Bb Wk1 180 Metrics

8/22/2019 Bb Wk1 180 Metrics

http://slidepdf.com/reader/full/bb-wk1-180-metrics 1/46

Copyright © 2001-2005Six Sigma Academy International, LLC All Rights Reserved

In-Process Metrics

Cost Quality Delivery

Process

X1

X2

Y(VOP)

“Correct”

(In-Spec.)

Xn

X3

Process InputVariables

(PIV)

Process OutputVariables

(POV)

Customer’s Needs and

Expectations(VOC)

Critical To Satisfaction(CTS)

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 1

The Breakthrough Strategy® And Metrics

1. Select Output Characteristic

2. Define Performance Standards

3. Validate Measurement System

4. Establish Baseline Process Capability

5. Define Performance Objectives

6. Identify Variation Sources

7. Screen Potential Causes

8. Discover Variable Relationships

9. Establish Operating Tolerances –

Implement Improvements

10. Validate Measurement System

11. Determine Final Process Capability

12. Implement Process Controls

• Throughout the project

we must be able tomeasure how good or bad the process is

• Metrics tell us where westarted, where we are,

and where we wishto go

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 2

Module Objectives

By the end of this module, the participant will be able to :

- Understand the importance of in-process metrics throughout the DMAICprocess to validate and maximize improvements aligned to the VOC andorganization’s key business objectives

- Define and calculate

• Final Yield (FY)

• Throughput Yield (TPY)• Rolled Throughput Yield (RTY)

• Normalized Yield (NY)

• Defects Per Unit (DPU)

• Opportunity Counting/Complexity

• Defects Per Million Opportunities (DPMO)

- Identify the “hidden factory” or “hidden process”

- Apply metrics to projects

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 3

Definition

Metrics:

• Measure key parameters describing how a process or productis performing

• Measure CTQs, CTCs and CTDs

• Critical in developing project baselines and improvement goals

• Allow us to measure ourselves against customer value expectations

• Communicate level of conformance to standards

Six Sigma projects begin and end with metrics.Measure the things that matter. All DMAIC projects have an initial and final DPMO and Z value.

Some companies will also use Cp, Cpk, Pp, and PPK.

Note: Sigma (or Z metrics), Cp, Cpk, Pp, and Ppk are defined in the Process Capability modules.

8/22/2019 Bb Wk1 180 Metrics



Yield In-Process Metrics


Process

X1

X2

Y(VOP)

“Correct”

(In-Spec.)

Xn

X3


(PIV)


(POV)


Expectations(VOC)


8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 5

Final Yield Or Classical Yield

Final Yield, YF = S/U = Successes/Units Submitted

Verify

Rework

Scrap

Product or Service

to Customer

U S Operation

Hidden Factory:

Defects andRework

Classical Yield Ignores Role of “Hidden Factory”.

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 6

C

U

S

T

O

M

E

R

Final Yield (YF) Yield at the end of the process, ignoring the Hidden

Factory (only scrap loss is accounted for).

Effect Of The Hidden Factory

Step 3100Units

70Units

10 Rework

10 unitslost as

scrap

Step 1 Step 2

The Hidden Factory or Hidden Process

Can you think of examples of Hidden Factory from your processes?

10 unitslost as

scrap

10 unitslost as

scrap

Final Yield, YF = S/U = Successes/Units Submitted

YF =(100-30)/100 = .70 or 70%

10 Rework 10 Rework

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 7

Example using Step 1

Throughput Yield (YTP)

• Used to measure quality at a single process step

• YTP = Units through the STEP, right the first time/units submitted

• Yield of step (excluding units scrapped or reworked)

Throughput Yield, YTP = (100 –10 -10)/100YTP = (100 - 20)/100 = .80 or 80%

Step 1100

Units

10 unitslost as

scrap

YTP = ?Hidden Factory

10 units arereworked and

placed back intothe process

C

U

ST

O

M

E

R

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 8

Throughput Yield Exercise

• Calculate the YTP for each Step

• Calculate the Final or Classical Yield (YF) for each step and compare

Step 3100

Units70

Units

10 unitslost asscrap

Step 1

10 Rework 10 Rework 10 Rework



Step 2

CU

S

T

O

M

E

R

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 9

YRT .80 x .778 x .75 = 46.7%

Process Step 1 Step 2 Step 3 Total

Input 100 90 80 100

Scrap 10 10 10 30

Rework 10 10 10 30

YTP 80% 77.8% 75.0%

Probability of zero defects

Rolled Throughput Yield (YRT) = Product of Throughput Yields across

the entire process.

