adeyl khan, faculty, bba, nsu quality control adeyl khan, faculty, bba, nsu phases of quality...

Adeyl Khan, Faculty, BBA, NSU

Chapter 10Quality Control


Phases of Quality Assurance

10-2Figure 10.1

Acceptancesampling

Processcontrol

Continuousimprovement

Inspection of lotsbefore/afterproduction

Inspection andcorrective

action duringproduction

Quality builtinto theprocess

The leastprogressive

The mostprogressive


Inspection

How Much/How OftenWhere/When Centralized vs. On-site

10-3

Inputs Transformation Outputs

Acceptancesampling

Processcontrol

Acceptancesampling

Figure 10.2


Inspection Costs

10-4

Co

st

OptimalAmount of Inspection

Cost of inspection

Cost of passingdefectives

Total Cost

Figure 10.3


Where to Inspect in the Process

Raw materials and purchased partsFinished productsBefore a costly operationBefore an irreversible processBefore a covering process

10-5


Examples of Inspection PointsType ofbusiness

Inspectionpoints

Characteristics

Fast Food CashierCounter areaEating areaBuildingKitchen

AccuracyAppearance, productivityCleanlinessAppearanceHealth regulations

Hotel/motel

Parking lotAccountingBuildingMain desk

Safe, well lightedAccuracy, timelinessAppearance, safetyWaiting times

Supermarket

CashiersDeliveries

Accuracy, courtesyQuality, quantity

10-6Table 10.1


Statistical Control

Statistical Process Control: Statistical evaluation of the output of a process during production The essence of statistical process control is to assure that the output of a process is random so that future output will be random.

Quality of Conformance :A product or service conforms to specifications

10-7


Control Chart

10-8

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

UCL

LCL

Sample number

Mean

Out ofcontrol

Normal variationdue to chance

Abnormal variationdue to assignable sources

Abnormal variationdue to assignable sources

Figure 10.4


Control ChartControl Chart

Purpose: to monitor process output to see if it is random

A time ordered plot representative sample statistics obtained from an on going process (e.g. sample means)

Upper and lower control limits define the range of acceptable variation

10-9

• Define• Measure• Compare• Evaluate• Correct• Monitor results

SPC (The Process)

• Random variation• Natural variations in the output of a process, created by countless minor factors

• Assignable variation• A variation whose source can be identified

Variations and Control


Normal Distribution

10-10

Mean

95.44%

99.74%

Standard deviation

Figure 10.6


Control Limits: Sampling Distribution & Process Distribution

10-11

Samplingdistribution

Processdistribution

Mean

Lowercontrol

limit

Uppercontrol

limit

Figure 10.7


SPC Errors

Type I error Concluding a process is not in control when it actually is.

Type II error Concluding a process is in control when it is not.

10-12

In control Out of control

In control No Error Type I error(producers risk)

Out of control Type II Error(consumers risk)

No error


Type I Error

10-13

Mean

LCL UCL

/2 /2

Probabilityof Type I error

Figure 10.8


Observations from Sample Distribution

10-14

Sample number

UCL

LCL

1 2 3 4

Figure 10.9


Control Charts for Variables

10-15

Variables generate data that are measured.Image: http://www.asprova.jp/mrp/glossary/en/cat254/x-r-1.html

Mean control charts

• Used to monitor the central tendency of a process.

• X bar charts

Range control charts

• Used to monitor the process dispersion

• R charts


Mean and Range Charts

10-16Figure 10.10A

UCL

LCL

UCL

LCL

R-chart

x-Chart Detects shift

Does notdetect shift

(process mean is shifting upward)

SamplingDistribution


Mean and Range Charts

10-17

x-Chart

UCL

Does notreveal increase

UCL

LCL

LCL

R-chart Reveals increase

Figure 10.10B

(process variability is increasing)SamplingDistribution


p chartUsed to monitor the proportion of

defectives in a process When observations can be placed into two

categories. Good or bad Pass or fail Operate or don’t operate

When the data consists of multiple samples of several observations each

10-18

Control Chart for AttributesAttributes generate data that are counted


c Chart

Used to monitor the number of defects per unitAdvantages

Use only when the number of occurrences per unit of measure can be counted; non-occurrences cannot be counted. Scratches, chips, dents, or errors per item Cracks or faults per unit of distance Breaks or Tears per unit of area Bacteria or pollutants per unit of volume Calls, complaints, failures per unit of time

10-19


Use of Control Charts

At what point in the process to use control chartsWhat size samples to takeWhat type of control chart to use

Variables Attributes

10-20


Run Tests

Run test – a test for randomnessAny sort of pattern in the data would suggest a non-random processAll points are within the control limits - the process may not be random

10-21


Nonrandom Patterns in Control charts

TrendCyclesBiasMean shiftToo much dispersion

10-22


Counting Runs

10-23

Counting Above/Below Median Runs (7 runs)

Counting Up/Down Runs (8 runs)

U U D U D U D U U D

B A A B A B B B A A B


NonRandom Variation

Managers should have response plans to investigate causeMay be false alarm (Type I error)May be assignable variation

10-24


Process CapabilityTolerances or specifications

Range of acceptable values established by engineering design or customer requirements

Process variability Natural variability in a process

Process capability Process variability relative to specification

10-25


Process Capability

10-26

LowerSpecification

UpperSpecification

A. Process variability matches specifications

LowerSpecification

UpperSpecification

B. Process variability well within specifications

LowerSpecification

UpperSpecification

C. Process variability exceeds specifications

Figure 10.15


Process Capability Ratio

3

X-UTLor

3

LTLXmin=C pk

10-27

Process capability ratio, Cp = specification widthprocess width

Upper specification – lower specification6

Cp =

If the process is centered use Cp

If the process is not centered use Cpk


Limitations of Capability Indexes

Process may not be stableProcess output may not be normally distributedProcess not centered but Cp is used

10-28


Example 8

MachineStandard Deviation

Machine Capability Cp

A 0.13 0.78 0.80/0.78 = 1.03

B 0.08 0.48 0.80/0.48 = 1.67

C 0.16 0.96 0.80/0.96 = 0.83

10-29

Cp > 1.33 is desirableCp = 1.00 process is barely capableCp < 1.00 process is not capable


3 Sigma and 6 Sigma Quality

10-30

Processmean

Lowerspecification

Upperspecification

1350 ppm 1350 ppm

1.7 ppm 1.7 ppm

+/- 3 Sigma

+/- 6 Sigma


Improving Process Capability

SimplifyStandardizeMistake-proofUpgrade equipmentAutomate

10-31


Taguchi Loss Function

10-32

Cost

TargetLowerspec

Upperspec

Traditionalcost function

Taguchicost function

Figure 10.17


Learning Objectives

List and briefly explain the elements of the control process. Explain how control charts are used to monitor a process, and the concepts that underlie their use. Use and interpret control charts. Use run tests to check for nonrandomness in process output. Assess process capability.

10-33

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