Analyze Phase
X Sifting
X Sifting
Welcome to Analyze
Welcome to Analyze
Multi-Vari Analysis
Multi-Vari Analysis
X Sifting
X Sifting
Classes and Causes
Classes and Causes
Inferential Statistics
Inferential Statistics
Intro to Hypothesis Testing
Intro to Hy
Define Phase
Wrap Up and Action
Items
Define Phase OverviewThe Goal
The goal of the Define Phase is to:
Identify a process to improve and develop a specific Lean
Six Sigma project.
Lean Six Sigma Belts define critical processes, subprocesses and identif
Improve Phase
Process Modeling
Regression
Process Modeling
Welcome to Improve
Welcome to Improve
Correlation
Correlation
Process Modeling:
Process Modeling:
Regression
Regression
Advanced Process
Advanced Process
Modeling: MLR
Modeling: MLR
Introduction t
Improve Phase
Fractional Factorial Experiments
Fractional Factorial Experiments
Welcome to Improve
Welcome to Improve
Process Modeling:
Process Modeling:
Regression
Regression
Advanced Process
Advanced Process
Modeling: MLR
Modeling: MLR
Designing Experim
Improve Phase
Wrap Up and Action Items
Improve Phase Overview - The Goal
The goal of the Improve Phase is to:
Determine the optimal levels of the variables which are
significantly impacting your Primary Metric.
Demonstrate a working knowledge of modelin
Six Sigma Project
Structured Project Guide
Project Name IE 6016 DMAIC Semester Project MANATEY Computer
Tool Purpose Guide to follow a structured approach to complete a Six Sigma project
Task
no
Stage
Task
Tool
Target Date Status
Identify the Project
2
Id
Example No.1
a. Histogram & Steam Leaf Diagram
b. Box plot & Dot Plot
Example No. 2
Box Plot
The Median difference between class 2 and 3 is in range of 74 to 77 but class 1 range is way higher
students in class 1 are better scoring than class 2&3.
Example
Inventory of tools in WBH PI-II Structured Project Guide
Task
Stage Task
no
Comments
Tool
Responsible
Project Summary
Green Belt, Black
Belt
Identify the Project
2
Identify the Champion, Sponsors
Project Summary
Green Belt, Black
Belt
3
Champion and Green
IE 6016 DMAIC Semester Project: Due August 2, 2016
MANATEY Computer (MC) Inc. is a leading computer company located in the West coast. They
assemble computer systems for industrial applications, 30% of their sales are for custom orders
where the profit ma
Decision over GST (Taxes) made by Indian Government
Decision Context
Goods and Service Tax bill took a decade to get implemented since it was been introduced,
the bill was passed in Indian Lok Sabha on 8th march 2016 and will be implemented from
next fina
Chapter 4
Structure Decisions with
Multiple Objectives
Create and structure objectives hierarchy
Create measures natural and categorical
Most of the chapters tables and figures are
included in the file.
Instructor must decide how many and which
examples t
IE 6610 Introduction to Six Sigma
Homework 3 - Due July 28, 2016
1. For a process quality characteristics, the specification limits are 0.4037 +/- 0.0013
(that is from 0.4024 to 0.4050). The following data gives the data for 14 measurement
data sets, each
EME 6673 Final Exam Case Study
Q1
15 Points max
A Six Sigma team is testing pilot test results to see if they have made a ststistically significant improvement.
They need to test this hypothesis statistically and determine the 95% Confidence Interval for
Take home portion of IE 7610 Mid Term exam Winter 2016
Mid Term Exam IE 7610 Win 2016
Deadline for submission on Blackboard: Wed March 16, 2016 11:59
pm.
This case study has information about the process that is existing in an Ice Cream Parlor.
