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# Cold Storage Problem : Fundamental of Business Statistics

Added on - 21 Jan 2022

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PGP - Data Science and Business Analytics
DSBA -Project 2 - Cold Storage Problem
Assessment -Fundamental of Business Statistics
Problem 1
Cold Storage started its operations in Jan 2016. They are in the business of storing Pasteurized Fresh
Whole or Skimmed Milk, Sweet Cream, Flavoured Milk Drinks. To ensure that there is no change of
texture, body appearance, separation of fats the optimal temperature to be maintained is between
20C-40C
In the first year of business, they outsourced the plant maintenance work to a professional company
with stiff penalty clauses. It was agreed that if it was statistically proven that the probability of
temperature going outside the20C-40Cduring the one-year contract was above 2.5% and less than
5% then the penalty would be 10% of AMC (annual maintenance contract). In case it exceeded 5%
then the penalty would be 25% of the AMC fee. The average temperature data at date level is given
in the file “Cold_Storage_Temp_Data.csv”
1.Find mean cold storage temperature for Summer, Winter and Rainy Season
The approach taken in this case is to filter the dataset provided based on seasons i.e. summer, rainy
and winter. The package ‘dplyr’ is being used here for data manipulation and filtering. By using this
package, we are able to extract the required columns i.e Seasons and Temperature, filter them by
seasons and get a summary of different seasons. The output and the mean cold storage temperature
for summer, winter and rainy season are provided below:
Therefore, the mean cold storage temperatures for Summer, Winter and Rainy seasons are3.1470C,
2.7760Cand3.0880Crespectively.
Boxplot:from the box plot we see that the temperature between Summer and Rainy season
is close
R Code:
#=======================================================================#
#=======================================================================#
# Set working directory
setwd("C:/Users/Desktop/Great_Learning/R_Project/Cold_Storage_Problem")
getwd()
# Import the CSV into R
cold_storage_data_temp<-read.csv("Cold_Storage_Temp_Data.csv", header=TRUE)
View(cold_storage_data_temp)
# Install package "dplyr" for data manipulation
install.packages("dplyr")
library("dplyr")
# Create a new data sub-set with the required columns i.e. Season and temperature
seasons_temp<-select(cold_storage_data_temp,Season,Temperature)
View(seasons_temp)
# Filter and view seasons_temp dataset w.r.t winter, summer and rainy
winter_temp<-filter(seasons_temp, Season == "Winter")
summer_temp<-filter(seasons_temp, Season == "Summer")
rainy_temp<-filter(seasons_temp, Season == "Rainy")
View(winter_temp)
View(summer_temp)
View(rainy_temp)
# Get summary of winter, summary and rainy temperatures

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