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- 1. Supply Chain: Adding Customer Value Anthony Orzechowski Director, R&D Quality Engineering 09-22511 v1.0
- 2. Conclusion We can improve our customers’ ability to be competitive by providing means to translate quality measures into business impact <ul><li>As suppliers, we should always want to help build our customers’ competitive advantage. </li></ul><ul><li>We can help them do this by … </li></ul><ul><ul><li>Relating the capability of key process outputs to safety and the financial impact on their business. </li></ul></ul><ul><ul><li>Helping drive expanded business for the customer will in turn lead to expanded business for ourselves. </li></ul></ul><ul><li>This presentation illustrate one example of this translation by looking at a creative and helpful view of process capability. </li></ul>
- 3. Putting the Focus on the Customer 50 o 3’N 8 o 21’E Family of integrated systems High first-pass acceptance Expanding assay menu Pre- and post-analytics Reliability Scientific Leadership Standardization Consolidation Workflow optimization Results Safety Labor efficiencies Cost pressures Error reduction Complexity
- 4. ARCHITECT “ Family Commonality…” Universal ARCHITECT Sample Carrier RSH Identical Software Common Immunoassay Reagents i 1000 SR Immunoassay Immunoassay i 2000 SR Immunoassay c 8000 Clinical Chemistry Common Clinical Chemistry Reagents c 16000 Clinical Chemistry Chemistry Integration ci 8200 Integrated ci 16200 Integrated
- 5. Assessing Performance in a Clinical Laboratory The Essential Question … <ul><li>“ What amount of medical harm due to analytical error is it OK to let go undetected?” </li></ul><ul><li>Dr. Frederick A. Smith Children’s Hospital - Chicago </li></ul>Dr. Frederick A. Smith - Children’s Hospital - Chicago
- 6. Measuring Capability Measuring a Suppliers ability to Meet Customer Needs is a well accepted practice <ul><li>As suppliers we want to deliver solutions that will consistently be capable meeting the need posed by this essential question. </li></ul><ul><li>This requires us to understand several key parameters … </li></ul><ul><ul><li>The Quality Specification (Total Error Allowable (TEa) </li></ul></ul><ul><ul><li>Our expected Deviation from Target </li></ul></ul><ul><ul><li>Our expected Variability about the process mean </li></ul></ul><ul><li>One traditional means to assess these together is a Capability index such as Cpk or Ppk. </li></ul>
- 7. Process Centered Capability Index — Cpk Cpk accounts for both inherent variation and a shift in mean. Target USL LSL y Cp is about 1.0, but Cpk is about 0.5.
- 8. Typical Translation of Capability <ul><ul><li>Sigma Level = C pk * 3 </li></ul></ul><ul><ul><li>But what does this mean to the customer relative to business impact ? </li></ul></ul><ul><ul><li>Is 12 Sigma twice as valuable as 6 Sigma to the customer? </li></ul></ul><ul><ul><li>How do we help optimize value for the customer ? </li></ul></ul>
- 9. Visualizing the Impact of Capability Customer Quality Control Visualizing the Impact of Capability on False Rejection and Acceptance Process with Capability = 1.5 USL = Upper Specification Limit (In-House) LSL = Lower Specification Limit Test Method Uncertainty True Variability of the Product Beta Risk LSL USL Process with Capability = 0.8 USL = Upper Specification Limit (In-House) LSL = Lower Specification Limit Test Method Uncertainty True Variability of the Product Beta Risk LSL USL
- 10. Translating Capability to Business Impact Measurement or improvement of capability should be related to tangible business results <ul><li>Simply supplying a measure of capability however does not directly translate into a quantified financial or business outcome for the customer. </li></ul><ul><li>To help in this translation from a QC standpoint, </li></ul><ul><ul><li>Relate it to the Essential Question … What level of error is OK to let go undetected? </li></ul></ul><ul><ul><li>Understanding how this question is assured at our customer will help to understand the value they place on higher levels of capability. </li></ul></ul>
