1. Product & Process Assessment Six Sigma Foundations Continuous Improvement Training Six Sigma Foundations Continuous Improvement Training Six Sigma Simplicity

2. Key Learning Points  Traditional Metrics vs. CI  DPU vs. DPMO  RTY vs. Hidden Factory  Traditional Metrics vs. CI  DPU vs. DPMO  RTY vs. Hidden Factory

3. AgendaAgenda Objectives of This Module  Introduction to defects  First time yield (FTY) vs. rolled throughput yield (RTY)  COPQ vs. yield  The hidden factory  Defects per opportunity metric  Complexity explained  Defects per unit metric  The basic Binomial model  The basic Poisson model  The application of defect data in process improvement efforts  Project metrics Objectives of This Module  Introduction to defects  First time yield (FTY) vs. rolled throughput yield (RTY)  COPQ vs. yield  The hidden factory  Defects per opportunity metric  Complexity explained  Defects per unit metric  The basic Binomial model  The basic Poisson model  The application of defect data in process improvement efforts  Project metrics

4. Will the first time yield be correlated to other major business metrics? What will the test yield be next week? First Time (End of Line) Yield by Week 90 92 94 96 98 100 Wk 1Wk 2Wk 3Wk 4Wk 5Wk 6Wk 7Wk 8Wk 9 Wk 10Wk 11Wk 12Wk 13Wk 14Wk 15 Weekly Yield (%) Where: FTY= First Time Yield (test yield) P = Number of units that pass test U= Number of units tested U FTY= P * 100% Traditional Method of Project Selection How does your organization identify poor quality products?

5.  Will traditional yield (end-of-line test yield) calculations correlate to business metrics?  End-of-line test yield has traditionally been considered a good predictor of profit margins and scrap rates. However, it rarely does a good job at either. Why not? What is missing? What’s the problem with classical yield calculations?  As managers and Black Belts, we shouldn’t select projects based on the FTY of a product.  Will traditional yield (end-of-line test yield) calculations correlate to business metrics?  End-of-line test yield has traditionally been considered a good predictor of profit margins and scrap rates. However, it rarely does a good job at either. Why not? What is missing? What’s the problem with classical yield calculations?  As managers and Black Belts, we shouldn’t select projects based on the FTY of a product. Expected Relationships The Traditional Method of Project Selection

6. Defects vs. Defectives  Defects:  Countable failures that are associated with a single unit. A single unit can be found to be defective, but it may have more than one defect.  Defectives:  Completed units that are classified as bad. The whole unit is said to be defective regardless of the number of defects it has. FTY = the number of non-defectives/the number of total units.  Defects:  Countable failures that are associated with a single unit. A single unit can be found to be defective, but it may have more than one defect.  Defectives:  Completed units that are classified as bad. The whole unit is said to be defective regardless of the number of defects it has. FTY = the number of non-defectives/the number of total units.

7. The Hidden Factory  To analyze, re-work, and/or scrap potential product requires:  More manpower  Extra floor space  Longer cycle time  More raw material  More $$$$  How big is your “hidden factory”?  What happens to cost as defects increase?  To analyze, re-work, and/or scrap potential product requires:  More manpower  Extra floor space  Longer cycle time  More raw material  More $$$$  How big is your “hidden factory”?  What happens to cost as defects increase? HiddenFactory Re-Work or Scrap Re-Work FailureAnalysisFailureAnalysis Test Operation 2 Test Operation 1 Product

8. Hint: Go collect defect data! 0 100 200 300 400 500 600 Defect-Based Cost Model  When we track individual defects rather than percent defective, we end up with a much better predictor of costs.  What constitutes a defect?  When we track individual defects rather than percent defective, we end up with a much better predictor of costs.  What constitutes a defect? DPU vs. COPQ 01234 DPU COPQ ($) High Low

9. Defects ? Defects ? Proportion =  Which metric do you need? DPU or DPMO?  In most cases, we end up converting defect data to a proportion as follows:  When we use defect data, we need to determine what number to put into the denominator of the equation above. Defects Total Opportunities Defects Total Opportunities DPMO = Defects Unit Produced Defects Unit Produced DPU = Collect Defect Data What do I need to know? Compare the quality level of non-identical parts, processes, or products. Model process efficiency or estimate the probability of producing defect-free parts. ? = A measure of complexity (i.e., opportunities) ? = The number of units produced (or processed through the operation) DPMODPU Management Metric Black Belt Project Metric Project Selection Metric Benchmarking Metric Black Belt Project Metric x 1M x 1M DPU vs. DPMO

