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“ZERO DEFECT” A buzz word or reality? Fefco Technical Seminar 2013 Wilbert Streefland Technology Coaching BvbA “There is nothing that is a more certain sign of insanity than to do the same thing over and over and expect the results to be different” Albert Einstein

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“ZERO DEFECT”

A buzz word or reality?Fefco Technical Seminar 2013

Wilbert Streefland

Technology Coaching BvbA

“There is nothing that is a more certain sign ofinsanity than to do the same thing over and over

and expect the results to be different”Albert Einstein

2

Zero Defect

• Conclusion

• Zero Defect & Innovation

• Zero Defect & Quality

• Zero Defect & Knowledge

• Zero Defect & Culture

• Introduction Zero Defect

3

Customer/Supplier & Zero Defect

• The customer presented his requirements

� Can the box plant supply accordingly?

• The machine suppliers showed what they have available

� Does this help to solve the current issues between customer and box plant?

ZD & Introduction

4

Introduction Zero Defect

• How many of you have made mistakes since waking-up this morning?

– If Zero � did you do anything?

• We all make mistakes every day!

• At best we can extend the time between consecutive mistakes

• The best way to achieve a longer time between consecutive mistakes is to record every mistake we make and learn how to avoid them

ZD & Introduction

5

Zero DefectDefinition

Infinite Time until the

next Defect to occur

ZD & Introduction

Zero Defect has no meaning

without Targets!

6

The impact of a defective product?

Use risk engineering:

• What will be the impact of a failing product?– Fatal accident or injury– Loss of production or product– Reputation damage

• How much do we invest to detect/avoid failing products?

• Analyse the product failing interval. This in time and number of products produced

• Do we provide training?

The biggest risk is: “Ignorance”

ZD & Introduction

7

Zero defect interpretations

1. Focus only on zero defect products send to the customer

2. Focus on all raw materials converted to zero defect products at maximum production speed

Interpretation 1 results in waste!

ZD & Introduction

� Customer ☺

� All ☺

8

The way to Zero Defect:

Process variability at target level

< (smaller)

Customer agreed tolerances

This for all property tolerances agreedwith the customer

ZD & Introduction

9

Zero DefectCustom or Standard product• Process capabilities set the standard for

what can be promised in terms of Zero Defect targets

• The process needs upgrading if it is not meeting customer requirements �Investment in higher level process! (Training, machines, retrofits etc.)

Never lower process performance

due to low customer demands!

ZD & Introduction

10

Zero Defect

• Conclusion

• Zero Defect & Innovation

• Zero Defect & Quality

• Zero Defect & Knowledge

• Zero Defect & Culture

Infinite Time until the next Defect to occur

• Introduction Zero Defect

11

Zero Defect & Culture

• Follow the airline principle:

– A pilot is never blamed if a major problem occurs so he will provide all information freely needed to

investigate the case

– The exception is in case of proven negligence, fraud

and or deception

• Management need to allow the making of mistakes �

else you can't detect and avoid them

No Blame Culture!

ZD & Culture

12

Zero Defect

• Conclusion

• Zero Defect & Innovation

• Zero Defect & Quality

• Zero Defect & Knowledge

No Blame!• Zero Defect & Culture

Infinite Time until the next Defect to occur

• Introduction Zero Defect

13

“Believing” or “Knowing”Why…?• ..is a box plant supplier allowed to

agree tolerances with a box plant customer not understanding the box plant process variability?

• ..is a box plant customer asking for Zero Defect products without a list of parameters and targets for these parameters?

• ..are box plants understanding so little and believing so much?

ZD & Knowledge

14

What is knowledge?

Theory

Knowledge and Insight

Experience

The right combination of

experience, evidence and theory allows to develop knowledge

and insight

ZD & Knowledge

Knowledge is valuable!• Document it• Use it for training

15

Long time between two defects

����Short time between

two defects

Relation between Knowledge and Defects

Knowledge about what you are doing

����Believing that you know what you are

doing

ZD & Knowledge

16

Zero Defect

• Conclusion

• Zero Defect & Innovation

• Zero Defect & Quality

Quantitative tests,

Observations, Training

• Zero Defect & Knowledge

No Blame!• Zero Defect & Culture

Infinite Time until the next Defect to occur

• Introduction Zero Defect

17

What is current practise?

