Basic Analytics Module for Sponsors
- 1. Action plan and SOP for Special Cause Variation Determine new Goals (UCL, LCL)
- 2. Module 8: Basic Analytics Welcome to Champion Training Facilitated by Kaplans Process Improvement Team
- 3. Agenda of Champion Training Modules # Module # Pages 1 Introduction 19 2 Project Selection and Engaging Process Improvement 22 3 Champion Role through Project Lifecycle 26 4 Calculating Financial Benefit/ the Cost of Poor Quality 13 5 Define Overview & Tools 26 6 Measure Overview & Tools 27 7 Analyze Overview & Tools 18 8 Basic Analytics 21 9 Improve Overview & Tools 33 10 Control Overview & Tools 28 11 How to Effectuate Change Using Change Management 31 12 Kaplans Work Out 15
- 4. Purpose of This Training Provide Kaplan champions with the knowledge and skills to be effective leaders and coaches to their people engaged in Process Improvement/Six Sigma projects Everything should be made as simple as possible, but not too simple. Albert Einstein
- 5. Types of Statistics Descriptive Statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. With descriptive statistics you are simply describing what is, what the data shows. Inferential Statistics investigate questions, models and hypotheses. In many cases, the conclusions from inferential statistics extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population thinks. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data.
- 6. Monitor Descriptive Statistics Monitor performance of the Xs and Ys over time Verify that the improvement actions on the Xs have made the desired improvement in the Y Mean, Median, Mode Standard Deviation
- 7. Numeric Display Terms The number of data points with non-missing values in the data set.N The Average Mean (Arithmetic Mean) The middle data point in the data set.Median (50th Percentile) The Value that occurs the most frequently in a data set.Mode The average distance from the mean.StDev (Standard Deviation) The highest value form the lowest 25% of the ranked data. Q1 (First Quartile or 25th Percentile) The lowest value from the highest 25% of the ranked data. Q3 (Third Quartile or 75th Percentile)
- 8. Defects A DEFECT is failure to conform to customer requirements DEFECTIVE is when an entire unit fails to meet acceptance criteria, regardless of the number of defects within the unit. Defective Defect Defective
- 9. How to Calculate Sigma 3.4 5 8 10 20 30 40 70 100 150 230 330 480 680 960 1,350 1,860 2,550 3,460 4,660 6,210 8,190 10,700 13,900 17,800 22,700 28,700 35,900 44,600 54,800 66,800 80,800 96,800 115,000 135,000 158,000 184,000 212,000 242,000 274,000 308,000 344,000 382,000 420,000 460,000 500,000 540,000 0.34 0.5 0.8 1 2 3 4 7 10 15 23 33 48 68 96 135 186 255 346 466 621 819 1,070 1,390 1,780 2,270 2,870 3,590 4,460 5,480 6,680 8,080 9,680 11,500 13,500 15,800 18,400 21,200 24,200 27,400 30,800 