Lean with Statistical Analysis for Engineers using Minitab

Duration

Lean with Statistical Analysis for Engineers using Minitab

3 days

Overview

This course will instill the awareness of Lean and the expectation that customer satisfaction can be improved by understanding continuous improvement strategies. The participant will learn how to quantify variation, both analytically and graphically. Another main objective is to teach the participant on how to make comparisons and understand if there is a statistical difference in the analysis. This will result in making better and data-driven decisions.

The three-day instructor-led course includes the following topics:

• Understanding on Lean Manufacturing Operation
• Understanding Variation and Impact of Variation in Continuous Improvement initiatives
• Understanding on Basic Statistics and using Basic Graphical Tools in MINITAB.
• Understanding on data collection concept – Sampling vs. Population.
• How to consider the risk of sampling and impact to the population by using Confidence Interval.
• How to do data comparison by utilizing Comparative Method tools using Mean, Median & Proportion.
• How to perform correlation and regression analysis between two continuous data.
• Learn how to apply appropriate statistical tools on numerous engineering applications.

These concepts are explained with every day analogies and real world examples. The learner will get hands-on experience with a fun game and step-by-step examples using MINITAB statistical analysis software.

IMPORTANT

This course has hands-on activities that require an extensive use of a computer. Unless your class is scheduled in a LAB, please plan to bring along your laptop computer to class with MINITAB software version 17 or 18 or 19 installed.

Target Audience

This course is intended for participants who have little or no background in statistical analysis. It is recommended for engineers and senior technicians; who are working in manufacturing, process, product, device, test, QA areas; and are directly involved in the continuous improvement activities through process characterization, control and maintenance.

This course is also applicable for engineers who are involved in product design and development and product services that require the use data collection and analysis to make critical decisions in their respective job.

Duration of Program

The duration of the complete program is approximately Twenty One (21) hours excluding rest or break time. However, the final duration will depend on the amount of customization, number of practice exercises, and other variables.

Course Outline / Content

Note: All sessions will be accompanied with full exercises with data provided by the trainer.

Module 0: Introduction to Lean Manufacturing & Six Sigma

• Wastes and Losses in Production
• Impact of Wastes on Cycle time and cost
• PQCDSM of manufacturing
• Six Sigma and Lean Relationship

Module 1: Variation and Reaction

• Understanding Variation
• Reaction to Variation
• Continuous Improvement Strategy in Six Sigma

Module 2: Data Collection Concepts

• Why collect data?
• Using Data As Aid to Decision Making
• Historical Data vs. New Data
• Sampling vs. Population
• Sample Size & Power
• Randomization & Types of Randomization
• Data Type

Module 3: Basic Statistics

• Introduction to Minitab Environment
• Function and Windows in Minitab
• Manipulating Data in Minitab
• Location
• Mean
• Median
• Mod
• Spread
• Range
• Variance
• Standard Deviation
• Basic Graphical Tools
• Histogram/Bar Chart
• Dot Plot
• Box Plot
• Pareto Charts
• Scatter Plot
• Matrix Plot
• Normal Distribution
• Properties of Normal Distribution
• Why Normal Distribution is Important in Statistical Analysis

Module 4: Confidence Interval

• What is Confidence Interval?
• Why use Confidence Interval?
• Confidence Interval in Decision Making

Module 5: Comparative Method

• Introduction – Why do we need comparison?
• Comparison Flow Chart
• Practical Problem Statement
• Statistical Problem Statement
• Hypothesis Testing
• Analysis, Statistical and Practical Conclusion
• Type of Comparison
• Side of Comparison
• Hypothesis Testing – analysis
• Type of error - α & β error
• p-value
• Test for Assumption
• Independent Test
• Graphical & Analytical
• Normality Test
• Graphical & Analytical
• Box-Cox Transformation
• X-Axis Discrete & Y-Axis Continuous – Mean Comparison
• One-To-Standard Comparison (1-Sample t)
• One-To-One Comparison (2-Sample t)
• Multiple Comparisons (One-way ANOVA)

Day 3

• X-Axis Discrete & Y-Axis Continuous – Variance Comparison
• One-To-Standard (Confidence Interval)
• One-To-One (F-Test)
• Multiple Comparisons (Bartlett/Levene Test)
• X-axis discrete & Y-axis discrete – Proportion Comparison
• One-to-Standard Comparison (1-proportion)
• One-to-One Comparison (2-proportions)
• Multiple Comparisons (Chi-square)

Module 6: Correlation & Linear Regression

X-axis continuous Y-axis continuous

• Correlation
• Correlation vs. Causation
• Correlation Analysis
• Linear Regression
• Simple Linear Regression Model
• Regression Analysis using ANOVA
• Residual and Residual Diagnosis
• Linear Regression Analysis
• Multiple Regression
• Multiple Linear Regression Model
• Regression Analysis using General Linear Model (GLM)

Programme scope, delivery arrangements and suitability are confirmed during enquiry.