quality management tools and techniques 2014 part 1 st.ver

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QUALITYMANAGEMENT

January 2014 1Quality Management



Part 2

Tools and Techniques used in Total

Quality Management

January 2014 2 Quality Management



Tools and Techniques used in Total

Quality Management

• Tools and Techniques

• Product and Process Improvements

January 2014 Quality Management 3



Part 2.1

Tools and Techniques

January 2014 4 Quality Management



Introduction

• One of the basic principles of Total Quality ismanagement by facts

• It requires that each decision, each solution to a

problem is based on relevant data andappropriate analysis

• Collecting and analyzing data can be difficult

• Use of Total Quality tools and techniques ensurebetter decision making, better solution toproblems, improvement in productivity, productsand services




Overview of Total Quality Tools

Basic 7 tools:

• The Pareto Chart

• Cause-and-Effect Diagrams• Check Sheets• Histograms• Scatter Diagrams

• Stratification• Run Charts and Control Charts





Other tools:

• Statistical Process Control (SPC)

• 5S• Flowcharts• Input-output diagram• Failure mode and effects analysis

• Design of Experiments (DOE)




Overview of Total Quality Techniques

• Process Capability

• Quality Loss Function and Robust Engineering

•

Risk Assesment• Problem Solving and Decision Making

– PDCA

–

8D – Kepner Traego

– Six Sigma




Pareto chart

January 2014 Quality Management

Pareto charts are useful for separating the important from thetrivial. Pareto charts are important because they can help anorganization decide where to focus limited resources

9




Pareto Chart

The purpose is to separate the “vital few” from the trivial many.

Type of InterruptionFrequency of

Occurrence

Machine Breakdown 180

Defective Production 135

No Material 63

Change-over 56

Tool Breakdown 27

Defective Material 23

Maintenance 14

No Labor 9

The number of production interruptions, and the reasons for the interruption, atan injection molding plant are recorded for one month.

Whatinformation dowe “see” fromthe Pareto?

MachineBreakdown andDefectiveProduction are thebiggestcontributors toproduction

interruption.

10



Pareto chart




The purpose of the cause-and-effect diagram

( Ishikawa of fishbone) diagram is to help

identify and isolate the causes of problems


Cause and Effect Diagram

12





13

• Example of problem:

• Contamination of product with iron.

• Possible (primary) causes of contamination: – Measurement – Material

– Methods

– Environment

– Manpower – Machines

5M+E




Iron in

product

Measurements Materials Methods

MachinesManpowerEnvironment






15

Measurements

Solvent contamination

Lab error

CalculationImproper calibrationAnalyst

At supplierIn laboratory





16





17

• Can be used in any function of an organization

• Example from Human Resources – Employee turnover

– Possible (primary) causes of turnover:• Economy

• Performance of the Organization

• Organizational Culture

• Job Characteristics

• Unrealisic Employee Expectations

• Personal Reasons



Check sheet


Check sheets make it easy to collect data for specific purposes and

to present it in a way that automatically converts it into useful

information

19



Histogram


Histograms have to do with variability. A histogram is a measurement scaleacross one axis and a frequency of measurements on the other.



Histogram


Adds to 100%



Scatter diagram


Scatter diagram is used to determine the

correlation between two variables.



What is a Correlation?• A correlation exists between two variables when

they are related to one another in some way.

Time Cost

Project (Days) ($k)

1. 14 80

2. 29 111

3. 26 76

4. 10 27

5. 18 55

6. 11 51

7. 34 150

8. 26 140

9. 24 80

10. 21 120


QUESTION:

What is the relationship between Project

time and Cost?



Scatter DiagramA scatter diagram is the graphical

representation of paired (x,y) data.

Time Cost

Project (Days) ($k)

1. 14 80

2. 29 111

3. 26 76

4. 10 27

5. 18 55

6. 11 517. 34 150

8. 26 140

9. 24 80

10. 21 120

3 0 2 0 1 0

1

5

0

1 0 0

5 0

T i m e ( D a y s )

C

o

s t ( $

k )

T i m e v s . C o s t o f P r o j e c t s




Scatter Diagram

Time Cost

Project (Days) ($k)

1. 14 80

2. 29 1113. 26 76

4. 10 27

5. 18 55

6. 11 51

7. 34 1508. 26 140

9. 24 80

10. 21 120

3

0

2

0

1

0

1 5 0

1 0 0

5 0

T i m e ( D a y s )

C

o

s t ( $

k )

T i m e v s . C o s t o f P r o j e c t s


As the project time increases,so does the cost.