YF 90% 88.9% 87.5% 70%

Rolled Throughput Yield (YRT)Getting Through The Entire Process “Clean”

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 10

Step 1 Step 2 Step 3

Serial Process

Step 1

Step 2a

Step 2b

Step 3

Parallel Process

Analysis of Serial and ParallelProcess Designs

C

U

S

T

O

M

E

R

C

U

S

T

O

M

E

R

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 11


Units 100 90 80 70

Scrap 10 10 10 30

Rework 10 10 10 30

YTP .80 .778 .75 N/A

Total

YRT = .80 x .778 x .75 = .467


Serial Process Example

CUSTOMER

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 12

YRT = .80 x (((40/90) x .75) + ((50/90) x.80)) x .75 = .467

Step 1

Step 2a

Step 2b

Step 3

Units 100 40 50 80 70

Scrap 10 5 5 10 30

Rework 10 5 5 10 30

YTP .80 .75 .80 . 75 N/A

FinalStep 1 Step 2a Step 2b Step 3

Parallel Process Example

CUSTOMER

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 13

Normalized Yield (NY)

Normalized yield (NY) is the average yield per process step or opportunity

Where k represents the total number of process steps or opportunities

Example:

- Given: Rolled Throughput Yield, YRT of a process is 53.64% and 100process steps

- What is the average yield per process step?

9938.05364.10011

RT

k

Y NY

NY represents the probability of a unit passing through ONE

process step or opportunity without rework or scrap

RT

k

Y NY 1

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 14

Exercise - Normalized Yield (NY)

YRT .80 x .778 x .75 = 46.7%

Process Step 1 Step 2 Step 3 Total

Input 100 90 80 100

Scrap 10 10 10 30

Rework 10 10 10 30

TPY 80% 77.8% 75.0%

FY 90% 88.9% 87.5% 70%

Calculate the normalized yield (NY) for this process with 3 steps

8/22/2019 Bb Wk1 180 Metrics



In-Process Metrics for Defects


Process

X1

X2

Y(VOP)

“Correct”

(In-Spec.)

Xn

X3


(PIV)


(POV)


Expectations(VOC)


8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 16

Defectives vs. Defects

• Defect is a single instance of non-conformance to a customer

requirement, e.g., a die has six numbered faces, if 1 face is not properlynumbered, it has 1 defect. If 3 faces are improperly numbered, it has 3defects

• Defective is a unit containing 1 or more defects, e.g., whether a die has1 bad face or 3, it is still a defective die

- Always considered a fail on a pass/fail scale

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 17

Defects Per Unit (DPU)

Defects per unit (DPU) is the ratio of defects to units and is calculated as:

Number of Defects

Total Units= DPU =

D

U

Note: A unit is an equal area of opportunity

• Each similar product made is a unit (die, tire, pen)

• For continuous surfaces, such as paper, the unit may be a linear yard

• For variable surfaces, such as various sized auto panels being painted,the unit may be a square foot

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 18

• DPU may be calculated for a single acceptance point, e.g., same size

hood or summed for an overall acceptance stage or area, e.g., all thebody panels of a car with car being the unit

• When summed, DPU is sometimes referred to as Total Defects Per Unit or TDU

• DPU is the count of all defects and not a measure of the consequencesof the defects

2 Late Deliveries10 Routes Delivered

Example: = 0.2 DPU

DPU Example

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 19

DPU Exercise

A store ships customer orders. Each order contains 1 item and is a

complete unit.

