1 / 30

Product & Process Assessment

Product & Process Assessment. Six Sigma Foundations Continuous Improvement Training. Six Sigma Simplicity. Key Learning Points. Traditional Metrics vs. CI DPU vs. DPMO RTY vs. Hidden Factory. Agenda. Objectives of This Module Introduction to defects

jchelsea
Télécharger la présentation

Product & Process Assessment

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

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

  5. The Traditional Methodof Project Selection • 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

  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.

  7. Hidden Factory Re-Work or Scrap Re-Work or Scrap Failure Analysis Failure Analysis Test Test Product Operation 1 Operation 2 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?

  8. Defect-Based Cost Model DPU vs. COPQ • When we track individual defects rather than percent defective, we end up with a much better predictor of costs. • What constitutes a defect? 600 High 500 400 300 COPQ ($) 200 100 Low 0 1 2 3 4 0 DPU Hint: Go collect defect data!

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

  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

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

  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)

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

  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!

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

  16. defects = = DPU Defects per unit units (Opps/unit) = 1,000,000 DPMO DPU * / = Defects / Total Opps * 1,000,000 DPU and DPMO Calculations • 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.

  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

  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.

  19. 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. x 1M DefectsOpportunity DPMO =

  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. x 1M DefectsOpportunity DPMO =

  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. x 1M DefectsOpportunity DPMO =

  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. x 1M DefectsOpportunity DPMO =

  23. DPMO Examples Application to a Measured Quantitative Parameter Part Specification: 1.240 ± .003 Spec Limit Probability of producing a “good” part = 0.97725 Probability of producing a “bad” part = 0.02275 Xbar = 1.241 S = 0.001 Measurement of a Product Characteristic 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.

  24. DPMO Examples -cont. 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 = dpu/opportunities/unit * 1,000,000 = (8/1)/(1,000/1) * 1,000,000 = 8,000

  25. 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. Defect Missing Part Opportunity for a Defect Fundamental Question:What Is the Likelihood Of Producing A Unit With Zero Defects?

  26. DPUs from the Process Fundamental Question: Given these facts, what is the likelihood of producing a unit with zero defects? Production Run • Tally the number of defects within each unit. Based on this sample, calculate the probability of producing a zero-defect unit. 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?

  27. 60 60*10 = 0.1 or 10% 60 60*10 = 0.9 or 90% 1 - DPU Modeling Fundamental Question: Given these facts, what is the likelihood of producing a unit with zero defects? • 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. 0.90 (10) = 0.3487 => 34.87%

  28. Operation 3 RTY = 98% DPU = 0.02 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. Operation 1 RTY = 99% DPU = 0.01 Operation 2 RTY = 96% DPU = 0.04 Final RTY = (0.99)*(0.96)*(0.98) = 0.93*100% = 93%

  29. 12000 Ecoat Defects 9000 PPM 6000 3000 0 Scratches Cracked Light Other 12000 Scratch Defects Process 9000 PPM 6000 Supplier Other 3000 0 Operator Dropped Material Cuts Other Operator Dropped 8000 6000 4000 PPM 2000 0 Part Sticks to Rack Packaging Other Analysis of Defect Data The Three-Level Pareto Principle

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

More Related