Block 6 - lecture 2 Flashcards

1
Q

why will the need for acceptance sampling decrease?

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

AQL?

A

acceptable quality level

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

acceptable quality level?

A

quality desired by the customer

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

where is the AQL specified?

A

contract

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

ways that AQL can be specified?

A
  • defective units per 10,000

- as a fraction eg. 0.0001

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

producer’s risk (alpha)?

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

typical producer’s risk?

A

5 percent

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

why are customers interested in low risk like the producer?

A

returning materials:

  • disrupts
  • wastes time
  • impacts relations
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

LTPD?

A

Lot tolerance proportion defective

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Lot tolerance proportion defective?

A

worse level of quality tolerable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

customer risk (beta)?

A

risk of accepting bad parts (Type II)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

typical customer’s risk?

A

10 percent

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

ANI?

A

Average Number of items Inspected

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

OC curve?

A

Operating Characteristics curve

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Operating Characteristics curve?

A

a graph for performance of a sampling plan

probability of accepting the lot over the proportion defective

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

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

A

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

17
Q

what is the type of distribution for proportion defective?

A

binomial distribution

18
Q

difference between binomial and poisson distributions?

A

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

19
Q

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

A

n>20

p<0.05

20
Q

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

21
Q

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

22
Q

steps to make an OC curve?

A
  • 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
23
Q

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

A

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

24
Q

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

A
  • np increases
  • probability of acceptance drops for given proportion defective
  • producer risk increases
  • customer risk decreases
25
how to decrease producer's and customer's risk with sampling?
- increase sample size (reduces customer's risk) | - increase acceptance number (reduces producer's risk)
26
AOQ?
Average Outgoing Quality
27
Average Outgoing Quality?
expected proportion of defects that the plan will allow to pass
28
Rectified inspection?
- rejected lots will be replaced | - defective accepted units will be replaced
29
AOQL?
Average Outgoing Quality Limit
30
Average Outgoing Quality Limit?
The value of proportion defective (there can't be less defects than in the sample)