Improving Manufacturing Processes

This popular 4-day track provides participants with data analysis techniques to improve manufacturing products and processes.

Our most popular offering for manufacturing, this track provides the foundation for effectively analyzing and improving products and processes. Participants will gain exposure to a broad range of graphical and statistical tools to assess if results are on target, explore relationships between variables, and minimize defects, using examples with metrics such as diameter, pressure, and hardness. Analytical and statistical principles will be presented through real-world examples and exercises.

This course is appropriate for members of a quality team, those leading and facilitating process improvement activities, such as Lean Six Sigma, and practitioners preparing to adopt process improvement in their manufacturing organization.

Training Track

DAYS 1-2

In this 2-day foundational course you will learn to minimize the time required for data analysis by using Minitab to import data, develop sound statistical approaches to exploring data, create and interpret compelling graphs, and export results. Analyze a variety of real world data sets to learn how to align your applications with the right statistical tool, and interpret statistical output to reveal problems with a process or evidence of an improvement. Learn the fundamentals of important statistical concepts, such as hypothesis testing and confidence intervals, and how to uncover and describe relationships between variables with statistical modeling tools.

This course places a strong emphasis on making sound decisions based upon the practical application of statistical techniques commonly found in manufacturing, engineering, and research and development endeavors.

Topics Include:

  • Importing and Formatting Data
  • Bar Charts
  • Histograms
  • Boxplots
  • Pareto Charts
  • Scatterplots
  • Tables and Chi-Square Analysis
  • Measures of Location and Variation
  • t-Tests
  • Proportion Tests
  • Tests for Equal Variance
  • Power and Sample Size
  • Correlation
  • Simple Linear and Multiple Regression
  • One-Way ANOVA
  • Multi-Variable ANOVA
Graph One for Improving Manufacturing Processes
Interaction Plot for PntWear - Data Means