Types of Relationships

Positive Correlation Strong Positive Perfect Positive No Correlation

Negative Correlation Strong Negative Perfect Negative Nonlinear Correlation




Correlation Coefficient

• The correlation coefficient, r,is a statistical measure of the strength of the

linear relationship between two variables.

• r is always between -1 and 1 (inclusive).

• When r is close to zero, no linear relationshipis present.




Correlation Coefficient

r = 0.82

3 0 2 0 1 0

1 5 0

1 0 0

5 0

T i m e ( D a y s )

C

o

s t ( $

k )

T

i

m

e

v

s

.

C

o

s

t

o

f

P

r

o

j

e

c

t

s


Time CostProject (Days) ($k)

1. 14 80

2. 29 111

3. 26 76

4. 10 27

5. 18 55

6. 11 51

7. 34 150

8. 26 140

9. 24 80

10. 21 120



Correlation Coefficient - Examples

Positive Correlation Strong Positive Perfect Positive No Correlation

Negative Correlation Strong Negative Perfect Negative Nonlinear Correlation

r = 0.52 r = 0.85 r = 1.0 r = 0.09

r = - 0.73 r = - 0.89 r = - 1.0 r 0

Note: r 0 means no linear relationship.

The variables might be related, just not in

a linear fashion.




Correlation Coefficient Discussion

1. The correlation coefficient, r,

is approximately (select one):

a.) 0.50

b.) 0.85

c.) 0.05

2. The correlation coefficient, r,

is approximately (select one):a.) 0.00

b.) 0.70

c.) -0.70




Stratification


Stratification is a tool used to investigate the cause of a problem by grouping

data into categories. Grouping of data by common elements or characteristicsmakes it easier to understand the data and to draw insights from it.

31



Run chartThe run chart records the output results of a process over time

For this reason, the run chart is sometimes called a trend chart




Run charts and Control charts

The weakness of the run chart is that it

does not tell whether the variation is

the result of common causes or specialcauses.

This weakness gave rise to the control

chart.




Control Chart

• Common Cause Variation – Routine, inherent process variation – the “steady

state” variation that persists over time

– Common-cause variation is the noise within thesystem.

– Common cause variation describes variability in aprocess that is inherent in the design of theprocess

– Reduction of common cause variation requires(usually) a redesign of the process




Control Chart

• Special Cause Variation

– Variation that demonstrates a deviation from theprocess’ “steady state”.

– Special-cause variation always arrives as asurprise. It is the signal within a system

– Special cause variation is a variability that comesfrom some extraordinary event




Control Chart

Two different sorts of data:

• Data collected by counting:

–

Attribute/discrete data charts – Number of wrong invoices received per day

• Data collected by measurements:

–

Variable/ continue data charts – Percentage of alcohol in beer




Control Chart

Two different control charts:

• The p-chart is used to monitor the number of(non) conforming units in a sample

• The x and R chart is used to monitor avariable's data when samples are collected atregular intervals from a business or industrial

process. – Range of a set of data is the difference between

the largest and smallest values




Week

P r o p o r t i o n

2018161412108642

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

_ P=0.0416

UCL=0.06839

LCL=0.01481

1

P Chart of Rejcted Invoices

Run (p) chartEach week, n=500 invoices are sampled.

The percentage of invoices needing correction isplotted for 20 weeks.


Wk 1 – 4%Wk 2 – 5,2%

Wk 3 – 5,3%

Wk 4 – 4,9%

Wk 5 – 2,5%

Etc. Average = 0,04126



Control Chart

On Control chart, data are plotted just

as they are on a run chart, but a lower

control limit, an upper control limit,

and a process average are added.




Control Chart


• Control limits are statistical bounds that

define the region within which the process

naturally varies.

• These bounds are computed from the data.



Upper and Lower Control Limit

(UCL & LCL) for p chart


0.0416 ( 1 – 0.0416)

500= 0,04126 + 0,02679= 0,06839UCL=0.0416+3

LCL=0.0416 – 0,02679 = 0, 01481

UCL/LCL =

p = average of all p values: (0,04+0,052+0,053+0,049+0,025)/5




Week

P r o p o r t

i o n

2018161412108642

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

_ P=0.0416

UCL=0.06839

LCL=0.01481

1


When the process randomly fluctuates within the control limits, it is impactedonly by common causes of variation and considered stable, or in statistical control.If the process is in control , 99.73% of all the points will fall between the controllimits.

Upper ControlLimit

Lower ControlLimit

WithinControlLimits

42

Control (p) Chart



Week

P r o p o r t i o n

2018161412108642

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

_ P=0.0416

UCL=0.06839

LCL=0.01481

1


Control (p) Chart

. Special CauseVariation

Variation thatdemonstrates adeviation from theprocess’ “steady

state”.

