Introduction to DOE The What and Why of DOE Who should use DOE? DOE terminology Reasons for using DOE (screening, modeling, performance validation and verification) The four pillars of DOE History and evolution of DOE along with key contributors Introduction to DOE Pro software

Introduction to Regression Analysis The What and Why of regression analysis Least squares regression Key terms in simple linear regression Intercept, slope, residual, prediction equation, R-squared, p-value, standard error Regression analysis examples

Back to Course Topics Discussed What is a control chart and how can it help us? 7 out-of-control symptoms 4 types of control charts Variables data (IMR chart, Xbar-R chart) Attribute data (p-chart, c-chart) Examples and uses of control charts

Topics Discussed Lean tools and concepts for improving a process 5S, poka yoke, visual controls Quick changeover (single minute exchange of dies (SMED)) Batch vs. single piece flow Cellular design Mistake proofing, Failure mode and effect analysis (FMEA) Kaizen Total productive maintenance (TPM) Overall equipment effectiveness (OEE) Flow and pull Kanbans Inventory Theory of Constraints [...]

Back to Course Topics Discussed Hypothesis testing basics Power and Sample Size Comparing means (t-Test, paired t-Test, ANOVA) Comparing standard deviations (F-test) Comparing attribute data (Test of Proportions, Chi-Square test for independence)

Topics Discussed Purpose and use of prioritization Best way to prioritize Quick tools and techniques when data is limited: Team voting, nominal group (ranking), effort impact analysis, pairwise comparisons, IPO (prioritization) matrix, Pugh concept selection (decision matrix)

Topics Discussed Sampling and the Central Limit Theorem What is a confidence interval? Confidence intervals for variables and attribute data (mean, proportions) Sample size considerations Determining sample size to estimate a mean or proportion with a desired margin of error and confidence level

Topics Discussed What is waste and variation? Techniques for identifying waste and variation (root cause analysis, brainstorming, process observation, cause and effect) 7 classic types of waste, plus 1 (DOWNTIME) Cost of Waste Analyzing work (Cycle time and Takt time)

Back to Course Topics Discussed The what and why of MSA How to set up, conduct and perform an MSA for attribute or variables data Interpreting MSA results and metrics Attribute data (effectiveness, probability of false accepts, probability of false rejects, bias) Variables data (repeatability, reproducibility, Precision-to-Tolerance (P/Tol), Precision-to-Total (P/Tot), discrimination (resolution), sigma measure) [...]

Back to Course Topics Discussed Why collect data? Graphs (pareto, histogram, run chart, box plots, scatter plots) Measures Variables data (summary statistics, sigma level, Cp, Cpk, dpm) Attribute data (yield, FPY, RTY, dpu)