Manufacturing Training Course Description

Please find below the course descriptions for France, Belgium, Switzerland, Luxembourg and French-Speaking Africa. You may access the training offering for the rest of Europe and the World on line.

We may provide training in English or French. Please contact us for information about Training in German or Italian.

Introduction to Minitab

Length of training: 1 to 1.5 Days

Decrease the time required for statistical analysis by quickly learning to navigate Minitab's user-friendly and customizable environment. Learn how to import/export data and output between Minitab and various software and database systems. Enhance your ability to create, manipulate, and restructure data. Develop sound statistical approaches to data analysis by learning how to create and interpret a wide variety of graphs and numerical measures useful for quality improvement initiatives. This course focuses on the utilization of these tools as they pertain to applications commonly found in manufacturing, engineering, and business processes.

Topics covered include: Charts, Histograms, Boxplots, Dotplots, Scatterplots, Tables, Measures of Location and Variation, ODBC.

Basic Statistics using Minitab

Length of training: 1 to 1.5 Days

Augment your graphical analysis skills using Minitab's powerful statistical tools. Develop the foundation for important statistical concepts such as hypothesis testing and confidence intervals. By analyzing a variety of real world data sets, learn how to match the appropriate statistical tool to your own applications and how to correctly interpret statistical output to quickly reveal problems with a process or to show evidence of an improvement. Learn how to explore critical features in your processes through statistical modeling tools that help to uncover and describe relationships between variables. A strong emphasis is placed on making good business decisions based upon the practical application of statistical techniques commonly found in manufacturing, engineering, and research and development endeavors.

Tools Covered Include: t-Tests, Proportion Tests, Tests for Equal Variance, Power and Sample Size, Correlation, Simple Linear and Multiple Regression, ANOVA and GLM.

Prerequisite: Introduction to Minitab.

Advanced ANOVA and Regression

Length of training: 1 Day

Continue to build on the fundamental statistical analysis concepts taught in the Basic Statistics course by learning additional statistical modeling tools that help to uncover and describe relationships between variables. Hands-on examples illuminate how modeling tools help reveal key inputs and sources of variation in your processes. Learn how to use statistical models to investigate how processes may behave under varying conditions. This course provides techniques to help you better understand your processes and to focus and verify your improvement efforts.

Topics Covered Include: Multiple and Stepwise Regression; GLM with Covariates, Nesting and Random Factors; MANOVA; Binary and Nominal Logistic Regression

Prerequisites: Introduction to Minitab and Basic Statistics.

Statistical Quality Analysis using Minitab

Length of training: 1 to 1.5 Days

Develop the necessary skills to successfully evaluate and certify manufacturing and engineering measurement systems. Learn the basic fundamentals of statistical process control and how these important quality tools can provide the necessary evidence to improve and control manufacturing processes. Develop the skills to know when and where to use the various types of control charts available in Minitab for your own processes. Learn how to utilize important capability analysis tools, many enhanced in Minitab Release 14, to evaluate your processes relative to internal and customer specifications. The course emphasis is placed on teaching quality tools as they relate to manufacturing processes.

Tools Covered Include: Gage R&R, Destructive Testing, Gage Linearity, Gage Stability, Attribute Agreement, Variables and Attribute Control Charts, Capability Analysis for Normal, Non-normal and Attribute data.

Prerequisite: Introduction to Minitab and Basic Statistics using Minitab.

Factorial DOE using Minitab

Length of training: 1 to 1.5 Days

Learn to generate a variety of full and fractional factorial designs using Minitab's intuitive DOE interface. Real-world applications demonstrate how the concepts of randomization, replication, and blocking form the basis for sound experimentation practices. Develop the skills necessary to correctly analyze resulting data to effectively and efficiently reach experimental objectives. Use Minitab's customizable and powerful graphical displays to interpret and communicate experimental results to improve products and processes, find critical factors that impact important response variables, reduce process variation, and expedite research and development projects.

Tools and topics Covered Include: Design of Factorial Experiments; Normal Effects Plot and Pareto of Effects; Power and Sample Size; Main Effect, Interaction, and Cube Plots; Center Points; Overlaid Contour Plots; Multiple Response Optimization.

Prerequisite: Introduction to Minitab and Basic Statistics using Minitab.

Response Surface Designs

Length of training: 1 Day

Expand your knowledge of basic 2 level full and fractional factorial designs to those that are ideal for process optimization. Learn how to use Minitab's DOE interface to create response surface designs, analyze experimental results, and find optimal factor settings. Learn how to experiment in the real world by using techniques such as sequential experimentation that balance the discovery of critical process information while being sensitive to the resources required to obtain that information. Learn how to find factor settings that simultaneously optimize multiple responses.

Topics Covered Include: Central Composite and Box-Behnken Designs, Calculations for Steepest Ascent, Overlaid Contour Plots, Multiple Response Optimization.

Prerequisite: Introduction to Minitab, Basic Statistics and Factorial DOE using Minitab.

DOE in Practice using Minitab

Length of training: 1 to 2 Days

Learn how to handle common DOE scenarios where classic factorial or response surface design and analysis techniques are neither appropriate nor possible due to the nature of the response variable or the data collection process. Develop techniques for experimental situations often encountered in practice such as missing data and hard-to-change factors. Understand how to account for variables (covariates) that may affect the response but cannot be controlled in the experiment. Explore the importance of minimizing process costs while simultaneously optimizing an important process characteristic. Learn how to find and quantify the effect that factors have on the probability of a critical event, such as a defect, occurring.

Topics and Tools Covered Include: ANCOVA, Unbalanced Designs, Split-Plot Designs, Multiple Response Optimization, Binary Logistic Regression.

Prerequisite: Introduction to Minitab, Basic Statistics, Factorial DOE and Response Surface Designs using Minitab.

Mixture Designs

Length of training: 1 Day

Learn the principles of designing experiments and analyzing the resulting data for processes that are comprised of the mixing and blending of ingredients such as those commonly found in the chemical, food, and beverage industries. By utilizing Minitab's easy to understand interface, create experiments designed to study and uncover important process information related to mixture processes with the minimal amount of experimental resources. Learn how to interpret graphical and statistical output to understand a mixtures blending properties and to choose the appropriate mixture of ingredients needed to optimize one or more critical process characteristics.

Tools and Topics Covered Include: Simplex Lattice and Centroid Designs, Upper and Lower Constraints, Extreme Verticies, Pseudocomponents, Response Trace Plots.

Prerequisite: Introduction to Minitab, Basic Statistics, Factorial DOE, Response Surface Designs and DOE in practice using Minitab.

Reliability Analysis

Length of training: 1 to 2 Day(s)

Determine lifetime characteristics of a product using both graphical and quantitative analysis methods. Examine case studies containing censored and uncensored data to learn how to correctly handle a wide variety of data structures commonly found in reliability. Explore the common distributions used to model failure rates and develop necessary skills in choosing these models. Study and describe the impact that explanatory variables have on product lifetime. Learn how to obtain reliability estimates on highly reliable products in a reasonable amount of time. A strong emphasis is placed on using appropriate probability models to predict important lifetime characteristics of your products once in the field.

Tools covered include: Parametric and Nonparametric Distribution Analysis, Estimation and Demonstration Test Plans, Growth Curves, Probit Analysis, Regression with life Data, Accelerated Life Testing and Test Plans.