Analysis and Measure of Quality Indicator

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ANALYSIS AND MEASURE OF QUALITY INDICATOR
by Your Full Name
Course
Professor
University
Date of Submission

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The Number of Calls Resolved by Telephone Advice
Part A: Identification of Chart Used
A process behavior chart is used to evaluate the variation of a process over a certain
period of time, and to keep track of quality. They are used in shaping the character of interest
by providing visuals that can be interpreted effectively. Graphical charts are usually used to
provide a context for interpreting provided data (Wanichthanarak et al., 2017). A process
behavior graph has three main components. The process behavior chart has a Central Line for
the average, an upper line for the upper natural process limit, a lower line for the lower
natural process limit, and all data is plotted based on a time order (Black, 2017). Process
behavior charts are more useful when portraying historical account of a procedure,
monitoring processes for their stability, detecting changes in the variation of previously stable
data, checking signals which may be necessary for making adjustments, and detecting special
cause variations. There are two kinds of causes that produce the variation as seen in a run
chart. First is the common cause. Common causes are usually slight and are caused by
random factors that make up the process. The second type of cause is the common causes,
and are usually systematic changes in the pattern of the process for which the real cause is
often found when a control chart signals their presence. Process behavior charts are used best
when predicting an expected range of results, to evaluate patterns, and to finding and solving
problems (Black, 2015). The chart is also suitable for detecting significant variations in a
process mean or variance.
Characteristics of Chart Chosen
For the analysis of the data that was provided, the process behavior chart that I used is
the XmR chart. X in an XmR chart represents the performance measure while the mR stands
for the moving range. An XmR chart has some assumptions (Ali, Pievatolo and Gob, 2016).
The first assumption is that there is one observation per period. Secondly, is that it has

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consistent values; that is, the vales will to be measured are of the same type, and the method
used is the same (Ali, Pievatolo and Gob, 2016). Lastly, the data also should be logically
comparable (Khaliq and Riaz, 2015). The data we have suited the conditions of an XmR
chart. The data has one observation per period; the data to be measured is of the same type,
and the data is also logically comparable. XmR charts are usually created using five or more
points of data and how data does have more than 5 points (Khaliq and Riaz, 2015). This
makes the chart even more suitable for the analysis of the data (Ali, Pievatolo and Gob,
2016). That is why I chose the XmR chart for the scrutiny of the information given on
telephone calls.
Process Behavior Charts
The charts below are the XmR charts for the data. Visualization of behavior happens
to be an important aspect. It gives a clear indication of limits and degree of variation among
the characteristics of interest (Wludarczyk-Sielika and Stateczy, 2016). Chart 1 below is the
first process behavior chart while Chart 2 is the second process behavior chart. They are
labeled Chart 1 and Chart 2 to minimize the caption written beside the chart. The first process
behavior chart contains three lines, the central line, upper and lower limits and finally, the
proportion of calls line (Wludarczyk-Sielika and Stateczy, 2016). In this manner, the
proportions are easy to interpret. The proportion of calls line shows the spread of the data in a
graph (Wludarczyk-Sielika and Stateczy, 2016). Therefore, the data is represented in the
graphs are analyzed in a process behavior chart that gives clear visuals. The first being an
individual chart and the second a moving range chart ( Wludarczyk-Sielika and Stateczy,
2016). The values used in the creation of the process behavior are calculated differently. For
the upper natural process limit to be obtained, the average moving range is multiplied by a
scaling factor of 3.27, which is a constant. The scaling factor produces an appropriate range
for the upper natural process limit. The analysis done is as shown below.

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Jan-2015
Mar-2015
May-2015
Jul-2015
Sep-2015
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Number of calls closed with tel. advice, X
Proportion of calls
resolved by advice
Central Line
Lower Natural Process
Limit
Upper Natural Process
Limit
Period
Proportion of calls
Chart 1
Jan-2015
Feb-2015
Mar-2015
Apr-2015
May-2015
Jun-2015
Jul-2015
Aug-2015
Sep-2015
0
0.01
0.02
0.03
Number of calls resolved by tel. advice, mR
Moving Ranges
Upper Limit Range
Average Moving Range
Period
Proportion of calls
Chart 2
Explanation of Findings
Next, we explain the findings from the process behavior charts above. Data is usually
collected as a source for action, but before data is used, it has to be interpreted first (Khurana,
Parthasarathy and Turaga, 2016). Data is analyzed to determine when a change has occurred
in a process or scheme. It is imminent that the changes are identified in time to take
appropriate action (Zügner, Akbarnejad, and Günnemann, 2018). The criteria used to discern
special cause variation is the use of lower and upper limits together with the central line.
Examining the run chart in Chart 1, which is the process behavior chart, it is seen that the