TOTAL QUALITY MANAGEMENT
IE673
ASSIGNMENT 5
By
DHRUV SHAH
UCID: dds29@njit.edu
ID: 31327792
Under Professor
Paul Ranky
Q1. Explain what is QFD, (Quality Function Deployment)?
Ans. Quality Function Deployment (QFD) is a way to deal with
item/administration plan and constant change that brings clients into the
outline process. It is utilized to decipher what the client needs into what the
association produces. The idea was initially grown by Dr. Yoji Akao in 1966,
consolidating quality methodologies with components of worth designing. QFD
requires the association of every practical division of the association toward
the beginning of, and all through the venture. This is additionally an
essential goal of TQM. There are numerous illustrations of items being conveyed
to advertise just to be dismisses on the grounds that they didn't fulfill
client needs/needs. A goal of QFD is to stay away from that disaster.
The four phases of QFD are:
1. Product
concept planning. It starts with customers and market research with leads to
product plans, ideas, sketches, concept models, and marketing plans.
2. Product
development and specification. It would lead to the development to prototypes
and tests.
3.
Manufacturing processes and production tools. They are designed based on the
product and component specifications.
4. Production of product. It starts after the
pilot has been resolved.
Q2. Explain the WHATs in a QFD matrix
Ans. The heart of QFD is the set of interrelated matrices
known as the House of Quality (HOQ), so named because the complete matrix takes
on the appearance of a house.
Ø
Gathering Customer Needs Input: The premise of
QFD is that before any product or service is designed, the producer should have
a good understanding of his potential customers’ needs in order to improve the
likelihood that the product or service will be a market success. That the
producer should be aware of customer needs seems logical, but it sounds far
easier than it is.
Ø
Refining the Customer Needs Inputs: Once the
cross-functional QFD team has assembled sufficient information on what
characteristics, attributes and features customers say they need, the
information must be distilled into something useful. Typically the problem is
that the inputs invariably cover the spectrum from some really good ideas and
nuggets of information to some that are trivial or frivolous, and the volume of
information so great that the designers are unable to cope with it. The data
must be sorted into a prioritized set of the most important customer needs.
Ø
Using the Affinity Diagram: Affinity diagrams
are used to promote creative thinking. They can be very helpful in breaking
down barriers created by past failures and in getting people to give up
ingrained paradigms that impede our ability to find new and different
approaches. This is a critical element in achieving continual improvement.
Affinity diagrams give structure to the creative process by organizing ideas in
a way that allows them to be discussed, improved, and interacted with by all
the participants.
Ø
Using the Tree Diagram: The next tool to be used
is the Tree Diagram. Tree diagrams can be used for countless purposes. It will
be used here simply to refine the affinity diagram results to make the list the
customer needs, or WHATs that will be placed in the HOQ. Although a tree
diagram could go all the way down into the nuts and bolts of a new design,
remember that the objective here is not to design the new product, but to list
the items to be addressed by the design team once the entire HOQ is completed.
Ø
Customer Importance: Also coming out of the
analysis is the team’s best estimate of the relative importance of each listed
customer need. Customer importance is usually based on a scale of 1 to 5 with 5
being the highest priority. This information is solicited from customer
sources, but unanimity in ranking by the customers is unlikely, so the team has
to do its best to evaluate and assign priorities, as they believe the aggregate
of customers would. These importance rankings are entered in the Customer
Importance column.
Q3. Explain the HOWs in a QFD matrix
Ans. The Technical Requirements room of the HOQ states how
the company intends to respond to each of the Customer Needs. It is sometimes
referred to as the voice of the company. We must state at the outset that the
technical requirements are not the design specifications of the product or
service. Rather, they are characteristics and features of a product that is
perceived as meeting the customer needs. They are measurable in terms of
satisfactory achievement. Some may be measured by weight, strength, speed, and
so on. Others by a simple yes or no, for example a desired feature, appearance,
test, or material is or is not incorporated. The other side of the coin is that
the technical requirements must not be limiting, but must be flexible enough to
allow the company to consider every creative possibility in its attempts to
satisfy the need. The technical requirements are generated by the QFD team
through discussion and consultation with the Customer Needs and Planning
matrices used as guidance. The team may use affinity or tree diagrams to
develop, sort, and rank the requirements, similar to the Customer Needs
development process. The difference here is that the input is from within the
company rather than from external customers.
Q4. Explain the 1, or 3, or 9 interrelationship values in a
QFD matrix.
Ans. The results will be shown in the Interrelationships
matrix, which links the HOWs and the WHATs. At each intersection cell of the
interrelationship matrix the team must assess the degree of relationship
between the WHAT and the corresponding HOW. This is usually done using scales
of significance of 1 to 5 or 1 to 9, with the higher number indicating a
stronger relationship. Sometimes these numbers are entered, but often symbols
are used.
