IE 405 IE 405 QUALITY MANAGEMENTQUALITY MANAGEMENT

RECITAITON FIVE:RECITAITON FIVE:

REVIEW SESSIONREVIEW SESSION

FALL 2002FALL 2002

BASIC CONCEPTS OF QUALITY• Traditional definition: ‘FITNESS FOR USE’QUALITY DIMENSIONS:QUALITY DIMENSIONS: Performance, Reliability, Durability,

Serviceability, aesthetics, features, perceived quality and conformance to standards.

QUALITY CONTROL: QUALITY CONTROL: Operational techniques and the activities which sustain a quality of product or services that will satisfy given needs.

e.g. inspection at the end of production, in-receiving inspection

QUALITY ASSURANCE: QUALITY ASSURANCE: All plant and systematic activities to provide adequate confidence that a product or service will satisfy a quality dimension.

e.g. training the people, statistical process control, preventive maintenance programs for bottleneck machines

QUALITY IMPROVEMENT: QUALITY IMPROVEMENT: Reduction of variability in processes and products.

STATISTICAL METHODS FOR QUALITY STATISTICAL METHODS FOR QUALITY CONTROL AND IMPROVEMENTCONTROL AND IMPROVEMENT

THREE MAJOR AREAS:THREE MAJOR AREAS:

1. Statistical Process Control i.e Control charts for process monitoring

2. Design of Experiments i.e. Factorial Experiments

3. Acceptance Sampling

CUSTOMER TYPES

1. Internal customer

2. External customer

Possible questionsPossible questions:: Who is the customer? Who are my customers? What do they need? What are their measures and expectations?

Customer types continued…

• INTERNAL CUSTOMERINTERNAL CUSTOMER: Person within the company who receives the work of another and then adds his or her contribution to the product or service before passing it on to someone else.

EXAMPLES:EXAMPLES: In manufacturing, the internal customer is the next

person down the line who builds the product. In a restaurant, the chef has the waiters and

waitresses as internal customers and the chef must meet their requirements if they are all to please their guests.

Sand Muller Operator to Mold Machine Operator Shipping Supervisor to Billing Clerk

Customer types continued…

• EXTERNAL CUSTOMEREXTERNAL CUSTOMER:People outside who are the end user’s of a firm’s products and services.

EXAMPLES:EXAMPLES:• Manufacturer of the nozzle for a gasoline

pump has the oil company, the service station owner and you as the users as external customers.

• An auto insurance company has brokers, customer service representatives and the insured as the external customers.

QUALITY COSTSQUALITY COSTS

FourFour Types of Costs:Types of Costs:

1. Prevention2. Appraisal3. Internal Failure 4. External Failure costs

QUALITY COSTSQUALITY COSTS

1.1. Prevention Costs:Prevention Costs: All costs incurred in an effort to ‘make it right the first time’.

i.ei.e Training

22. . Appraisal Costs:Appraisal Costs: These are costs to determine conformance with quality standards.

i.ei.e. Inspection, Auditing

QUALITY COSTS continued..QUALITY COSTS continued..

3.3.Internal Failure CostsInternal Failure Costs:: Incurred when products, components, materials and services fail to meet quality requirements and this failure is discovered prior to the delivery of the product to the customer.

i.e.i.e. scrap, yield losses4.External Failure Costs:4.External Failure Costs: Occur when the product

does not perform satisfactorily after it is supplied to the customer.

