bb wk1 180 metrics
TRANSCRIPT
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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)
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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
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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
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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.
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Yield 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)
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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”.
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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
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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
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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
10 unitslost asscrap
10 unitslost asscrap
Step 2
CU
S
T
O
M
E
R
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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”
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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
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Metrics Pg 11
Step 1 Step 2 Step 3
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
Step 1 Step 2 Step 3
Serial Process Example
CUSTOMER
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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
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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
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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
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In-Process Metrics for Defects
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)
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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
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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
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• 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
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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.
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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)
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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
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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
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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
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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.
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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
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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
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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
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In-Process Metrics Exercises
M t i E i
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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 )
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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
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Key Learning Points
•
•
•
•
•
Obj ti R i
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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
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Appendix ADPMO, Counting Opportunities
DPMO Co nting Opport nities
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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
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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
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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
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Appendix BCard Drop Optional Exercise
Card Drop Optional Exercise
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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:
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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
Card Drop Optional Exercise:
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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
Card Drop Optional Exercise:
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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
Card Drop Optional Exercise:
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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
Card Drop Optional Exercise:
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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
Card Drop Optional Exercise:
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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
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