Common CauseVariation

Routine, inherent

process variation – the“steady state” variationthat persists over time.

P Chart of Rejected Invoices





Run (x) Chart

Range of Daily Production Cost per Week

R a n g e

Week Number

44



Control Chart

To construct a Control chart, we need

to establish a lower control limit, an

upper control limit, both for the

process averages (x) and for the

ranges (R)





R

chart

Sample # 8:00 8:30 9:00 9:30 10:00 10:30 11:00

1 10.0 7.0 5.0 9.0 2.0 2.0 5.0

2 1.0 4.0 2.0 3.0 4.0 4.0 6.0

3 4.0 10.0 6.0 7.0 2.0 8.0 4.0

4 9.0 2.0 2.0 3.0 6.0 8.0 10.0

5 8.0 8.0 3.0 1.0 1.0 6.0 3.0

Average 6.4 6.2 3.6 4.6 3.0 5.6 5.6

Range 9.0 8.0 4.0 8.0 5.0 6.0 7.0

Control (x – R) Chart

1.1)7.6577.0(0.5LCL

RAXLCL

9.8)7.6577.0(0.5UCLRAXUCL

x

2x

x

2x

= - =

- =

= + = + =

Subgroup

Size (n) A2 D3 D42 1.880 0.000 3.267

3 1.023 0.000 2.574

4 0.729 0.000 2.282

5 0.577 0.000 2.114

Subgroup

Size (n) A2 D3 D42 1.880 0.000 3.267

3 1.023 0.000 2.574

4 0.729 0.000 2.282

5 0.577 0.000 2.114

0.0

4.0

8.0

12.0

16.0

R

U C L

0.0

2.0

4.0

6.0

8.0

10.0 U C L

L C L

UCL = 8.9

LCL = 1.1

Compute and plot the controllimits for the “Averages” chart.

46

Xchart

LCL




Sample # 8:00 8:30 9:00 9:30 10:00 10:30 11:00

1 10.0 7.0 5.0 9.0 2.0 2.0 5.0

2 1.0 4.0 2.0 3.0 4.0 4.0 6.0

3 4.0 10.0 6.0 7.0 2.0 8.0 4.0

4 9.0 2.0 2.0 3.0 6.0 8.0 10.0

5 8.0 8.0 3.0 1.0 1.0 6.0 3.0

Average 6.4 6.2 3.6 4.6 3.0 5.6 5.6

Range 9.0 8.0 4.0 8.0 5.0 6.0 7.0

Control (x – R) Chart

Subgroup

Size (n) A2 D3 D42 1.880 0.000 3.267

3 1.023 0.000 2.574

4 0.729 0.000 2.282

5 0.577 0.000 2.114

Subgroup

Size (n) A2 D3 D42 1.880 0.000 3.267

3 1.023 0.000 2.574

4 0.729 0.000 2.282

5 0.577 0.000 2.114

0.0

4.0

8.0

12.0

16.0

R

U C L

0.0

2.0

4.0

6.0

8.0

10.0 U C L

L C L

UCL = 8.9

LCL = 1.1

Compute and plot the controllimits for the “Range” chart.

UCL = 14

LCL = 0

LCL = D3R

LCL = 0 x 6.7 = 0

UCL = D4R

UCL = 2.114 x 6.7 = 14

47

R

chart

X

chart

LCL



Control Chart

• As long as the variation is the result of

common causes such as statistical variation

only, the plotted data stays between theupper control limit and lower control limit

while varying about the center line or average.





Other tools:

• Statistical Process Control (SPC)•

5S• Flowcharts• Input-output diagram• Failure mode and effects analysis•

Design of Experiments (DOE)




Overview of Total Quality Techniques

• Process Capability

• Quality Loss Function and Robust Engineering

• Risk Assesment

• Problem Solving and Decision Making

– PDCA

– 8D

– Kepner Traego

– Six Sigma




Statistical Process Control (SPC)

• Traditionally, in mass-manufacturing,

traditionally, the quality of a finished article is

ensured by end of line inspection of the

product.• Each article (or a sample of articles from a

production lot) may be accepted or rejected

according to how well it meets its design





• In contrast, SPC use statistical tools to observe

the performance of the production process in

order to predict significant variations which may

result in the production of a sub-standard article.• An advantage of SPC over end of line inspection is

that it allows early detection and prevention of

problems, rather than the correction of problems

after they have occurred





• Statistical Process Control (SPC) is a statistical

method of separating variation resulting from

special causes from natural variation in order

to eliminate the special causes.• It is used to monitor processes and indicate

when they get out of control

• It can be applied to any process




Collecting Data for an SPC Chart

• At least 20 subgroups of about n=5 data arerequired.

