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Process Improvement

Process Improvement. (Continued). Introduction to Design of Experiments (DOE). Quickly optimize processes Reduce development time Reduce manufacturing costs Reduce scrap and rework Increase throughput Improve product quality Make products/processes more robust

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Process Improvement

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  1. Process Improvement (Continued) Session 7 - Process Improvement (Continued)

  2. Introduction to Design of Experiments (DOE) • Quickly optimize processes • Reduce development time • Reduce manufacturing costs • Reduce scrap and rework • Increase throughput • Improve product quality • Make products/processes more robust • Reduce need for control charting Session 7 - Process Improvement (Continued)

  3. Experimental designs are specific collections of trials run so the information content about a multi-variable process is maximized. With response-surface experimental designs, the goal is to put this information into a picture of the process. J. Stuart Hunter - Definition of DOE Session 7 - Process Improvement (Continued)

  4. Important Contributions From Different Approaches Session 7 - Process Improvement (Continued)

  5. Process Knowledge If what we know about our processes can’t be expresses in numbers, we don’t know much about them. If we don’t know much about them, we can’t control them. If we can’t control them, we can’t compete. Motorola University - Session 7 - Process Improvement (Continued)

  6. Quality is based on conformance to specifications Old Philosophy of Quality Loss due to scrap & rework Loss due to scrap & rework LSL USL Session 7 - Process Improvement (Continued)

  7. New Philosophy of Quality L3 L3 > L2 > L1 L2 L1 LSL USL Target Session 7 - Process Improvement (Continued)

  8. Taguchi’s Concept • DESIGN quality into the product and process. • Design the PRODUCT to be least sensitive to variations rather than trying to control the factors. • Design the product so that its performance parameters are CLOSEST TO THE TARGET. • Minimize costs within quality constraints rather than maximize quality within cost constraints. Session 7 - Process Improvement (Continued)

  9. Quality Effort by Activity Development Design Manufacturing Solve Problems Session 7 - Process Improvement (Continued)

  10. Taguchi’s Quadratic Loss Function L0 LSL Target USL L1 = k(y1 - T)2 Session 7 - Process Improvement (Continued)

  11. Example Let V(out) = 115 Vdc y = V(out) m = 115Vdc LD(50) = 115 +/- 20 Vdc (Consumer’s Tolerance) Repair Cost = $100 L(y) = k(y - 115) 2 k = L(y)/(y - m) 2 = $100/20 2 k = 0.25 If V(out) = 110 Vdc L(110) = 0.25(110 - 115)^2 = $6.25 Session 7 - Process Improvement (Continued)

  12. Example Suppose adjustment costs $2.00, i.e., the cost to rework. When should a unit be reworked? L(y) = 0.25 (y - 115)^2 $2.00 = 0.25 (y - 115)^2 8 = (y - 115)^2 y = 115 +/- 8 ^ 0.5 y = 115 +/- 2.83 Session 7 - Process Improvement (Continued)

  13. System Design Parameter Design Tolerance Design Basis of The Taguchi Method Session 7 - Process Improvement (Continued)

  14. Purposeful changes of the inputs (factors) to a process in order to observe corresponding changes in the output (responses). What is Designed Experiments? Session 7 - Process Improvement (Continued)

  15. Screening Modeling (Characterization) Sensitivity Optimization Robust (parameter) Design Tolerance Design Strategies for Experimentation Session 7 - Process Improvement (Continued)

  16. Obtain maximum information using minimum resources. Determine which factors shift average response, which shift variability, which have no effect. Find factor settings that optimize the response and minimize the cost. Build empirical models relating the response of interest to input factors Objectives of an Experimental Design Session 7 - Process Improvement (Continued)

  17. Full Factorials Fractional Factorials Plackett-Burman Latin Square Hadamard Matrices Foldover Designs Box-Behnken Designs D-Optimal Designs Taguchi Designs Methods of Experimentation Session 7 - Process Improvement (Continued)

  18. Full Factorial Experiments • Advantages • Tests all factors at all levels • Evaluates all main effects • Evaluates all interactions Session 7 - Process Improvement (Continued)

  19. Full Factorial Experiments • Disadvantages • Large number of runs • Large number of samples • Takes long time to run • Expensive Session 7 - Process Improvement (Continued)

  20. Fractional Factorial Experiments • Advantages • Fewer runs • Faster to complete • Fewer Samples • Less costly Session 7 - Process Improvement (Continued)

  21. Fractional Factorial Experiments • Disadvantages • Cannot test all factors at all levels • Cannot evaluate all main effects • Cannot evaluates all interactions Session 7 - Process Improvement (Continued)

  22. Factorial Versus Taguchi Session 7 - Process Improvement (Continued)

  23. Orthogonal Arrays Screening Designs Robust Designs Minimal Runs Taguchi Designs Session 7 - Process Improvement (Continued)

  24. Layout for Taguchi L-8 Session 7 - Process Improvement (Continued)

  25. Example Session 7 - Process Improvement (Continued)

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