i.e.i.e. complaint adjustment, returned product/material

QUALITY COSTS MATRIXQUALITY COSTS MATRIX

INTERNAL FAILURE COSTS

Manufacturing losses,rework,reinspection, scrap costs

APPRAISAL COSTS

Maintaining a quality assurancesystem

Inpsection and test of incomingmaterial

Product inspection and test

EXTERNAL FAILURE COSTS

Warranty claims, transportationdefects, repair, customer and product

services

PREVENTION COSTS

Maintaining a quality control system

New products reviewProcess control

Training

STATISTICAL PROCESS CONTROL TOOLSSTATISTICAL PROCESS CONTROL TOOLS

• HISTOGRAMS, CHECK SHEETS, SCATTER DIAGRAMS, DEFECT CONCENTRATION DIAGRAMS

• PARETO CHART

• CAUSE AND EFFECT DIAGRAM

• CONTROL CHARTS

274 59 43 19 10 1864,8 13,9 10,2 4,5 2,4 4,3

64,8 78,7 88,9 93,4 95,7 100,0

0

100

200

300

400

0

20

40

60

80

100

Defect

CountPercentCum %

Per

cent

Cou

nt

Pareto Chart for Defects

Personnel

Machines

Materials

Methods

Measurements

Environment

Cause-and-Effect Diagram

Why do we use STATISTICAL PROCESS CONTROL TOOLS?

IN ANY PRODUCTION PROCESS, REGARDLESS OF HOW WELL DESIGNED OR CAREFULLY MAINTAINED IT IS, A CERTAIN AMOUNT OF INHERENT OR NATURAL VARIABILITYVARIABILITY WILL ALWAYS EXIST.

.

3 SIGMA2 SIGMA1 SIGMA1 SIGMA2 SIGMA3 SIGMA

Chance and Assignable causes of Quality Chance and Assignable causes of Quality VariationVariation

• A process that is operating with only chance causeschance causes of of variation variation present is said to be IN IN STATISTICAL STATISTICAL CONTROLCONTROL.

• Sources of variability that are notare not part of the chance cause,pattern as ‘assignable causes’assignable causes’. A process that is operating in the presence of assignable causes is said to be OUT OF OUT OF CONTROLCONTROL.

TYPES OF CONTROL CHARTSTYPES OF CONTROL CHARTS

• VARIABLE CONTROL CHARTSVARIABLE CONTROL CHARTS: Data are usually continuous measurements, such as length, voltage or viscosity

Exp. X chart, R chart, S chart

• ATTRIBUTE CONTROL CHARTS:ATTRIBUTE CONTROL CHARTS: Are for discrete data often taking the form of counts.

• Exp. P chart, np chart, c and u charts.

CHART CONSIDERATIONS INCLUDE SAMPLE CHART CONSIDERATIONS INCLUDE SAMPLE SIZE AND FREQUENCYSIZE AND FREQUENCY

Analysis of Patterns on Control ChartsAnalysis of Patterns on Control Charts A control chart may indicate and out-of-control condition

either when one or more points fall beyond the control limits or when the plotted points exhibit some nonrandom pattern of behavior.

Therefore, the problem is one of pattern recognition, that is recognizing systematic or nonrandom patterns on the control chart and identifying the reasons for this behavior.

THUS use SENSITIZING RULESSENSITIZING RULES..

LOOK forLOOK for CYCLING PATTERNS, TRENDS, SHIFTS IN CYCLING PATTERNS, TRENDS, SHIFTS IN PROCESS LEVEL AND STRATIFICATIONPROCESS LEVEL AND STRATIFICATION

SUMMARY OF SHEWART CONTROL CHARTSSUMMARY OF SHEWART CONTROL CHARTS

CATEGORY TYPE OF CHART PURPOSEX Controls process averageR Controls spread of process

VARIABLE S Controls spread of process (large subgroup size)

p Fraction nonconformingATTRIBUTE np Number nonconforming

u Number of nonconformities per unit (usually variable samplec Number of nonconfomities

ADVANTAGES AND DISADVANTAGES OF CONTROL CHARTS

• Attributes control charts have the advantage that several quality characteristics can be considered jointly an the unit classified as nonconforming if it fails to meet the specification on any one characteristic

• Variable control charts provide much more useful information about process performance than does an attributes control chart. Specific information about the process mean and variability is obtained directly.

The most important advantage of X and R chartss is that they often provide an indication of impending trouble and allow operating personnel to take corrective action BEFORE any defectives are actually produced. Thus, X and R charts are leading indicators of trouble.

