TM 720 Lecture 11 Acceptance Sampling Plans 9162020

TM 720 - Lecture 11 Acceptance Sampling Plans 9/16/2020 TM 720: Statistical Process Control 1

Assignment: l Reading: • • l Finish Chapter 14 • • Sections 14. 1 – 14. 2 Sections 14. 4 Start Chapter 12 Assignment: • • Download and complete Assign 08: Acceptance Sampling • • Requires MS Word for Nomograph Requires MS Excel for AOQ Solutions for 8 will post on Thursday 9/16/2020 TM 720: Statistical Process Control 2

Acceptance Sampling 9/16/2020 TM 720: Statistical Process Control 3

Three Important Aspects of Acceptance Sampling 1. Purpose is to sentence lots, not to estimate lot quality 2. Acceptance sampling does not provide any direct form of quality control. It simply rejects or accepts lots. Process controls are used to control and systematically improve quality, but acceptance sampling is not. 3. Most effective use of acceptance sampling is not to “inspect quality into the product, ” but rather as audit tool to insure that output of process conforms to requirements. 9/16/2020 TM 720: Statistical Process Control 4

Three Approaches to Lot Sentencing 1. Accept with no inspection 2. 100% inspection – inspect every item in the lot, remove all defectives Defectives – returned to vendor, reworked, replaced or discarded 3. Acceptance sampling – sample is taken from lot, a quality characteristic is inspected; then on the basis of information in sample, a decision is made regarding lot disposition. 9/16/2020 TM 720: Statistical Process Control 5

Acceptance Sampling Used When: l l l Testing is destructive 100% inspection is not technologically feasible 100% inspection error rate results in higher percentage of defectives being passed than is inherent to product Cost of 100% inspection extremely high Vender has excellent quality history so reduction from 100% is desired but not high enough to eliminate inspection altogether Potential for serious product liability risks; program for continuously monitoring product required 9/16/2020 TM 720: Statistical Process Control 6

Advantages of Acceptance Sampling over 100% Inspection l l l Less expensive because there is less sampling Less handling of product hence reduced damage Applicable to destructive testing Fewer personnel are involved in inspection activities Greatly reduces amount of inspection error Rejection of entire lots as opposed to return of defectives provides stronger motivation to vendor for quality improvements 9/16/2020 TM 720: Statistical Process Control 7

Disadvantages of Acceptance Sampling (vs 100% Inspection) l Always a risk of accepting “bad” lots and rejecting “good” lots • • Producer’s Risk: chance of rejecting a “good” lot – Consumer’s Risk: chance of accepting a “bad” lot – l Less information is generated about the product or the process that manufactured the product l Requires planning and documentation of the procedure – 100% inspection does not 9/16/2020 TM 720: Statistical Process Control 8

Lot Formation l Lots should be homogeneous • Units in a lot should be produced by the same: • If lots are not homogeneous – acceptance-sampling scheme may not function effectively and make it difficult to eliminate the source of defective products. • • machines, operators, from common raw materials, approximately same time l Larger lots preferred to smaller ones – more economically efficient l Lots should conform to the materials-handling systems in both the vendor and consumer facilities • Lots should be packaged to minimized shipping risks and make selection of sample units easy 9/16/2020 TM 720: Statistical Process Control 9

Random Sampling l l l IMPORTANT: • • Units selected for inspection from lot must be chosen at random Should be representative of all units in a lot Watch for Salting: • Vendor may put “good” units on top layer of lot knowing a lax inspector might only sample from the top layer Suggested technique: 1. 2. 3. 4. Assign a number to each unit, or use location of unit in lot Generate/pick a random number for each unit/location in lot Sort on the random number – reordering the lot/location pairs Select first (or last) n items to make sample 9/16/2020 TM 720: Statistical Process Control 10

Single Sampling Plans for Attributes l Quality characteristic is an attribute, i. e. , conforming or nonconforming • • • l N - Lot size n - sample size c - acceptance number Ex. Consider N = 10, 000 with sampling plan n = 89 and c = 2 • • From lot of size N = 10, 000 Draw sample of size n = 89 If # of defectives c = 2 • Accept lot If # of defectives > c = 2 • Reject lot 9/16/2020 TM 720: Statistical Process Control 11

