statistical process control seminar jireh semiconductorprocess capability process capability rule of...

SPC - Jireh Semiconductor - 2018ualityQJohn Breckline – Key Quality

In association withBW (Ben) Marguglio, LLC

845-265-0123

Statistical Process Control Seminarat

Jireh Semiconductor

Instructor: John Breckline

January 24, 2018


Topic Agenda

• SPC / Stats Review• Critical Characteristics

• Product or Process

• Measurement Capability• Process Capability

• SPC: Attribute / Variable• Establishing SPC in Operations

• SPC Interpretation• Renewed Engagement

8 hoursoverview – what/why, not how

2


John Breckline

Retired but…

Motorola: Auto Electronics, Commercial, Telecom (pagers)

Nokia: MBB / Continuous Improvement Manager

AT&T: MBB – Transactional / Lean Projects

MBB Credentials: Stat-a-Matrix (99) / Nokia (again)

ASQ Certifications: CQE (87), CBB (03), CSQP (17)

Certification Preparation Instructor since 1997

30+ years in Quality Disciplines

– Inspection, Sampling, Data Systems

– SPC, Measurement, Supplier Quality

– Quality Systems Management

– Six Sigma Master Black Belt

Fort Worth TX Mobile: 817-401-0412

email: [email protected]

3


Seminar Objectives

To reinforce the concepts and practices of SPC

To bring a fresh perspective of the utilization of SPC

To lay a foundation of statistical thinking as a basisfor effective SPC

To challenge those responsible for SPC to optimizethe use SPC to manage production operations

4


Statistical Thinking






5


Statistical Thinking Map

DesignFMEA

ProcessFMEA

ProcessControl

DefineCritical

Characteristics

ApprovalPlanning

MeasurementSystemAnalysis

ProcessCapabilityStudies

ProcessImprovement

ControlPlan

ProcessCapable

Yes

No

StatisticalProcessControl

6


Statistical Process Control (SPC)

Objectives & Benefits

– To monitor, control, and improve process performanceover time by studying variation and its source.

– Focuses on monitoring and detecting process variation

– Provides ‘on-line’ process feedback and control signals

– Distinguished between common and special causes ofvariation

– Helps improve process to perform consistently andpredictably

7


Review:

– Basic ‘rules’ for Out of Control performance:

– Point out of control – why?

– Shifts – why?

– Runs – why?

– Others – why?

30

40

50

60

70

80

90

X=60.2

UCL=87.7

LCL=32.7

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Batch

100

ABC

Application of Control Charts8


Data Use

Why Collect and Track Data

– Data Information Decision

– Understand – Evaluate – Control – Predict

Objective of Statistical Usage

– Identifying problem areas through trend analysis

– Assisting in identifying and alleviating risk

– Improving the current process

– Preventing problems

9


Data Graphics

Data Plotting vs Recording

– Plotting data into a chart is used for visibility andrecognition (easier to understand a visual aid than abunch of numbers

– Recording data is necessary to avoid risks:

• No record of adjustments

• Subtle changes are lost

• Measurement errors are lost

• Plotting errors missed

• Sampling errors missed

10


Control Charts

– Line graphs that display a dynamic picture of processbehavior

– Focuses attention on monitoring and detecting processvariation over time

– Used to analyze variation in processes

– Distinguishes special from common causes of variationas a guide to action

Control Charts11


Common Cause:

– ~85% of all process problems are due tocommon cause variation.

– Common cause variation is predictable

– Special Cause:

– ~15% of all process problems are due tospecial cause variation.

– Special cause variation is unpredictable

Theory of Variation

12


Theory of Variation

Common Cause• Always present• Standard practices• Built into the process• Predictable variation

Special Causes• Unpredictable occurrences• Significant variation• Assignable cause

Understand the Difference• Different tools to improve / control each

13


The Sources of Variation

Process/System

Customer Satisfaction

Environment

Measurement

Man

Material

Machine

Methods

14


Basic Statistics… a Refresher

Terms Description Symbol

Mean /Average

mathematical center of the sample/population

Median “geographic” center of the sample/population N/A

Range width of distribution (largest – smallest) R

StandardDeviation

“statistical” width of distributionmeasured by “sigma” increments s

Histogram

Normal Curve 4s 3s 2s 1s 0 1s 2s 3s 4s

99.73%

Normal Curve

15


If common causes of variationdominate, the output of a processforms a distribution that is stableand predictable over time.

