Visual Six Sigma E-Book

Ian Cox

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: SAS Institute Inc
• Sprache: Englisch

Seitenzahl: 693

Veröffentlichungsjahr: 2016

Beschreibung

Streamline data analysis with an intuitive, visual Six Sigma strategy Visual Six Sigma provides the statistical techniques that help you get more information from your data. A unique emphasis on the visual allows you to take a more active role in data-driven decision making, so you can leverage your contextual knowledge to pose relevant questions and make more sound decisions. You'll learn dynamic visualization and exploratory data analysis techniques that help you identify occurrences and sources of variation, and the strategies and processes that make Six Sigma work for your organization. The Six Sigma strategy helps you identify and remove causes of defects and errors in manufacturing and business processes; the more pragmatic Visual approach opens the strategy beyond the realms of statisticians to provide value to all business leaders amid the growing need for more accessible quality management tools. * See where, why, and how your data varies * Find clues to underlying behavior in your data * Identify key models and drivers * Build your own Six-Sigma experience Whether your work involves a Six Sigma improvement project, a design project, a data-mining inquiry, or a scientific study, this practical breakthrough guide equips you with the skills and understanding to get more from your data. With intuitive, easy-to-use tools and clear explanations, Visual Six Sigma is a roadmap to putting this strategy to work for your company.

Leseprobe

Table of Contents

Wiley & SAS Business Series

Title Page

Copyright

Preface to the Second Edition

Preface to the First Edition

Acknowledgments

About the Authors

Part One: Background

Chapter 1: Introduction

What Is Visual Six Sigma?

Chapter 2: Six Sigma and Visual Six Sigma

Background: Models, Data, and Variation

Models

Measurements

Observational versus Experimental Data

Six Sigma

Variation and Statistics

Making Detective Work Easier through Dynamic Visualization

Visual Six Sigma: Strategies, Process, Roadmap, and Guidelines

Conclusion

Notes

Chapter 3: A First Look at JMP

The Anatomy of JMP

Visual Displays and Analyses Featured in the Book

Scripts

Personalizing JMP

Visual Six Sigma Data Analysis Process and Roadmap

Techniques Illustrated in the Remaining Chapters

Conclusion

Notes

Chapter 4: Managing Data and Data Quality

Data Quality for Visual Six Sigma

The Collect Data Step

Example 1: Domestic Power Consumption

Example 2: Biscuit Sales

Conclusion

Notes

Part Two: Case Studies

Chapter 5: Reducing Hospital Late Charge Incidents

Framing the Problem

Collecting Data

Uncovering Relationships

Uncovering the Hot Xs

Identifying Projects

Conclusion

Chapter 6: Transforming Pricing Management in a Chemical Supplier

Setting the Scene

Framing the Problem: Understanding the Current State Pricing Process

Collecting Baseline Data

Uncovering Relationships

Modeling Relationships

Revising Knowledge

Utilizing Knowledge: Sustaining the Benefits

Conclusion

Chapter 7: Improving the Quality of Anodized Parts

Setting the Scene

Framing the Problem

Collecting Data

Uncovering Relationships

Locating the Team on the VSS Roadmap

Modeling Relationships

Revising Knowledge

Utilizing Knowledge

Conclusion

Notes

Chapter 8: Informing Pharmaceutical Sales and Marketing

Setting the Scene

Collecting the Data

Validating and Scoping the Data

Uncovering Relationships

Investigating Promotional Activity

A Deeper Understanding of Regional Differences

Summary

Conclusion

Note

Chapter 9: Improving a Polymer Manufacturing Process

Setting the Scene

Framing the Problem

Reviewing Historical Data

Measurement System Analysis (MSA)

Uncovering Relationships

Modeling Relationships

Revising Knowledge

Utilizing Knowledge

Conclusion

Notes

Chapter 10: Classification of Cells

Setting the Scene

Framing the Problem and Collecting the Data: The Wisconsin Breast Cancer Diagnostic Data Set

