Staffing Business Case study:
- Store Staff Allocation Models
- A superstore business is growing rapidly and they are unable to maintain proper staffing at peak business hours
- Now they want to know whether to hire new people?
- (or) rescheduling will be sufficient?
Data Generation process:
# Date start = Monday 25th week of 2017
# Each day has two observations( 1,2) = (am, pm)
# Each week 10 observations; mon-fri
Packages to be used
library(pacman)
pacman::p_load(forecast)
p_load(tidyverse, lubridate, rio, pdfetch, tidyverse, readxl,dygraphs)
library(dygraphs)
# Secondary packages
pacman::p_load(tsibble, fable)
pacman::p_load(tsibbledata)
pacman::p_load(feasts, fpp3)
library(ggthemes)
library(tsbox) #convert xts to tsibble
library(TSstudio)
library(xts)
library(zoo)
library(ggthemes)
Import and preview data
dr1 = import("staffing.csv")
head(dr1)
## V1 V2
## 1 1 203
## 2 2 243
## 3 3 146
## 4 4 204
## 5 5 167
## 6 6 197
str(dr1) # note that this is an indexed vector not a time series
## 'data.frame': 144 obs. of 2 variables:
## $ V1: int 1 2 3 4 5 6 7 8 9 10 ...
## $ V2: int 203 243 146 204 167 197 174 201 209 589 ...
Convert Indexed vector to time-series variable
# We are picking only the staffing volumes to analyze the patterns in the data.
# we are generating the date twice in a day from mon-fri, so the frequency is 10 for a week
mycounts = ts(dr1[2], start = c(1,1), frequency = 10)
Understand the nature of the data
str(mycounts)
## Time-Series [1:144, 1] from 1 to 15.3: 203 243 146 204 167 197 174 201 209 589 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr "V2"
head(mycounts)
## Time Series:
## Start = c(1, 1)
## End = c(1, 6)
## Frequency = 10
## V2
## [1,] 203
## [2,] 243
## [3,] 146
## [4,] 204
## [5,] 167
## [6,] 197
time(mycounts)
## Time Series:
## Start = c(1, 1)
## End = c(15, 4)
## Frequency = 10
## [1] 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4
## [16] 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9
## [31] 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5.0 5.1 5.2 5.3 5.4
## [46] 5.5 5.6 5.7 5.8 5.9 6.0 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9
## [61] 7.0 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8.0 8.1 8.2 8.3 8.4
## [76] 8.5 8.6 8.7 8.8 8.9 9.0 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9
## [91] 10.0 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 11.0 11.1 11.2 11.3 11.4
## [106] 11.5 11.6 11.7 11.8 11.9 12.0 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 12.9
## [121] 13.0 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9 14.0 14.1 14.2 14.3 14.4
## [136] 14.5 14.6 14.7 14.8 14.9 15.0 15.1 15.2 15.3
cycle(mycounts)
## Time Series:
## Start = c(1, 1)
## End = c(15, 4)
## Frequency = 10
## [1] 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5
## [26] 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
## [51] 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5
## [76] 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
## [101] 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5
## [126] 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4
frequency(mycounts)
## [1] 10
plot(mycounts)
#These commands helps us to understand how data is generated and their frequency
#More importantly, how cycle is defined in the dataset.
#high-level understanding of patterns using plot function.
Visualize the data:
labs = c( "Mam", "Mpm", "Tam", "Tpm", "Wam", "Wpm", "Tham", "Thpm", "Fam", "Fpm")
monthplot(mycounts, main = "Staffing", labels = labs, xlab = "Bidaily Units")
ggsubseriesplot(mycounts, main = "Staffing" , labels = labs)
Conclusion:
- It’s is very clear from the viz that there is a strong pattern in the data.
- Afternoon is the time where this superstore needs more staffing.
- Morning’s have less staffing needs.
Recommendations:
- Super-store business owners can now reschedule the employees based on the average staffing count as displayed on the y-axis of the plot.
- This strong pattern in the data guides the business to effectively plan the staffing needs. Eventually business grows.