Block 6 - lecture 2

Question 1

why will the need for acceptance sampling decrease?

Answer 1

TQM (total quality management) being used more - working with suppliers more so need less data for acceptance

Question 2

AQL?

Answer 2

acceptable quality level

Question 3

acceptable quality level?

Answer 3

quality desired by the customer

Question 4

where is the AQL specified?

Answer 4

contract

Question 5

ways that AQL can be specified?

Answer 5

• defective units per 10,000

- as a fraction eg. 0.0001

Question 6

producer’s risk (alpha)?

Answer 6

risk that the sampling plan will fail to accept good parts (Type I)

Question 7

typical producer’s risk?

Answer 7

5 percent

Question 8

why are customers interested in low risk like the producer?

Answer 8

returning materials:

• disrupts
• wastes time
• impacts relations

Question 9

LTPD?

Answer 9

Lot tolerance proportion defective

Question 10

Lot tolerance proportion defective?

Answer 10

worse level of quality tolerable

Question 11

customer risk (beta)?

Answer 11

risk of accepting bad parts (Type II)

Question 12

typical customer’s risk?

Answer 12

10 percent

Question 13

ANI?

Answer 13

Average Number of items Inspected

Question 14

OC curve?

Answer 14

Operating Characteristics curve

Question 15

Operating Characteristics curve?

Answer 15

a graph for performance of a sampling plan

probability of accepting the lot over the proportion defective

Question 16

what should n and c (sample size and acceptance number) be based on?

Answer 16

AQL (acceptable quality level)
alpha (producer’s risk)
LTPD (lot tolerance proportion defective)
beta (customer risk)

Question 17

what is the type of distribution for proportion defective?

Answer 17

binomial distribution

Question 18

difference between binomial and poisson distributions?

Answer 18

Both for attribute data.
binomial distributions = discrete events (eg. proportion of separate parts)
Poisson distributions = continuous events (eg. count of parts)

Question 19

when can a poisson distribution be used to approximate proportion defective (binomial)?

Answer 19

n>20

p<0.05

Question 20

when does the producer’s risk = 1 - probability of acceptance?

Answer 20

at AQL

Question 21

when does the customer’s risk = the probability of acceptance?

Answer 21

at LTPD

Question 22

steps to make an OC curve?

Answer 22

• find p for AQL and LTPD (probability for 1 part)
• find np (probability for sample)
• use ‘cumulative poisson probabilities’ chart in bklet (probability under c)
• repeat for many p values
• plot on graph

Question 23

how does the sample size affect the shape of the OC curve?

Answer 23

larger n = lower proportion defective, thus the graph is squashed steeper

Question 24

how does the sample size affect the producer’s and customer’s risk? why?

Answer 24

• np increases
• probability of acceptance drops for given proportion defective
• producer risk increases
• customer risk decreases

Question 25

how to decrease producer’s and customer’s risk with sampling?

Answer 25

• increase sample size (reduces customer’s risk)

- increase acceptance number (reduces producer’s risk)

Question 26

AOQ?

Answer 26

Average Outgoing Quality

Question 27

Average Outgoing Quality?

Answer 27

expected proportion of defects that the plan will allow to pass

Question 28

Rectified inspection?

Answer 28

• rejected lots will be replaced

- defective accepted units will be replaced

Question 29

AOQL?

Answer 29

Average Outgoing Quality Limit

Question 30

Average Outgoing Quality Limit?

Answer 30

The value of proportion defective (there can’t be less defects than in the sample)