• All box plants have a lab facility with measuring equipment

• For what is the lab facility used?

1. To check the board grade of a competitor box?

2. When there is a customer complaint?

3. To systematically monitor product performance?

ZD & Quality

Let’s look at the box plant process

18

Box Plant “Process”All elements need to “Pass” for Zero defect

Substrate Corrugating Printing Die Cutting Folding

•BCT•Erection•# Boxes•On time delivery•Etc.

•Fishtailing •Gap•Glue•Box Dimension•Etc.

•Colour•Grade•Moisture•Strength•Delaminating•Etc.

Box(Customer)

ZD & Quality

All elements need a quantitativeand measurable specification

•Board grade•Calliper•ECT•FCT•No Wash Boarding•No Warp•Bond•Etc.

•Colour to colour register•Dot size•Colour target•Colour variation•Bar-code readable•Defects•Edge Sharpness•Etc.

•Print to die cut register•Box Dimension•Scoring depth•Cutting•Cracking•Etc.

19

Box Plant “Process”All elements need to “Pass” for Zero defect

Substrate Corrugating Printing Die Cutting Folding Box(Customer)

If one element fails then the total box fails!

ZD & Quality

•BCT•Erection•# Boxes•On time delivery•Etc.

•Fishtailing •Gap•Glue•Box Dimension•Etc.

•Colour•Grade•Moisture•Strength•Delaminating•Etc.

•Board grade•Calliper•ECT•FCT•No Wash Boarding•No Warp•Bond•Etc.

•Colour to colour register•Dot size•Colour target•Colour variation•Bar-code readable•Defects•Edge Sharpness•Etc.

•Print to die cut register•Box Dimension•Scoring depth•Cutting•Cracking•Etc.

20

Zero Defect & QualityImportant about “Acceptable” Quality!

• Quality is a True/False property. Better than 100% is not possible � high quality is a buzz word!

• Measure properties at regular interval

• For destructive measurements use a product that is produced weekly– Do not take one measurement of all products– Take regular many measurements of a few

regularly produced products � it will provide the process quality assurance evidence needed for zero defect production

ZD & Quality

21

A closer look at variability and the impact reducing it

1. Determine process variability by systematic

measuring e.g. WARP

2. Reduce process variability by implementing

working standards and procedures (magenta curve). Resulting in:

• Increased product level

• Variation reduction

3. Lower specification while the product performance still meets customer demands

(Yellow curve)

0

5

10

15

20

25

30

35

40

45

0 20 40 60 80 100 120 140

Measured value

Fre

qu

en

cy

Y1

Target

V alue

Current

A verage

0

5

10

15

20

25

30

35

40

45

0 20 40 60 80 100 120 140

Measured value

Fre

qu

en

cy

Y1

Y2

Target

V alue

Current

A verage

0

5

10

15

20

25

30

35

40

45

0 20 40 60 80 100 120 140

Measured value

Fre

qu

en

cy

Y1

Y2

Y3

Target

V alue

Current

A verage

Step 1 Step 2

Step 3

•ZD & Quality

22

• It is a must to understand the relation between process variables and product quality

• A feed-forward closed loop is not common available yet!

Extending time between Defects

What is current practise?

• Measure properties

• Decide if properties are within customer tolerance

• Eject if not!

• Next product produced might need again ejection because process was not corrected

• Waste is produced �

What would be better to do?

• Agree tolerances

• Set “control” limits (lower than agreed tolerance)

• Measure properties, preferably in line

• Adjust process setting when outside “control” limits

• Next product is always within tolerance � No waste ☺

ZD & Quality

23

Zero DefectDo and Don’ts, 2 examples

1. Colour case � The wrong solution for a problem?

2. Folding case � Does measuring and ejecting solve the problem?

ZD & Quality

24

1 Colour CaseWhat has the largest impact on colour?

• On an off line printer a spectrophotometer and colour matching software was used to correct the ink formulation before starting production due to colour deviations

� This only corrects the consequence of a process defect but not the reason

• Downtime evaluation on this off line printer revealed:

– 33% colour related downtime of the total production time

• The implemented working procedure for correcting colour on the press is the source for high downtime

• Tests showed that the main problem for the start-up colour deviation was the screen roll specification and unpredictable status

ZD & Quality

The target:

Print all sheets “Zero Defect” starting with the first

25

Colour case solution• Screen roll related ink transfer problems result in:

– Unpredictable colour!