• The data within a subgroup should be collectedclose together in time (for example, 5 consecutively

produced parts)• Longer time intervals are used between subgroups.

(Depending on the process and purpose of thestudy, these time intervals could be 15 min., 30min., 1hr., 2 hr., or longer).

• Use a sampling frequency that captures normalchanges in the process (changes in material,operators, etc.).




Collecting Data for an SPC Chart


Measure 1 2 3 4 5 6 7 20

Time 08:00 09:00 10:00 11:00 12:00 13:00 14:00 ......... 03:00

Sample 1

Sample 2Sample 3

Sample 4

Sample 5

x x=

R R=



Creating a SPC Chart

• Calculate the UCL and LCL

– In case of variable data also for the Range

• Create a SPC chart with time intervals on the horizontalaxis and x or p on the vertical axis

– In case of variable data create additional chart forRange

• Plot the UCL and LCL on the chart

• Start collecting data from the process

• Generally, the average and range are monitoredsimultaneously, so that the entire system can beevaluated




Quality Management

SPC (X Bar and R) Chart

Range of Daily Production Cost per Week

X B a r C o n t r o l C h a r t – A v e r a g e D a i l y P r o d u c t i o n C o s t s b y W e e k

January 2014 57



Interpretation of SPC charts





• Out of Control Conditions:• If one or more points falls outside of the upper control limit

(UCL), or lower control limit (LCL). see section A

• If two out of three successive points fall in the area that isbeyond two standard deviations from the mean, eitherabove or below - see section B

• If four out of five successive points fall in the area that isbeyond one standard deviation from the mean, eitherabove or below - see section C

•

If there is a run of six or more points that are all eithersuccessively higher or successively lower - see section D

• If eight or more points fall on either side of the mean - seesection E




SPC and Process Capability

• Does it mean that if all results on an SPC chart arewithin Control limits at least 99,73% of the productsare good?

• No, it only means that the process is under statistical

control• To determine if the process is producing only good

products we need to understand the relation betweencontrol limits and product specification

• A specification is an explicit set of requirements to be

satisfied by a material, design, product, or service=> PROCESS CAPABILITY




Normal Distribution

• The central limit theorem states that under certain

(fairly common) conditions, the sum of many random

variables will have an approximately normal

distribution


U p p e r C o n t r o l L i m i t

L o w e r C o n t r o l L i m i t




Properties of the Normal Distribution

99.73% of the parts will fall

between

+/- 3 standard deviations

from mean

68% of parts will fall

between


from the mean

95% of the parts will fall

between


from mean

63



Standard deviation (Sigma)


http://localhost/var/www/apps/conversion/tmp/scratch_4//upload.wikimedia.org/wikipedia/commons/8/8c/Standard_deviation_diagram.svg



Standard deviation (Sigma)


The standard deviation of a statisticalpopulation is the square root of its variance



Example

•

Consider a population consisting of the following eight values:2 4 4 4 5 5 7 9

• These eight data points have the mean (average) of 40:8 =5

• To calculate the population standard deviation, first compute thedifference of each data point from the mean, and square the result ofeach:

• Next compute the average of these values, and take the square root:

• This is the population standard deviation; it is equal to the square root ofthe variance.


Population vs sample standard



Population vs sample standard

deviation

• The formula is valid only if the eight values webegan with form the complete population.

• If they instead were a random sample, drawn

from some larger, "parent" population, thenwe should have used 7 (which is n − 1) insteadof 8 (which is n) in the denominator of theformula, and then the quantity thus obtained

would have been called the sample standarddeviation




Process capability

• Process capability shows the relationship

between the natural process limits (the

control limits) and specifications

• Process in (statistical) control vs. Capable

process





Specification Limits

LSL USL

Specification Limits areapplied to individual

measurements.

Specification limits are decided by people.

Control limits are determined by the data

(voice of the process).

69

LCL UCL




Process Capability Improvement

F r e q u e n c y

2.52.01.51.00.50.0

25

20

15

10

5

0

Histogram of Dimension B

After

F r e q u e n c y

2.52.01.51.00.50.0

20

15

10

5

0

Histogram of Dimension B (m.m.)

F r e q u e n c y

2.52.01.51.00.50.0

16

14

12

10

8

6

4

2

0

Histogram of Dimension B (m.m.)

LSL USL

LSL USL

Cut Dimension (mm)

Cut Dimension (mm)

Cut Dimension (mm)

Initial State:

POORCAPABILITY

After Modified

Collision Sensor

InstalledBETTER CAPABILITY

After Auto Clamp

Installed

HIGH PROCESS

CAPABILITY!

