Gráficos Sankey ou Alluvial

Sankey plot representes the information flow.

The plot below shows how information from different business areas were addressed during the Fit & Gap Sesssions.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(gt)
library(gtsummary)
library(readxl)
library(networkD3)

in_data1 <- read_excel(path = "~/workspace/ALA/fit and bid analysis/ABR BID and Change Impact Tracker.xlsx",
                 sheet = "Parking Lot") |> 
  janitor::clean_names() 

links <- 
  in_data1 |> 
  select(stream, moved_to) |> 
  rename(source = stream, target = moved_to) |> 
  select(source, target) |> 
  mutate(value = 1,
         target = if_else(is.na(target) | str_detect(target, "N/A"), "Closed", target)) |>
  filter(!is.na(source)) 

links <- 
  links |> 
  mutate(group = factor(source))
  
nodes <- tibble(
  name = c(as.character(links$source), as.character(links$target)) |> unique()
)

links <- 
  links |> 
  mutate(IDsource = match(links$source, nodes$name)-1,
         IDtarget = match(links$target, nodes$name)-1)


colors_group <- links$group |> unique() |> as.character() 
my_color <- 'd3.scaleOrdinal() .domain(["MAS", "DTC", "B2B/ O2C/ DTC", "O2C", "B2B", "SCM", "SCM/MAS", "DTC/ FIN", "FIN/ SCM", "FIN", "MAS/ O2C", "DTC/ MAS", "SCM/REP", "B2B/ O2C", "FIN/ MAS", "FIN/ O2C", "SCM/FIN"]) .range(["#ffff55", "#cc4444", "#33aabb",  "#bb9988", "#ea7766", "#bf4499", "#33aabb", "#33aabb", "#33aabb", "#9f4400", "#33aabb", "#33aabb", "#33aabb", "#33aabb", "#33aabb", "#33aabb", "#33aabb"])'

sankeyNetwork(Links = links, 
              Nodes = nodes,
              Source = "IDsource",
              Target = "IDtarget",
              Value = "value",
              NodeID = "name",
             # colourScale = my_color,
              LinkGroup = "group",
              sinksRight = FALSE,
            nodeWidth = 40,
            fontSize = 14)

Links is a tbl_df. Converting to a plain data frame.
Nodes is a tbl_df. Converting to a plain data frame.

Taken the information that was carried to a BID evaluation process - FMS GAPs (BIDs) - this is how they were addressed during the evaluation of BIDs process:

in_data_s2 <- read_excel(path = "~/workspace/ALA/fit and bid analysis/ABR BID and Change Impact Tracker.xlsx",
                 sheet = "FMS GAPs (BIDs)",
                 skip = 1) |> 
  janitor::clean_names()

links <- 
  in_data_s2 |> 
  filter(!is.na(stream)) |> 
  select(stream, status, effort_days_total, description, comment) |> 
  rename(source = stream, target = status, value = effort_days_total) |> 
  filter(!is.na(value))


links <- 
  links |> 
  mutate(group = factor(source),
         value = 1)
  
nodes <- tibble(
  name = c(as.character(links$source), as.character(links$target)) |> unique()
)

links <- 
  links |> 
  mutate(IDsource = match(links$source, nodes$name)-1,
         IDtarget = match(links$target, nodes$name)-1)

sankeyNetwork(Links = links, 
              Nodes = nodes,
              Source = "IDsource",
              Target = "IDtarget",
              Value = "value",
              NodeID = "name",
              LinkGroup = "group",
               sinksRight = FALSE,
            nodeWidth = 40,
              fontSize = 22)

Links is a tbl_df. Converting to a plain data frame.

Nodes is a tbl_df. Converting to a plain data frame.

O que foi Cancelado?

links |> 
  filter(str_detect(target, "Cancel")) |> 
  arrange(source) |> 
  gt()