The custom
Improve Phase
Experimental Methods
Experimental Methods
Welcome to Improve
Welcome to Improve
Process Modeling:
Process Modeling:
Regression
Regression
Advanced Process
Advanced Process
Modeling: MLR
Modeling: MLR
Designing Experiments
Designing Experimen
Improve Phase
Designing Experiments
Designing Experiments
Welcome to Improve
Welcome to Improve
Process Modeling:
Process Modeling:
Regression
Regression
Advanced Process
Advanced Process
Modeling: MLR
Modeling: MLR
Reasons for Experiments
Reasons for Exp
Improve Phase
Full Factorial
Experiments
Full Factorial Experiments
Welcome to Improve
Welcome to Improve
Process Modeling:
Process Modeling:
Regression
Regression
Advanced Process
Advanced Process
Modeling: MLR
Modeling: MLR
Designing Experiments
Designi
Measure Phase
Welcome to Measure
Welcome to Measure Phase
Welcome to Measure
Welcome to Measure
Process Discovery
Process Discovery
Six Sigma Statistics
Six Sigma Statistics
Measurement System Analysis
Measurement System Analysis
Process Capability
Proces
Measure Phase
Process Capability
Process Capability
Welcome to Measure
Welcome to Measure
Process Discovery
Process Discovery
Six Sigma Statistics
Six Sigma Statistics
Measurement System
Measurement System
Analysis
Analysis
Process Capability
Process Capa
Measure Phase
Wrap Up and Action
Items
Measure Phase Overview - The Goal
The goal of the Measure Phase is to:
Define, explore and classify X variables using a variety of tools.
Detailed Process Mapping
Fishbone Diagrams
X-Y Matrixes
FMEA
Acquire a worki
Analyze Phase
Welcome to Analyze
Welcome to Analyze
Welcome to Analyze
Welcome to Analyze
X Sifting
X Sifting
Inferential Statistics
Inferential Statistics
Intro to Hypothesis Testing
Intro to Hypothesis Testing
Hypothesis Testing ND P1
Hypothesis Testing
Analyze Phase
Introduction to Hypothesis
Testing
Hypothesis Testing (ND)
Welcome to Analyze
Welcome to Analyze
X Sifting
X Sifting
Hypothesis Testing Purpose
Hypothesis Testing Purpose
Inferential Statistics
Inferential Statistics
Tests for Central Tenden
Analyze Phase
Inferential Statistics
Inferential Statistics
Welcome to Analyze
Welcome to Analyze
X Sifting
X Sifting
Inferential Statistics
Inferential Statistics
Inferential Statistics
Inferential Statistics
Nature of Sampling
Nature of Sampling
Intro t
Analyze Phase
Hypothesis Testing Normal Data
Part 2
Hypothesis Testing Normal Data Part 2
Welcome to Analyze
Welcome to Analyze
X Sifting
X Sifting
Inferential Statistics
Inferential Statistics
Intro to Hypothesis Testing
Intro to Hypothesis Testing
Hypot
Analyze
Wrap Up and Action Items
Analyze Wrap Up Overview
The goal of the Analyze Phase is to:
Locate the variables significantly impacting your Primary
Metric. Then establish Root Causes for X variables
using Inferential Statistical Analysis such as Hyp
Analyze Phase
Hypothesis Testing Non Normal Data
Part 1
Hypothesis Testing Non Normal Data Part 1
Welcome to Analyze
Welcome to Analyze
X Sifting
X Sifting
Inferential Statistics
Inferential Statistics
Intro to Hypothesis Testing
Intro to Hypothesis Testi
Analyze Phase
Hypothesis Testing Normal Data
Part 1
Hypothesis Testing Normal Data Part 1
Welcome to Analyze
Welcome to Analyze
X Sifting
X Sifting
Inferential Statistics
Inferential Statistics
Intro to Hypothesis Testing
Intro to Hypothesis Testing
Sampl
Analyze Phase
Hypothesis Testing Non Normal
Data Part 2
Hypothesis Testing Non Normal Data Part 2
Welcome to Analyze
Welcome to Analyze
X Sifting
X Sifting
Inferential Statistics
Inferential Statistics
Intro to Hypothesis Testing
Intro to Hypothesis Testi
Improve Phase
Welcome to Improve
Welcome to Improve
Welcome to Improve
Process Modeling: Regression
Advanced Process Modeling:
MLR
Designing Experiments
Experimental Methods
Full Factorial Experiments
Fractional Factorial
Experiments
Wrap Up & Action Item
Improve Phase
Advanced Process Modeling
Multiple Linear Regression
(MLR)
Advanced Modeling & Regression
Welcome to Improve
Welcome to Improve
Review Corr./Regression
Review Corr./Regression
Process Modeling:
Process Modeling:
Regression
Regression
Advance