70

Cp Index Demonstrates Potential




Cp = (USL – LSL)/ 6s

Cp is the ratio of TotalTolerance to the 6sProcess Spread.

It shows how capable theprocess would be if it wereperfectly centered.

Cp = Potential Capability

Cp Index Demonstrates Potential

Capability

3s

L S L

U S L

L S L

L S L

U S L

U S L

Cp = 1

Cp = 2

3s

4s 4s

Cp =1.33

6s 6s

This is a

“Six Sigma”

process.

71

Cpk Index Demonstrates Actual



Cpk Index Demonstrates Actual

Capability

Cp = 2

Cpk = 2

L S L

U S L

Cp = 2

Cpk = 1.33

When process is

centered, Cp = Cpk.

Cpk takes into

account any off-

centering that actualoccurs.

USL – Avg.

3sCpk =





Process Sigma Level - Table

Six Sigma “thinking” employs a 1.5 sigma shift.

For example: if a process exhibits 4 Sigma capability in the

short term, it would probably exhibit 2.5 Sigma capability in

the long term.

PPM Sigma Level

3.4 6

233 5

6210 4

66807 3308538 2

691462 1

73

Process Performance Index



Process Performance Index

(Pp and Ppk)

• Process Performance Index basically tries toverify if the sample that you have generatedfrom the process is capable to meet the

requirements• It differs from Process Capability (Cp & Cpk) in

that Process Performance only applies to aspecific batch of material.

• An example of this is for a short pre-production run.




Process Control and Capability

• Examples of process control and capability

• Three different production processes






Sample

S a m p l e M e a n

30272421181512963

1.40

1.35

1.30

1.25

1.20

_ _ X=1.3022

UC L=1.4127

LCL=1.1916

Sample

S a m p l e R a

n g e

30272421181512963

0.4

0.3

0.2

0.1

0.0

_ R=0.1917

UC L=0.4053

LCL=0

Xbar-R Chart of Nugget Diameter

Process In Control (Stable)





1.801.621.441.261.080.90

LSL

Process Data

Sample N 150

StDev(Within) 0.08320

StDev(Ov erall) 0.08138

LSL 0.80000

Target *

USL *

Sample Mean 1.30220

Potential (Within) C apability

CC pk 2.01

O v erall Capability

Pp *

P PL 2.06

PPU *

Ppk

C p

2.06

C pm *

*

C P L 2.01

C PU *

C pk 2.01

O bserv ed P erformance

PPM < LSL 0.00

PPM > USL *

PPM Total 0.00

Exp. Within Performance

PPM < LSL 0.00

PPM > U SL *

PPM Total 0.00

Exp. O v erall P erformance

PPM < LSL 0.00

PPM > USL *

PPM Total 0.00

Within

Overall

Process Capability of Nugget Diameter

Process Is Capable (Cpk > 1.67)





Sample

S a m

p l e M e a n

24222018161412108642

730.0

727.5

725.0

722.5

720.0

_ _ X=725.25

UC L=730.85

LCL=719.64

Sample

S a m p l e R

a n g e

24222018161412108642

20

15

10

5

0

_ R=7.69

UC L=17.55

LCL=0

Xbar-R Chart of Preform Length

Process In Control (Stable)

l d b l





724720716712708704700

LSL USL

Process Data

Sample N 120

Shape 1.67064

Scale 7.84297

Threshold 701.20479

LSL 700.00000

Target *

USL 720.00000

Sample Mean 708.21482

Overall C apability

Pp 0.83

PPL 1.22

P PU 0.69

P pk 0.69

Observed Performance

P PM < LS L 0.00

PPM > USL 8333.33

PPM Total 8333.33

Exp. Ov erall Performance

PPM < LSL 0.0

PPM > USL 13481.4

PPM Total 13481.4

Process Capability of Preform LengthCalculations Based on Weibull Distribution Model

Process Is Not Capable (Ppk < 1.67)

l d b l





Sample

S a m p l e M e a n

24222018161412108642

0.031

0.030

0.029

0.028

0.027

_ _ X=0.028057

UC L=0.029480

LCL=0.026634

Sample

S a m p

l e R a n g e

24222018161412108642

0.0060

0.0045

0.0030

0.0015

0.0000

_

R=0.002468

UC L=0.005218

LCL=0

1

Xbar-R Chart of Strip Caster Thickness

Special Cause

Process Not In Control (Not Stable)

P C t l d C bilit





Thickness

F r e q u e n c y

0.0340.0320.0300.0280.0260.0240.0220.020

20

15

10

5

0

Histogram of Strip Caster Thickness

LSL=

0.024

USL=

0.030

Process Is Not Capable(Cpk will be calculated after special cause is eliminated.)