10. A Metric to Expose the Hidden Factory Rolled Throughput Yield(RTY)  The product of total throughput at each step in the process Definition  A measurement of yield which exposes the extent and location of scrap and rework  The proportion processed with no defects Rolled Throughput Yield(RTY)  The product of total throughput at each step in the process Definition  A measurement of yield which exposes the extent and location of scrap and rework  The proportion processed with no defects

11. Rolled Throughput Yield (RTY) Y1=0.92 First Pass Yield=804/1000=0.804 Y2=0.82 Y3=0.84 Scrap 4% - 40 units Rework 4% 960 units Scrap 9% Rework 9% - 86 units 874 units Scrap 8% Rework 8% 804 units RTY=633/1000=0.633 920 units 754 units 633 units RTY=0.92x0.82x0.84=0.633 - 70 units 1000 units HiddenFactory

12. Cost of Hidden Factory  To analyze, to rework, and to scrap requires:  More raw material (Scrap / re-order)  Manpower(Unproductive Hours)  Floor space(Capacity)  Longer cycle time(DSO)  To analyze, to rework, and to scrap requires:  More raw material (Scrap / re-order)  Manpower(Unproductive Hours)  Floor space(Capacity)  Longer cycle time(DSO)

13. YieldYield Rolled Throughput Yield = 63% Test Yield = 84% First Pass Yield = 80% Identifies Result of Final Inspection Identifies Result of Final Inspection Identifies Process Yield (Scrap) Identifies Process Yield (Scrap) Identifies Extent & Location of COPQ (Our Opportunity) Identifies Extent & Location of COPQ (Our Opportunity) Customer View Internal View Internal Performance

14. The Greater Hidden Factory  Beyond the direct costs associated with finding and fixing defects, “Cost of Poor Quality” also includes:  The hidden cost of failing to meet customer expectations the first time  The hidden opportunity for increased efficiency  The hidden potential for higher profits  The hidden loss in market share  The hidden increase in total cycle times  For an average company, the cost of poor quality can be as high as 25% of annual sales  COPQ can exceed the profit margin  COPQ is our Opportunity!  Beyond the direct costs associated with finding and fixing defects, “Cost of Poor Quality” also includes:  The hidden cost of failing to meet customer expectations the first time  The hidden opportunity for increased efficiency  The hidden potential for higher profits  The hidden loss in market share  The hidden increase in total cycle times  For an average company, the cost of poor quality can be as high as 25% of annual sales  COPQ can exceed the profit margin  COPQ is our Opportunity!

15. Measure Analyze ImproveControl Management Measure Analyze Improve Control Select a new project Goal: Y = f (x) Review progress and modify Use DPMO Here Use DPU Here Remember: Strategic Black Belt Overview

16.  Black Belts should use the DPU (or PPM) metric to track their project performance.  Management should use the DPMO metric to select projects and conduct benchmark studies for dissimilar goods and services.  Black Belts should use the DPU (or PPM) metric to track their project performance.  Management should use the DPMO metric to select projects and conduct benchmark studies for dissimilar goods and services. Defects per unit == unitsdefectsDPU 1,000,000 * / 1,000,000 * Opps Total / Defects(Opps/unit)DPUDPMO = = DPU and DPMO Calculations

17. DPMO, Measures of Complexity  Product complexity  Number of parts  Number of functions  Process complexity  Number of attachments  Number of welds  Transactional complexity  Number of entries  Software complexity  Lines of code  Product complexity  Number of parts  Number of functions  Process complexity  Number of attachments  Number of welds  Transactional complexity  Number of entries  Software complexity  Lines of code

18. DPMO, Measures of Complexity - continued  “Complexity” is a measure of how complicated a particular good or service is. Theoretically, it’s doubtful that we will ever be able to quantify complexity in an exacting manner.  If we assume that all characteristics are independent and mutually exclusive, we may say that “complexity” can be reasonably estimated by a simple count. This count is referred to as an “Opportunity Count”.  In terms of quality, each product and/or process characteristic represents a unique “opportunity” to either add or subtract value.  Remember, we only need to count opportunities if we want to estimate a sigma level for comparisons of goods and services that are not necessarily similar.  “Complexity” is a measure of how complicated a particular good or service is. Theoretically, it’s doubtful that we will ever be able to quantify complexity in an exacting manner.  If we assume that all characteristics are independent and mutually exclusive, we may say that “complexity” can be reasonably estimated by a simple count. This count is referred to as an “Opportunity Count”.  In terms of quality, each product and/or process characteristic represents a unique “opportunity” to either add or subtract value.  Remember, we only need to count opportunities if we want to estimate a sigma level for comparisons of goods and services that are not necessarily similar.