34,400 38,200 42,000 46,000 50,000 54,000 0.034 0.05 0.08 0.1 0.2 0.3 0.4 0.7 1.0 1.5 2.3 3.3 4.8 6.8 9.6 13.5 18.6 25.5 34.6 46.6 62.1 81.9 107 139 178 227 287 359 446 548 668 808 968 1,150 1,350 1,580 1,840 2,120 2,420 2,740 3,080 3,440 3,820 4,200 4,600 5,000 5,400 0.0034 0.005 0.008 0.01 0.02 0.03 0.04 0.07 0.1 0.15 0.23 0.33 0.48 0.68 0.96 1.35 1.86 2.55 3.46 4.66 6.21 8.19 10.7 13.9 17.8 22.7 28.7 35.9 44.6 54.8 66.8 80.8 96.8 115 135 158 184 212 242 274 308 344 382 420 460 500 540 0.00034 0.0005 0.0008 0.001 0.002 0.003 0.004 0.007 0.01 0.015 0.023 0.033 0.048 0.068 0.096 0.135 0.186 0.255 0.346 0.466 0.621 0.819 1.07 1.39 1.78 2.27 2.87 3.59 4.46 5.48 6.68 8.08 9.68 11.5 13.5 15.8 18.4 21.2 24.2 27.4 30.8 34.4 38.2 42 46 50 54 99.99966% 99.9995% 99.9992% 99.9990% 99.9980% 99.9970% 99.9960% 99.9930% 99.9900% 99.9850% 99.9770% 99.9670% 99.9520% 99.9320% 99.9040% 99.8650% 99.8140% 99.7450% 99.6540% 99.5340% 99.3790% 99.1810% 98.930% 98.610% 98.220% 97.730% 97.130% 96.410% 95.540% 94.520% 93.320% 91.920% 90.320% 88.50% 86.50% 84.20% 81.60% 78.80% 75.80% 72.60% 69.20% 65.60% 61.80% 58.00% 54.00% 50% 46% 6.0 5.9 5.8 5.7 5.6 5.5 5.4 5.3 5.2 5.1 5.0 4.9 4.8 4.7 4.6 4.5 4.4 4.3 4.2 4.1 4.0 3.9 3.8 3.7 3.6 3.5 3.4 3.3 3.2 3.1 3.0 2.9 2.8 2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.0 1.9 1.8 1.7 1.6 1.5 1.4 Long-Term Yield ST Process Sigma Defects Per 1,000,000 Defects Per 100,000 Defects Per 10,000 Defects Per 1,000 Defects Per 100 3.4 5 8 10 20 30 40 70 100 150 230 330 480 680 960 1,350 1,860 2,550 3,460 4,660 6,210 8,190 10,700 13,900 17,800 22,700 28,700 35,900 44,600 54,800 66,800 80,800 96,800 115,000 135,000 158,000 184,000 212,000 242,000 274,000 308,000 344,000 382,000 420,000 460,000 500,000 540,000 0.34 0.5 0.8 1 2 3 4 7 10 15 23 33 48 68 96 135 186 255 346 466 621 819 1,070 1,390 1,780 2,270 2,870 3,590 4,460 5,480 6,680 8,080 9,680 11,500 13,500 15,800 18,400 21,200 24,200 27,400 30,800 34,400 38,200 42,000 46,000 50,000 54,000 0.034 0.05 0.08 0.1 0.2 0.3 0.4 0.7 1.0 1.5 2.3 3.3 4.8 6.8 9.6 13.5 18.6 25.5 34.6 46.6 62.1 81.9 107 139 178 227 287 359 446 548 668 808 968 1,150 1,350 1,580 1,840 2,120 2,420 2,740 3,080 3,440 3,820 4,200 4,600 5,000 5,400 0.0034 0.005 0.008 0.01 0.02 0.03 0.04 0.07 0.1 0.15 0.23 0.33 0.48 0.68 0.96 1.35 1.86 2.55 3.46 4.66 6.21 8.19 10.7 13.9 17.8 22.7 28.7 35.9 44.6 54.8 66.8 80.8 96.8 115 135 158 184 212 242 274 308 344 382 420 460 500 540 0.00034 0.0005 0.0008 0.001 0.002 0.003 0.004 0.007 0.01 0.015 0.023 0.033 0.048 0.068 0.096 0.135 0.186 0.255 0.346 0.466 0.621 0.819 1.07 1.39 1.78 2.27 2.87 3.59 4.46 5.48 6.68 8.08 9.68 11.5 13.5 15.8 18.4 21.2 24.2 27.4 30.8 34.4 38.2 42 46 50 54 99.99966% 99.9995% 99.9992% 99.9990% 99.9980% 99.9970% 99.9960% 99.9930% 99.9900% 99.9850% 99.9770% 99.9670% 99.9520% 99.9320% 99.9040% 99.8650% 99.8140% 99.7450% 99.6540% 99.5340% 