P C l d C bili




• Determine what to measure, and verify measurementsystem

• Determine appropriate sub grouping, and collect data in

time order.

• Create control chart and assess stability.

– If special cause(s) present, then remove the special

cause(s), collect new data.

(Apply problem solving: KT, 8D, etc.)


P C l d C bili




• Once stable, evaluate process capability. – Make histogram.

– Check for normality.

– Compute Cpk or Ppk, PPM.

–

If not capable, then reduce variation (conduct DOE ifneeded), and collect new data.

(Note: Cpk/Ppk > 1.67 is often the goal.)

• Establish preventive maintenance

(needed to maintain stable & capable condition).


P C l d C bili




• A process is in statistical control when it is stableover time, and therefore predictable

• Common Capability Indices:

– Cp, Cpk (used when data isnormal and process is in control)

– Pp, Ppk (used for non-normal data, and for processesthat have not yet been stabilized).


P C t l d C bilit




• If we produce a product in a stable andcapable process it means that almost all parts

are produced within the tolerance limits

(99,9777% with Cpk of 1,67)• Does that mean that all those products have

same quality?


Q lit L F ti (T hi)



Quality Loss Function (Taguchi)

• Taguchi showed that "loss" in capabilities did notbegin only after exceeding these tolerances, butincreased as described by the Taguchi LossFunction at any condition exceeding the nominal

condition• The customer wants a target value

– => any deviation will cause loss

• The quality loss function attempts to measure

quality as loss due to deviation from target• Highest quality system is the one which has the

least functional variability


Q lit L F ti



Quality Loss Function


The quality loss function is quantitative evaluation ofloss caused by functional variation of a product

USL LSL

Q lit L F ti



Quality Loss Function

• Quality Loss Function approach aims atimproving Quality by reducing the functional

variation of a product.

Another approach

• Robust Engineering


R b t E i i



Robust Engineering


• Robustness is the state where technology,product or process performance is minimally

sensitive to factors causing variability (in

user’s environment and manufacturing) at thelowest cost.

Robust Engineering



90

Robust Engineering

• Robust Engineering isprevention.

• Robust Engineering focuses

on …

How to prevent failures,

How to reduce

variability in product

function,

How to reduce cost.

Applicable in:

•Electronic

•Mechanical

•Chemical•Software

Engineering

Systems


Robust Engineering



91

Robust ngineering

All failures and defects are caused by 3 types of noise: Various usage conditions

•End-user/ Environmental conditions

•Neighboring subsystems

Deterioration or wear (degradation over time) Individual difference (manufacturing

imperfections)

How can we prevent problems due to these types of

noise factors?


Robust Engineering



92

Countermeasures against Noise

1. Ignore

2. Control/ eliminate Noise (reactive)

(standardization, control charting, Error Proofing,

Tolerance design)3. Compensate effect of Noise

(feedback control, feed-forward control)

4. Minimize effect of Noise (proactive)

(Robust optimization – Parameter Design)

The more we can do #4, the less money we spend on the

others.

$$$

Robust Engineering


Quality Loss Function vs. Robust



y

Engineering

• Quality Loss Function approach aims atimproving Quality by reducing the functional

variation of a product.

while

• Robust Engineering approach aims at

improving Quality by minimizing the

sensitiveness of a product to factors causingvariability (noise)


Quality Loss Function vs. Robust



y

Engineering


Target

Quality Loss

0 Value

Robust

Engineering

Quality Loss

Function

5S



5S

• 5S is the name of a workplace organizationmethodology

• It describes how to organize a work space for

efficiency and effectiveness by identifying andstoring the items used, maintaining the area

and items, and sustaining the new order.


5S



5S(Seri, Seiton, Seiso, Seiketsu, Shitsuke)

• 1S - Separate and Scrap – Sort useful form useless

• 2S – Straighten – Everything in its place

• 3S – Scrub – Workplace and equipment clean

• 4S – Standardize – Select the best practice

• 5S – Sustain

– Make sure rules are followedBy organizing the workplace, unstable or wasteful situationsbecome visible earlier, allowing for a quick, effective response.


Purpose of 5S



Purpose of 5S

A structured system to make

abnormalities stick out

These abnormalities can then

be addressed


5S example



5S example


Flowchart



Flowchart


A flowchart is a picture of the separate steps of a process in

sequential order

High level flowchart

99

Flowchart



Flowchart


Detailed flowchart

100

The Input-Process-Output (IPO)



Diagram

• IPO Diagrams are “high-level” process maps.