– Unpredictable dot gain!

• Change the screen roll specification to an ink film thickness and line count allowing:– Releasing the ink

– Being cleanable

• Implement strict screen roll cleaning procedures

• Check everyday the screen roll on cleanliness

ZD & Quality

Colour related downtime reduced to 0%!

Colour was printed with Zero defect

26

2 Folding Case• What is the problem?

• Inline data collection• In depth Analysis

• Gap fishtailing simulation

ZD & Quality

Lead Edge1

2

3

8

60

27

What is the problem?Gap at the glue flap not under control + Skew + Alignment edge

ZD & Quality

28

Folding data of 3,200 boxesLead/Trail edge Gap

Le ad Edge

0

50

100

150

200

250

300

350

400

Fre

qu

en

cy

Le a d Edge Tre nd

0

2

4

6

8

10

12

14

16

18

20

1

128

255

382

509

636

763

890

1017

1144

1271

1398

1525

1652

1779

1906

2033

2160

2287

2414

2541

2668

2795

2922

3049

3176

Gap

in

mm

T r ail Ed g e

0

50

100

150

200

250

300

350

400

Fre

qu

en

cy

Tra il Edge T re nd

0

2

4

6

8

10

12

14

16

18

20

1

12

8

25

5

38

2

50

9

63

6

76

3

89

0

101

7

114

4

127

1

139

8

152

5

165

2

177

9

190

6

203

3

216

0

228

7

241

4

254

1

266

8

279

5

292

2

304

9

317

6

Ga

p i

n m

m

ZD & Quality

29

Fishtailing

• Is this approach OK or is it “Lottery” data?

• What happens to the faulty product?

• What can be done to reduce variability?

• What is really happening?

Fis h T ailin g

0

100

200

300

400

500

600

Fre

qu

en

cy

Fish ta i ling Tre nd

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

8.00

10.00

1

13

2

26

3

39

4

52

5

65

6

78

7

91

8

104

9

118

0

131

1

144

2

157

3

170

4

183

5

196

6

209

7

222

8

235

9

249

0

262

1

275

2

288

3

301

4

314

5

327

6

Fis

h t

ailin

g in

mm

ZD & Quality

30

Feeding skewed board using trim knife

Skewed 1°

Skewed

Skewed feeding of board in combination with trim knife:

• Lead edge gap = Trail edge gap ���� within target

• Can we detect fishtailing by only measuring gaps?

• How does the box look after squaring?

No Skew

ZD & Quality

Skewed 1°

31

In depth folding analysis(No trim knife used!)

Max 3.10 Max 2.00

Min -3.11 Min -5.00

Avg 0.00 Avg -1.90

Avg GP -2.14 Std 3.09

Avg GL 2.14

Std 2.23

Skew/m -3.51

Raw data

Cor. Data Cor. CPD,

PD Slot

Max 6.78 Max 1.92 Max 0.96 Max 1.47 Max 1.26 Max 0.57

Min -0.07 Correction Min -0.28 Correction Min -1.18 Correction Min -0.40 Correction Min -0.32 Correction Min -4.30

Avg 3.28 3.28 Avg 0.56 0.56 Avg 0.00 0.00 Avg 0.45 0.45 Avg 0.50 0.50 Avg -2.76 -2.76 2.32

Avg 1357 1.56 1.56 Avg Pos 3 0.20 0.20 Avg Pos 5 0.00 0.00 Avg Pos 7 0.64 0.64 Avg 1357 0.33 0.33 Prediction: -6.71

Avg 2468 5.00 5.00 Avg Pos 4 0.93 0.93 Avg Pos 6 0.00 0.00 Avg Pos 8 0.27 0.27 Avg 2468 0.67 0.67

