LEAN - SIX SIGMA TRAINING TOPICS C1. DESCRIPTIVE STATISTICS AND GRAPHICAL ANALYSIS
-Use graphs and descriptive statistics to gain insight into objects, processes, events, and people.
-Distinguish among difference types of data.
-Choose the most appropriate visual representation for a given type of data and for the question we are trying to answer.
-Choose the most appropriate descriptive statistics to summarize different aspects of a given data set, such as the central tendency and spread.
C2. STATISTICAL INFERENCE
.-Examine populations by looking at a subset of the population – a sample – and using inferential statistics.
-Define data collection methods that allow you to draw conclusions with a known level of risk.
-Define the sampling distribution, an important theoretical construct in statistics, and explain its impact on how well a sample statistic estimates a population parameter.
-Identify basic characteristics of the normal distribution and use them to calculate how well a sample mean estimates a population mean.
C3. HYPOTHESIS TESTS AND CONFIDENCE INTERVALS
1. Establish hypotheses about business problems and use statistics to test those hypotheses.
2. Use a 1-sample t-test to determine whether a population mean is equal to a hypothesized value.
3. Use a 2 variances test to determine whether two populations have the same variance for a given parameter.
4. Use a 2-sample t-test to determine whether two populations have equal means
5. Use a paired t-test to determine whether two dependent populations have equal means
6. Use a 1 proportion test to determine whether a population proportion is equal to a hypothesized value.
7. Use a 2 proportions test to determine whether two populations have equal proportions.
8. Use chi-square test to determine whether the values of two categorical variables are related.
C4. CONTROL CHARTS
– Identify the common goals and applications of control charts. Monitor processes that are measured with variables data collected in subgroups.
Monitor processes that are measured with individual observations of variables data. Monitor processes whose performance is most meaningfully described by attribute data.
C5. PROCESS CAPABILITY
-Describe how well a process is performing in relation to its specification limits by using capability indices.
-Recognize when data about a business process permit a reliable capability analysis.
-Interpret various measures of process capability.
-Apply capability analysis to processes that involve non-normal data.
C6. (ANOVA) ANALYSIS OF VARIANCE
– Detect significant differences in the mean responses from two or more groups.
-Use individual value plots to visualize within – and between-group variation and identify group means.
-Identify groups whose mean responses differ from the mean responses of other groups in the set.
-Detect significant differences in a mean response due to either of two factors or to the interaction between those factors.
C7. CORRELATION AND REGRESSION
– Identify and characterize relationships between variables and use the relationships to predict the outcomes of business decisions.
-Use scatterplots and correlation to visualize and quantify the strength and nature of relationships between numeric variables.
-Use regression to define linear relationships between numeric variables mathematically, producing equations to predict one value from another.
C8. MEASUREMENT AND SYSTEM ANALYSIS (MSA)
.– Design measurement systems for business processes.
-Distinguish between the accuracy and the precision of a measurement system.
-Distinguish between repeatability and reproducibility.
Use graphs to assess the repeatability and reproducibility of a measurement system.
Examine sources of variation in a measurement system.
Use ANOVA to assess the repeatability and reproducibility of a measurement system.
Assess the linearity and bias of a measurement system.
Use attribute agreement analysis to assess a measurement process that records attribute responses.
C9. (DOE) DESIGN OF EXPERIMENTS
– Create and analyse factorial designs to find the optimal settings of multiple factors in a process.
-Use blocking to account for unwanted variation in an experiment.
-use Center points to detect curvature in the design space and estimate error without replicating corner points.
-Create and analyse factional factorial designs to find the optimal settings of multiple factors in a process without running a full design.
-Use Minitab’s response optimizer to find optimum factor setting.
C10. LEAN SIX SIGMA
- Introducing Lean Six Sigma (Why six sigma, Why Lean, Why Lean Six Sigma)
- Understanding Six Sigma (Lean Six Sigma Foundation, DMAIC)
- Understanding Lean (Lean Metrics, 5S, JIT, TPM, Quality, CI)
- Implementing Lean Six Sigma ( Leading the project, Controlling the project, Analyzing Lean Six Sigma...)
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