- 11. Relationship of Sigma to C pk Sigma is simply a different view of Capability <ul><li>The Sigma Level is directly analogous to measuring capability through Cpk. </li></ul><ul><li>Sigma = (TEa – Bias)/(SD) </li></ul><ul><li>The difference between Cpk and Sigma Level is: </li></ul><ul><ul><li>Sigma Level measures the deviation from target (truth) rather than the Spec Limit and, </li></ul></ul><ul><ul><li>The value is normalized by dividing by the variability, not 3x the variability. </li></ul></ul><ul><ul><li>Sigma Level = C pk * 3 </li></ul></ul>USL (TEa) LSL (TEa) Sigma is about 1.5 Cp is about 1.0 but, Cpk is about 0.5 Bias %CV Defects True Value <ul><li>See: </li></ul><ul><li>Six Sigma Quality Design and Control, Second Edition and </li></ul><ul><li>Westgard.com </li></ul>
- 12. Application of Sigma Concepts and Metrics for QC Selection Higher Sigma Levels allow Larger Shifts to go undetected and still produce a safe result -7s -6s -5s -4s -3s -2s -1s 0s 1s 2s 3s 4s 5s 6s 7s Critical Shift Limit risk of a “bad” test result to 5% (this can be tuned to the situation) <ul><li>Risk of 5% means quality requirement cuts the error distribution at 1.65s from mean. </li></ul><ul><li>Shifts greater than this must be detected. </li></ul>See: Six Sigma Quality Design and Control, Second Edition The higher the Sigma Level Delivers higher Allowable Critical Shifts 1.65s
- 13. Relationship of Sigma to SE crit Higher Sigma Levels allow Greater Shifts to go undetected and still produce a safe result <ul><li>Critical Systematic Error ( SE crit ) </li></ul><ul><ul><li>Index used to describe size of error that needs to be detected by QC procedure </li></ul></ul><ul><ul><li> SE crit = [ (TEa – Bias)/SD ] – 1.65 </li></ul></ul><ul><ul><li>Sigma = SE crit + 1.65 </li></ul></ul><ul><ul><li>Can relate SE to rejection characteristics of QC rules and numbers of QC measurements using known power curves </li></ul></ul>USL LSL Sigma is about 1.5 Cp is about 1.0 but, Cpk is about 0.5 Bias %CV Defects True Value <ul><li>See: </li></ul><ul><li>Six Sigma Quality Design and Control, Second Edition </li></ul>Sigma Level
- 14. Method Decision Chart - Evaluating the System Capability Method Decision Chart 0.0% 2.0% 4. 0% 6.0% 8.0% 10.0% 12.0% Allowable Inaccuracy In a method decision chart, allowable error is plotted with the systematic error component on the Y-Axis and the random error component on the X-Axis. Allowable Imprecision (%CV) 0.0% 4.0% 8.0% 12.0% 16.0% 20.0% 24.0%
- 15. Method Decision Chart - Evaluating the System Capability Method Decision Chart 0.0% 2.0% 4. 0% 6.0% 8.0% 10.0% 12.0% Allowable Imprecision (%CV) Allowable Inaccuracy Example: Total Allowable Analytical Error (TEa) = 20% If a particular analyte could allow up to 20% (at 95% Confidence the allowable random and systematic error could be plotted as shown below Total Allowable Error bound at 95% Confidence (i.e.. 2 Sigma) 0.0% 4.0% 8.0% 12.0% 16.0% 20.0% 24.0%
- 16. Method Decision Chart - Evaluating the System Capability Sigma Value = (TEa – BIAS) / (SD) Method Decision Chart 0.0% 2.0% 4. 0% 6.0% 8.0% 10.0% 12.0% Allowable Imprecision (%CV) Allowable Inaccuracy Zone A Less than 2 Sigma Zone B Example: Total Allowable Analytical Error (TEa) = 20% Zone A < 2 Sigma B > 2 Sigma 0.0% 4.0% 8.0% 12.0% 16.0% 20.0% 24.0%
- 17. Method Decision Chart - Evaluating the System Capability Zone I < 2 Sigma II 2 to 3 Sigma III 3 to 4 Sigma IV 4 to 5 Sigma V > 5 Sigma <ul><li>Error Budget Window </li></ul><ul><ul><li>Will the design meet requirements? With what confidence? </li></ul></ul>Sigma Value = (TEa – BIAS) / (SD) Method Decision Chart 0.0% 4.0% 8.0% 12.0% 16.0% 20.0% 24.0% 0.0% 2.0% 4. 0% 6.0% 8.0% 10.0% 12.0% Allowable Imprecision (%CV) Allowable Inaccuracy Zone I Less than 2 Sigma II V Example: Total Allowable Analytical Error (TEa) = 20% III IV
- 18. Relationship of Sigma Level to QC As the Sigma Level Rises, the amount of Customer QC drops and the financial impact to the customer is improved making the Customer more Competitive <ul><li>Reference: Westgard Workshops - Quality Assessment from Test Outcome Data: Use of PT Data to Estimate Quality of Lab Tests, 2006 – Madison, WI </li></ul>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 1.0 2.0 3.0 4.0 1.65 2.65 3.65 4.65 5.65 1 3s /2of3 2s /R 4s /3 1s /6 x 0.07 6 1 3s /2 2s /R 4s /4 1s 0.03 4 1 2.5s 0.04 4 1 2.5s 0.03 2 1 3s /2 2s /R 4s 0.01 2 1 3s 0.00 2 1 3.5s 0.00 2 1 3s 0.00 1 P fr N Probability for Rejection (P) 3 4 5 Desirable Error Detection Desirable False Rejection Systematic Error (SE, multiples of s) Sigma Scale
