1. Retail service qualitymanagement
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I. Contents of retail service quality management
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The growing globalization, increasing customer expectations, regulatory compliance
requirements, complexity of the supply chain, as well as the pressure on retailers to quickly bring
new products to the market, have created a number of product quality and safety issues. These
issues have manifested in numerous product recalls which have shaken consumers' trust in the
ability of companies to make quality and safe products. The year 2007 was a tipping point.
Widely dubbed "The Year of the Recall," 2007 saw millions of consumer products, including
toys, apparel, cribs, heaters, ovens, and jewelry being recalled for defective or harmful
components. Many of these products had been manufactured by trusted brands. Even if a
supplier or sub-supplier is to blame for a defective product, ultimately the retailer or the brand
faces the brunt of customer dissatisfaction.
In response, retailers are striving to step up integrated enterprise quality management programs,
to ensure process and product quality as well as to improve visibility and traceability across the
supply chain. Also, an enterprise-wide quality management system will enable retailers to unify
their quality processes across their global operations. Retailers can thus effectively fulfill their
supplier, customer and internal quality obligations, adhere to industry standard quality
management methodologies such as cGMPs and Six Sigma, as well as other quality standards,
help companies make profitable decisions across the value chain, keep up with the new and
complex market trends, and enhance their overall product and process quality.
MetricStream Quality Management Software Solutions
MetricStream offers comprehensive enterprise quality management software solutions with pre-
defined templates, checklists and workflows for streamlining quality processes, and proactively
detecting, monitoring, and remediating quality issues. The solution provides capabilities to

2. manage supplier and factory information, supplier quality audits and assessments, risk
assessments and management, internal quality inspections, production management, testing,
incident / issue management, corrective actions, employee training, product recalls, customer
complaints and compliance with standards and regulations.
MetricStream solution enables retailers to adopt an automated and integrated risk-based
approach to quality management, and thus efficiently prioritize key areas of concern. Ultimately,
retailers can achieve quality and compliance at a lower cost, considerably reduce the risk of poor
quality, achieve control over the end-to-end processes, enhance visibility, improve correlation
between programs, and establish coherent metrics to monitor and manage program performance.
Replete with charts and metrics, the solutions allow retailers to monitor supplier performance,
and product quality performance in real-time.
The solutions uniquely combine the reporting of product and process problems with flexible
workflows, ensuring collaboration with suppliers, internal quality teams, third-party auditors and
testing labs. Retailers can thus achieve resolution of issues in quick time. Retailers can further
securely store, manage, and retrieve documents pertaining to products, processes and training.
Key benefits of the solutions include:
 The scalable and flexible enterprise system facilitates standardization of multiple quality
management methods and processes across a company and its supply chain
 Helps automate internal and supplier quality processes, and streamline the management
of quality issues reported by customers
 Helps eliminate resource-intensive manual efforts and documentation, duplication of
actions, and redundancies
 Provides a unified view of the quality and compliance programs across the enterprise and
the supply chain, right from the procurement stage and supplier due diligence process to
distribution and customer service
 Facilitates timely risk management by helping retailers manage internal processes, which
capture the latest alerts and events including supplier related data and industry regulations
 Enables retailers to define measurable key performance indicators for assessing
performance and corrective actions
 Helps remove silos, enabling multiple operational and business units spread across
locations to collaborate and investigate quality issues, and also implement consistent
remediation plans
 Offers enhanced measuring and monitoring process as well as the right set of values and
reports in real time, to accurately evaluate the performance of various business units
==================
III. Quality management tools
1. Check sheet

3. 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,
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

4. 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.
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

5. algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
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

6. 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
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

7. 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]
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