This document discusses quality management and quality control concepts. It addresses questions on the differences between quality assurance and quality control, the importance of human factors in quality, how quality planning and auditing can be centralized, and how organizational structure can impact quality. It also discusses the roles of "policeman" and "adviser" in quality management, limitations of statistical quality control, how acceptance quality limits can insulate production from defects, and interfaces between quality control and purchasing.

1. CHAPTER 10: QUALITY MANAGEMENT – II
Response to Questions
1. Quality control is post facto i.e. ‘after the fact’; Quality Assurance is apriori
i.e. ‘before’ something can occur.
Control is for repair and restoration whereas assurance is for prevention.
Assurance diminishes/eliminates the need for control.
2. Quality and motivation are intimately linked. Whatever may be the level of
automation in processing / operations, the human factor is very important
in quality. Behavioural sciences, therefore, can play a big role in quality.
An organization has to be quality-conscious at every step of its
functioning.
3. The implementation and control of quality will be with the respective
division. Quality Planning and Quality Audit may be centralized functions
so as to maintain the thrust and uniformity of quality policies across the
various divisions.
4. Organizational structure would indicate as to how quality will be planned,
implemented and controlled. The organizational structure would also help
or hinder the flow of information related to quality. For instance, too many
hierarchical levels tend to dissipate information that could be vital. Also,
certain kinds of organization structures would encourage greater lateral
flows and interactions for quality.
5. Quality management needs both: the policeman and the adviser. Bad
quality product has to be ruthlessly weeded out, like a policeman would
keep out the trouble-makers. However, just like crime in society cannot be
eliminated by police action alone, quality cannot be established unless
there is good conceptualization, conscious design, clear plan and
excellent motivation levels for quality across the entire organization.
The advisory role will be evident in conceptualization, design and planning
for quality; the policing role would be in strict implementation and quality
control.
6. The limitation of statistical quality control, ironically, is in its strength viz.
statistics. Statistics is applicable when there is a large population, i.e. it is
applicable in mass production or repeated operations. Now-a-days,
customers are asking for a large variety and almost ‘customized’ or
unique products/services. Unitized production and Just-in-Time
production are quite common place. The ‘science of averages’ viz.
statistics has limited use in such cases. The emphasis is, therefore,

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increasingly on producing ‘right quality first time’ and ‘zero defects.’ An
error, when it occurs is attended to and examined as an event that stands
out rather than as a part of the statistics of errors.
7. The vital information given by an OC curve is:
a. for the consumer : the ‘ bad’ quality materials that can enter into his
operations system; and
b. for the producer/supplier: the ‘good’ quality products that can get
rejected, thus contributing to the cost of the supplier organization.
In sensitive production systems (i.e. systems sensitive to bad quality),
above mentioned point (a) is vital. Sampling plans and the corresponding
OC curves are, therefore, very important.
8. A sample is taken and a measurement of a specified quality characteristic
is made on each unit. These measurements are then summarized into a
simple statistic (e.g., sample average) and the observed value compared
with an allowed value defined in the plan. A decision is then made to
accept or reject the lot.
9. Refer to the response to Question # 7. The idea behind AOQL is that at
no point in time should bad quality inputs enter the production system
beyond a certain level. The AOQL and the corresponding sampling plan
would thus insulate the production system from any damage.
10.If most of the parts are procured from outside, the vendors need to have a
‘zero defect’ programme in their firms. Companies that have ‘zero defect’
programmes need to convince their vendors to have ‘zero defects’
programme in their firms.
11.The interfaces of Quality Control and Purchasing are at:
a. the quality of the materials being purchased.
b. the selection of the vendor/s who could consistently supply
materials / components of the required quality.
c. The training, motivation and development of vendors to have
policies and programmes in line with the purchasing firm’s
objectives and policies.
12.‘Producer’s risk’ and ‘Consumer’s risk’ together can fix or determine an
OC curve (i.e. a sampling plan). A particular value of AOQL may
correspond to various pairs of these two risks. If the producer’s risk and
AOQL are specified, the sampling plan also gets specified.
13. Sequential and double sampling may not always produce cost
economies. Sometimes the first sampling in ‘double sampling’ and the