– Input: Substance(s) that enter the system.

– Process: Actions taken upon or using theinput.

– Output: Tangible item(s) that result from

the processing, and exit the process.


IPO High Level Process Maps



IPO - High Level Process Maps

Post Position

Review Resumes

Select Candidates

Interview

Make Offer

INPUTS PROCESS OUTPUTS

• Personnel Request Form

• Candidates for the

position

• Resumes

• Person placed in position

• Rejected candidates

• Closed-out PR Form

“Hire Employee”


Risk Assessment



Risk Assessment

• Risk assessment is the determination ofquantitative or qualitative value of risk related

to a concrete situation and a recognized threat

• Quantitative risk assessment requirescalculations of two components of risk (R):,

the magnitude of the potential loss (L), and

the probability (p) that the loss will occur• Total Risk: R = L x p


Risk Assessment



Risk Assessment

• Important problem of products are risks whichare caused by bad or insufficent quality. In the

first line these are safety risks (danger to

human life, health, property). Both in theproduction phase and usage phase

• Producer and/or distributor has a moral and

legal responsibilty for the risks in usage phase..


Risk Assessment



Risk Assessment


often

possible

seldom

not

probable

small medium big

catastrop

hicPotential

damage

Damage

occurence

Area of extreme

risks

Product liability



Product liability

• Product liability is the area of law in whichmanufacturers, distributors, suppliers,retailers, and others who make productsavailable to the public are held responsible forthe injuries those products cause.

• Although the word "product" has broadmeaning, product liability as an area of law is

traditionally limited to products in the form oftangible personal property.


Product liability



Product liability

Types of liability:

• Manufacturing defect, – Manufacturing defects are those that occur in the

manufacturing process and usually involve poor-qualitymaterials or poor workmanship

• Design defect, – Design defects occur where the product design is inherently

dangerous or useless (and hence defective) no matter howcarefully manufactured

• Failure to warn – Failure-to-warn defects arise in products that carry inherent

nonobvious dangers which could be mitigated through adequatewarnings to the user, and these dangers are present regardlessof how well the product is manufactured and designed for itsintended purpose.


Product liability



Product liability

European Union:

European Union Directive 85/374/EEC

Czech Republic

Zákon č. 59/1998 Sb. o odpovědnosti za škodu

způsobenou vadou výrobku


Risk management



Risk management

• In design and development – DFMEA

– Design verification

– Design validation

– Prototyping and testing

• In production – PFMEA

– SPC

– Poka Yoke

– Final product inspection / Product audit – (Quick) Problem Solving


FMEA



FMEA

• Failure modes and effects analysis (FMEA) is astep-by-step approach for identifying all possiblefailures in a design, a manufacturing or assemblyprocess, or a product or service.

• “Failure modes” means the ways, or modes, inwhich something might fail. Failures are anyerrors or defects, especially ones that affect thecustomer, and can be potential or actual.

• “Effects analysis” refers to studying theconsequences of those failures.


FMEA



FMEA

• The purpose of the FMEA is to take actions toeliminate or reduce failures, starting with thehighest-priority ones.

• Failure modes and effects analysis also

documents current knowledge and actions aboutthe risks of failures, for use in continuousimprovement.

• FMEA is used during design to prevent failures.

• Ideally, FMEA begins during the earliestconceptual stages of design and continuesthroughout the life of the product or service.


FMEA



FMEA

• Failures are prioritized according to – how serious their consequences are

• (severity)

– how frequently they occur• (occurrence)

– how easily they can be detected

• (detection).


Occurrence

• Rating Meaning

• 1 No known occurrences on similar products or processes



• 1 No known occurrences on similar products or processes

• 2/3 Low (relatively few failures)

• 4/5/6 Moderate (occasional failures)

• 7/8 High (repeated failures)

• 9/10 Very high (failure is almost inevitable

• Rating Meaning

• 1 No effect

• 2 Very minor (only noticed by discriminating customers)

• 3 Minor (affects very little of the system, noticed by average customer)

•

4/5/6 Moderate (most customers are annoyed)• 7/8 High (causes a loss of primary function; customers are dissatisfied)

• 9/10 Very high and hazardous (product becomes inoperative; customers angered; the failure may result unsafe

operation and possible injury)

• Rating Meaning

• 1 Certain - fault will be caught on test

• 2 Almost Certain

• 3 High

• 4/5/6 Moderate

• 7/8 Low

• 9/10 Fault will be passed to customer


Severity

Detection

113

FMEA process



FMEA process


RPN (Risk Priority Number) = S x O x D

FMEA example



FMEA example


FMEA example



FMEA example


FMEA cycle



FMEA cycle


Types of FMEA



Types of FMEA

• Process: analysis of manufacturing and assembly processes

• Design: analysis of products prior to production

• Concept: analysis of systems or subsystems in the earlydesign concept stages

• Equipment: analysis of machinery and equipment designbefore purchase

• Service: analysis of service industry processes before theyare released to impact the customer

• System: analysis of the global system functions• Software: analysis of the software functions


Design of Experiments



Design of Experiments

• Design of experiments (DOE) is a sophisticatedmethod for experimenting with complexprocesses for the purpose of optimizing them.