source	target	value	description	comment	group	IDsource	IDtarget
B2B	99. Cancelled	1	Order confirmation report	Order confirmations are not sent per sales order. Currently the customer checks his orders on Asics.net. Global BID planned for 2024 - Cancelled as orders are created live to FMS the user can see immediately in order history. No confirmation required	B2B	7	11
B2B	99. Cancelled	1	Brand portal assets - 360 images on B2B	06/12 TZ: to be covered by 101772 in item 97 22/11 BO: Global requirement. Effort 4 days. Will be delivered as part of the 2024 portfolio. Not all assets are shot and loaded into the brand portal. This would need to be approved by ADI currently the quality doesn't not meet global standards. Is Brazil able to load images to Brand portal for locally produced product. 360 image are a nice to have but not a must have.	B2B	7	11
DTC	99. Cancelled	1	ABR - Baseline configuration - Localization	01/12 BO: Integrated in BID 101484. Cancelled this one. Setup localization topics for DTC	DTC	3	11
FIN	99. Cancelled	1	Tax calculation mismatches	22/11 BO: Replaced by (Base) Location per Team, this is covered by the FIN Localization topic. Generic GAP for possible mismatches related to tax in both SCM and O2C pricing.	FIN	4	11
FIN	99. Cancelled	1	Approval of journal entries	29/11: JS: Provided solution of Park/Post is accepted by the ABR team (Ives and Fabricio). 22/12 BO: Currently there is a solution in BPM. In my view this requirement must be challenged. Not part of Global template. Park/ Post functionality or Report to review executed manual posting. Journal entries are currently being approved by the business. We don't have a solution in FMS for this. Will this be covered in the BPM tool?	FIN	4	11
FIN	99. Cancelled	1	ABR - Baseline configuration - Localization	01/12 BO: Integrated in BID 101485. Cancelled this one. Setup localization topics for FIN, for now the tax benefit topic is part of this estimate but it depends if standard SAP can cover dealing with this.	FIN	4	11
MAS	99. Cancelled	1	Local pictures management	07/12 BO I cancelled this FMS Gap. No software topic 29/11 BO: No software change needed More process related	MAS	0	11
MAS	99. Cancelled	1	Customer delivery schedule	Customers can indicate when they want to be delivered (days & time) 16/11 - CM: discussed with O2C. Requirement will be covered with Customer VAS.	MAS	0	11
MAS	99. Cancelled	1	Customised assortments in Brazil	This will be solved with GAP#52 or stadard solution will be used.	MAS	0	11
MAS	99. Cancelled	1	Product description in PT	Needed at product creation - to be discussed if we can use the existing FMS upload or we need to intervene upstream 23/11 - CM:	MAS	0	11
O2C	99. Cancelled	1	Sales order upload tool	07/12 BO: Cancelled this Gap. Effort estimation was 4 days in total. 07/12 LSC: this functionality is already supported by the current SO Upload The sales order upload tool splits on PO number. ABR wants it to split on RDD	O2C	2	11
O2C	99. Cancelled	1	ABR - Baseline configuration - Localization	01/12 BO: Integrated in BID 101483. Cancelled this one. Setup localization topics for O2C other than pricing: '- NFe generation, setup DRC for O2C - XML generation and mapping - Printing and form of DANFE, CCC (NF correction letter) and Simplified NFe (PL SAP BE: OTC_011_BR) - XML for carrier PL SAP BR OTC_007_BR	O2C	2	11
O2C	99. Cancelled	1	O2C-E-Invoicing and DRC	included in #89 '- NFe generation, setup DRC for O2C - XML generation and mapping - Printing and form of DANFE, CCC (NF correction letter) and Simplified NFe (PL SAP BE: OTC_011_BR) - XML for carrier PL SAP BR OTC_007_BR	O2C	2	11
SCM	99. Cancelled	1	Different purchase prices needed for Fedex and WilsonSons for local purchasing. Both DC's have the same site.	13/12 WV: Vanessa confirmed that 1 Purchasing price will be negociated for both Fedex and Wilson Sons --> Closed. (No estimate was given) 29/11 BO: Wilder will discuss this topic with rBPO. - Having 1 price for both Fedex and Wilson Sons is costing R$ 240.000 in 10 months. - Manual pricing in the PO is not an option according to Vanessa. Frequency to WS Cleared is once a month - To check with Tatiana if an additional price condition is an option with DRC	SCM	1	11
SCM	99. Cancelled	1	Possibility needed to change address on Ecom delivery after Goods Issue	30/11 WV: Aligned with Vanessa. Can be cancelled 24/11 BO: Challenged by Alberto if really needed. Used for re-delivery of ecom deliveries with incorrect address	SCM	1	11
SCM	99. Cancelled	1	Several interfaces	Partner = Brokers Brokers will work in Thomson Reuters	SCM	1	11
SCM	99. Cancelled	1	Several interfaces	Partner = Arquivei Out of scope - replaced by VIM & DRC (Jurriaan and Bert)	SCM	1	11
SCM	99. Cancelled	1	Several interfaces	Partner = VTEX - Tracking information to VTEX Included in the DTC stream	SCM	1	11
SCM	99. Cancelled	1	Adapt Buy-Via process for ZPB Back-to-Back via Wilson Sons (ACL/ACO buy from local ABR vendor)	24/11 BO: Ward discussed with Vanessa and they agreed to do this manual. BID cancelled.	SCM	1	11
SCM	99. Cancelled	1	Support change of direct shipment to purchasing for stockm, based on use of Back to back process: - Change output to local vendor - Changes related AOS - Changes in APL interface - Remove sales order assignment of stock - Change of site of PO - …....	12/12 WV: Processes discussed in case orders are cancelled. Standard SAP with a lot of work. Import process still to be checked. ==> Cancelled (Estimate was 45 days) 24/11 BO: How do other regions handle this exception? In my view the customer should accept the goods and if the customer doesn't want the goods, they can return it. Ward will discuss further if this process is really needed. Used in case a customer of Back-to-Back stock cancels the purchase. Should work for warehouses with a WM and Without a WM	SCM	1	11
SCM	99. Cancelled	1	ABR - Baseline configuration - Localization	01/12 BO: Integrated in BID 101482. Cancelled this one. Setup localization topics for SCM	SCM	1	11