Common and Special Causes

Time

Prediction

Metric = Lines on Time

If special causes of variation dominate,the output of a process is not stableover time and not predictable.

Time

Prediction

Metric = Lines on Time

?

16


Normal Curve – Area Under the Curve

Normal Curve• Accounts for Common Cause variation

average+2*sigma

average-2*sigma

13.60 % 13.60 %

average-3*sigma

average+3*sigma

2.14 %2.14 %

0.13 % 0.13 %

Upper naturallimit (UNL)

Lower naturallimit (LNL)

average average+1*sigma

average-1*sigma

34.13 %34.13 %

17


Distributions:

– Not all distributions are Normal

– Exponential Distribution is common for one-sided specs (PPB rates)

– Predictive statistics require Normal Distributions (SPC is predictive)

Basic Statistics… Distributions

Normal Distribution – not perfect Exponential Distribution – Jireh?

18


Central Limit Theorem

Concept– The sample mean become normally distributed as sample

size increases– The spread of the sample means are less than the spread

of the individuals of the sample

Value– Non-normal distributions can be assessed using normal

distribution statistics and tools (X-bar-R, etc.)– Most inferential statistical tools assume normality of data

• Confidence Intervals• SPC / Control Charts

19


Performance vs. Requirements

Requirements

– Customer Specifications

– Internal Operating Control Levels

– Compare Performance to Requirements

1615141312111098

USLLSL

O

20


Taguchi Loss Function

Nominal

Lower SpecificationLimit (LSL)

Upper SpecificationLimit (USL)

Acceptable Product Rejected ProductRejected Product

BestOutput

The LossFunctioninvertednormal

distributioncurve

21


1. _____________________________________________

2. _____________________________________________

3. _____________________________________________

4. _____________________________________________

5. _____________________________________________

Key Learnings22


Critical Characteristics






23


Key Characteristics

What is Important• to End Customer• to Manufacturers• to Jireh Operations• to Supplier Operations

Identifying Characteristics• Design FMEA• Early Supplier Involvement• Process FMEA• White / Black Diamond Designation

24


Product:

1. _____________________________________________

2. _____________________________________________

3. _____________________________________________

Process:

1. _____________________________________________

2. _____________________________________________

3. _____________________________________________

4. _____________________________________________

5. _____________________________________________

6. _____________________________________________

Key Characteristics25


Key Process Characteristics

Measurement Machine Methods

. .

Mother Earth ManMaterials


. .


26


Measurement / Process Capability






27


Measurement

Measurement Systems Analysis (MSA)• Gage Repeatability & Reproducibility (GR&R)

Variable GR&R• Understand capability of measurement

• Percent of Contribution – Variance• Percent of Study – Std Dev• Percent of Tolerance (P/T Ratio)• Effect of sample selection• <10% acceptable• 10-30% marginal• >30% high risk

Attribute R&R• Understand capability of inspection

28


Gage R&R Terms

Repeatability– The ability of a single instrument to generate accurate measurements consistently – same

operator, same part, same time

Reproducibility– The ability of a system to generate accurate measurements consistently – different

operators or times (accuracy/precision)

P/T Ratio– Precision to Tolerance – how much measurement error is related to specification

– Critical to product acceptance & capability studies (decision to Spec)

R&R Contribution– Measurement error related to process / characteristic variation

– Important to process control (SPC) and process improvement (DOE)

29


Variable Gage R&R

average

Observed =Variability

Observedvalues

total2

ProductVariability

Product Variability

product2 systemtmeasuremen

2+ Measurement Variability

Measurement Variability- Additive -

30


Why care about Process Capability?

• Satisfy our customers

• Understand our processes

• Prioritize areas in need of quality improvement (variationreduction) activities

• Verify that process improvements are successful

• Track improvements over time

• Give us information to set realistic tolerances

• Help us identify how to cost products

• Select the best qualified supplier

• We can improve our company Bottom Line

31


Process Capability Roadmap

Improve

Improve

Select Characteristic

Study ScopeShort-term - Long-term

MeasurementCapability?

Is ProcessStable?

UnderstandConfidence Interval(based on sample size)

Improve /Transform

Segregate/Understand

ImproveProcess

Is DataNormal?

Maintain the Gain

MultipleSources?

Cpk / PpkAcceptable?