99.3790% 99.1810% 98.930% 98.610% 98.220% 97.730% 97.130% 96.410% 95.540% 94.520% 93.320% 91.920% 90.320% 88.50% 86.50% 84.20% 81.60% 78.80% 75.80% 72.60% 69.20% 65.60% 61.80% 58.00% 54.00% 50% 46% 6.0 5.9 5.8 5.7 5.6 5.5 5.4 5.3 5.2 5.1 5.0 4.9 4.8 4.7 4.6 4.5 4.4 4.3 4.2 4.1 4.0 3.9 3.8 3.7 3.6 3.5 3.4 3.3 3.2 3.1 3.0 2.9 2.8 2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.0 1.9 1.8 1.7 1.6 1.5 1.4 Long-Term Yield ST Process Sigma Defects Per 1,000,000 Defects Per 100,000 Defects Per 10,000 Defects Per 1,000 Defects Per 100 3.4 5 8 10 20 30 40 70 100 150 230 330 480 680 960 1,350 1,860 2,550 3,460 4,660 6,210 8,190 10,700 13,900 17,800 22,700 28,700 35,900 44,600 54,800 66,800 80,800 96,800 115,000 135,000 158,000 184,000 212,000 242,000 274,000 308,000 344,000 382,000 420,000 460,000 500,000 540,000 0.34 0.5 0.8 1 2 3 4 7 10 15 23 33 48 68 96 135 186 255 346 466 621 819 1,070 1,390 1,780 2,270 2,870 3,590 4,460 5,480 6,680 8,080 9,680 11,500 13,500 15,800 18,400 21,200 24,200 27,400 30,800 34,400 38,200 42,000 46,000 50,000 54,000 0.034 0.05 0.08 0.1 0.2 0.3 0.4 0.7 1.0 1.5 2.3 3.3 4.8 6.8 9.6 13.5 18.6 25.5 34.6 46.6 62.1 81.9 107 139 178 227 287 359 446 548 668 808 968 1,150 1,350 1,580 1,840 2,120 2,420 2,740 3,080 3,440 3,820 4,200 4,600 5,000 5,400 0.0034 0.005 0.008 0.01 0.02 0.03 0.04 0.07 0.1 0.15 0.23 0.33 0.48 0.68 0.96 1.35 1.86 2.55 3.46 4.66 6.21 8.19 10.7 13.9 17.8 22.7 28.7 35.9 44.6 54.8 66.8 80.8 96.8 115 135 158 184 212 242 274 308 344 382 420 460 500 540 0.00034 0.0005 0.0008 0.001 0.002 0.003 0.004 0.007 0.01 0.015 0.023 0.033 0.048 0.068 0.096 0.135 0.186 0.255 0.346 0.466 0.621 0.819 1.07 1.39 1.78 2.27 2.87 3.59 4.46 5.48 6.68 8.08 9.68 11.5 13.5 15.8 18.4 21.2 24.2 27.4 30.8 34.4 38.2 42 46 50 54 99.99966% 99.9995% 99.9992% 99.9990% 99.9980% 99.9970% 99.9960% 99.9930% 99.9900% 99.9850% 99.9770% 99.9670% 99.9520% 99.9320% 99.9040% 99.8650% 99.8140% 99.7450% 99.6540% 99.5340% 99.3790% 99.1810% 98.930% 98.610% 98.220% 97.730% 97.130% 96.410% 95.540% 94.520% 93.320% 91.920% 90.320% 88.50% 86.50% 84.20% 81.60% 78.80% 75.80% 72.60% 69.20% 65.60% 61.80% 58.00% 54.00% 50% 46% 6.0 5.9 5.8 5.7 5.6 5.5 5.4 5.3 5.2 5.1 5.0 4.9 4.8 4.7 4.6 4.5 4.4 4.3 4.2 4.1 4.0 3.9 3.8 3.7 3.6 3.5 3.4 3.3 3.2 3.1 3.0 2.9 2.8 2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.0 1.9 1.8 1.7 1.6 1.5 1.4 Long-Term Yield ST Process Sigma Defects Per 1,000,000 Defects Per 100,000 Defects Per 10,000 Defects Per 1,000 Defects Per 100 Long-Term Yield ST Process Sigma Defects Per 1,000,000 Defects Per 100,000 Defects Per 10,000 Defects Per 1,000 Defects Per 100 A KU student expected to speak in person to their Financial Aid Officer every time they contact the FAO. 500 Defects = 100,000 Defects Per 5000 Opportunities Million Opportunities 2.8 Sigma A DEFECT is any time that the customers requirement is not met.
