1. Service qualitymanagement
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I. Contents of service quality management
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Other TLAs NOT covered here:
Software Quality Management
Square Meters
Key Themes for SQM Applications in Enterprise (inc MSP/ASP, Cloud) and Communications
Service Providers (CSP)
Quality of Experience (QoE)
Quality of Customer Experience (QoCE)
monitor quality of service (QoS)
bridge the gap between the conventional methods of managing network performance and the
customer’s perception of QoS
manage the entire service delivery process
Service quality can be defined as “the collective effect of service performances which determine
the degree of satisfaction of a user of the service” (ITU E.800). In other words, quality is the
customer’s perception of a delivered service. By service-quality management, we refer to the

2. monitoring and maintenance of end-to-end services for specific customers or classes of
customers
As larger varieties of services are offered to customers, the impact of network performance on
the quality of service will be more complex. It is vital that service engineers identify network-
performance issues that impact customer service. They also must quantify revenue lost due to
service degradation.
Two major software building blocks are required to proactively manage service quality: a
powerful data-aggregation engine and an end-to-end service-mapping tool.
The data aggregator is designed to collect data from a diverse range of sources. In other words, it
collects data from different types and locations like UDR, performance data, and network alarms.
It also gathers data that was produced by multi-vendor equipment. The aggregator processes
large volumes of data to the order of several hundreds of megabytes per day. For example,
around 60 million usage data records are produced every day for a network with 10 million
customers.
The service-mapping tool comes in next. Performance data is mapped onto service-quality data.
Take a customer using Multimedia Messaging Services, or MMS (FIG. 4). If a video download
is interrupted many times during a session, the customer will lose interest. The operator’s
revenue will be lost with it. To avoid this situation, key quality indicators (KQIs) like availability
can monitor the QoS offered to customers.
From a customer’s point of view, the availability KQI measures how successfully he or she can
access and use the MMS service.
With the service-mapping tool, it’s possible to combine KQIs from multiple key performance
indicators (KPIs) across different service resources (FIG. 5). As defined in TMF GB923, KPIs
measure a specific aspect of the performance of either a service resource or a group of service
resources of the same type. A KPI is restricted to a specific resource type and derived from
network measurements.
By following this top-down approach, the service-mapping tool provides several benefits. It
helps operators manage end-to-end quality of service from a customer’s perspective. It also
allows them to reuse key performance indicators and key quality indicators across services and
products. Lastly, it helps operators drill down to the service elements that are responsible for
quality degradations.
Service quality also demands a simple and easy-to-use user interface. With this interface,

3. Network Operations Center (NOC) staff and service managers can monitor service-quality
objectives against thresholds. These thresholds may be internal targets for the network operator.
Or they could be derived from Service Level Agreement (SLA) definitions.
When the service quality falls below the contracted levels, managers could then initiate
corrective actions. They could focus on the service degradations that affect the greatest number
of customers. A set of standard reports for different user communities should also be available.
Network Operations, for example, may request reports on service capacity, the number of
customers affected by service degradation, N-Worst or N-Best services, and N-Worst or N-Best
service elements. For new services, marketing and sales may be interested in reports on service
usage and service uptake. National regulators may also request historical service quality against
given service objectives.
Telecommunications Management Forum (TMF)
TMF has numerous standards and specifications for service quality management within the
communications service provider market. It is interesting that the TMF has recently rebranded
their efforts from SQM to something they are calling Managing Customer Experience (MCE).
Details:
http://www.tmforum.org/CollaborationProgram/ManagingCustomerExperience/6513/home.html
To address both the end-to-end view of the customer lifecycle and of the value chain, TM
Forum’s Managing the Customer Experience Program takes a phased approach to:
* Developing a single framework for measuring and effectively managing service quality;
* Defining key service quality metrics at each point along the service delivery network;
* Identifying service quality issues and the necessary accounting and rebating information; usage
information, and problem resolution information;
* Defining management capabilities to support each step in the service delivery network;
* Specifying appropriate interfaces/API’s to enable the interchange of such information
electronically between the various providers in a service value network.
Managing Customer Experience (SQM) Charter for Phase 1
TR 148 Managing the Quality of Customer Experience – This document describes the principals
and models for assuring the Quality of Customer Experience (QoCE) through end to end (e2e)
Service Quality Management (SQM) that forms the basis of the e2e Holistic Customer
Experience Framework Ecosystem that supports:

4. * B2B processes (Assurance focus);
* SLA Agreements (implicit, explicit customer defined) for services and resources;
* Linkage to customer and user perception information.
TR149 Technical Report–Managing the Quality of Customer Experience
Customer Experience Service Level Agreements, and End-to-End Service Quality
Management—reviews five scenarios where e2e Customer experience is needed, IPTV ,
MobileTV, IPVPN, Service syndication, VoIP, and Blackberry.
Part 1: Holistic e2e Customer Experience Framework Customer Experience/ SLA/ Service
Quality Management
Part 2: Key Factor Analysis Workbook—Provides a Customer Experience model and analysis
methodology called Key Factor Analysis. Proposes as set of APIs metrics and design principles
for establishing Customer Experience and end to end Service Quality Management.
==================
III. Quality management tools
1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
 Who filled out the check sheet
 What was collected (what each check represents,
an identifying batch or lot number)
 Where the collection took place (facility, room,

5. apparatus)
 When the collection took place (hour, shift, day
of the week)
 Why the data were collected
2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.

6. 3. Pareto chart
A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method

7. A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that
is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line

8. exactly.
5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
 People: Anyone involved with the process
 Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
 Machines: Any equipment, computers, tools, etc.
required to accomplish the job
 Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
 Measurements: Data generated from the process
that are used to evaluate its quality
 Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method

9. A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
III. Other topics related to Service quality management (pdf download)
quality management systems
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