Manufacturing Quality Series

Your Trainer

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Vineet Maurya

AI & Data Science Expert

Vineet has over fifteen years of experience in building analytical solutions and digital strategy for large organization. He is also data visualization expert & has passion for telling stories from big data. He has lead data science teams in top consulting firms such as Deloitte Singapore, AON Innovation Singapore, Accenture, IBM etc and helped several Fortune 500 companies with innovative analytical solutions on big data.

He helps Senior Leadership, VPs and advice clients primarily on Consulting and Big Data Analytics for Financial Advisory, Business Valuation, Real Estate consulting, Corporate Investigations, AML consulting, Big data Analytical Models and Six Sigma implementation using various tools and technologies. Over ten years, his clients ranged from dozens of global Fortune 500 companies in non financial business (e.g., Wallmart, McDonalds ), financial services firms (e.g., S&P rating, DBS Bank , ICBC), investment funds & hedge funds.

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

Prerequisite:

None. This course is a prerequisite for all other general Minitab courses.

Optional Topic for On-Site Training:

Nonparametric Tests

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 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.

Topics include:

  • Gage R&R
  • Destructive Testing
  • Gage Linearity and Bias
  • Attribute Agreement
  • Variables and Attribute Control Charts
  • Capability Analysis for Normal, Nonnormal, and Attribute Data

Prerequisite:

Minitab Essentials

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.

Topics 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:

Minitab Essentials

Continue to build on the fundamental statistical analysis concepts taught in the Minitab Essentials 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 include:

  • Multiple and Stepwise Regression
  • Nonlinear Regression
  • Partial Least Squares
  • Multi-Variable ANOVA with Covariates
  • Nesting and Random Factors
  • MANOVA
  • Binary and Nominal Logistic Regression

Prerequisite:

Minitab Essentials

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 using a model that includes quadratics, 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 include:

  • Central Composite and Box-Behnken Designs
  • Calculations for Steepest Ascent
  • Overlaid Contour Plots
  • Multiple Response Optimization

Prerequisite:

Minitab Essentials and Factorial Designs

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 mixture’s blending properties and to choose the appropriate mixture of ingredients needed to optimize one or more critical process characteristics.

Topics include:

  • Simplex Lattice and Centroid Designs
  • Upper and Lower Constraints
  • Extreme Vertices Designs
  • Pseudocomponents
  • Response Trace Plots
  • Mixtures with Process Variables
  • Mixture Amounts

Prerequisite:

Minitab Essentials and Factorial Designs

Learn how to handle common DOE scenarios where modifications to the analysis of classic factorial and response surface designs are necessary 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 opportunities to minimize costs or variability while simultaneously optimizing an important product or 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 include:

  • ANCOVA
  • Unbalanced Designs
  • Split-Plot Designs
  • Multiple Response Optimization
  • Analyze Variability
  • Binary Logistic Regression

Prerequisite:

Minitab Essentials and Factorial Designs

Optional Topic for On-Site Training:

Taguchi Designs

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.

Topics include:

  • Parametric and Nonparametric Distribution Analysis
  • Estimation and Demonstration Test Plans
  • Growth Curves
  • Multiple Failure Modes
  • Warranty Predictions
  • Weibayes Analysis

Prerequisite:

Minitab Essentials

Study and describe the impact that explanatory variables have on product lifetime. Determine the effect of factors and covariates on product failure and the risk of failure to a population of products. Learn how to obtain reliability estimates on highly reliable products in a reasonable amount of time and assess when those components are expected to fail.

Establish appropriate sample sizes and allocation of units to stress levels for an accelerated life test, and determine the effect of a stress variable on the probability of failure. A strong emphasis is placed on using appropriate probability models to predict important lifetime characteristics of your products once in the field.

Topics include:

  • Probit Analysis
  • Regression with Life Data
  • Accelerated Life Testing and Test Plans

Prerequisite:

Minitab Essentials and Introduction to Reliability

Continue to build on the fundamental concepts taught in the Manufacturing Statistical Quality Analysis course by learning additional tools that help to improve and control your processes. Develop the necessary skills to successfully evaluate and certify manufacturing and engineering measurement systems with multiple gages or locations on a part. Learn how to evaluate a random sample of product from a lot to determine whether to accept or reject the entire lot. Expand your knowledge of control charting to handle rare events and time weighted data.

Learn how to utilize important capability analysis tools 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.

Topics include:

  • Gage R&R Expanded
  • Orthogonal Regression
  • Tolerance Intervals
  • Acceptance Sampling
  • Between-Within Analysis
  • Control Charts including EWMA, Short-Run, CUSUM, and Rare Events

Prerequisite:

Minitab Essentials and Statistical Quality Analysis

Learn to apply Minitab tools commonly used in the pharmaceutical industry. Develop sound statistical approaches to data analysis by understanding how to select the right tool for a given scenario and to correctly interpret the results of the analysis. Learn how to easily import data and export output.