OK

No

No

Poor

Yes

OK

N/A

OK

Yes

No

32



Output


Process Capability

Long Term vs. Short Term Variation• Fishbone diagram

5 Min Variation

10 Hr Variation

6 Mo Variation

Short Term

Long Term

Performance

33


Stability

– Without stability, cannot use data as a predictor offuture

– Special Causes create instability – removal for study?

Normality:

– Calculations are based on Normal Distribution

– Further from ‘normal’ less accurate totally invalid

– Calculation Tests & Probability Plotting

Stability & Normality

Stability Question Normality Question

34


Process Capability

• Cpk – ratio to the smallestvalue (USL/LSL)

• Ppk – ratio to the smallestvalue (USL/LSL)

How it’s measured

• Cp – capability index

• Pp – performance index

35


Process Capability

Process Capability rule of thumb:– Cpk > 1.50 Process is Six Sigma

– Cpk > 1.00 All Product Meets Requirements

– Cpk = 1.00 Most Product Meets Requirements

– Cpk < 1.00 Some Product Does Not Meet Requirements

– Ppk typically 0.33 less than Cpk

Note: Above calculates take in 1.5 s shift of mean over time

What are Jireh standards for Cpk ?What is difference between Cpk & Ppk in Jireh?

36


Process Capability

Objective of Process Capability Analysis

– Determine how the natural process limits compare with thespecification range

– Depending on the comparison to standard:

Study Outcome

Do nothing -1- -2- Change the specs

Center the process -3- -4- Reduce process variability

-5-

Accept the losses

37


1. _____________________________________________

2. _____________________________________________

3. _____________________________________________

4. _____________________________________________

5. _____________________________________________

Key Learnings38


SPC: Attribute & Variable Data






39


Data Types

What About… %

Type Description Model Symbol

AttributeDiscrete

counted

VariableContinuous

measured

40


Data Collection

Attribute Data– Counted:

• How many• How often• What kind

– Discrete

– Convert Attribute to Variable• Measure attribute (length/area)• Create a scale: Likert

Check Sheets – for Attribute data– NOT check-lists

– Defects / errors by time frame – ‘farmer’s count’

– Quick visual analysis – sum by column or row

41


• Binomial Distribution

– Defectives – number of pieces unacceptable

• Poisson Distribution

– Defects – errors to a single piece

Attribute Distributions42


Attribute Control Charts

Defect: is each nonconformance to the specified acceptance criteria

Defective: is an item (unit) in a sample that has one or morenonconformance to the acceptance criteria

– A “defective” contains at least one “defect”

Defectives Control Charts

– np Plots number of non-conforming units (needs fixed n)

– p Plots proportion of non-conforming units (n varies)

Defects Control Charts

– c Plots number of defects (requires fixed n)

– u Plots number of defects per “inspection unit” (n varies)

43


Attribute Charts

Defectives(binomial)

Defects(Poisson)

Sample Size Varies Sample Size Fixed

p Chart (most common)

% or portion defective

np Chart

Number of defectives

u Chart

Average number of defects

c Chart

Actual number of defects

44


Human Error Modes

1. Omission

2. Excessive / insufficient repetition

3. Wrong order

4. Early / late execution

5. Execution of restricted work

6. Incorrect selection

7. Incorrect counting

8. Misrecognition

9. Failing to sense danger

10. Incorrect holding

11. Incorrect positioning

12. Incorrect orientation

13. Incorrect motion

14. Improper holding

15. Inaccurate motion

16. Insufficient avoidance

BW (Ben) Marguglio, LLC845-265-0123

45


Xbar-R (Xbar = average of readings; R = range)

– n sample size

– X or x reading (data)

– Xbar average of readings

– Xbarbar average of averages

– R range of data points

– Rbar average of ranges

– S or s standard deviation

NOTE: UCL/LCL are boundaries for 99.73% of the data population

Variable Data SPC Terms46


The Control Chart

Time

Measu

re

UCL

LCL

AVG

PLOT POINT

99.73%

Remember: Control limits are determined by the process average values...