How to Compute the OC Curve Probabilities l Assume that the lot size N is large (infinite) l d - # defectives ~ Binomial() where • • l p - fraction defective items in lot n - sample size Probability of acceptance: 9/16/2020 TM 720: Statistical Process Control 12

Example l Lot fraction defective is p = 0. 01, n = 89 and c = 2. Find probability of accepting lot. 9/16/2020 TM 720: Statistical Process Control 13

OC Curve l l Performance measure of acceptance-sampling plan • displays discriminatory power of sampling plan Plot of: Pa vs. p • • Pa = P[Accepting Lot] p = lot fraction defective p = fraction defective in lot Pa = P[Accepting Lot] 0. 005 0. 9897 0. 010 0. 9397 0. 015 0. 8502 0. 020 0. 7366 0. 025 0. 6153 0. 030 0. 4985 0. 035 0. 3936 9/16/2020 TM 720: Statistical Process Control 14

OC Curve l OC curve displays the probability that a lot submitted with a certain fraction defective will be either accepted or rejected given the current sampling plan 9/16/2020 TM 720: Statistical Process Control 15

Ideal OC Curve l l Suppose the lot quality is considered bad if p = 0. 01 or more A sampling plan that discriminated perfectly between good and bad lots would have an OC curve like: 9/16/2020 TM 720: Statistical Process Control 16

Ideal OC Curve l In theory it is obtainable by 100% inspection IF inspection were error free. l Obviously, ideal OC curve is unobtainable in practice l But, ideal OC curve can be approached by increasing sample size, n. 9/16/2020 TM 720: Statistical Process Control 17

Effect of n on OC Curve l The precision with which a sampling plan differentiates between good and bad lots increases as the sample size increases 9/16/2020 TM 720: Statistical Process Control 18

Effect of c on OC Curve l Changing acceptance number, c, does not dramatically change slope of OC curve. l Plans with smaller values of c provide discrimination at lower levels of lot fraction defective 9/16/2020 TM 720: Statistical Process Control 19

Producer and Consumer Risks in Acceptance Sampling l Because we take only a sub-sample from a lot, there is a risk that: • a good lot will be rejected (Producer’s Risk – a ) and • a bad lot will be accepted (Consumer’s Risk – b ) 9/16/2020 TM 720: Statistical Process Control 20

Producer’s Risk - a l Producer wants as many lots accepted by consumer as possible so • Producer “makes sure” the process produces a level of fraction defective equal to or less than: p 1 = AQL = Acceptable Quality Level a is the probability that a good lot will be rejected by the consumer even though the lot really has a fraction defective p 1 l That is, 9/16/2020 TM 720: Statistical Process Control 21

Consumer’s Risk - b l Consumer wants to make sure that no bad lots are accepted • Consumer says, “I will not accept a lot if percent defective is greater than or equal to p 2” p 2 = LPTD = Lot Tolerance Percent Defective b probability bad lot is accepted by the consumer when lot really has a fraction defective p 2 l That is, 9/16/2020 TM 720: Statistical Process Control 22

Designing a Single-Sampling Plan with a Specified OC Curve l Use a chart called a Binomial Nomograph to design plan l Specify: • p 1 = AQL (Acceptable Quality Level) • p 2 = LTPD (Lot Tolerance Percent Defective) • 1 – = P[Lot is accepted | p = AQL] • β = P[Lot is accepted | p = LTPD] 9/16/2020 TM 720: Statistical Process Control 23

Use a Binomial Nomograph to Find Sampling Plan (Figure 14 -9, p. 658) l l Draw two lines on nomograph • • • Line 1 connects p 1 = AQL to (1 - ) Line 2 connects p 2 = LTPD to Pick n and c from intersection of lines Example: Suppose • • p 1 = 0. 01, α = 0. 05, p 2 = 0. 06, β = 0. 10. Find the acceptance sampling plan. 9/16/2020 TM 720: Statistical Process Control 24

