om0016 –quality management - summer/spring 2012

Summer 2011- May drive

Master of Business Administration - MBA Semester IV

OM0016 –Quality Management - 4 Credits

(Book ID: B1341)

Assignment - Set- 1 (60 Marks)

Note: Each Question carries 10 marks. Answer all the questions.

Q1. Elucidate a relationship between strategy and data quality.

Correlation Between Strategy and Data Quality

Organisations are turning to be more dependent on data; nearly every modern organisation

depends upon data and creates huge quantities of data. So, to meet the requirements of the

organisation, a comprehensive data management program is necessary. But data is

distinctive from other resources and requires diverse management techniques.

Organisational framework required to tackle the above concerns often does not exist.

Hence, it is necessary to develop a comprehensive data quality strategy that can address

many of these issues. For example, as with many organisations, formal data quality

strategy was not documented for the New Zealand Ministry of Health. A planned data

quality framework was later introduced which informed the overall development of a data

quality strategy for the Ministry of Health. All results of the development of the strategy

were documented for each collection of data. And it enabled internal members to access all

the information about data collection. The documents are:

· Guidelines for the usage of data and original purpose for the collection

· Roles and responsibilities

· Results of assessment of collection using data quality framework

· Action plan for quality improvement

· Current and previous data quality initiatives

The data was available both on paper and Web.

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As defined by Joseph Juran[3], “Data are of high quality, if they are fit for their intended

uses in operations, decision-making and planning. Data are fit for use if they are free of

defects and possess desired features”. Hence, a data quality strategy in an organisation is a

must to define the level of quality required to make the data useful. The data quality

strategy also needs to look forward to the future potential uses of the data.

Q2. Briefly describe Deming's 14 points philosophy. Elucidate Juran's trilogy in brief.

Q3. Describe the evolution of Six Sigma in General Electric. Explain the advantages and

disadvantages of Six Sigma in brief.

The Evolution of Six Sigma

Q4. What were the improvements made to QMS? Why do think management

responsibility is an essential requirement of QMS?

Improvements made to Quality Management Systems

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Q5. Explain the goals and programs of strategic plan. What are the reasons of failure of

the strategic plans?

Q6. What are the characteristics of Ideal indicator? What are the two Six Sigma models

undertaken for Quality improvement?

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Summer 2011- May drive

Master of Business Administration - MBA Semester IV

OM0016 –Quality Management - 4 Credits

(Book ID: B1341)

Assignment - Set- 2 (60 Marks)

Note: Each Question carries 10 marks. Answer all the questions.

Q1. What are the different types of Reliability estimates? What are the important

components of retest reliability?

Reliability Estimations

A major part of any quality program is the assurance that the products are in accordance

with the performance objectives. Reliability can be estimated through a number of methods

that are grouped into two types: single-administration and multiple-administration.

Multiple-administration methods require two assessments. In the test-retest method,

reliability is estimated as the correlation of the Pearson product-moment coefficient

between two administrations of the same measure. In the alternate forms method,

reliability is estimated by the correlation of the Pearson product-moment coefficient

between two different forms of a measure administrated together. The idea behind

test/retest is that one should get the same score on test 1 as well as in the test 2.

The three main components to this method are as follows:

1. Apply the measurement instrument at two separate times for each subject.

2. Calculate the correlation between the two separate measurements.

3. Assume there is no change in the underlying condition between test 1 and 2.

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Single-administration methods include the split-half and internal consistency reliability

methods. The split-half method considers the two halves of a measure as alternate forms.

This "halves reliability" estimate is then moved to the full test length using the Spearman-

Brown prediction formula. Internal consistency estimate groups questions in a

questionnaire to estimate reliability. For example, one could write two sets of four

questions that measure the same concept (example, class performance) and after collecting

the responses, run a correlation between those two groups of four questions to decide if the

instrument is consistently measuring the concept. The most common internal consistency

estimate is Cronbach’s alpha, which is usually interpreted as the mean of all possible split-

half coefficients.

Clarity of expression (for written assessments), increasing the measure and informal means

help to improve reliability. However, item analysis, a formal psychometric is considered

the most effective way to increase reliability. This analysis is a combination of

computation of item difficulties and item discrimination indices. The latter index involving

calculation of correlations between the items and sum of the item scores of the complete

test. If too difficult, too easy, and/or near-zero or negative discrimination items are

replaced with better items, the reliability of the measure will increase.[3]

(where λ is the failure rate)

Measures of Reliability

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Most common form of reliability is the retest reliability, which refers to the reproducibility

of values of a variable when the measurement is done more than once on the same product.

Here, we take the example of 10 people weighed twice with a gap of two weeks between

tests. This example is used to explain the three important components of retest reliability:

change in the mean – typical error, retest correlation – kappa coefficient and alpha

reliability.

1. Change in the Mean

The difference between the two tests conducted is the change in the mean. The change

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consists of two components: a random change and a systematic change.

Random change in the mean is due to the sampling error. This kind of change arises from

the typical error, which is like a randomly selected number added to or subtracted from the

true value every time the measurement is conducted.

Systematic change in the mean is a systematic change in the value between two measures.

This variation in the mean value is an important issue when subjects perform a series of

trials as part of a tracking the program. The subjects are usually tracked to determine the

effects of an intervention (example, a change in diet or training), so it is important to

perform trials to make learning effects or other systematic changes insignificant before

applying the intervention.

Typical Error of Measurement

In the above example, the first few weights show a slight trend downwards –it shows that

the subjects lose a bit of weight, so there is a random variation of about a kilogram. This

random variation is the typical error. It is quantified as the standard deviation in each

subject’s measurements between tests, after any shifts in the mean have been taken into

account. The variation/error is also known as the within-subject standard deviation, or the

standard error of measurement.

Coefficient of variation is an important form of typical error. This is the typical error is

expressed as a percentage of the subject’s mean score. Another form of within-subject

variation is reliability limits of agreement, which represent the 95% likely range for the

difference between a subject’s scores in two tests[4].

2. Retest Correlation

When the test and retest values are determined, it is obvious that the closer the values are

to the true value, the higher is the reliability. Therefore, a retest correlation is one way to

quantify reliability. A correlation of 1.00 represents perfect agreement between tests,

whereas 0.00 represents no agreement whatever. In our example the correlation is 0.95,

which represents very high reliability.

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Kappa Coefficient: Reliability of Nominal Variables

Reliability can also be defined for supposed variables, to represent the constancy with

which something is classified on several occasions. The best measure is something called


the kappa coefficient. It is equivalent to a correlation coefficient and has the same range of

values (-1 to +1).

3. Alpha Reliability

The alpha reliability of the variable is derived by assuming that each item represents a

retest of a single item. For example, if there are five items, it is as though the five scores

are the retest scores for one item. But reliability is calculated such that it represents the

reliability of the mean of the items and not the reliability of any single item. Hence, the

alpha reliability of 10 items would be higher than that of 5 similar items.[5]

Alpha reliability is regarded as a measure of internal consistency of the mean of the items at the

time of supervision of the questionnaire. It refers to the ability to reproduce the results whenever

it is required. This is essentially enhances faith in the statistical analysis and the results obtained.

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Q2. What are the different types of Quality Costs? What is the relevance of Cost and

Schedule Information in an organisation?


Q3. What are the basic steps involved in audit reporting? Explain. What is the purpose of

product auditing?

Q4. What is quality culture? Explain the five key drivers that are essential for developing

quality culture.

Q5. For modern products, explain the concepts on what on which design reviews are

based.

Q6. What are the five dimensions of the quality that were identified by the SERVQUAL

model?

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