Aim: To analyze performance of 16 consecutive months and predict the performance for the month of May 2015.

##    Month Month Desc Received Resolved Reopen Backlog
## 1      1   Jan 2014      444      420     12       0
## 2      2   Feb 2014      452      433     13      24
## 3      3   Mar 2014      677      632      8      19
## 4      4   Apr 2014      612      592      8      45
## 5      5   May 2014      514      465      5      20
## 6      6   Jun 2014      512      491     19      49
## 7      7   Jul 2014      476      472     15      93
## 8      8   Aug 2014      381      399      7       4
## 9      9  Sept 2014      394      397     12       0
## 10    10   Oct 2014      419      408     10       0
## 11    11   Nov 2014      249      257      2      11
## 12    12   Dec 2014      374      353     10       0
## 13    13   Jan 2015      314      346      5      21
## 14    14   Feb 2015      344      331      6       0
## 15    15   Mar 2015      431      450      6      13
## 16    16   Apr 2015      654      635      9       0

Plotting graph:

library(ggplot2)
gL1 <- ggplot(data, aes(x=Month, y=NumberOfCases)) + geom_line(aes(y=Received, colour="Received"))+labs(title="Performance Analysis") + geom_line(aes(y=Resolved, colour="Resolved")) + geom_line(aes(y=Reopen, colour="Reopen")) + scale_colour_manual("", breaks=c("Received","Resolved","Reopen"), values=c("purple","maroon","orange"))
gL1

plot of chunk performance

Analysis:

A sudden increase in inflow experienced in March 2014 caused an increase in the number of reopened cases in the following weeks. There was an unexpected rise in the number of reopened cases in December 2014. This has been classified as an anomaly. The number of cases received was seen to rise tremdously in April 2015. This is expected to cause another increase in the number of reopened cases in the coming months.

Load Analysis

Plotting graph:

library(ggplot2)
gL1 <- ggplot(data, aes(x=Month, y=NumberOfCases)) + geom_line(aes(y=Received, colour="Received"))+labs(title="Load Analysis") + geom_line(aes(y=Resolved, colour="Resolved")) + geom_line(aes(y=Backlog, colour="Backlog")) + scale_colour_manual("", breaks=c("Received","Resolved","Backlog"), values=c("purple","maroon","orange"))
gL1

plot of chunk load

Analysis:

As the inflow increased early 2014, an increase in backlog was observed in the following weeks. A similar increase in the number of cases for April 2015 is expected to cause an increase in the backlog in the following weeks.

Predicting Performance for the Next Month

For Received cases:

library(lattice)
library(caret)
meanReceived <- mean(data$Received)
meanReceived
## [1] 452.9
stdev <- sd (data$Received)
stdev
## [1] 119.2
n <- 18
meanReceived + c(-1,1) * qt(.975, n-1) * stdev / sqrt(n)
## [1] 393.7 512.2

Therefore, there is a 95% chance that the number of Received cases for the next month will be between 393.7 and 512.2.

For Resolved cases:

meanResolved <- mean(data$Resolved)
meanResolved
## [1] 442.6
stdevRes <- sd (data$Resolved)
stdevRes
## [1] 106.1
meanResolved + c(-1,1) * qt(.975, n-1) * stdevRes / sqrt(n)
## [1] 389.8 495.3

Therefore, there is a 95% chance that the number of Resolved cases for the next month will be between 389.8 and 495.3.

For Reopened cases:

meanReopen <- mean(data$Reopen)
meanReopen
## [1] 9.188
stdevReo <- sd (data$Reopen)
stdevReo
## [1] 4.293
meanReopen + c(-1,1) * qt(.975, n-1) * stdevReo / sqrt(n)
## [1]  7.053 11.322

Therefore, there is a 95% chance that the number of Reopened cases for the next month will be between 7.05 and 11.32.