Initial Data Exploration

Constructing the Training, Validation, and Test Sets

Prediction Models

Recursive Partitioning

Stepwise Logistic Model

Generalized Regression

Neural Net Models

Comparison of Classification Models

Conclusion

Notes

Part Three: Supplementary Material

Chapter 11: Beyond “Point and Click” with JMP

Programming and Application Building in JMP

A Motivating Example: Democracy and Trade Policy

Building the Missing Data Application

Conclusion

Notes

Index

End User License Agreement

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Guide

Cover

Table of Contents

Begin Reading

List of Exhibits

Chapter 2: Six Sigma and Visual Six Sigma

Exhibit 2.1 Modeling of Causes before Improvement

Exhibit 2.2 Modeling of Causes after Improvement

Exhibit 2.3 Visual Six Sigma Data Analysis Process

Exhibit 2.4 The Visual Six Sigma Roadmap: What We Do

Chapter 3: A First Look at JMP

Exhibit 3.1 JMP Home Window and Tip of the Day Window

Exhibit 3.2 Partial View of

PharmaSales.jmp

Data Table

Exhibit 3.3 Icons Representing Modeling Types

Exhibit 3.4 Menu Bar and Default Data Table Toolbars

Exhibit 3.5

Analyze

Menu for JMP Pro 12.2.0

Exhibit 3.6

Graph

Menu for JMP Pro 12.2.0

Exhibit 3.7

Distribution

Dialog for

PharmaSales.jmp

Exhibit 3.8

Distribution

Dialog with Three Variables Entered as Ys

Exhibit 3.9

Distribution

Reports for

Region Name

,

Visits

, and

Prescriptions

Exhibit 3.10

Distribution

Report Options

Exhibit 3.11 Stacked Layout for Three

Distribution

Reports

Exhibit 3.12 Variable-Specific Report Commands

Exhibit 3.13

Distribution

Reports for

Region Name

and

Salesrep Name

Exhibit 3.14 Bar for

Region Name

Scotland Selected

Exhibit 3.15 Partial View of Data Table Showing Selection of Rows with

Region Name

Scotland

Exhibit 3.16

Distribution

of

Salesrep Name

with Adrienne Stoyanov Selected

Exhibit 3.17 Data Table Consisting of 2,440 Rows with

Salesrep Name

Adrienne Stoyanov

Exhibit 3.18 Deselecting Rows or Columns

Exhibit 3.19 JMP Home Window with List of Open Windows

Exhibit 3.20

Tables

Menu

Exhibit 3.21

Rows

Menu with Commands for

Row Selection

Shown

Exhibit 3.22

Cols

Menu with

Utilities

Shown

Exhibit 3.23

Column Info

Dialog for

Visits

Showing Column Properties

Exhibit 3.24

DOE

Menu

Exhibit 3.25 Running the Script

Distribution Plots for Three Outcome Variables

Exhibit 3.26

Distribution

Report Obtained by Running

Distribution Plots for Three Outcome Variables

Exhibit 3.27 Saving a Script to the Data Table

Exhibit 3.28

Distribution

Script

Exhibit 3.29 Visual Six Sigma Data Analysis Process

Exhibit 3.30 Visual Six Sigma Roadmap

Exhibit 3.31 Platforms and Options Illustrated in the Remaining Chapters

Chapter 4: Managing Data and Data Quality

Exhibit 4.1 Data Management Activities in the Collect Data Step of Visual Six Sigma

Exhibit 4.2 Text Import Preview Options

Exhibit 4.3 Text Import Preview Window with Column Option

Exhibit 4.4 Warning Dialog

Exhibit 4.5 The JMP Table with Data from

household_power_consumption.txt

Exhibit 4.6

Missing Data Pattern

Exhibit 4.7 Formula to Find Number of Seconds

Exhibit 4.8

Distribution

of Columns (Partial View)

Exhibit 4.9 Were Measurements Attempted Every Minute?

Exhibit 4.10 Constructing a Virtual Column

Exhibit 4.11 Number of Measurements by Month

Exhibit 4.12 Number of Measurements Each Month by Day of the Week

Exhibit 4.13 Seasonal and Weekly Trends in

Global_active_power

Exhibit 4.14 Daily Trend in

Global_active_power

Exhibit 4.15 Daily Trend in

Global Active Power

on Day 7

Exhibit 4.16 Univariate Distributions of Power, Voltage, and Current

Exhibit 4.17 Selecting All Rows for June 23, 2009

Exhibit 4.18 Defining

No_Sub_metering

with a Formula

Exhibit 4.19 Power Drawn by Different Appliances on June 23rd 2009

Exhibit 4.20 Power Drawn by Different Appliances, Filtering by Day

Exhibit 4.21 Table

Biscuit Products.jmp

(Partial View)

Exhibit 4.22 Table

Biscuit Sales.jmp

(Partial View)

Exhibit 4.23 The

Join

Dialog

Exhibit 4.24

Biscuits.jmp

(Partial View)

Exhibit 4.25 Recoding

Number in Multipack

Exhibit 4.26 Pack Sizes for Different Biscuit Categories

Exhibit 4.27 Value at Risk for Different Biscuit Categories