•

DOE allows multiple factor adjustmentssimultaneously

• It reduces the number of tests needed to findan optimal situation by factor 10

• It also shows which factors are critical andwhich are not


Problem Solving and Decision Making




• A problem is a situation in which what exists doesnot match what is desired or, put another way, thediscrepancy between the current and the desired

state of affairs.

• Problem solving in a total quality setting is notabout putting out fires. It is about continual

improvement.






• Securing reliable information is an importantpart of problem solving and decision making.Recommended tools:

Cause-and-effect diagrams

Flowcharts

Pareto charts

Run charts

Histograms

Control charts Scatter diagrams


PDCA



PDCA


Identify and analyze the problem. Set a

measurable goal . Identify root cause(s) of the

problem

Develop and implement the solution

Evaluate the actual results (measured andcollected in "DO" above) and compare against

the expected results (targets or goals from the

"PLAN") to ascertain any differences

Request corrective actions onsignificant differences between actual

and planned results . Standardize the

solutions

122

8D methodology (TOPS)




8D methodology



gy

• D0: Problem Awareness: – Plan for solving the problem and determine the

prerequisites.

• D1: Team:

– Establish a team of people with product/processknowledge.

• D2: Defining the Problem:

– Specify the problem by identifying in quantifiableterms the who, what, where, when, why, how, andhow many (5W2H) for the problem.


8D methodology



gy

• D3: Contain : – Define and implement containment actions to isolate

the problem from any customer

– Verify effectiveness of actions

• D4: Diagnose (Define and verify root causes) : – Identify all applicable causes that could explain why

the problem has occurred.

– Identify why the problem has not been noticed at thetime it occurred.

– All causes shall be verified or proved byexperimentation and statistical data, not determinedby fuzzy brainstorming.


8D methodology



gy

• D5: Action (Choose and verify permanent correctiveactions) – Verify that the correction will actually solve the problem

– Evaluate the degree of problem reduction or elimination

• D6: Verify – Verify the effectiveness of the corrective actions

• D7: Prevent (Take Preventive Measures): – Modify the management systems, operation systems,

practices, and procedures to prevent recurrence of this

and all similar problems.• D8: Closure:

– Recognize the collective efforts of the team.


8D and FMEA



• The Failure Modes in a FMEA are equivalentto the problem statement or description in an8D.

•

Causes in a FMEA are equivalent to potentialcauses in an 8D.

• Effects of failure in a FMEA are problemsymptoms in an 8D.


Diagnose the problem



Continuous Improvement

Basic 5 times Why

The intent of asking "Five times Why“ is to assure that the root causes and

not symptoms are corrected.

The "Five-Why Process" was introduced at Toyota to find solution to

manufacturing problems, but this approach can be applied to any other areaas well.

Ask "Why this problem happened?" to discover its underlying problem then

ask "Why?" again to go deeper by another level until you reach the root cause.


5 times Why




Why did the machine stop?

A fuse in the machine has blown

Why did the fuse blow?

Circuits overloaded

Why did the circuit overload?

The bearings have been damaged and locked up

Why have the bearings been damaged?

There was insufficient lubrication

Why was there insufficient lubrication?

The oil pump on the machine is not circulating enough oil

Why is the pump not circulating enough oil?

Pump intake is clogged with metal shavings

Why is the intake clogged with metal shavings?

There is no filter on the pump intake

Asking "why" repeatedly, possibly more than five times, directs the focus towards real

causes, so problems can be solved permanently.


D4 – Use Problem Solving Tools to Diagnose

the probable Root Cause




“Problem Analysis (PA)“ is used to find the true cause of a positive or

negative deviation.

When people, machinery, systems or processes are not performing asexpected, problem analysis provides a structured process to identify and verify

the cause.

The PA process describes the problem with a clear Problem Statement and

Problem Specification (“IS / IS NOT”).


IS / IS NOT



Continuous ImprovementJanuary 2014 Quality Management 131

Six Sigma



g

• The most complex and sophisticated

methodology, within Total Quality, for

problem solving and process improvements isSix Sigma

quality management tools and techniques 2014 part 1 st.ver

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