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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Explore the dramatic changes brought on by the new manufacturing technologies of Industry 4.0 In Smart Manufacturing, The Lean Six Sigma Way, Dr. Anthony Tarantino delivers an insightful and eye-opening exploration of the ways the Fourth Industrial Revolution is dramatically changing the way we manufacture products across the world and especially how it will revitalize manufacturing in North America and Europe. The author examines the role and impact of a variety of new Smart technologies including industrial IoT, computer vision, mobile/edge computing, 3D printing, robots, big data analytics, and the cloud. He demonstrates how to apply these new technologies to over 20 continuous improvement/Lean Six Sigma tools, greatly enhancing their effectiveness and ease of use. The book also discusses the role Smart technologies will play in improving: * Career opportunities for women in manufacturing * Cyber security, supply chain risk, and logistics resiliency * Workplace health, safety, and security * Life on the manufacturing floor * Operational efficiencies and customer satisfaction Perfect for anyone involved in the manufacturing or distribution of products in the 21st century, Smart Manufacturing, The Lean Six Sigma Way belongs in the libraries of anyone interested in the intersection of technology, commerce, and physical manufacturing.
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Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Title Page
Copyright
Dedication
Foreword
Acknowledgments
About the Author
About the Contributors
Introduction
CHAPTER 1: Introduction to Industry 4.0 and Smart Manufacturing
Introduction
The First Industrial Revolution
The Second Industrial Revolution
The Third Industrial Revolution
The Fourth Industrial Revolution
The Major Components of Smart Manufacturing
Summary: The Advantages of Smart Manufacturing
Sample Questions
Notes
CHAPTER 2: Lean Six Sigma in the Age of Smart Manufacturing
Introduction
The History of Lean – American Assembly Lines
The History of Lean – Toyota Embraces Deming and Piggly Wiggly
The Toyota Production System: The Birthplace of Lean
Lean Empowers Employees, Treating Them with Respect
Resilient Supply Chain Management: How Toyota Fared During the COVID-19 Pandemic
The History of Six Sigma: Bill Smith and Jack Welch
Six Sigma's DMAIC Framework to Fix an Existing Process
The DMAIC Framework Using Smart Technologies
Six Sigma's DMADV Framework to Design a New Process
The Statistics Behind Six Sigma
Six Sigma Professionals in the Age of Smart Manufacturing
Six Sigma Project Charters and SMART Goals
Lean and Six Sigma Uses of the Scientific Method
Summary: Six Sigma's Marriage to Lean
Sample Questions
Notes
CHAPTER 3: Continuous Improvement Tools for Smart Manufacturing
Introduction
Voice of the Customer in the Age of Smart Manufacturing
Voice of the Customer Using Net Promoter Score
Voice of the Customer Using the Delphi Technique
Voice of the Customer Using the Kano Model
Affinity Diagrams to Organize Many Ideas into Common Themes
Critical to Quality to Convert the VOC to Measurable Objectives
Types of Data
Benchmarking
Process Maps
Types of Process Maps
SIPOC
Process Maps with Decision Points
Process Maps with Swim Lanes
Limited Data Collection and the Hawthorne Effect Impacting Process Mapping
Value Stream Maps to Eliminate Waste
Value-Added Activity versus Non-Value-Added Activity
Root Cause Analysis Using a Fishbone Diagram and Risk Matrix
Root Cause Analysis Using the Five Whys
Changes Coming to Root Cause Analysis with Smart Technologies
Pareto Chart
Kanban Pull System
Poka-Yoke to Error-Proof Processes and Products
Five S
Heijunka
Plan-Do-Check-Act
Kaizen
Setup Time Reduction Using Single Minute Exchange of Dies
Gage Repeatability and Reproducibility (Gage R&R)
Failure Modes and Effects Analysis (FMEA) to Solve Complex Problems
Pugh Matrix to Design New Processes and Products
Quality Function Deployment (House of Quality)
Summary
Sample Questions
Notes
CHAPTER 4: Improving Supply Chain Resiliency Using Smart Technologies
Introduction
Supply Chain Resilience
Supply Chain Risk Heat Maps
Supply Chain Mapping at a Macro and Micro Level
Preferred Supplier Programs
Bill of Material Risk Grading Tools
Environmental Risk Solutions
The Global Driver Shortage and Poor Utilization
Vehicle Monitoring Tools
Computer Vision Systems Using Smart Cameras
Autonomous Trucks
Supply Chain Resilency in a Post-COVID World
Criticism and Defense of Lean Inventory Management
Good Sourcing Strategies
Supply Chain Stress Testing
Summary
Sample Questions
Notes
CHAPTER 5: Improving Cybersecurity Using Smart Technology
Introduction
Trends Increasing the Risk of Manufacturing and Supply Chain Cyberattacks
So Why Is Manufacturing and the Supply Chain an Attractive Target?