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initial few samples in ‘sequential sampling’ may not produce a conclusive
result (viz. accept the lot or reject the lot). When that happens, the
cumulative sample size may be larger than that in single sampling.
14.Reliability is the probability of survival and, hence, depends upon the
defectives/defects rate. Quality Control function controls the defectives
rate and, thus, controls the ‘reliability.’
15.Manufacturing personnel can check the quality of their own produce; they
can check the process variables and thus control quality. Such self-
feedback is quick and timely.
While manufacturing takes over some of these functions, Quality
Management may carry out other functions such as quality planning,
quality audit, etc. These functions can help Quality Management to retain
essential control over quality.
16.Answers are evident from the text.
17. Larger sample size would increase the discrimination of the acceptance
sampling procedure, i.e. the OC curve would get tighter.
For the same sample size if the acceptance number is decreased, the
discrimination of the sampling procedure would increase, i.e. the OC curve
would get tighter.
Doubling the sample size and the acceptance number simultaneously will
make the OC curve tighter.
Decreasing the sample size or increasing the acceptance number will
have the reverse effect.
18. n=300 & C=6 corresponds to curve a
n=100 & C=2 corresponds to curve b
n=25 & C=4 corresponds to curve c
Note: (ii) > (i) > (iii) with respect to discrimination and a > b > c.
Therefore:
(ii) corresponds to a,
(i) corresponds to b, and
(iii) corresponds to c.
19.Dodge-Romig tables make it simpler to compute the sampling plan
corresponding to the requirements of quality.

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20.In the case of a continuous production process, the issue is not one of
acceptance sampling but of process control. Accordingly, the process has
to be periodically monitored and, if necessary, corrected.
21.To summarize the problem: There is a sampling plan (n=70, c=2) which
has so far been quite adequate. However, now the process average (of
defectives) has changed. In order to continue having the same AOQL
protection (2 per cent), should the sampling plan be modified?
If we have understood the concept of AOQL by means of the earlier
examples, it should be clear to us that the AOQ vs fraction defectives (p)
graph (i.e. relationship) is independent of the average level of defectives.
The AOQ vs ‘p’ behaviour is dependent only on the sampling plan. So, as
long as the sampling plan remains the same, the ordnance factory should
not be worried about any changes in the average level of defectives
arriving in the shipments from the supplier, so long as all the rejected
consignments are 100 per cent inspected by the supplier (or at his cost)
and returned (or replaced) by consignments containing all good items.
However, since the process average has changed, the inspection load
also would have changed for the supplier. We can use the Dodge-Romig
Single Sampling table, to reduce the average total inspection load. The
table given in Appendix III is for AOQL = 2.0 per cent and therefore is
suitable. The plan of (n=70, c=2) is not quite the best plan (from the total
inspection load point of view only) for the earlier process average of 0.5
per cent (1 in 200) defectives. Looking under the column for process
averages range 0.41-0.80, we get (n=95, c=3) for a lot size of 15,000
(range 10,000 – 20,000 as in the table). n=95, c=3 would therefore have
provided the minimum inspection load.
Now the process average falls in the range, 0.81 – 1.20 (1 in 100).
Correspondingly the most appropriate sampling plan is: (n=190, c=6). The
ordnance factory may consider adopting it.
Incidentally, it may be interesting to note in the table (Appendix III) that as
we move to higher levels of process average defectives, the LTPD keeps
dropping. For the same level of AOQL protection, why is it necessary for
the LTPD to decline? How does it help in minimizing the inspection load?
22. As per the problem, HMML’s only requirement is that AOQL = 2.0 per
cent, whereas Lele needs reduced inspection load and better
discrimination (i.e. higher probability of acceptance for his really good
lots).
Dodge-Romig Single Sampling table for AOQL of 2.0 per cent (Appendix
III) contains various sampling plans which all have AOQL of 2.0 per cent.