Let’s see how this works. Refer to Figure above, and
consider the first customer need, Comprehensible text. Now look at each of the
intersections on that row to see which HOWs have a relationship with
Comprehensible text. Authors/ Editors Guide seems to offer a relationship.
Certainly the publisher’s guidance to the author, and the level and
effectiveness of the editing process will impact the quality and
comprehensibility of the text. We have identified an inter-relationship, but
how strong is it? The team has to decide, and the result may not be very exact,
but rather is a well- discussed estimate. Let’s say that the strength is high.
We should enter either a “9” or the double-circle symbol in that cell. The next
Comprehensible Text relationship cell appears to be under Text Clarity. The
interrelationship between this WHAT and HOW is strong, so a 9 or the
double-circle symbol is entered. All cells must be checked for interrelation-
ships, and when such exists, the strength of the relationship must be
evaluated.
As we have mentioned, either numbers or symbols may be used.
If you use numbers, use only 1, 3, and 5 or 1, 3, and 9 rather than 1, 2, 3, 4,
5, and so on. Remember, we are only estimating the interrelationship’s
strength: Is it strong, medium, weak, or nonexistent? There is little to be
gained by trying to be precise in an area where the result is a best guess or
an estimate.
Q5. Explain how you calculate the technical priorities in
the design target matrix.
Ans. To determine the relative importance, or priorities, of
each of the stated Technical Requirements (HOWs) in meeting the Customer Needs
(WANTs), the QFD team simply multiplies each of the interrelationship ratings
of the technical requirement (0, 1, 3, or 9) from the Interrelationship matrix,
times the corresponding customer needs. Overall Weighting value in the Planning
matrix; and then sums the columns. Moreover, some QFD users translate the
priority values into a percentage scale. This is done, of course, by dividing
the individual technical priority values by the sum of all the priority values,
and multiplying by 100.
% Total priority =
(Technical Requirement Priority, Ʃ Technical Priorities) * 100
The rest of the % of Total Priority values are calculated
and placed in a row just below the Technical Priorities. This information is
used by the organization as guidance for the appropriate deployment of
resources for the project.
Q6. Define statistical process control.
Ans.
Statistical process control (SPC) is a statistical method of separating
variation resulting from special causes from variation resulting from natural causes
in order to eliminate the special causes and to establish and maintain
consistency in the process, enabling process improvement.
Although SPC
is normally thought of in industrial applications, it can be applied to
virtually any process. Everything done in the workplace is a process. All
processes are affected by multiple factors. For example, in the workplace a
process can be affected by the environment and the machines employed, the
materials used, the methods (work instructions) provided, the measurements
taken, and the manpower (people) that operate the process—the Five M’s. If
these are the only factors that can affect the process output, and if all of
these are perfect—meaning the work environment facilitates quality work; there
are no misadjustments in the machines; there are no flaws in the materials; and
there are totally accurate and precisely followed work instructions, accurate
and repeatable measurements, and people who work with extreme care, following
the work instructions perfectly and concentrating fully on their work—and if
all of these factors come into congruence, then the process will be in
statistical control. This means that there are no special causes adversely
affecting the process’s output.
SPC does not eliminate all variation in the processes, but
it does something that is absolutely essential if the process is to be
consistent and if the process is to be improved. SPC allows workers to separate
the special causes of variation (e.g., environment and the Five M’s) from the
natural variation found in all processes. After the special causes have been
identified and eliminated, leaving only natural variation, the process is said
to be in statistical control (or simply in control). When that state is
achieved, the process is stable, and in a 3-sigma process, 99.73% of the output
can be counted on to be within the statistical control limits.
Q7. Explain control charts for variables, with a simple
mathematical example.
Ans. Consider an example using x-charts and R-charts. These
charts are individual, directly related graphs plotting the mean (average) of
samples (x) over time and the variation in each sample (R) over time. The basic
steps for developing a control chart for data with measured values are these:
1. Determine sampling procedure. Sample size may depend on
the kind of product, production rate, measurement expense, and likely ability
to reveal changes in the process. Sample measurements are taken in subgroups of
a specific size (n), typically from 3 to 10. Sampling frequency should be often
enough that changes in the process are not missed but not so often as to mask
slow drifts. If the object is to set up control charts for a new process, the
number of subgroups for the initial calculations should be 25 or more. For
existing processes that appear stable, that number can be reduced to 10 or so,
and sample size (n) can be smaller, say, 3 to 5.
2. Collect initial data of 100 or so individual data points
in k subgroups of n measurements. The process must not be tinkered with during
this time—let it run. Don’t use old data—they may be irrelevant to the current
process. Take notes on anything that may have significance. Log data on a data
sheet designed for control chart use.
3. Calculate the mean (average) values of the data in each
subgroup x.
4. Calculate the data range for each subgroup (R).
5. Calculate the average of the subgroup averages x. This is
the process average and will be the centerline for the x -chart.
