SPC & WinSPC Software Courses

• Statistical Process Control and Process Capability: The objective of this 3-day course curriculum is to provide participants with the analytical tools and methods necessary to: understand SPC fundamentals and the importance of reducing variation; implement control charting in order to assess process stability; interpret control chart signals and implement appropriate reaction plans; determine appropriate sampling plans and sample sizes; assess process capability (for normal and non-normal data); apply charting techniques for short production runs; apply charting methods where multiple sources of variation may exist (e.g., multiple cavities, filling heads); apply SPC for naturally trending data (e.g. due to tool wear); determine the appropriate type of chart for a given process; and avoid common misapplications of SPC in practice.
• Design of Experiments: This 4-day course teaches participants how to plan and conduct experiments in an effective and efficient manner. Participants gain a solid understanding of important concepts and methods to develop predictive models that allow the optimization of product designs or manufacturing processes. Practical examples are presented to illustrate the application of technical concepts. Participants get a chance to design an experiment, analyze the results, and utilize the model(s) to develop optimal solutions.
• Reliability/Weibull Analysis: The objective of this 3-day course is to train participants to: understand reliability concepts and unique aspects of reliability data; understand underlying probability and statistical concepts for reliability analysis; develop competency in the modeling and analysis of time-to-failure data; understand reliability metrics and how to estimate and report them; handle multiple failure modes; estimate reliability of subsystems and systems; become aware of system reliability activities such as Reliability Block diagramming, Reliability importance, and Reliability allocation; determine if reliability specifications are met (at specified confidence level) or whether design improvements are required; develop competency in the planning of reliability tests (sample sizes); develop reliability demonstration test plans (testing time vs. number of units tradeoffs); and analyze existing warranty data to predict future returns.
• Statistics, Hypothesis Testing, and Regression: The objective of this 3-day course is to provide participants with the analytical tools and methods necessary to: describe and summarize data effectively with descriptive statistics and graphical methods; correctly compare groups with respect to means, variability, and proportions by testing hypotheses; estimate key statistics and quantify uncertainty (confidence intervals); characterize expected variation from sample data (tolerance intervals); determine appropriate sample sizes to achieve adequate power for hypothesis tests; and develop, validate, and utilize predictive models. Many practical examples are presented to illustrate the application of technical concepts.
• Measurement Systems Assessment: This 2-day course’s purpose is to provide participants with the analytical tools and methods necessary to: understand key sources of measurement error; design and conduct Gage R&R studies to estimate measurement error components (repeatability, reproducibility); interpret Gage R&R results and identify corrective actions if necessary; plan and conduct Gage R&R studies for attribute systems; apply control charts to monitor measurement systems over time; assess accuracy and linearity of measurement systems; and handle non-replicable systems (such as destructive tests).

For more information on these courses, including their delivery methods and costs, go to Integral Concepts or call at +1 (248) 884-2276.