19. Defects Opportunity Defects Opportunity DPMO = x 1M x 1M DPMO, Counting Opportunities  Non-value-added rules: No opportunity count should be applied to any operation that does not add value.  Transportation and storage of materials provide no opportunities. Deburring operations can also be considered.  Testing, inspection, gauging, etc., do not count. The product, in most cases, remains unchanged. An exception: An electrical tester where the tester is also used to program an EPROM. The product was altered and value was added.  Supplied components rules: Each supplied part provides one opportunity.  Supplied materials such as solder, machine oil, coolants, etc., do not count as supplied components.  Non-value-added rules: No opportunity count should be applied to any operation that does not add value.  Transportation and storage of materials provide no opportunities. Deburring operations can also be considered.  Testing, inspection, gauging, etc., do not count. The product, in most cases, remains unchanged. An exception: An electrical tester where the tester is also used to program an EPROM. The product was altered and value was added.  Supplied components rules: Each supplied part provides one opportunity.  Supplied materials such as solder, machine oil, coolants, etc., do not count as supplied components.

20. DPMO, Counting Opportunities -cont.  Connections rules: Each “attachment” or “connection” counts as one.  If a device requires four bolts, there is an opportunity of four  one for each bolt connected.  A 60-pin integrated circuit, surface mount device, soldered to a printed circuit board counts as 60 connections.  A 16-pin dual in-line package with through-hole mounting counts as 16 joints. There is no double counting of joints  one for the top side and one for the bottom side is not correct.  Once you define an “opportunity,” you must institutionalize that definition to maintain consistency.  Connections rules: Each “attachment” or “connection” counts as one.  If a device requires four bolts, there is an opportunity of four  one for each bolt connected.  A 60-pin integrated circuit, surface mount device, soldered to a printed circuit board counts as 60 connections.  A 16-pin dual in-line package with through-hole mounting counts as 16 joints. There is no double counting of joints  one for the top side and one for the bottom side is not correct.  Once you define an “opportunity,” you must institutionalize that definition to maintain consistency. Defects Opportunity Defects Opportunity DPMO = x 1M x 1M

21. DPMO, Counting Opportunities -cont.  Machine shop equipment rules:  There is one opportunity count for each machined surface.  If one tool makes five separate cuts, the count is five opportunities.  When a hole is drilled and counter- bored, the count is two because there are two separate operations.  A hole that is drilled and honed because the drilling operation is not trusted to hit the dimension is only a count of one. The honing operation is re-work of the drilling operation.  Machine shop equipment rules:  There is one opportunity count for each machined surface.  If one tool makes five separate cuts, the count is five opportunities.  When a hole is drilled and counter- bored, the count is two because there are two separate operations.  A hole that is drilled and honed because the drilling operation is not trusted to hit the dimension is only a count of one. The honing operation is re-work of the drilling operation. Defects Opportunity Defects Opportunity DPMO = x 1M x 1M

22. DPMO, Counting Opportunities -cont.  Transactional process rules:  Filling out a form provides one opportunity per data-entry field, not one opportunity for each character.  One line of assembly equivalent code counts as one opportunity for software programs.  Sanity check rule:  “Will applying counts in these operations take my business in the direction it is intended to go?”  If counting each dimension adds no value, and increases cycle time then this type of count would be contrary to the company objective and would not provide an opportunity.  Once you define an “opportunity”, you must institutionalize that definition to maintain consistency.  Transactional process rules:  Filling out a form provides one opportunity per data-entry field, not one opportunity for each character.  One line of assembly equivalent code counts as one opportunity for software programs.  Sanity check rule:  “Will applying counts in these operations take my business in the direction it is intended to go?”  If counting each dimension adds no value, and increases cycle time then this type of count would be contrary to the company objective and would not provide an opportunity.  Once you define an “opportunity”, you must institutionalize that definition to maintain consistency. Defects Opportunity Defects Opportunity DPMO = x 1M x 1M

