Biodelta a contract manufacturing company that is changing the way manufacturing is perceived in the value chain of getting products to the consumer.

By applying applying Artificial Intelligence, it hopes to measure its progress and improve efficiecny to better serve its costomers as well as reach new people.

Data Science can only be done with data, so we start off by loading the given data

## Parsed with column specification:
## cols(
##   SUPNO = col_double(),
##   SUPNAME = col_character(),
##   BRANCH = col_character(),
##   CUSTNO = col_double(),
##   CUSTNAME = col_character(),
##   CUSTTOWN = col_character(),
##   CUSTPCODE = col_character(),
##   ITEMCODE = col_double(),
##   ITEMDESCR1 = col_character(),
##   ITEMDESCR2 = col_character(),
##   QTYSHIPPED = col_double(),
##   EXTPRC = col_double(),
##   X13 = col_character()
## )
#Aprilcustsales

dim(CustSalesAPR2019)
## [1] 428  13
head(CustSalesAPR2019)
## # A tibble: 6 x 13
##   SUPNO SUPNAME BRANCH CUSTNO CUSTNAME CUSTTOWN CUSTPCODE ITEMCODE
##   <dbl> <chr>   <chr>   <dbl> <chr>    <chr>    <chr>        <dbl>
## 1 11843 BIO DE… BFN    504869 ASSEMBL… KIMBERL… <NA>       1846964
## 2 11843 BIO DE… BFN    504869 ASSEMBL… KIMBERL… <NA>       1848708
## 3 11843 BIO DE… BFN    504869 ASSEMBL… KIMBERL… <NA>       1848716
## 4 11843 BIO DE… BFN    504888 BRANDFO… BRANDFO… 9400       1488431
## 5 11843 BIO DE… BFN    502099 C KROON… VILJOEN… 9520       1846876
## 6 11843 BIO DE… BFN    502099 C KROON… VILJOEN… 9520       1706427
## # ... with 5 more variables: ITEMDESCR1 <chr>, ITEMDESCR2 <chr>,
## #   QTYSHIPPED <dbl>, EXTPRC <dbl>, X13 <chr>

They are 428 observations and 13 variables in the data consisting of customer name, branch, item description 1 and 2, quantity shipped and so on

x <- c( 21,36,261,111,10)
labels <- c("ALPHAB", "ALPHAC", "ALPHAI", "563684", "563687")
pct <- round(x/sum(x)*100)
lbls <- paste(labels, pct) # add percents to labels
lbls <- paste(lbls,"%",sep="") # ad % to labels
pie(x,labels = lbls, col=rainbow(length(lbls)),main="item description2 pie chart")

ALPHAI looks to be the most sold product with 59%, followed by item 563684 with 25%. item 563687 has the lowest market share of 2%

H <- c(21,36,261,111,10)
M <- c("ALPHAB", "ALPHAC", "ALPHAI", "563684", "563687")
barplot(H,xlab="April 2019",ylab="Quantity shipped",col="blue",names.arg=M, main="item description2 Bar chart",border="red")

Concrete breakdown of shipped products for April. 261 cartons of ALPHAI was shipped, 111 cartons of item 563684, 36 cartons of ALPHAC, 21 cartons of ALPHAB, then 10 cartons of item 563687

max(CustSalesAPR2019$QTYSHIPPED)
## [1] 1000

At peak 1000 products were shipped

min(CustSalesAPR2019$QTYSHIPPED)
## [1] -11

11 items were faulty or sold for credit

max(CustSalesAPR2019$EXTPRC)
## [1] 24150

Sales peaked at $24,150

min(CustSalesAPR2019$EXTPRC)
## [1] -2373.69

Loss of $2,373.69 was recorded

max(CustSalesAPR2019$ITEMDESCR1)
## [1] "LIVETWICE FAMILY SINGLE"

The most sold item overall is LIVETWICE FAMILY SINGLE

min(CustSalesAPR2019$ITEMDESCR1)
## [1] "ALPHA B CAPS 60"

The least sold item overall is “ALPHA B CAPS 60”

max(CustSalesAPR2019$BRANCH)
## [1] "PTA"

“PTA” is the branch with most patronage of Biodelta products

min(CustSalesAPR2019$BRANCH)
## [1] "BFN"

“BFN” is the branch with least patronage of Biodelta products

max(CustSalesAPR2019$CUSTNAME, na.rm=TRUE)
## [1] "WOLLIES PHARMACY"

Award for the most valuable customer goes to “WOLLIES PHARMACY”

min(CustSalesAPR2019$CUSTNAME, na.rm=TRUE)
## [1] "ALFA APTEEK BRITS"

“ALFA APTEEK BRITS” tried but they did the least in terms of patrnoage

This report was made based on CustSalesAPR2019.

We would like to know more what the variables represent and the renumerations.

Are there any questions you want to ask using the data or methodology? Send report to alinnorugo@gmail.com