Rectifying Inspection Programs l Acceptance sampling programs usually require corrective action when lots are rejected, that is, • l Screening rejected lots • Screening means doing 100% inspection on lot In screening, defective items are • • Removed or Reworked or Returned to vendor or Replaced with known good items 9/16/2020 TM 720: Statistical Process Control 25

Rectifying Inspection Programs 9/16/2020 TM 720: Statistical Process Control 26

Where to Use Rectifying Inspection l Used when manufacturer wishes to know average level of quality that is likely to result at given stage of manufacturing l Example stages: l • • • Receiving inspection In-process inspection of semi-finished goods Final inspection of finished goods Objective: give assurance regarding average quality of material used in next stage of manufacturing operations 9/16/2020 TM 720: Statistical Process Control 27

Average Outgoing Quality: AOQ l Quality that results from application of rectifying inspection • l l Average value obtained over long sequence of lots from process with fraction defective p N - Lot size, n = # units in sample Assumes all known defective units replaced with good ones, that is, • • If lot rejected, replace all bad units in lot If lot accepted, just replace the bad units in sample 9/16/2020 TM 720: Statistical Process Control 28

Development of AOQ l If lot accepted: Number defective units in lot: l Expected number of defective units: l Average fraction defective, Average Outgoing Quality, AOQ: 9/16/2020 TM 720: Statistical Process Control 29

Example for AOQ l Suppose N = 10, 000, n = 89, c = 2, and incoming lot quality is p = 0. 01. Find the average outgoing lot quality. 9/16/2020 TM 720: Statistical Process Control 30

Military Standard 105 E (MIL STD 105 E) (ANSI/ASQC Z 1. 4, ISO 2859) l Most widely used acceptance sampling system for attributes l MIL STD 105 E is Acceptance Sampling System l • collection of sampling schemes Can be used with single, double or multiple sampling plans • We will consider single sampling plans for this course 9/16/2020 TM 720: Statistical Process Control 31

Inspection Types l l Normal Inspection • Tightened Inspection • • l Used at start of inspection activity Instituted when vendor’s recent quality history has deteriorated Acceptance requirements for lots are more stringent Reduced Inspection • • Instituted when vendor’s recent quality history has been exceptionally good Sample size is usually smaller than under normal inspection 9/16/2020 TM 720: Statistical Process Control 32

Switching Rules 9/16/2020 TM 720: Statistical Process Control 33

Procedure for MIL STD 105 E l STEP 1: Choose AQL • MIL STD 105 E designed around Acceptable Quality Level, AQL • Recall that the Acceptable Quality Level, AQL, is producer's largest acceptable fraction defective in process • Typical AQL range: • 0. 01% AQL 10% • Specified by contract or authority responsible for sampling 9/16/2020 TM 720: Statistical Process Control 34

Procedure for MIL STD 105 E l STEP 2: Choose inspection level • • Level II • Designated as normal Level I • • Requires about one-half the amount of inspection as Level II Use when less discrimination needed Level III • • Requires about twice as much Use when more discrimination needed Four special inspection levels used if very small samples necessary • S-1, S-2, S-3, S-4 9/16/2020 TM 720: Statistical Process Control 35

Procedure for MIL STD 105 E l l l STEP 3–Determine lot size, N • Lot size most likely dictated by vendor STEP 4: Find sample size code letter • • From Table 14 -4, p 675 Given lot size, N, and Inspection Level, use table to determine sample size code letters STEP 5: Determine appropriate type sampling plan • Decide if Single, Double or Multiple sampling plan is to be used 9/16/2020 TM 720: Statistical Process Control 36

Procedure for MIL STD 105 E l STEP 6: Find Sample Size, n, and Acceptance Level, c • Given sample size letter code, use Master Tables: 14 -5, 14 -6, and 14 -7 on pp. 676 -678 • Find n and c for all three inspection types: • Normal Inspection • Tightened Inspection • Reduced Inspection 9/16/2020 TM 720: Statistical Process Control 37

Example l Suppose product comes from vendor in lots of size 2000 units. The acceptable quality level is 0. 65%. Determine the MIL STD 105 E acceptance-sampling system. 9/16/2020 TM 720: Statistical Process Control 38

Questions & Issues 9/16/2020 TM 720: Statistical Process Control 39