entity 1 supplies a Product A to the Entity 2, we could :
project the inventories at the Entity 1
project the inventories at the Entity 1
project the inventories in transit between both entities
For the 3 couples Entity x Product.
# set a working df
df1 <- Calculated_GIT_DB
# filter : make the horizon shorter
df1 <- filter(df1, df1$Period <= (min(df1$Period) + 120) )
# keep only needed variables
df1 <- df1 |> select(DFU, Period, Proj.GIT)
# spread
df1 <- df1 |> spread(DFU, Proj.GIT)
#--------------------
# Chart
u <- highchart() |>
hc_title(text = "Projected All In Transit") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$Period) |>
hc_add_series(name = "8 weeks",
color = "gold",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$`Entity 1_Product A`) |>
hc_add_series(name = "4 weeks",
color = "steelblue",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$`Entity 2_Product B`) |>
hc_add_series(name = "2 weeks",
color = "mediumseagreen",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$`Entity 3_Product C`) |>
hc_chart(type = "column") |>
hc_plotOptions(series = list(stacking = "normal"))
# display chart
u Interesting to visualize where are the most important projected inventories in transit for a supply network :
which products
between which entities
how much inventories
Let’s consider the Entity 1 x Product A and calculate a DRP.
The idea is to then visualize together 2 parts :
local projected inventories
projected in-transit
It’s an easy way to simulate, based on some DRP parameters (and also the Demand Forecasts) the total (local + in transit) projected inventories of an Entity.
# set a working df
df1 <- Calculated_GIT_DB
# filter
df1 <- filter(df1, df1$DFU == "Entity 1_Product A")
# add Demand
df1$Demand <- 100
# add Supply
df1$Supply <- 0
# add Opening
df1$Opening <- if_else(df1$Period == min(df1$Period), 600, 0)
df1$Opening <- df1$Opening |> replace_na(0)
# keep only needed variables
df1 <- df1 |> select(DFU,
Period,
Demand,
Opening,
Supply)
glimpse(df1)## Rows: 149
## Columns: 5
## $ DFU <chr> "Entity 1_Product A", "Entity 1_Product A", "Entity 1_Product …
## $ Period <date> 2024-02-18, 2024-02-25, 2024-03-03, 2024-03-10, 2024-03-17, 2…
## $ Demand <dbl> 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 10…
## $ Opening <dbl> 600, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ Supply <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…df1$SSCov <- 2
df1$DRPCovDur <- 3
df1$MOQ <- 1
df1$FH <- if_else(df1$Period <= '2024-04-07', "Frozen", "Free")
glimpse(df1)## Rows: 149
## Columns: 9
## $ DFU <chr> "Entity 1_Product A", "Entity 1_Product A", "Entity 1_Produc…
## $ Period <date> 2024-02-18, 2024-02-25, 2024-03-03, 2024-03-10, 2024-03-17,…
## $ Demand <dbl> 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, …
## $ Opening <dbl> 600, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Supply <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ SSCov <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
## $ DRPCovDur <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, …
## $ MOQ <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ FH <chr> "Frozen", "Frozen", "Frozen", "Frozen", "Frozen", "Frozen", …Now let’s calculate the DRP using the drp() function of the planr package.
We will get especially :
the local projected inventories
the DRP Replenishment Plan
# calculate DRP
Calculated_DRP_DB <- planr::drp(dataset = df1,
DFU,
Period,
Demand,
Opening,
Supply,
SSCov,
DRPCovDur,
MOQ,
FH)
glimpse(Calculated_DRP_DB)## Rows: 149
## Columns: 15
## $ DFU <chr> "Entity 1_Product A", "Entity 1_Pro…
## $ Period <date> 2024-02-18, 2024-02-25, 2024-03-03…
## $ Demand <dbl> 100, 100, 100, 100, 100, 100, 100, …
## $ Opening <dbl> 600, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ Supply <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ SSCov <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ DRPCovDur <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…
## $ Stock.Max <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…
## $ MOQ <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ FH <chr> "Frozen", "Frozen", "Frozen", "Froz…
## $ Safety.Stocks <dbl> 200, 200, 200, 200, 200, 200, 200, …
## $ Maximum.Stocks <dbl> 500, 500, 500, 500, 500, 500, 500, …
## $ DRP.Calculated.Coverage.in.Periods <dbl> 5, 4, 3, 2, 1, 0, -1, -2, 5, 4, 3, …
## $ DRP.Projected.Inventories.Qty <dbl> 500, 400, 300, 200, 100, 0, -100, -…
## $ DRP.plan <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 800, 0, 0, …We will keep the current in transit from the initial dataset demo_in_transit.
