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Quality Control and Improvement with MINITAB

NPTEL and Indian Institute of Technology Bombay via YouTube


This course will emphasize on application of different theories, tools, and techniques for Quality Control and Improvement. Most of the topics will be discussed with relevant problems and solutions in MINITAB 19 software interface.
The course will emphasize two broad areas (e.g., Quality of Design and Quality of Conformance). In Quality of Design, relevant topics, such as VOC, Kano model, QFD, and FMEA, will be discussed with examples. Subsequently, the Quality of Conformance topics, such as quality control (e.g., statistical process control) and various topics related to process capability analysis, are discussed. With an objective to discuss topics related to the design of experiments, a few important statistical techniques, such as hypothesis testing, ANOVA, regression analysis, and MSA are covered in this course. Finally, various Design of Experiment (DOE) techniques for factor screening and quality improvement are elaborated with examples. These techniques include factorial designs, fractional factorial design, multiple response optimization, and the Taguchi method.


Course Introduction - Quality Control and Improvement with MINITAB..
Lecture 1: Introduction of Quality.
Lecture 2: Voice of the Customer and Kano Model.
Lecture 3: Quality Function Deployment.
Lecture 4: Critical to Quality Characteristics.
Lecture 5 : Data Visualization for Quality Control and Improvement.
Lecture 6: Importance of Pareto Chart and Cause and Effect Diagram.
Lecture 7: Design Failure Mode and Effect Analysis.
Lecture 8: Introduction to Statistical Process Control.
Lecture 9: X-bar and R Chart.
Lecture 10: X-bar and S Chart.
Lecture 11: Individual Moving Range Chart and Attribute Chart.
Lecture 12: Attribute Control Charts and Process Capability.
Lecture 13: Process Capability Index.
Lecture 14: Process Performance and Sigma Level.
Lecture 15: Process Capability for Attribute data.
Lecture 16: Basic Statistics & Confidence Interval.
Lecture 17: Hypothesis Testing.
Lecture 18: One-sample t Test.
Lecture 19: Two-sample t Test.
Lecture 20: Paired t Test and ANOVA.
Lecture 21: One-way ANOVA.
Lecture 22: One-way ANOVA (Continued).
Lecture 23: ANCOVA and Nonparametric Test.
Lecture 24: Linear Regression.
Lecture 25: Linear Regression(Continued) and Multiple Regression.
Lecture 26:Best Subset Regression, Multicollinearity.
Lecture 27: Multicollinearity, Best Subset Regression, Multiple Regression....
Lecture 28: Design of Experiment, One-factor-at-a-time experiment.
Lecture 29: Two-factor asymmetric Design, Symmetric Factorial Design, Two-way ANOVA.
Lecture 30: Two-factor symmetric Design, Robust setting, Two-way ANOVA.
Lecture 31: Measurement System Analysis.
Lecture 32: Measurement System Analysis (Contd.).
Lecture 33: Measurement System Analysis (Contd.), Introduction to Factorial Experiments.
Lecture 34: Factorial Experiments.
Lecture 35: Factorial Experiments (Contd.).
Lecture 36: Factorial Experiments (Contd.).
Lecture 37: Blocking in Factorial Design..
Lecture 38: Multiple response Optimization & RSM.
Lecture 39: Fractional Factorial Design.
Lecture 40: Taguchi Method.

Taught by

IIT Bombay July 2018

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