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HMML can pick any one of them that corresponds to the lot size range
10,000 – 20,000. In fact, the present sampling plan of (n=100, c=2)
appears to be far too stringent. If we refer to the other Dodge-Romig table
(of given LTPD) i.e. Appendix II, we find that for a large lot size such as
5,000 to 10,000, the plan of (n=105, c=2) gives an AOQL of 1.3 per cent.
The plan of (100, 2) would be close to that value of AOQL. We can
compute the AOQL, but it is not necessary to make complicated
calculations, particularly when we have a workable approximation.
The present plan can be replaced by the one that gives an AOQL of 2.0
per cent. For a lot size of 10,000 to 20,000, Appendix III offers several
sampling plans:
(70, 2); (95, 3); (190, 6); (290, 9); and (460, 14)
Any of the above can be chosen; but the best of them would be the one
that corresponds to the process average defectives in Lele’s shipments (in
the long-run). However, the latter figure has not been furnished. Hence,
we shall at least try to choose the plan option which has about the same
LTPD value (i.e. for β of 0.10) for the old (100, 2) plan. The old plan has
an LTPD of roughly five per cent. Referring to Dodge-Romig AOQL table
viz. Appendix III, this is obtained in the option: (n=290, c=9).
We can accept this solution. However it is possible that the inspection load
of HMML will go up. Also, one may try a sample size of a little higher than
100 and keep the cut-off value at 3 (i.e., only other integer value possible
after 2). The reader may find that it is difficult to keep the cut-off value at
an integer, have an AOQL of around 2.0 per cent and LTPD of around 5
per cent (i.e. for β of 0.10). Thus, we will settle at (n=290, c=9).

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CHAPTER 10: QUALITY MANAGEMENT – II
Objective Questions
1. In controlling quality, sampling is resorted to because:
a. it saves cost
√b. it saves time and cost
c. it offers more insight
d. a, b & c
2. Acceptance Sampling procedure follows:
a. Binomial distribution
b. Normal distribution
c. Poisson distribution
√d. none of the above
3. Sequential sampling, carried to the fullest extent, can sometimes lead to
no result.
a. True √b. False
4. A larger acceptance number tends to make the OC curve tighter.
a. True √b. False
5. The discrimination under the sampling plan of (50, 1) is greater than under
the sampling plan of (100, 2)
a. True √b. False
6. Dodge-Romig tables are for:
a. acceptance sampling on variables
√b. acceptance sampling on attributes
c. a & b
d. statistical process control
7. A high cost of quality indicates:
a. bad process
b. competitive market
√c. a & b
d. none of the above
8. Acceptance sampling occurs because we accept defectives production
and then search for them through sampling.
√a. True b. False
9. The inspection load of sequential sampling is always lower than that for
single sampling for the same quality characteristics.
a. True √b. False

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10.A flat organization structure is conducive to better quality
√a. True b. False
11.AQL is:
a. the best quality that the supplier sends in
b. the worst quality that a firm accepts
√c. a notional point for good quality
d. none of the above
12.An OC curve is unique for a sampling plan.
√a. Yes
b. No
c. A maximum of two sampling plans are possible.
13.(AQL, producer’s risk) and (LTPD, Consumer’s risk) would be sufficient to
define a sampling plan.
√a. True b. False
14.AOQL is:
a. average of AQLs (acceptable quality levels)
b. the best quality output that a firm can send out
c. a & b
√d. none of the above
15.LTPD is:
a. the worst quality that a firm accepts
b. the worst quality that can occur in a lot
c. per cent of the time the lot with given defectives is accepted
(tolerated)
√d. none of the above.
16.AOQL depends upon:
a. the process and its variation
√b. the sampling plan
c. a & b
d. none of the above
17.Average outgoing quality limit is:
√a. limit of bad quality input
b. upper limit of good quality output
c. a & b
d. average of the quality of output

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18.For obtaining the same acceptance quality performance, one may have
different LTPDs with corresponding different consumer risks.
√a. True b. False