Std 2.02 Std 0.49 Std 0.40 Std 0.38 Std 0.38 Std 1.03 1.03 1.06

Skew/m -4.52 Skew/m -0.96 Skew/m 0.00 Skew/m 0.49 Skew/m -0.45

Raw Data

CPD feed

corrected

PD feed

corrected

Cor. Data Cor. CPD,

PD Slot

Glue Lash A Avg -0.25 -0.17 0.16 0.24 0.19

Std 0.12 0.10 0.12 0.10 0.12

Glue Panel B Avg -0.21 -0.24 0.19 0.16 0.18

STD 0.07 0.08 0.08 0.08 0.08 Prediction Raw data

Cor. Data Cor. CPD,

PD Slot

Edge 1, 3, 5, 7: Max 1.81

Min -7.52

Edge 2, 4, 6, 8: Max 0.60

Min -5.77

Avg ALL: 2.56 -2.65

Avg Edge 1, 3, 5, 7: 3.08 -3.15 -4.72 -5.27

Delta Glue panel: Delta short panel: Delta long panel Delta glue Lash Std Edge 1, 3, 5, 7: 2.46 2.11 2.30

ALL: -2.72 ALL: -0.56 ALL: 0.45 ALL: 0.05 Avg Edge 2, 4, 6, 8: 2.03 -2.15 -7.14 -5.15

Edge 1, 3, 5, 7: -1.36 Edge 1, 3, 5, 7: -0.20 Edge 1, 3, 5, 7: 0.64 Edge 1, 3, 5, 7: -0.31 Std Edge 2, 4, 6, 8: 1.73 1.36 1.69

-4.07 -0.93 Edge 2, 4, 6, 8: 0.27 Edge 2, 4, 6, 8: 0.41Edge 2, 4, 6, 8: Edge 2, 4, 6, 8:

Panel Angel Results

Gap results

Drive Side Edge Pos 1b (Glue Panel)

-5 -4 -3 -2 -1 0 1 2 3 4 5

Error in mm

Drive Side Edge Pos 2b (Glue Panel)

-5 -4 -3 -2 -1 0 1 2 3 4 5

Error in mm

Slot 1, Pos 3, CPD Variation

-5 -4 -3 -2 -1 0 1 2 3 4 5

Error in mm

Slot 1, Pos 4, CPD Variation

-5 -4 -3 -2 -1 0 1 2 3 4 5

Error in mm

Slot 2, Pos 5, CPD Variation

-5 -4 -3 -2 -1 0 1 2 3 4 5

Error in mm

Slot 2, Pos 6, CPD Variation

-5 -4 -3 -2 -1 0 1 2 3 4 5

Error in mm

Slot 3, Pos 7, CPD Variation

-5 -4 -3 -2 -1 0 1 2 3 4 5

Error in mm

Slot 3, Pos 8, CPD Variation

-5 -4 -3 -2 -1 0 1 2 3 4 5

Error in mm

OS, Pos 1a (Glue Lash)

-5 -4 -3 -2 -1 0 1 2 3 4 5

Error in mm

OS, Pos 2a (Glue Lash)

-5 -4 -3 -2 -1 0 1 2 3 4 5

Error in mm

Edge to first colour PD, Glue Panel

-5

-4

-3

-2

-1

0

1

2

3

4

5

Edge 1357

Err

or

in m

m

Edge to first colour PD, Glue Lash

-5

-4

-3

-2

-1

0

1

2

3

4

5

Edge 1357

Err

or

in m

m

Gap, Pos 1

-12 -9 -6 -3 0 3 6 9 12

Error in mm

Gap, Pos 2

-12 -9 -6 -3 0 3 6 9 12

Error in mm

Fishtailing

-5

-4

-3

-2

-1

0

1

2

3

4

5

Err

or

in m

m

Slot Var., PD, Edge, Pos.: 5

-5

-4

-3

-2

-1

0

1

2

3

4

5

Err

or in

mm

Slot Var., PD, Edge, Pos.: 6

-5

-4

-3

-2

-1

0

1

2

3

4

5

Err

or in

mm

ZD & Quality

Note the feed skew problem!

32

Correcting the fishtailing for the feeding error

h values

-6

-4

-2

0

2

4

6

h i

n m

m

h Raw Data

h Skew Corrected

Note the impact of correcting

fishtailing data for the feed error!