NOT SPECIFICATIONS

NOTE: Process spread is equal to a +/– 3 sigma from the mean

47


• Constructing a chart– Historic Data: minimum 20 data-sets (time increments)– Reference table values

• Calculate / post UCL / LCL

• Visualize Zones (+/– 3 std deviation)

• Plot data points

• Interpret Immediately

Using Variable Data Charts

Variable Charts

48


Variables Control Charts

X-Bar and R Chart (2 charts)

– Plot (1) subgroup average

– Plot (2) subgroup range

X-Bar and S Chart (2 charts)

– Plot (1) subgroup average

– Plot (2) subgroup standard deviation – typically when n>9

ImR/ XmR Chart (individuals moving range – 2 charts)

– Plot (1) individual value

– Plot (2) difference between 2 consecutive individuals

49


1. _____________________________________________

2. _____________________________________________

3. _____________________________________________

4. _____________________________________________

5. _____________________________________________

Key Learnings50


Operational SPC






51


Control Plan / SPC

Based on Capability & Stability• Understand source of input variation• Understand frequency of input variation• SPC: Run-time, Set-up, Special Event

Control Plan• Document who, what, when, sample• Out of Control Action Plan

52


Control Plan

Components of a Control Plan– Process Step to be ‘controlled’ (critical to process/output)– Variable to control (input or output)

– Specification / acceptable ‘management limits’– Measurement Method

– Control Method – SPC / other monitoring– Sample Size / Sample Frequency

– OCAP – Out-of-Control Action Plan• Who to contact – initiate responsibility for action• What action to take (investigative or prescribed)• What documentation (SOP or records)

53


Key Process Characteristics


. .


Per YOUR determination of Key Process Inputs

54


Variable Data

– Xbar – R

– Xbar – S

– X – MR

– Median

Types of Control Charts

Attribute Data

– p Chart

– np Chart

– c Chart

– u Chart

55


Antenna AssemblyLong Term Process Capability

-150%

-100%

-50%

0%

50%

100%

150%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Tool Wear Direction

Locating Slot Width Upper Crush Rib Snap Height

Upper Spec

Lower Spec

Nom-

X

XX

SuggestedMonitoring

XX

X

X

X

X

XX

X

Lower Crush Rib

X

AssemblyFeatures

Effective SPC

56


Set up controls at:• Changes of authority

• Start of significant, irreversible activity

• After critical quality feature created

• Vital few process inputs (Xs)

• Significant cost decision points

• Service gates

• Set-up operations

• Material Introduction

Use your process flow chart to identify control points

Where Should I Control ?57


Subgrouping

– Select SPC samples in a way that makes each subgroupas homogeneous (same) as possible

– A sampling process that reflects the actual process

Sources of Variability

–Time-to-Time (lot-to-lot)

–Within Piece

–Between Pieces

SPC Sampling - Rational Subgrouping

M T W T F S S M T W T F S S M T W T F S S M T W T F S S

58


1. _____________________________________________

2. _____________________________________________

3. _____________________________________________

4. _____________________________________________

5. _____________________________________________

Key Learnings59


Interpretation of SPC Charts






60


Statistical Thinking Map

DesignFMEA

ProcessFMEA

ProcessControl

DefineCritical

Characteristics

ApprovalPlanning

MeasurementSystemAnalysis

ProcessCapabilityStudies

ProcessImprovement

ControlPlan

ProcessCapable

Yes

No

StatisticalProcessControl

61


Control Chart Interpretation

Control Charts– Average and control limits are a function of past history

common cause variation– If process remains stable, only common cause variation

is existent in the process– Special Cause conditions create Out-of-Control signals

on the control chart– Four general rules:

1. Average: Out-of-Control Range: In-Control2. Average: In-Control Range: Out-of-Control3. Average: Out-of-Control Range: Out-of-Control4. Average: In-Control Range: In-Control

– 5+ Specific Rules (sigma zones)

62


“Out of Control”

– Special causes of variation are present

– Is detected by either having any point outside thecontrol limits or by unnatural patterns

7 Rules to interpret control charts

Out of Control63


Interpreting a Control Chart

Time

Measu

re

UCL

LCL

AVE

1. A lack of control is indicated whenever a single point fallsoutside the control limits.

64



Time

Measu

re

UCL

LCL

AVE

2. 4 out of 5 points in zone B

(A)

(B)

(C)

(A)

(B)

(C)

65



3. 2 out of 3 points in zone A

Time

Measu

re

UCL

LCL

AVE

(A)

(B)

(C)

(A)

(B)

(C)

66



4. 8 or more consecutive points on one side of centerline

Time

Measu

re

UCL

LCL

AVE

(A)

(B)

(C)

(A)

(B)

(C)

67



5. A trend of 6 or more consecutive pointsincreasing or decreasing

Time

Measu

re

UCL

LCL

AVE

(A)