10 orders are randomly selected prior to shipment and inspected. 2 itemsare incorrect, another item is damaged. 2 other items have incorrectquantities.

• How many units are being inspected? ____ • How many defects are there? ____

• Calculate the DPU? ____

Note that DPU is not necessarily the same as the qualityimpact to the customer – Some defects are likely worse than others.

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 20

An Opportunity Is An OpportunityOnly If It Is Measured

• Opportunity: A chance for a defect to occur

• Factors that must be present for an opportunity to exist

- Characteristic (what it is that you are evaluating)

- Scale (How you measure the characteristic)

- Standard or specification (Criteria upon which to pass/fail)- Density or measure

(The result of an actual measurement of the characteristic)

• Active opportunity if all factors are present

• Passive opportunity if density is absent(you could have measured it if you had wanted)

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 21

Defects Per Opportunity (DPO)

• Used to calculate the probability of defect free units produced by

the process

DPO: Defects per Opportunity

D: Number of defects

TOP: Total number of opportunities

OP: Number of opportunities per unit

U: Number of units

• Only active or measurable opportunities are considered

U OP

D

TOP

D DPO

*

D f t P Milli O t iti (DPMO) d

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 22

PPM =

Total Defects x 1,000,000

Total Units x Opportunities Per UnitDPMO =

Defects Per Million Opportunities (DPMO) andParts Per Million Opportunities (PPM)

• The DPMO considers total opportunities for defect occurrence existing

in a process

• DPMO or PPM, is generally considered long term as it is most oftencalculated from long term data

Defective Units x 1,000,000

Total Units Processed

• DPMO and PPM can be directly converted into a long term Sigma value

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 23

DPMO Example/Industrial

• An inspection of 200 components at an industrial facility revealed a

total of 10 defects

• Each component has 5 characteristics and each is consideredan opportunity

• What are the DPU, TOP, and DPO statistics?

• How do we convert these statistics into a DPMO statistic?

10 x 1,000,000

(200 x 5)DPMO = = 10,000

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 24

Useful Formulas

If the DPU of the process is known, the effective YRT can be estimated

YRT = e - DPU

Or similarly, DPU of the process can be estimated if YRT is known

DPU = - ln (YRT)

A key advantage of this method is that we do not have to know or countdiscrete opportunities. We simply need the count of the units and howmany defects were found.

The math behind this formula is the Poisson distribution. The next slidepresents the derivation for those interested.

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 25

Derivation Of FormulaBackground For Those Interested

• YRT = e-dpu comes from the Poison distribution

• P(X=x) = [(λ t)x (e) –λ t ] / x!

• Where λ = lambda = rate of defect occurrences = DPU

• And t = number of units

• Set t =1 as we are interested in 1 unit’s chance of making it through theprocess without being scrapped or reworked (λ t) = λ = DPU

• x is the number of defects for which we are defining our probability

• We are looking for the YRT or the probability of making it all the waythrough the process with no defects (rework or scrap), so x = o

• 0! Is defined as 1, so with the denominator being 1, we get

• P(X=0) = [(dpu)0 (e)-dpu] [(1)(e)-dpu]e-dpu

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 26

Leveraging The Process

Metrics also help in project identification… where would you work?

YTP 80% 77.8% 75%COPQ 500K 100K 50K

Throughput/Hr 1,000 500 750

Category Step 1 Step 2 Step 3

COPQ: Cost of Poor Quality

L i Th P

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 27

Leveraging The Process

Category Step 1 Step 2 Step 3

Metrics also help in project identification… where would you work?

YTP 80% 77.8% 75%COPQ 500K 100K 50K

Throughput/Hr 1,000 500 750

8/22/2019 Bb Wk1 180 Metrics


Copyright © 2001-2005

Six Sigma Academy International, LLC All Rights Reserved

In-Process Metrics Exercises

M t i E i

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 29

Metrics Exercise(May Be Assigned As Homework)

• We have a manufacturing facility with 3 departments. Our product must

be painted, assembled and then aligned.• We gather data that we consider to be long term data

(full variation expected to be seen)

• As is typical, the production from each department is not identical

Shop Units Defects Opportunities per Unit

Paint 546 21 36

Assembly 479 102 50

Alignment 502 57 21

Our management wants to know the answers to 2 questions:1) How good is our quality at the most basic (opportunity) level?2) Given that quality level AND our complexity (107 total

opportunities), what is the probability a unit will make it all theway through without being scrapped or reworked (YRT)?