- 10. Shift The Mean And Reduce Variation Calculate new process capability after implementing the improvement or design Determine if the new process capability (process sigma) meets stated goals See if you achieved the desired shift, variance reduction, or DPMO reduction
- 11. Sigma and Normal Distribution As you can see, the curve is divided into a series of equal increments, each representing one standard deviation from the mean.
- 12. Histogram 100806040200-20 25 20 15 10 5 0 DAYS Frequency Mean 29.41 StDev 24.45 N 68 Days it Takes for FAO to Return calls Spec Limit is 5 days maximum
- 13. Causes for Greenbelts Not Completing Project 55 25 8 6 3 3 Not enough time Sponsor does not understand value Did not pass exam Unclear of what needed to be done on template Lost template Office closed for a month 55% 80% 88% 94% 100%97% Causes CumulativePercent
- 14. Central Limit Theorem http://www.intuitor.com/statistics/CentralLim.html If a random sample is drawn from any population, the sampling distribution of the sample mean is approximately normal for a sufficiently large sample size. The larger the sample size, the more closely the sampling distribution of the sample mean will resemble a normal distribution 1 3 15 30
- 15. Yields Rolled Throughput Yield Receive request for Financial Aid 45,000 DPMO wasted Step 1 in Financial Aid 28,650 DPMO wasted Step 2 in Awarding Financial Aid 51,876 DPMO wasted Financial Aid Awarded Right First Time 125,526 DPMO wasted opportunities 95.5% Yield (YTP) 97% Yield (YTP) 94.4% Yield (YTP) Yields can be multiplied with many process steps. Assumes independent sources of defects. YRT = .955*.97*.944 = 87.5%
- 16. Correlations are not Necessarily Causal City of Oldenburg, Germany 1930- 1936 X-axis: stork population Y-axis: human population What your mother told you about babies when you were three is still not right, despite the strong correlation evidence. Causal means that one variable results in the other thing occurring. In general, it is extremely difficult to establish causality between two correlated events or observances. There are many statistical tools to establish a statistical significant correlation. Source: Box, Hunter, hunter Statistics For Experiments 1978
- 17. Regression Regression can be used for prediction, inference, hypothesis testing, and modeling of causal relationships The procedure calculates estimates of the relationship between the independent variables (advertising, price, etc.) and the dependent variable (sales). Simple Linear Regression Analysis: Y = b0 + b1X
- 18. The Control Chart or Shewhart Chart Observation IndividualValue 28252219161310741 60 50 40 30 20 10 0 _ X=29.06 UCL=55.24 LCL=2.87 1 Control Chart of Recycle Process Center (usually the Mean) Special Cause Variation Detected Control Limits Common Cause Variation
- 19. Common Distributions Sample size - Normal Distributions As the number of samples measured increases, to 30, the distribution becomes more representative of the population. Population sample NORMAL DISTRIBUTIONS IMPORTANCE Most variables are approximately normally distributed. This means we can use the normal distribution as a model to help us better understand these variables. NORMALITY TESTS Normality tests are used to determine if any group of data fits a standard normal distribution
- 20. Confirm New Capability Calculate new process capability Determine if the new process sigma meets stated goals
- 21. Action plan and SOP for Special Cause Variation Determine new Goals (UCL, LCL)
- 22. Module Wrap Up Questions/Comments Discussions of Process Improvement Projects in Your Area Discuss Next Module Establish Time/Date of Next Meeting