(B)

(C)

(A)

(B)

(C)

68



6. Stratification, 15 or more points in zone C

Time

Measu

re

UCL

LCL

AVE

(A)

(B)

(C)

(A)

(B)

(C)

69



7. Mixture or systematic variation

Time

Measu

re

UCL

LCL

AVE

(A)

(B)

(C)

(A)

(B)

(C)

70


Out-of-Control Action Plan (OCAP)

Activators

Activators - out-of-control decision rules

Checkpoints

Checkpoints - list of possible assignable causes

Terminators

Terminators - corrective actions

Start

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

End

No

No

Out-of-Control Action Plan (OCAP)

Actions:Who to contact – initiate responsibility for actionWhat action to take (investigative or prescribed)What documentation (SOP or records)

71


Engagement Renewal






72


Taguchi Loss Function

Nominal

Lower SpecificationLimit (LSL)

Upper SpecificationLimit (USL)

Acceptable Product Rejected ProductRejected Product

BestOutput

The LossFunctioninvertednormal

distributioncurve

73


Multi-Vari Studies

Studying Relationships– Between suspect input variables and the output variables of

a process while in normal operation

Passive Data Collection– Process is monitored in its natural state

– without intervention or can be done with historical data

Full Range of Variation– To allow inputs to vary so we can observe their effects on

the outputs

Inherent Issues– To identify process problems and limitations

74


Data Collection

The identified inputs (Xs) are monitored in concert with thecritical outputs (Ys)

– Relate variation in the inputs to variation inoutputs

Xs Ys

75


Multi-Vari Study - Graphical

Wed

Tue

ThuSu

nSa

tM

onFri

0.0141

0.0140

0.0139

0.0138

0.0137

0.0136

0.0135

4321

XR71

VT6

87

B2000

AX10

0

Med

i um

Low

High

2703

2B

2679

2A

2662

1C

2550

1D

2443

1C

2211

0A

1998

2D

1872

1A Red

Ora

nge

Gre

en

Bro

wn

Blue

Black 321

4-(Q

3,m

ax)

3-(m

ed, Q

3)

2-(Q

1,m

ed)

1-(m

in, Q

1)

4-(Q

3,max

)

3-(m

ed, Q

3)

2-

(Q1,

med

)

1-(m

in, Q

1)

4-(Q

3,m

ax)

3-(m

ed, Q

3)

2-(Q

1,m

ed)

1-(m

in, Q

1)

4-(Q

3,max

)

3-(m

ed, Q

3)

2-(Q

1,m

ed)

1-(m

in, Q

1)

Day

Mean

Shift Product Opr Exp Lot Code Color Booth SolCode TempCode RHCode SpeedCode

Main Effects Plot for ThicknessData Means

76


54321

1087654321010876543210108765432101087654321010876543210

1.53

1.48

1.43

1.38

Part

A

1

2

Multi-Vari Chart for A by Trial - PartOper

Trial

Multi-Vari Study Example

Range of measurement – all inspectors – single part

Master Inspector measurement

77


SPC Checklist Activity

Team Activity

• Form Teams of 4-5

• Using current SPC Procedure & Process Specs

• Create a Checklist for daily/weekly review of SPC

• Activity: 30 minutes to complete

• Report-Out – 5 minutes per team of line items

• Debrief: collective sharing of team Checklists

• Similarities & Differences

• Value of SPC Checklist ?

78


1. _____________________________________________

2. _____________________________________________

3. _____________________________________________

4. _____________________________________________

5. _____________________________________________

Key Learnings79


Overall Feedback – Key Learnings• Cpk based individual; SPC on X-bar chart

• Datainterpretationaction

• Stable chart as the foundation for predictable future

• Clear steps to set up SPC

• Interpretation of WE rules

• Fully understand the key parameters then create specific chart tomonitor

• OCAP design review to come up with good procedure

• Daily review and in some cases to reset SPC limits

• Charting SPC on a paper a good exercise

• Multi-variable correlation to generate actions to improve SPC

• Customer always sees individual not average of the sample; thisreminds us to look at and address SPC more carefully

• Interpretation immediately; looking at SPC chart real-time

• Process capability, stability, normality review before SPC setup

• Daily review checklist item exercise/OCAP for continuousimprovement

80


Overall Feedback – Key Learnings

81


For your attention and active participation!

John


John Breckline

83

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