6. Calculate the average of the subgroup ranges R. This will
be the centerline for the R-chart.
7. Calculate the process upper and lower control limits, UCL
and LCL respectively. UCL and LCL represent limits of the process averages and
are drawn as dashed lines on the control charts.
8. Draw the control chart to fit the calculated values.
9. Plot the data on the chart.
We calculate the mean (average) values for each subgroup.
This is done by dividing the sum of x1 through x10 by the number of data points
in the subgroup.
Where n = the number of data
points in the subgroup. The x values are listed in the Mean Value column.
The average x of the subgroup
average x is calculated by summing the values of x and dividing by the number
of subgroups (k):
The range (R) for each subgroup
is calculated by subtracting the smallest value of x from the largest value of
x in the subgroup.
R = (maximum value of x) –
(minimum value of x)
From the R values, calculate the
average of the sub-group ranges.
Q8. Explain control charts for
attributes, with a simple mathematical example.
Ans. When a process is stable and
in control, it displays common cause variation, variation that is inherent to
the process. A process is in control when based on past experience it can be
predicted how the process will vary (within limits) in the future. If the
process is unstable, the process displays special cause variation, non-random
variation from external factors. Attributes data are concerned not with
measurement but with something that can be counted. For example, the number of
defects is attributes data. Whereas the X – charts and R -charts are used for
certain kinds of variables data, where measurement is involved, the p -chart is
used for certain attributes data. Actually, the p -chart is used when the data
are the fraction defective of some set of process output. It may also be shown
as percentage defective. The points plotted on a p -chart are the fraction (or
percentage) of defective pieces found in the sample of n pieces.
C-Chart used when identifying the
total count of defects per unit (c) that occurred during the sampling period,
the c-chart allows the practitioner to assign each sample more than one defect.
This chart is used when the number of samples of each sampling period is
essentially the same.
Used when each unit can be considered pass or fail – no
matter the number of defects – a p-chart shows the number of tracked failures
(np) divided by the number of total units (n).
Q9. Discuss and explain various
continual quality improvement methods and tools.
Ans. Control charts of all types are
fundamental tools for continual improvement. They provide alerts when special
causes are at work in the process, and they prompt investigation and
correction. When the initial special causes have been removed and the data stay
between the control limits (within; 3s), work can begin on process improvement.
As process improvements are implemented, the control charts will either ratify
the improvement or reveal that the anticipated results were not achieved.
Whether the anticipated results were achieved is virtually impossible to know
unless the process is under control. This is because there are special causes
affecting the process; hence, one never knows whether the change made to the
process was responsible for any subsequent shift in the data or if it was
caused by some- thing else entirely. However, once the process is in
statistical control, any change you put into it can be linked directly to any
shift in the subsequent data. You find out quickly what works and what doesn’t.
Keep the favorable changes, and discard the others. As the process is refined
and improved, it will be necessary to update the chart parameters. The UCL,
LCL, and process average will all shift, so you cannot continue to plot data on
the original set of limits and process aver- age.
An important thing to remember
about control charts is that once they are established and the process is in
statistical control, the charting does not stop. In fact, only then can the
chart live up to its name, control chart. Having done the initial work of
establishing limits and centerlines, plot- ting initial data, and eliminating
any special causes that were found, we have arrived at the starting point. Data
will have to be continually collected from the process in the same way they
were for the initial chart.
This discussion of control charts
has illustrated only the x-chart, R-chart, p-chart, and c-chart. Lists common
control charts and their applications. The methods used in constructing the
other charts are essentially the same as for the four we discussed in detail.
Each chart type is intended for special application. You must determine which
best fits your need.
Q10. Explain the way control
charts could be used for quality improvements.
Ans. Continual improvement is
fundamental to success in the global marketplace. A company that is just
maintaining the status quo in such key areas as quality, new product
development, adoption of new technologies, and process performance is like a
runner who is standing still in a race. Competing in the global marketplace is
like competing in the Olympics. Last year’s records are sure to be broken this
year.
Athletes who don’t improve
continually are not likely to remain long in the winner’s circle. The same is
true of companies that must compete globally. Customer needs are not static;
they change continually. A special product feature that is considered
innovative today will be considered just routine tomorrow. A product cost that
is considered a bargain today will be too high to compete tomorrow. A good case
in point in this regard is the ever-falling price for each new feature
introduced in the personal computer. The only way a company can hope to compete
in the modern marketplace is to improve continually.
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Article3: Here’s the Scoop: Why
My First Job Mattered
Indeed,
I figure the President and I have something in like manner; one of my mid year
employments was working at a frozen yogurt/froyo store. I trust dessert shops
are quite often conspicuously utilized with children as their first employment.
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Summer
of Opportunity is an extraordinary cause and I trust it extends the nation over
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