1st Edition

Data Analytics and Visualization in Quality Analysis using Tableau

By Jaejin Hwang, Youngjin Yoon Copyright 2022
    222 Pages 227 B/W Illustrations
    by CRC Press

    222 Pages 227 B/W Illustrations
    by CRC Press

    Data Analytics and Visualization in Quality Analysis using Tableau goes beyond the existing quality statistical analysis. It helps quality practitioners perform effective quality control and analysis using Tableau, a user-friendly data analytics and visualization software. It begins with a basic introduction to quality analysis with Tableau including differentiating factors from other platforms. It is followed by a description of features and functions of quality analysis tools followed by step-by-step instructions on how to use Tableau. Further, quality analysis through Tableau based on open source data is explained based on five case studies. Lastly, it systematically describes the implementation of quality analysis through Tableau in an actual workplace via a dashboard example.


    • Describes a step-by-step method of Tableau to effectively apply data visualization techniques in quality analysis
    • Focuses on a visualization approach for practical quality analysis
    • Provides comprehensive coverage of quality analysis topics using state-of-the-art concepts and applications
    • Illustrates pragmatic implementation methodology and instructions applicable to real-world and business cases
    • Include examples of ready-to-use templates of customizable Tableau dashboards

    This book is aimed at professionals, graduate students and senior undergraduate students in industrial systems and quality engineering, process engineering, systems engineering, quality control, quality assurance and quality analysis.

    Chapter 1: Introduction

    Chapter Abstract

    Chapter Overview and Expected Learning Outcomes

    1.1 Basic Concepts in Quality Analysis

    1.2 What is Tableau?

    1.3 How to Leverage Tableau in Quality Analysis

    Chapter 2: Commonly Used Quality Analysis Tools with Tableau

    Chapter Abstract

    Chapter Overview and Expected Learning Outcomes

    What is the Chart?

    Tableau Categorizing Fields

    2.1 Stacked Bar Chart

    2.2 Histogram

    2.3 Butterfly Chart

    2.4 Donut Chart

    2.5 Scatter Plot

    2.6 Bubble Chart

    2.7 Box Plot

    2.8 Pareto Chart

    2.9 Bump Chart

    2.10 Maps

    2.11 Gantt Chart

    2.12 Control Chart for Variables

    2.13 Control Chart for Attributes

    Chapter 3: Quality Dashboard

    Chapter Abstract

    Chapter Overview and Expected Learning Outcomes

    3.1 What is a Dashboard?

    3.2 Dashboard Type

    3.3 Dashboard Design Approach

    3.4 Healthcare Quality Dashboard

    3.5 Airline Quality Dashboard

    3.6 Manufacturing Quality Dashboard

    3.7 Warehouse Quality Dashboard

    Chapter 4: Case Studies

    Chapter Abstract

    Chapter Overview and Expected Learning Outcomes

    4.1 Case Studies and Data Storytelling

    4.2 Red Wine Quality (Case 1)

    4.3 Airline Passenger Satisfaction (Case 2)

    4.4 Driverless Car Failure (Case 3)

    4.5 Real Time Voice Call Quality Data from Customers (Case 4)

    4.6 Brewery Production (Case 5)

    4.7 Seoul Bike Sharing Demand (Case 6)


    Jaejin Hwang is an assistant professor of Industrial and Systems Engineering at Northern Illinois University. He received his B.E. and M.S. degrees in Industrial and Systems Engineering from Ajou University. He received a Ph.D. degree in Industrial and Systems Engineering from the Ohio State University. His research and teaching interests include statistical quality control, engineering statistics, reliability engineering, work measurement & design, ergonomics, and occupational biomechanics. He has published more than 50 technical papers including peer-reviewed journal articles and international conference proceeding papers. He has received a Bagam Paper Award from the Korean Institute of Industrial Engineers. He is an executive committee member of the International Society for Occupational Ergonomics and Safety. He serves on the editorial board of the journal Work: A Journal of Prevention, Assessment & Rehabilitation. He is a guest editor of the International Journal of Environmental Research and Public Health (Special Issue).

    Youngjin Yoon is IT Assurance Team Leader at Pitney Bowes and was Consultant at Deloitte with 10 + years of broad global experience (U.S. and Asia) in developing and executing strategies in alignment with business objectives. He has experience in diverse consulting projects for 25+ multi-national companies with a successful track record of on-time and high-performance project completion. He received his B.S. in Computer Science Education from Korea University, M.B.A. from Washington University in St. Louis, and M.S in Project Management from Harrisburg University of Science and Technology. His interests include business strategy & operation, data analytics & visualization, 4th industrial revolution technologies, and risk management. He is a self-starter, author, storyteller, innovator, and excels at leading teams by influencing, motivating, and delivering results.