Learn the foundation for important statistical concepts for determining if a process mean is off target, whether two means are significantly different, and for demonstrating if a process change does not significantly affect a critical response. Develop the necessary skills to successfully evaluate and certify measurement systems. Understand how to utilize important capability analysis tools to evaluate your processes relative to internal and customer specifications. Learn how to evaluate a random sample of product from a lot to determine whether to accept or reject the entire lot. Understand how to apply DOE for process improvement. Learn how to use stability analysis for determining the shelf life of a product. All applications place emphasis on making good business decisions based upon the practical application of statistical techniques commonly used in the pharmaceutical industry.

Topics include:

  • Importing and Formatting Data
  • Bar Charts
  • Histograms
  • Boxplots
  • Scatterplots
  • Power and Sample Size Determination
  • t-Tests
  • Equivalence Tests
  • Proportion Tests
  • Tolerance Intervals
  • Variables and Attribute Control Charts
  • Regression
  • One-Way ANOVA
  • Multi-Variable ANOVA
  • DOE
  • Attribute Agreement Analysis
  • Gage R&R
  • Attribute Acceptance Sampling
  • Capability Analysis for Normal and Nonnormal Data

Prerequisite:

None. This course can be used as a pre-requisite to Response Surface Designs and DOE in Practice.

Learn to apply Minitab tools commonly used in the medical devices industry. Develop sound statistical approaches to data analysis by understanding how to select the right tool for a given scenario and correctly interpret the results. Learn how to easily import data and export output.

Learn the foundation for important statistical concepts for determining if a process mean is off target, whether two means are significantly different, and for demonstrating if a process change does not significantly affect a critical response. Develop the necessary skills to successfully evaluate and certify measurement systems. Understand how to utilize important capability analysis tools to evaluate your product quality relative to internal and customer specifications. Learn how to evaluate a random sample of product from a lot in final inspection to determine whether the lot of product should be shipped. Understand how to apply DOE for improving critical to quality characteristics. All applications place emphasis on making good business decisions based upon the practical application of statistical techniques commonly used in the medical device industry.

Topics include:

  • Importing and Formatting Data
  • Bar Charts
  • Histograms
  • Boxplots
  • Scatterplots
  • Power and Sample Size Determination
  • t-Tests
  • Equivalence Tests
  • Proportion Tests
  • Tolerance Intervals
  • Variables and Attribute Control Charts
  • Regression
  • One-Way ANOVA
  • Multi-Variable ANOVA
  • DOE
  • Attribute Agreement Analysis
  • Gage R&R
  • Attribute Acceptance Sampling
  • Capability Analysis for Normal and Nonnormal Data

Prerequisite:

None. This course can be used as a pre-requisite to Response Surface Designs and DOE in Practice.

Automate your Minitab analysis and save time with macros. Learn how to use Minitab’s command syntax to write macros that instantaneously import data from a database, manipulate poorly structured Excel files, and perform statistical analysis with minimal user input. By the end of this hands-on course, you will be able to write and execute your own custom macros.

 

Prerequisite:

Minitab Essentials

 

Learn the basics of Continuous Improvement tools – project charters, FMEA, value stream maps, process maps, Cause and Effect diagrams and matrices, brainstorming tools, Voice of the Customer and more – in this simulation of a real-world technical problem. Use Companion by Minitab® to organize data analysis and reports within a project.

This course places a strong emphasis on teaching root cause analysis, quality and non-statistical tools as they relate to continuous improvement projects commonly found in manufacturing and engineering processes.

Topics include:

  • Overview of the Companion Toolkit
  • Use pre-defined Roadmaps such as DMAIC, PDCA, CDOV to create a project plan
  • Customize the Roadmap phases with the appropriate mix of tools
  • Manage project data
  • Flow charts
  • Value stream maps
  • Data sharing between the tools
  • Adding X and Y Variables and connecting between tools
  • Cause and Effect Diagram
  • Analysis capture tools
  • Cause and Effect Matrix
  • Failure Modes and Effects Analysis (FMEA)
  • Graph data using a scatterplot and bar chart
  • Validate process improvements
 
 
 
 

Continue to build on the fundamental concepts taught in the Manufacturing Statistical Quality Analysis course by learning additional tools for measuring quality levels when your data are skewed, have extreme outliers, are multimodal, or are clustered. Expand your knowledge of control charting by learning how to correctly identify special cause variation in the presence of nonnormal data.

Develop the necessary skills to successfully use graphical methods and statistical tests for detecting nonnormal data and choosing an appropriate distribution or transformation for the analysis. Learn about the impact of poor measurement resolution and sample size on normality testing.

Topics include:

  • Probability Plots
  • Tests for Normality
  • Capability Analysis for Nonnormal Data
  • Box-Cox and Johnson transformations
  • Multiple Variables Capability Analysis
  • Tolerance Intervals
  • Individuals Control Charts
  • Multiple Failure Modes Analysis

Prerequisite:

Minitab Essentials and Statistical Quality Analysis