- 19. Why High Analytical Capability Is Important to a Laboratory Director <ul><li>Lower performance leads to increased costs </li></ul><ul><li>6 sigma performance is our ideal benchmark for practical reasons </li></ul>Higher Costs and Complaints 1 3s /2 2s /R 4s /3 1s /6 x with 6 controls (e.g. 2 reps at 3 ctrl levels) 1 3s /2 2s /R 4s /4 1s with 4 controls (e.g. 2 reps at 2 ctrl levels) 1 3s /2 2s /R 4s with 2 controls 3.5 SD Rule with 2 controls Example QC (at 90% AQA based upon OpSpec Charts) ~1% Relatively simple QC Rules 5 Sigma ~7% Extensive QC. QC alone will not assure quality <3 Sigma ~3% Complex QC 4 Sigma ~0% Any rule will do 6 Sigma False Rejects QC Impact Sigma Metric
- 20. Value Stream Mapping A Flow Chart with Data to Help Visualize Problems 1 Suppliers Customers 2 3 4 Sub-Optimal Process: 1 Suppliers Customers 2 4 Future State Process: > 99% FPA 65% FPA Redo QC QC QC QC 99% 98% 90% Rework Scrap Delay
- 21. Independent Analysis Confirms Six Sigma <ul><ul><li>Sten Westgard independently reviewed data from Abbott’s 2007 AACC ARCHITECT c16000 technical poster </li></ul></ul><ul><ul><ul><li>"7 out of 9 methods are world class” - “It is rare to find so much good news in a method evaluation study </li></ul></ul></ul>http://westgard.com/qcapp45.htm - QC APPLICATIONA Site Evaluation of Abbott Architect c16000 < 3 Sigma = Poor Performance 3 – 6 Sigma = Good Performance 6 Sigma = World Class Performance
- 22. How do our competitors perform with Six Sigma? <ul><li>An evaluation of several common, high volume assays on a competitive chemistry system is published on www.westgard.com </li></ul><ul><li>4 out of 8 assays are < 6 Sigma (50%) </li></ul><ul><li>2 out of the 4 assays are < 3 Sigma = Poor performance! </li></ul>6 Sigma
- 23. Error Budgeting Designing a Safe Product that will make our Customers more Competitive Conventional Requirements Approach Analytical Quality Planning Budget Stable Imprecision Stable Inaccuracy Stable Imprecision Stable Inaccuracy QC Safety Margin Traditional Error Budget Approach Total Error Allowable in a Patient Result
- 24. Reference: Westgard Workshops - Quality Assessment from Test Outcome Data: Use of PT Data to Estimate Quality of Lab Tests, 2006 – Madison, WI Assessing Capability Impact to the Business Lower Sigma Levels will result in Significant QC and Costs
- 25. Assessing Capability Impact to the Business Higher Sigma Levels will result in Reduced QC and Costs Reference: Westgard Workshops - Quality Assessment from Test Outcome Data: Use of PT Data to Estimate Quality of Lab Tests, 2006 – Madison, WI
- 26. Assessing Capability Impact to the Business Difference in Sigma Levels can be Used as a Competitive Advantage due to Differences in the Impact to the Business - Safety, Process Flow and Cost Reference: Westgard Workshops - Quality Assessment from Test Outcome Data: Use of PT Data to Estimate Quality of Lab Tests, 2006 – Madison, WI
- 27. Assessing Capability Impact to the Business Difference in Sigma Levels can be Used as a Competitive Advantage due to Differences in the Impact to the Business - Safety, Process Flow and Cost Reference: Westgard Workshops - Quality Assessment from Test Outcome Data: Use of PT Data to Estimate Quality of Lab Tests, 2006 – Madison, WI
- 28. Conclusions Benefits of Using Quality Specifications <ul><li>Clinical Laboratories </li></ul><ul><ul><li>Directly links assay performance requirements to medical utility and to Laboratory workflow impact due to Quality Control. </li></ul></ul><ul><ul><li>Provides a clear unambiguous means for clinical laboratories to communicate their performance needs to diagnostic manufacturers. </li></ul></ul><ul><li>Diagnostic Manufacturers </li></ul><ul><ul><li>Provides a means for effective development and control of performance specifications </li></ul></ul><ul><ul><li>Provides a means for diagnostic manufacturers to clearly discuss on-going Quality performance with their clinical laboratory customers. </li></ul></ul>
- 29. Thank You !!! A Promise for Life

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