23. Application to a Measured Quantitative Parameter If This Part Were A Supplied Part, It Would Count As One Opportunity. As Supplied Parts, At Least 2.275% Of Them Had Defects. Therefore, The DPMO = 0.02275*1,000,000 = 22750. Spec Limit Measurement of a Product Characteristic Probability of producing a “bad” part = 0.02275 Probability of producing a “good” part = 0.97725 Part Specification: 1.240 ±.003 Xbar = 1.241 S = 0.001 DPMO Examples

24. DPMO = dpu/opportunities/unit * 1,000,000 = (8/1)/(1,000/1) * 1,000,000 = 8,000 Application to an Inspected Parameter  A circuit board has 800 solder joints and 200 components.  How many opportunities do we have?  Six defective joints and two defective components were found in this unit.  What is the DPMO? DPMO Examples -cont.

25. Fundamental Question: What Is the Likelihood Of Producing A Unit With Zero Defects? Defect Missing Part Opportunity for a Defect The DPU Metric  Suppose we have a unit with 10 components. Each component within the unit is a chance (or opportunity) for a defect to occur. Thus, each unit can contain up to 10 defects.  Note: This means you need to be able to track more than one defect per unit through your data- collection system.  Suppose we have a unit with 10 components. Each component within the unit is a chance (or opportunity) for a defect to occur. Thus, each unit can contain up to 10 defects.  Note: This means you need to be able to track more than one defect per unit through your data- collection system.

26. Fundamental Question: Given these facts, what is the likelihood of producing a unit with zero defects? How many units had: _____ Zero defects _____ One defect _____ Two defects _____ Three defects _____ Four defects _____ Five or more defects Question 1:How many total defects are observed? Question 2:What is the number of DPUs? Production Run DPUs from the Process  Tally the number of defects within each unit. Based on this sample, calculate the probability of producing a zero-defect unit.

27.  Given:  60 defects observed  60 units processed  10 opps per unit  The probability that any given opportunity will be a defect is:  The probability that any given opportunity will NOT be a defect is:  The probability that all 10 opportunities on a single unit will be defect free is:  Given:  60 defects observed  60 units processed  10 opps per unit  The probability that any given opportunity will be a defect is:  The probability that any given opportunity will NOT be a defect is:  The probability that all 10 opportunities on a single unit will be defect free is: If we extend the concept to an infinite number of opportunities, all at a DPU of 1.0, we will approach the value of 0.368. Fundamental Question: Given these facts, what is the likelihood of producing a unit with zero defects? 60 60*10 = 0.1 or 10% 60 60*10 = 0.9 or 90% 1 - 0.90 (10) = 0.3487 => 34.87% DPU Modeling

28. Two Major Uses of Defect Data  Prediction of true factory yield (RTY)  Defect data can be used in an analysis to predict factory (or line) yield, as shown below. Data analysis for project scoping  Defect data is most commonly used to perform Pareto analysis on information to determine priorities for action items within the project planning cycle. The following slides demonstrate this idea.  Prediction of true factory yield (RTY)  Defect data can be used in an analysis to predict factory (or line) yield, as shown below. Data analysis for project scoping  Defect data is most commonly used to perform Pareto analysis on information to determine priorities for action items within the project planning cycle. The following slides demonstrate this idea. Operation 2 RTY = 96% DPU = 0.04 Final RTY = (0.99)*(0.96)*(0.98) = 0.93*100% = 93% Operation 1 RTY = 99% DPU = 0.01 Operation 3 RTY = 98% DPU = 0.02

29. OtherSupplier Process 0 3000 6000 9000 12000 Operator DroppedMaterial CutsOther PPM Scratch Defects The Three-Level Pareto Principle Analysis of Defect Data 0 2000 4000 6000 8000 Part Sticks to RackPackagingOther PPM Operator Dropped 0 3000 6000 9000 12000 ScratchesCrackedLightOther PPM Ecoat Defects

30. Product & Process Assessment Six Sigma Foundations Continuous Improvement Training Six Sigma Foundations Continuous Improvement Training