M t i E i ( t )

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 30

Metrics Exercise (cont.)File: 145 Metrics Exercise 1.xls

Example 1a

Step 1 Step 2 Step 3Paint Assemble Alignment

Units 546 479 502

Defects 21 102 57

Opp 36 50 21

DefectsTot Opp

DPO <==

DPMO

ZLT

DPU Total DPUs = TDU

Ytp RTY Obtain by multiplying the 3 Ytps

RTY Obtain by calculating e^ -DPU

Yna

ZLT

ZST

Note that we cannot simply addall the defects, add all the

opportunities, and then

calculate an overall DPO

because the shops did not have

equal production.

The Ytp and 1-Ytp are the best estimates we have for

the individual quality of the three shops. They do not

depend on having an equal volume in each shop.

Question 1

Question 2

Fill in Form

K L i P i t

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 31

Key Learning Points

•

•

•

•

•

Obj ti R i

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 32

Objectives Review

The participant should be able to :

- Understand the importance of in-process metrics throughout the DMAICprocess to validate and maximize improvements aligned to the VOC andorganization’s key business objectives

- Define and calculate

• Final Yield (YF)

• Throughput Yield (YTP)

• Rolled Throughput Yield (YRT)

• Normalized Yield (NY)

• Defects Per Unit (DPU)

• Opportunity Counting/Complexity

• Defects Per Million Opportunities (DPMO)

- Identify the “hidden factory” or “hidden process”

- Apply metrics to projects

8/22/2019 Bb Wk1 180 Metrics




Appendix ADPMO, Counting Opportunities

DPMO Co nting Opport nities

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 34

DPMO, Counting Opportunities:These Are GUIDELINES Only

• Non-Value-Add Rules:

No opportunity count should be applied to any operation whichdoes not add value

- Transportation and storage of materials generally provide noopportunities (obvious exceptions: Transportation/delivery/cycleprojects)

- Testing, inspection, gauging, etc. do not count – The product in mostcases remains unchanged

- Supplied Components Rules: Each supplied part provides1 opportunity

- Supplied materials such as machine oil, coolants, etc. do not countas supplied components

DPMO Counting Opportunities

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 35

DPMO, Counting Opportunities(Cont’d)

• Connections Rules:

Each “attachment” or “connection” counts as 1 - If a device requires 4 bolts, there would be an opportunity count of 4, 1

for each bolt connected

- A sixty pin integrated circuit, SMD, soldered to a PCB counts assixty connections

- A sixteen pin dual in-line package (DIP) with through hole mountingcounts as sixteen joints. No double counting of joints, e.g., do notcount 1 for top side and 1 for bottom side.

DPMO Counting Opportunities

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 36

DPMO, Counting Opportunities(Cont’d)

• Transactional Process Rules:

- Filling out a form provides 1 opportunity per data entry field, not anopportunity for each character

- 1 line of assembly equivalent code counts as one opportunity for software programs

• Sanity Check Rule: “Will applying counts in these operations take mybusiness in the direction it is intended to go?” If counting each dimension

checked on a CMM inflates the denominator of the equation, adds novalue and increases Cycle Time when the company objective is to takecost out of the product, then this type of count would be counter to the

company objective. Hence it would not provide an opportunity.• Once you define an “opportunity”, you must institutionalize that definition

to maintain consistency

8/22/2019 Bb Wk1 180 Metrics




Appendix BCard Drop Optional Exercise

Card Drop Optional Exercise

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 38

Card Drop Optional Exercise

• Objectives:

- To demonstrate the impact of process variation on

• Rolled Throughput Yield

• Product cost

• Cycle Time

• Production Goals:

- Ship 25 good units to customer

- Maximize yield

- Minimize

• Rework• Total cost

• Total Cycle Time

Card Drop Optional Exercise:

8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 39

Card Drop Optional Exercise:Instructions

• Per Team

- 3 targets (sheets of flip chart paper)- 1 deck of 52 cards, you will use only 25

- Stop watch

- 1 laptop to record data in Excel file: 145 Card Metrics.xls

• At each process-step- Operator: Drops cards

- Material handler: Moves good cards to the next step and recyclesdefects back to operator. Keeps track of how many TOTAL dropswere made at their station

• Each team will also need- Raw Material Clerk: Provides cards to 1st operator and measures

the time to completion(1st card drop to delivery of final card to customer)

- Customer: Receives and counts good units


8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 40

Card Drop Optional Exercise:Production Information

• Only start 25 cards

• Operator job instruction:

- Stand next to the target

- Drop 1 card at a time

- Drop card vertically, at arm’s length and at shoulder height

- Aim the card toward center of target

• Customer specification:

- “Good” unit: Must land completely within the bounds of the target ateach process step

- “Defective” unit: Lands on or beyond the paper’s edge, all these unitsmust be reworked

• Customer Order:

- 25 good units are processed through all 3 steps


8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 41

Card Drop Optional Exercise:Orientation Of Card At Release

Standard drop for all teams except 1

Fun drop for a team wishing

(or ASSIGNED) a challenge


8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 42

Card Drop Optional Exercise:Required Data Collection (Via Excel)

How many drops were made ateach step. Includes bad

drops and successful drops.

Time from 1st card issued to 1st operator to the 25th good product

handed to the customer.

File: 145 Card Metrics.xls


8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 43

Card Drop Optional Exercise:The Production Process Standard Setup

Rework Rework Rework

GoodUnits

Input=?

?

Output:

25 Units

GoodUnits

Step #1:

Drop Card

Step #2:

Drop Card

Step #3:

Drop Card(and Final Test)

?? ??

Input=?? Input=??

OP#1

OP#2

OP#3

Raw MtlInventory

clerk

Inspect &Transport

#1

Inspect &Transport

#2

Inspect &Transport

#1Fail FailFail

Pass Pass Pass

Customer


8/22/2019 Bb Wk1 180 Metrics



Metrics Pg 44

Card Drop Optional Exercise:Required Production Report (Via Excel)

• Yield and defect calculations

- Final Test Station Yield = Yield at Station 3- YRT = Yield1 * Yield2 * Yield3

- DPMO = (Total defects/Total Opportunities)*1,000,000

- DPU = Total defects/25 units shipped

• Cost calculations

- Materials cost = $5 per card used in Step 1

- Process cost = $2 per drop

• Efficiency calculations

- Cycle Time = Total time/# of units shipped

- Penalty of $0.50 per second for every second over 300• Business calculations

- Net profit = Total price – Total cost and penalties

- Client price = $18/unit

8/22/2019 Bb Wk1 180 Metrics



The following are trademarks and service marks of Six Sigma Academy International, LLC: Breakthrough Lean®, Breakthrough Strategy®, Breakthrough Value Services®,Breakthrough Change StrategySM, Breakthrough DesignSM, Breakthrough DiagnosisSM, Breakthrough ExecutionSM, Breakthrough Sigma LeanSM, Breakthrough Six SigmaSM,Breakthrough Software DesignSM, FASTARTSM, Six Sigma Gold BeltTM, SOLVING YOUR BUSINESS PROBLEMS FOR THE LAST TIMESM. Six Sigma is a federallyregistered trademark of Motorola, Inc. MINITAB is a federally registered trademark of Minitab, Inc. SigmaFlow is a federally registered trademark of Compass Partners, Inc.VarTran is a federally registered trademark of Taylor Enterprises.

Six Sigma Academy International, LLC

8876 East Pinnacle Peak Road, Suite 100

Scottsdale, Arizona 85255

Tel. (480) 515-9501 • Fax (480) 515-9507

www.6-sigma.com

Documents

Bb Wk1 180 Metrics