Therefore, the variables :
DFU Period
ETA.Current TLT
And we will add the new variables coming from the DRP Calculation :
ETA.Next
ETD.Next
#--------------------
# Get Current In Transit Components
#--------------------
# set a working df
df1 <- Calculated_GIT_DB
# select DFU
df1 <- filter(df1, df1$DFU == "Entity 1_Product A")
# keep only the needed columns
df1 <- df1 |> select(DFU,
Period,
ETA.Current,
TLT)
# keep results
Current_GIT_DB <- df1
#--------------------
# Get Next In Transit Components
#--------------------
# set a working df
df1 <- Calculated_DRP_DB
# select DFU
df1 <- filter(df1, df1$DFU == "Entity 1_Product A")
# keep only the needed columns
df1 <- df1 |> select(DFU,
Period,
DRP.plan)
# rename
df1 <- df1 |> rename(ETA.Next = DRP.plan)
# keep results
Next_ETA_GIT_DB <- df1
#--------------------
# Assemble to calculate the ETD.Next
#--------------------
# merge
df1 <- left_join(Next_ETA_GIT_DB, Current_GIT_DB)
# keep only the needed variables
df1 <- df1 |> select(DFU, Period, ETA.Next, TLT)
# calculate ETD.Period
df1$ETD.Period <- df1$Period - (df1$TLT * 7)
# get the beginning of the week for the Period
# to ensure we are following an english standard
df1$ETD.Period <- floor_date(as.Date(df1$ETD.Period, "%Y-%m-%d"), unit = "week")
# keep only the needed variables
df1 <- df1 |> select(DFU, ETD.Period, ETD.Period, ETA.Next)
# rename
df1 <- df1 |> rename(Period = ETD.Period,
ETD.Next = ETA.Next)
# keep results
Next_ETD_GIT_DB <- df1
#--------------------
# Get Template
#--------------------
# merge
df1 <- left_join(Current_GIT_DB, Next_ETA_GIT_DB)
df1 <- left_join(df1, Next_ETD_GIT_DB)
# keep results
Template_GIT_DB <- df1
glimpse(Template_GIT_DB)## Rows: 149
## Columns: 6
## $ DFU <chr> "Entity 1_Product A", "Entity 1_Product A", "Entity 1_Prod…
## $ Period <date> 2024-02-18, 2024-02-25, 2024-03-03, 2024-03-10, 2024-03-1…
## $ ETA.Current <dbl> 0, 1008, 0, 252, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ TLT <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8…
## $ ETA.Next <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 800, 0, 0, 300, 0, 0, 300, 0, 0, 3…
## $ ETD.Next <dbl> 800, 0, 0, 300, 0, 0, 300, 0, 0, 300, 0, 0, 300, 0, 0, 300…Now we’re ready to calculate a new projection of the in transit inventories.
# apply proj_git()
New_Calculated_GIT_DB <- planr::proj_git(dataset = Template_GIT_DB,
DFU,
Period,
ETA.Current,
ETA.Next,
ETD.Next,
TLT)
glimpse(New_Calculated_GIT_DB)## Rows: 149
## Columns: 11
## $ DFU <chr> "Entity 1_Product A", "Entity 1_Product A", "Entity 1…
## $ Period <date> 2024-02-18, 2024-02-25, 2024-03-03, 2024-03-10, 2024…
## $ ETA.Current <dbl> 0, 1008, 0, 252, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ TLT <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,…
## $ ETA.Next <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 800, 0, 0, 300, 0, 0, 300, 0,…
## $ ETD.Next <dbl> 800, 0, 0, 300, 0, 0, 300, 0, 0, 300, 0, 0, 300, 0, 0…
## $ Current.GIT <dbl> 1260, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ ETD.Current <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ Proj.Current.GIT <dbl> 1260, 252, 252, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Proj.Future.GIT <dbl> 800, 800, 800, 1100, 1100, 1100, 1400, 1400, 600, 900…
## $ Proj.GIT <dbl> 2060, 1052, 1052, 1100, 1100, 1100, 1400, 1400, 600, …Just a visual of the local projected inventories through the DRP calculation.
