# Business Method - Six Sigma

Six Sigma (6s) is an approach to improve the performance of business process.

where:

The 6s strategy was developed by Motorola, Inc. in the mid-1980s to help boost the quality level of its products. After Motorola became the first company to win the Malcolm Baldrige National Quality Award in 1988, the ensuing media exposure introduced the 6s approach to many other manufacturing companies, most notably Allied Signal (now Honeywell International) and General Electric. Today, with thousands of companies around the world adopting this philosophy, 6s is arguably the most popular process improvement strategy ever devised.

6s has done an admirable job of organizing statistical techniques with a solid strategy (DMAIC : Define–Measure–Analyze–Improve–Control) for applying them in a logical manner to efficiently enhance process performance. However, all this has been done before in various forms. One of the reasons 6s is still around today—and the others aren’t—is because 6s evolved from its original focus on quality improvement to concentrate on profit improvement.

Therefore, to achieve 6s quality levels on the shop floor, forward-thinking companies must start at the beginning, with the design of the product and the process that will produce it. Improving a product in the design phase is almost always much easier (and much cheaper) than attempting to make improvements after it is in production. By preventing future problems, DFSS is definitely a much more proactive approach than the DMAIC strategy, which is mainly used to fix existing problems.

## Statistical Tools for Six Sigma and DFSS

Number Name Purpose
1 Run chart Visualize a process over time
2 Scatter plot Visualize relationships between two or more variables
3 IX, MR Control Chart Test a process for stability over time
4 Dot graph Visualize distributions of one or more samples
5 Boxplot Visualize distributions of one or more samples
6 Histogram Visualize distribution of a sample
7 Stem-and-Leaf Displays Visualize distribution of a sample
8 Isogram Visualize paired data
9 Tukey mean - difference plot Visualize paired data
10 Multi-vari plot Visualize relationships between one Y and many X variables
11 Laws of probability Calculate probability of events; Background for most statistical tools
12 Hypergeometric distribution Calculate probability of counts of defective units in a sample selected from a finite population
13 Binomial distribution Calculate probability of counts of defective units in a sample with a constant probability of defects
14 Poisson distribution Calculate probability of counts of defects or events in a sample from a continuous medium
15 Normal distribution Calculate probability of characteristics in certain ranges of values
16 Sample mean with confidence interval Estimate location of a population based on a sample
17 Sample standard deviation with interval Estimate variation of a population based on a sample confidence
18 Rational subgrouping Collect data to estimate both short-term and long-term process behavior, Plan statistical process control
19 Control charts for variables: X,s and X, R Test a process for stability over time
20 Statistical tolerance intervals Calculate limits which contain a percentage of a population values with high probability
21 Exponential distribution Estimate reliability of systems, estimate times between independent events
22 Weibull distribution Estimate reliability of systems
23 Failure rate estimation with confidence interval Estimate reliability of systems
24 Binomial proportion estimation with confidence interval Estimate probability of counts of defective units in samples with a constant probability of defects
25 Control charts for attributes: np, p, c and u Test a process producing count data for stability over time
26 Poisson rate estimation with confidence interval Estimate rates of defects or events in space or time
27 Variable Gage R&R study Assess precision of variable measurement systems
28 Attribute agreement study Assess agreement of attribute measurement systems to each other
29 Attribute gage study Assess accuracy and precision of attribute measurement systems
30 Control chart interpretation Test processes for stability over time, Identify possible causes of instability
31 Measures of potential capability (CP and PP) with confidence intervals Estimate potential capability of a process to produce non-defective , products, if the process were centered
32 Measures of actual capability (CPK and PPK), with confidence intervals Estimate actual capability of a process to produce non-defective products

To continue p18 of 59 tools for Six Sigma