P charts (or c and u charts) will not react unless the process has already changed so that MORE NONCONFORMING UNITS are produced.

Recitation 5 Q1

SERIAL N0. COUNT OF NON-CONFORMITIESMY102 7MY113 6MY121 6MY125 3MY132 20MY143 8MY150 6MY152 1MY164 0MY166 5MY172 14MY184 3MY185 1MY198 3MY208 2MY222 7MY235 5MY241 7MY258 2MY259 8MY264 0MY267 4MY278 14MY281 4MY288 5

Recitation 5 Q1

2520151050

20

10

0

Sample Number

Sam

ple

Cou

nt

nonconformities (c chart)Control chart for count of

1

1 1

C=5,64

UCL=12,76

LCL=0

• C: count of nonconformities• Cbar=x/n= 141/25=5.64• UCL=12,76• LCL=-1,4 approximately 0

• Remember in this chart low values that do not have an assignable cause represent exceptionally good quality.

• CANOE NO 132,172 AND 278 are out of control. Since canoes 132 and 278 have an assignable cause, they are dicarded, however canoe 172 maybe due to chance cause and not discarded in this case.

• Revised c =141-20-14/25-2=4.65

• UCL=11.1 LCL=1.82 APPROX. 0

Recitation 5 Q1

2520151050

20

10

0

UCL=11,1Revised

Revised

Sample Number

Sam

ple

Cou

ntnon-conformitiesRevised control cart for count of

1

1 1

c= 4,65Revised

LCL=1,82

Recitation 5 Q2Sample Sample size No. of defects per Sample Avg. No. of Defects per Unit LCL UCL

1 16 23 1,44 0,49 2,252 20 30 1,5 0,59 2,163 26 35 1,35 0,68 2,064 8 12 1,5 0,13 2,615 22 29 1,32 0,62 2,126 29 35 1,21 0,72 2,027 31 50 1,61 0,74 28 13 15 1,15 0,4 2,359 28 36 1,29 0,71 2,04

10 23 38 1,65 0,64 2,111 19 24 1,26 0,57 2,1812 23 32 1,39 0,64 2,113 14 24 1,71 0,43 2,3114 29 34 1,17 0,72 0,7215 27 38 1,41 0,7 2,0516 15 25 1,67 0,46 2,2817 22 26 1,18 0,62 2,1218 22 24 1,09 0,62 2,1219 14 22 1,57 0,43 2,3120 16 17 1,06 0,49 2,2521 22 33 1,5 0,62 2,1222 16 21 1,31 0,49 2,2523 14 18 1,29 0,43 2,3124 5 9 1,8 0 2,9425 13 18 1,38 0,4 2,3526 19 26 1,37 0,57 2,1827 10 12 1,2 0,26 2,48

Recitation 5 Q2

3020100

1,5

1,0

0,5

0,0

Sample

Def

ects

per

Uni

t

Moonroof InstallationU chart for the

U=1,37

UCL

LCL

N: sample sizeC: number of defects per sampleU: Avg. No of Defects per unitu bar= c/n 706/516=1.37

• U bar=Centerline

• UCL= 2.25 LCL=0,49

• Although it seems in statistical control the defect rate is high, almost 1.4 defects per unit.

• Therefore, the company has decided to investigate the causes of the large number of defects

Recitation 5 Q2

28 10 8 0,8 0,26 2,4829 14 14 1 0,43 2,3130 11 8 0,73 0,31 2,4331 29 14 0,48 0,72 2,0232 19 7 0,37 0,57 2,1833 19 12 0,63 0,57 2,1834 45 25 0,56 0,85 1,9

Sample Sample size No. of defects per Sample Avg. No. of Defects per Unit LCL UCL

Recitation 5 Q2

35302520151050

3

2

1

0

Sample

Uni

tD

efec

t pe

r

implementing the new sealu chart for the moonroof example after

U=1,372

UCL

LCL

• It is clear that the process has changed, in particular that the average number of defects per unit has been significantly reduced. The values are in the range of 0.4 and 0.7, previously being 1.4.