Ejecting faulty boxes will not solve the problem!

ZD & Quality

33

Folding Case• Data collected inline will show if there is a

problem but not the problem source

• Detailed testing of production equipment is needed to find the problem source

• Ejecting bundles will avoid problems at the customer � Is that the best option for Zero Defect product?

• Do we know what folding tolerances are acceptable?

ZD & Quality

What about a folding error simulation?

34

What folding tolerances are machine suppliers promising and what is delivered?

• Check during machine commissioning if the supplier delivers what is promised?

• The next slide will show a mathematical simulation on how gap and fishtailing are affected by:– Feed variation

– Skew (Due to a feed skew)

– Slot position– Glue lash position

– Glue panel position

ZD & Quality

35

Folding Error Simulation

• How accurate is your FFG feeder and slot head positioning?

• Does your customer accept these gaps and fis

htailing deviations?

• Will the squaring unit c

orrect the fis

htailing error and not

increase the gap error?

• We have not yet looked at th

e influence of the folding arms!

ZD & Quality

36

Specification• Quantitative �

– Based on facts and figures

– Represent the objectives to achieve

– Realistic tolerance based on the capabilities of:

• Process

• Equipment

• Raw materials

• Measurable � Measuring equipment that has sufficient resolution to detect if the property is inside or outside the set tolerance

Measuring is done systematically and not only when there is a complaint!

•ZD & Quality

37

Always agree “SMART” targets

• S � Specific

• M � Measurable

• A � Achievable

• R � Realistic

• T � Timed

ZD & Quality

• It is easy to make this slide!

• Brain power is needed to

define “SMART” targets!

38

Zero Defect

• Conclusion

• Zero Defect & Innovation

SMART Target• Zero Defect & Quality

Quantitative tests,

Observations, Training

• Zero Defect & Knowledge

No Blame!• Zero Defect & Culture

Infinite Time until the next Defect to occur

• Introduction Zero Defect

39

Zero defect & InnovationThe paradox

• Innovation is based on the evolution of

correcting mistakes

• Would avoiding defects stop Innovation?

• According Murphy’s law it is likely that

(unknown) defects will continue to occur

At best we can extend the time between

the events!

ZD & Innovation

And… New technology might be needed to improve

40

Zero defect & InnovationWhat can be used now and in 2025

• How to produce Zero Defect if testing damages the product?

• Is it possible to investigate paper and board non destructive?

• We know CT scanning for medical use

• Inside Matters, a University Gent spinoff company, make CT scans of any material and/or object

CT scanning of paper and boardwill provide new ways for

detecting defects!

ZD & Innovation

41

CT scan of wood

ZD & Innovation

42

CT scans paper3D image 2D cross sectionInk

ZD & Innovation

43

CT scans corrugated board3D image 2D cross section

ZD & Innovation

44

Zero Defect

• Conclusion

New technology will support improvement

• Zero Defect & Innovation

SMART Target• Zero Defect & Quality

Quantitative tests,

Observations, Training

• Zero Defect & Knowledge

No Blame!• Zero Defect & Culture

Infinite Time until the next Defect to occur

• Introduction Zero Defect

45

# of defects

Co

st Production

Cost

Sales cost

Conclusion• Zero Defect might be a “ghost” objective at

best the time between two detectable defects can be extended

• Minimizing defects will reduce cost for all up to a certain level after that the customer and supplier have to understand the economics

• It requires:– Open fact based mind set

– Specifications that confirm quality targets

– No blame mentality

– Responsibility for what you claim

– Accepting the need for changes

– New technology

– Training and education

ZD & Conclusion

There are no shortcuts to Zero Defect only detours

0

46

Zero Defect

There are no shortcuts

to Zero Defect only detours

• Conclusion

New technology will support improvement

• Zero Defect & Innovation

SMART Target• Zero Defect & Quality

Quantitative tests,

Observations, Training

• Zero Defect & Knowledge

No Blame!• Zero Defect & Culture

Infinite Time until the next Defect to occur

• Introduction Zero Defect

Thank you for your attention

Wilbert Streefland

Technology Coaching BvbA

www.tcbvba.be