Presentation is loading. Please wait.

Presentation is loading. Please wait.

ISQA 572/ 449 Models for Quality Control/ Process Control and Improvement Dr. David Raffo Tel: 725-8508, Fax: 725-5850 Email:

Similar presentations

Presentation on theme: "ISQA 572/ 449 Models for Quality Control/ Process Control and Improvement Dr. David Raffo Tel: 725-8508, Fax: 725-5850 Email:"— Presentation transcript:

1 ISQA 572/ 449 Models for Quality Control/ Process Control and Improvement
Dr. David Raffo Tel: , Fax:

2 Agenda Announcements Questions HW1 Control Charts (Variables) Cont.
Quality Costs SPC Vs SQC (Inspections and Acceptance Sampling) Control Charts (Attributes) Process Capability

3 Quality Costs

4 Quality Costs

5 Quality Cost: Traditional View TM2-5

6 Competitive Benefits of TQM Exhibit 2-8

7 SPC Vs SQC (Inspections and Acceptance Sampling)

8 Approaches to Quality TM 4-1

9 Quality Control Modes TM 4-2

10 Statistical Process Control: Prevention TM 4-3

11 Disadvantages of Inspection
Wasteful Sampling and inspection add cost and decrease value Inaccurate Even 100% inspection is only 80% effective because of the possibility of human errors Impractical(Costly) Inspection may involve destructive testing

12 Disadvantages of Inspection
Wrong message Inspection communicates to people and suppliers that bad parts will still be tolerated. Risks In sampling and inspection there is a risk of accepting bad lots and rejecting good lots No continuous improvement Sampling is still inspection, not prevention, so that quality is not typically continuously improved.

13 Advantages of a Stable Process
Management and workers know the process capability and can predict performance, costs and quality levels. Productivity will be at a maximum and costs will be minimized. Management will be able to measure the effects of changes in the system with greater speed and reliability.

14 Advantages of a Stable Process
If management wants to alter specification limits, it will have the data to back up its decision. (A stable process does not necessarily meet specs nor exhibit minimal variation - it’s just predictable)

15 Acceptance Sampling Acceptance sampling has three basic decisions: accept, reject, or resample. Reason for using acceptance sampling: Cost of passing defects is low Destructive testing is required Cost of inspection high relative to cost of loss Assumes stable process Large number of items must be processed in a short time

16 Acceptance Sampling Terms
Producer’s Risk (): Risk of rejecting a lot with acceptable quality level. (type I error) Consumer’s Risk (): Risk of accepting a lot with unacceptable quality level. (type II error) Acceptable Quality Level (AQL): The maximum percentage defective that can be considered satisfactory. Lot Tolerance Percent Defective (LTPD): The percent defective where the consumer desires the probability of acceptance to be at a low level.

17 Acceptance Sampling - Attributes
Types of plans Single N, n, c (1000, 50, 1) Double N, n1, n2, c1, c2 , c3 (3000, 50, 80, 1, 3, 5) Sequential n, ca, cr (50, 0, 4); (50, 1, 5)

18 Acceptance Sampling - Attributes
Measures Average Outgoing Quality (AOQ) Average Total Inspection Average Sample Number Standard Sampling Plans MIL-STD-105E Dodge-Romig Chain Sampling Skip-Lot Deming kp

19 Acceptance Sampling - Variables
Advantages Smaller sample than equivalent attribute plan Provides more information Provides insight into quality improvements Disadvantages Separate plan for each variable Inspection costs are higher Distribution estimate required

20 Acceptance Sampling - Variables
Process Parameter Average quality of the product/process or variability of the quality is known Single Specification n & Xa Double Specification n, XLa, XUa Lot Proportion Nonconforming Form 1 (k-method) Form 2 (M-method)

21 Control Charts (Attributes)

22 Advantages & Disadvantages of Attribute Charts
Some quality characteristics can only be viewed as a attribute. Quality characteristic may be measurable as a variable but an attribute is used for time, cost or convenience. Combination of variables can be measured as an attribute rather than use a multivariate chart.

23 Advantages & Disadvantages of Attribute Charts
Attributes don’t measure the degree to which specifications are met or not met. Doesn’t provide much information on cause. Variable chart can indicate potential changes which allow preventive actions. Larger sample size required.

24 Types of Attribute Charts
p-Chart - Fraction Nonconforming Can have constant or variable sample size. Good tool for relating information about average quality level. np-Chart - Number of Nonconforming Number of nonconforming items may be easier for user to understand.

25 Types of Attribute Charts
c-Chart - Number of Nonconformities Used when desire is to control the number of defects where one defect may not cause the entire product to be defective. Often used where area of opportunity is continuous and a constant size

26 Types of Attribute Charts
u-Chart - Number of Nonconformities/unit Area of opportunity is of variable size. U-Chart - Number of Demerits/unit Allows the use of variable weights for different classes of defects.

27 p Chart TM 4-12

28 p Chart TM 4-13

29 p Chart TM 4-14

30 p-Chart Exhibit 4-26

31 Hotel Suite Inspection - Defects Discovered Exhibit 4-27

32 C-Chart Calculations Centerline c-bar = (S c)/m #sub-groups
UCLc = c-bar + 3*sqrt(c-bar) LCLc = c-bar - 3*sqrt(c-bar)

33 c Chart for Hotel Suite Inspection Exhibit 4-28

34 Process Capability

35 Process Capability Analysis
Creates uniformity of output Level of quality is maintained or improved Facilitates product and process design Assists in supplier selection and control Reduces total costs

36 Process Capability : Normal Curve TM 4-15

37 Process Capability TM 4-17

38 Capability Indexes Cp Ability to meet two-sided specification limits
Cp = (USL-LSL)/(6 ) Assumes Stable process Normal distribution Variables data Centered process Goal Cp>1.0

39 Capability Indexes Capability Ratio CR = (6)/(USL-LSL)
Poor if CR>1

40 Capability Indexes CPU & CPL
Ability to meet one-sided specification limit CPU = (USL-X)/(3) CPL = (X-LSL)/(3) Assumes Stable process Normal distribution Variables data

41 Process Capability Chart Exhibit 4-20

42 Capability Indexes Cpk
Ability to meet two sided specification but the process does not have to be centered Cpk = Cp - [|m-X|/(3)] where m=nominal centerline

43 Process Capability Index TM 4-18

44 Process Capability: Varieties TM 4-19


Download ppt "ISQA 572/ 449 Models for Quality Control/ Process Control and Improvement Dr. David Raffo Tel: 725-8508, Fax: 725-5850 Email:"

Similar presentations

Ads by Google