# set a working df
df1 <- Calculated_DRP_DB
#--------------
# Select Item
#--------------
# select DFU
df1 <- filter(df1, df1$DFU == "Entity 1_Product A")
# remove the end horizon (where the calculation is not effective)
df1 <- filter(df1, df1$Period <= min(df1$Period) + 120)
#--------------
# Transform
#--------------
# keep only the needed columns
df1 <- df1 |> select(Period,
Demand,
DRP.Calculated.Coverage.in.Periods,
DRP.Projected.Inventories.Qty,
DRP.plan
)
#--------------
# create a f_colorpal field
#--------------
df1 <- df1 |> mutate(f_colorpal = case_when(
DRP.Calculated.Coverage.in.Periods > 8 ~ "#FFA500",
DRP.Calculated.Coverage.in.Periods > 2 ~ "#32CD32",
DRP.Calculated.Coverage.in.Periods > 0 ~ "#FFFF99",
TRUE ~ "#FF0000" ))
#--------------
# Create Table
#--------------
# create reactable
reactable(df1, resizable = TRUE, showPageSizeOptions = TRUE,
striped = TRUE, highlight = TRUE, compact = TRUE,
defaultPageSize = 20,
columns = list(
Demand = colDef(
name = "Demand (units)",
cell = data_bars(df1,
fill_color = "#3fc1c9",
text_position = "outside-end"
)
),
DRP.Calculated.Coverage.in.Periods = colDef(
name = "Projected Coverage (Periods)",
maxWidth = 90,
cell= color_tiles(df1, color_ref = "f_colorpal")
),
f_colorpal = colDef(show = FALSE), # hidden, just used for the coverages
`DRP.Projected.Inventories.Qty`= colDef(
name = "Projected Inventories (units)",
format = colFormat(separators = TRUE, digits=0),
style = function(value) {
if (value > 0) {
color <- "#008000"
} else if (value < 0) {
color <- "#e00000"
} else {
color <- "#777"
}
list(color = color
#fontWeight = "bold"
)
}
),
DRP.plan = colDef(
name = "Replenishment Plan (units)",
cell = data_bars(df1,
fill_color = "#3CB371",
text_position = "outside-end"
)
)
), # close columns lits
columnGroups = list(
colGroup(name = "Projected Inventories & Coverages",
columns = c("DRP.Calculated.Coverage.in.Periods",
"DRP.Projected.Inventories.Qty"
))
)
) # close reactableDisplay of the local projected inventories calculated through DRP.
# set a working df
df1 <- Calculated_DRP_DB
#--------------
# Select Item
#--------------
# select DFU
df1 <- filter(df1, df1$DFU == "Entity 1_Product A")
# remove the end horizon (where the calculation is not effective)
df1 <- filter(df1, df1$Period <= min(df1$Period) + 120)
#--------------
# Transform
#--------------
# keep only the needed columns
df1 <- df1 |> select(Period,
Demand,
DRP.Projected.Inventories.Qty
)
# create a value.index
df1$Value.Index <- if_else(df1$DRP.Projected.Inventories.Qty < 0, "Shortage", "Stock")
# spread
df1 <- df1 |> spread(Value.Index, DRP.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$Shortage) |>
hc_chart(type = "column") |>
hc_plotOptions(series = list(stacking = "normal"))
u Now we’re going to display the Total Projected Inventories for the Entity 1 & Product A :
local projected inventories (calculated based on the DRP)
projected in-transit (calculated based on the DRP’s Replenishment Plan)
#------------------
# Get Local Projected Inventories
#------------------
# set a working df
df1 <- Calculated_DRP_DB
# select DFU
df1 <- filter(df1, df1$DFU == "Entity 1_Product A")
# remove the end horizon (where the calculation is not effective)
df1 <- filter(df1, df1$Period <= min(df1$Period) + 120)
# keep only the needed columns
df1 <- df1 |> select(Period,
DRP.Projected.Inventories.Qty
)
# keep results
Local_PI_DB <- df1
#------------------
# Get Projected in Transit
#------------------
# set a working df
df1 <- New_Calculated_GIT_DB
# select DFU
df1 <- filter(df1, df1$DFU == "Entity 1_Product A")
# remove the end horizon (where the calculation is not effective)
df1 <- filter(df1, df1$Period <= min(df1$Period) + 120)
# keep only the needed columns
df1 <- df1 |> select(Period,
Proj.GIT)
# keep results
GIT_PI_DB <- df1
#------------------
# Combine
#------------------
# merge
df1 <- left_join(Local_PI_DB, GIT_PI_DB)
# calculate ratio In Transit vs Total
df1$ratio <- df1$Proj.GIT / (df1$DRP.Projected.Inventories.Qty + df1$Proj.GIT)
#------------------
# Chart
#------------------
u <- highchart() |>
hc_title(text = "Projected Local & In Transit Inventories") |>
hc_subtitle(text = "in units") |>
hc_add_theme(hc_theme_google()) |>
hc_xAxis(categories = df1$Period) |>
hc_add_series(name = "Local",
color = "steelblue",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$DRP.Projected.Inventories.Qty) |>
hc_add_series(name = "In Transit",
color = "gold",
dataLabels = list(align = "center", enabled = TRUE),
data = df1$Proj.GIT) |>
hc_chart(type = "column") |>
hc_plotOptions(series = list(stacking = "normal"))
# display chart
u Due to the long transit time (8weeks), we can notice the importance of the in transit inventories within the total inventories hold by the entity.
It’s about 60% of the total weekly inventories of this entity, on this product.
This value could change, depending on the DRP parameters, especially :
(local) safety stocks level : SSCov
frequency of supply : DRPCovDur