6-sigma

1. 6-sigma S,S&L Chapt. 19

2. 6-sigma on Normal Distribution2 Errors/billion Mean = 10 sigma=2

3. 6-sigma Characteristics • Total Quality Management Program • The whole organization needs to work together to achieve this goal • Purchasing • Production • Sales

4. History of 6-sigma • Business School Trend • Started with Automakers and Electronics Firms in Japan • Focus on high quality workmanship, Few Lemons! • Design product so that it can be manufactured with high quality • Design product so that it can be fixed on the assembly line easily • Management focus on the quality of the product • This is a shift of management focus of profitability only • Meet quality inspections on all products before product left the factory • Manage to improve product quality year after year

5. History • USA was being beat in the market place by the Japanese in the 1970’s and early 1980’s & to some degree also today, e.g. VCR, flat screen monitors and TV’s • US academics and manufacturers studied the Japanese successes and brought home the reasons they were winning • US Business Schools developed 6-sigma as a US version of Japanese success • Becomes Business School Mantra

6. 6-sigma Has Become • A statistical tool to clearly identify production problems • A statistical tool to understand your production’s tolerance for variability in the raw materials and in the production process • A methodology to provide quantitative answers as to how to improve product quality. • You get a quantitative idea of which variables will get you the best quality improvement

7. 6-sigma is also • Management Mantra with its own Ethos and structure • Management Philosophy • Leadership training skills are built into this

8. 2 Categories of Variables to Reduce Errors • Quality of Raw Materials • Reproducibility of the Process

9. How Does it Work • Develop a process model for each product • The model can be a complex one • Aspen model of the process • Fluent model of the process • Y=G(xi’s) • The model can be a simple one using existing historical data on the process • Measure variability of raw materials, xi • Measure variability of process variables, xi • Measure variable of the resulting product, Y • Develop statistical based model using Statistically Designed Experiments Methodology • Y=a1x1+a2x2+a3x3+a12x1x2+a23x2x3+a13x1x3+a123x1x2x3 • Multivariable best fit

10. How Does it Work • Use model to predict what is the effect of • Raw material variations, xiσi • Process variations, xj σj • Combinations of raw material and process variations, (xi σi)(xj σj) • Calculate the number/quantity of out of specification products/material that will result. • Probablity(Z >Y +σY) = 2[1-F(Z)] • You can use this to determine lost profit as a result • You can use this to determine the increased costs for reworking products

11. How it Works • Use this data to develop contingency plans • Some consumers of your product can tolerate lower quality product in certain specs so sell these products to them. • Some products can be reworked to meet specs • Design out-of-spec re injection points into the process • Add reprocessing capability • Send back out-of-spec raw materials • Ask supplier to supply detailed analysis with each shipment showing that the raw material meets spec. • Run analysis of each batch of raw material entering the factory • Maintain records in a data base for later statistical analysis • Lower variability of process • Improve process control of critical units

12. How it Works • Use the error rate predictions to evaluate corporate decision making • Purchasing decisions • Cost/benefit for various raw material suppliers with different product quality, e.g. impurity levels and impurity types • Process Improvement • Cost/benefit for various process improvements • Not just increased capacity = more profit • Not just to meet more stringent pollution standards

13. 2 Categories of Variables to Reduce Errors • Quality of Raw Materials • Reproducibility of the Process

14. I-Variables to Reduce Errors • Quality of raw materials used to make product • Storage/Shipping Conditions of Raw Materials • Impurities in Raw Materials • Define specifications to be met by 6-sigma supplier • Quality control of raw materials • Reject out-of-spec raw material • Do not let an out-of-spec raw material enter the process • Quarantine out-of-spec raw materials • Reprocess raw materials to meet specs

15. Heat Used NaOH Rxn-4

16. II. Variables to Reduce Errors • Reproducibility of the Process • Raw Material storage/feeding • Process variables • All operating conditions of the process variables • Define ranges allowed to make good product • Product Storage/Shipping Procedures

17. Process Variables • All operating conditions of the process variables • Define ranges allowed to make good product • Temperatures • Feed, transfer lines, storage tanks • Operating Temperatures • Feed to Reactors • Feed to Separators • Flow Rates • Feed to Reactors • Feed to Separations Units • Critical Storage Tank Levels

18. Heat Used NaOH Rxn-4

19. Aspects of Design • Design can define critical process variables • Add more/advanced process control for the critical process variables • Define tolerance of a process for impurities/leaks • Different designs can be more impurity tolerant • Design for stable operation in various weather conditions • Rain/Snow • Summer/Winter • Humid/Dry