Deviation from the Global Way of Working

in_data_s2 |> 
  filter(!is.na(stream)) |> 
  filter(!str_detect(status, "99. Cancelled")) |> 
  filter(!is.na(charm_number)) |> 
  filter(!str_detect(charm_number, "Wait")) |> 
  filter(!str_detect(charm_number, "NA")) |> 
  select(stream, charm_number, bid_classification) |> 
  count(stream, bid_classification, sort = T) |> 
  filter(!is.na(bid_classification)) |>
  filter(!str_detect(bid_classification, "NA")) |>
  filter(!str_detect(bid_classification, "template")) |> 
  mutate(bid_classification = case_when(
    str_detect(bid_classification, "Legal") ~ "Legal Requirement",
    str_detect(bid_classification, "Integration") ~ "3rd Party Integration",
    str_detect(bid_classification, "Waiting") ~ "3rd Party Integration",
    TRUE ~ bid_classification
  )) |> 
  count(stream, bid_classification, wt = n) |> 
  group_by(stream) |> 
  mutate(`Total by Area` = sum(n)) |> 
  ungroup() |> 
  pivot_wider(names_from = bid_classification,
              values_from = n) |> 
  relocate(`Legal Requirement`, .after = last_col()) |> 
  relocate(`3rd Party Integration`, .after = last_col()) |> 
  relocate(`Baseline & Localization`, .after = last_col()) |> 
  relocate(`Deviation from Global way of working`, .after = last_col()) |> 
  relocate(`Total by Area`, .after = last_col()) |> 
  arrange(`Total by Area`) |>
  # mutate(color = "") |> 
  gt(rowname_col = "stream") |> 
  tab_header(
    title = md(paste("BID Quantities", fontawesome::fa("city") , "FMS-SAP")),
    subtitle = "Status at 2023-12-19"
  ) |>
  sub_missing()  |> 
  grand_summary_rows(
    # columns = -c(color),
     fns = list(
      total = ~ sum(., na.rm = TRUE)
    )
  ) |> 
  tab_options(
    table.font.size = 24
  ) |> 
  tab_style(
    style = list(
      cell_fill(color = "darkred"),
      cell_text(color = "white")),
    locations = cells_grand_summary(
      columns = `Deviation from Global way of working`,
      rows = 1
    )
  ) |> 
  # data_color(
  #   columns = `3rd Party Integration`,
  #   rows =  `3rd Party Integration` > 5,
  #   method = "numeric",
  #   palette = c("lightblue", "pink"),
  #   domain = c(0, 10)
  # )  |> 
  cols_label(
    `Legal Requirement` = "Legal<br>Requirement",
    `3rd Party Integration` = "3rd Party<br>Integration",
    `Baseline & Localization` = "Baseline &<br>Localization",
    `Deviation from Global way of working` = "Deviation<br>from Global<br>way of<br>working",
    `Total by Area` = "Totals",
    .fn = md
  ) |> 
  data_color(
    columns = `Total by Area`,
    #rows =  `Total by Area` > 15,
    method = "numeric",
    palette = c("lightblue", "pink"),
    domain = c(0, 16)
  ) 

BID Quantities FMS-SAP
Status at 2023-12-19
	Legal Requirement	3rd Party Integration	Baseline & Localization	Deviation from Global way of working	Totals
AUTH	—	—	1	—	1
REP	—	—	1	—	1
B2B	1	—	—	1	2
O2C	4	—	3	3	10
DTC	2	7	1	2	12
FIN	4	5	1	2	12
MAS	3	5	3	1	12
SCM	4	8	2	2	16
total	18	25	12	11	66

teste

countrypops |>
  dplyr::filter(country_code_3 %in% c("FRA", "GBR")) |>
  dplyr::filter(year %% 10 == 0) |>
  dplyr::select(-contains("code")) |>
  dplyr::mutate(color = "") |>
  gt(groupname_col = "country_name") |>
  fmt_integer(columns = population) |>
  data_color(
    columns = population,
    target_columns = color,
    method = "numeric",
    palette = "viridis",
    domain = c(4E7, 7E7)
  ) |>
  cols_label(
    year = "",
    population = "Population",
    color = ""
  ) |>
  opt_vertical_padding(scale = 0.65)

	Population
France
1960	46,649,927
1970	51,724,116
1980	55,052,582
1990	58,044,701
2000	60,921,384
2010	65,030,575
2020	67,571,107
United Kingdom
1960	52,400,000
1970	55,663,250
1980	56,314,216
1990	57,247,586
2000	58,892,514
2010	62,766,365
2020	67,081,234