Primary Motives Behind Manufacturing and Supply Chain Attacks
Methods Used to Breach Target Systems
What Are the Potential Costs of a Cyberattack?
Protecting Against Cyberattacks
Summary
Sample Questions
Notes
CHAPTER 6: Improving Logistics Using Smart Technology
Introduction: Why Logistics?
Megatrends in Logistics That Impact Brands/Manufacturers
The Different Expectation of Your Customer-by-Customer Type
The Cost of Not Paying Attention to Logistics
The Benefits of Making Logistics a Strategic Competency
Steps to Make Logistics Your Competitive Advantage
Why Technology Is So Important to Logistics
Area 1: Insight/Planning/Monitoring
Area 2: Task Execution
Area 3: Exchanges and Collaborations
Area 4: Safety, Security, and Compliance
Summary
Sample Questions
Notes
CHAPTER 7: Big Data for Small, Midsize, and Large Operations
Introduction
Structured Data and Relational Databases
Unstructured Data
Why Manufacturing Needs Big Data Analytics
The Four Levels of Data Analytics
Descriptive Analytics – What Happened?
Diagnostic Analytics – Why Did It Happen?
Predictive Analytics – What May Have Happened?
Prescriptive Analytics – What Is the Best Next Step?
Future of Big Data Analytics
Data Science Tools
Data Analytics Pipeline
The Benefits of Big Data for SMEs
Big Data Tools for SMEs
Problems SMEs Face in Adopting Big Data Analytics
Best Practices in Data Analytics for SMEs
Summary
Sample Questions
Notes
CHAPTER 8: Industrial Internet of Things (IIoT) Sensors
Introduction
PLCs
Carnegie Mellon
Consumer-Oriented IoT
Webcams
IIoT -Enabling Technologies
IIoT Platform Building Blocks
IIoT Sensors
Application Areas for IIoT
Industries Where IIoT Can and Does Play a Role
Future Trends in IIoT
Summary
Sample Questions
Notes
CHAPTER 9: Artificial Intelligence, Machine Learning, and Computer Vision
Introduction
History of AI and Computer Vision
Understanding Machine Learning and Computer Vision
Issues with Artificial Intelligence
Conclusion
Sample Questions
Notes
CHAPTER 10: Networking for Mobile Edge Computing
Introduction
Brief History of Networking
Basic Networking Concepts, Architecture, and Capabilities
Subnets
Basic Wi-Fi Concepts, Architecture, and Capabilities
Mobile Cell Phone Concepts, Architecture, and Capabilities
IT and Telecommunications Networking Convergence
Summary
Sample Questions
References
Popular Acronyms Used in Networking and Mobile Computing
Notes
CHAPTER 11: Edge Computing
Introduction: What Is Edge Computing?
Benefits of Edge Computing
Top Use Cases for the Edge in Smart Manufacturing
The Data Challenge
Deployment Challenges
Solving Deployment Challenges with an Edge Computing Platform
The Edge Computing Platform Landscape
Edge-to-Cloud Computing
How a Successful Edge Computing Rollout Works
Summary
Sample Questions
Notes
CHAPTER 12: 3D Printing and Additive Manufacturing
Introduction
History
Additive Manufacturing Process
Applications
Summary
Sample Questions
References
CHAPTER 13: Robotics
Introduction
Industrial Robots
Manipulator
Actuators
Controllers
End Effectors
Types of Robots
Robotics Timeline: 1961 to 2011
Collaborative Robots
The Outlook
Sample Questions
Bibliography
Notes
CHAPTER 14: Improving Life on the Factory Floor with Smart Technology
Introduction
Life on the Factory Floor from 1700 to Today
The Smart Manufacturing Factory Floor
How AI Is Powering Smart Manufacturing
Smart Manufacturing Is Optimizing Factory Processes
Hurdles Faced in Implementing Smart Technologies
Three Essential Job Types in Smart Manufacturing
Three Types of Tools Needed in Smart Manufacturing
Smart Manufacturing Design Choices
Summary
Sample Questions
Notes
CHAPTER 15: Growing the Roles for Women in Smart Manufacturing
Introduction
Women as Innovators
Women Hold the Answers (Skills Where Women Excel)
Women's Inspiration
Companies Working to Overcome Barriers to Women's Entry
Programs to Develop STEM Skills for Women
Growing the Role of Women in Smart Manufacturing
Maria Villamil's Story
Deborah Walkup's Story
Summary
Sample Questions
Notes
CASE STUDIES
CASE STUDY 1: Automating Visual Inspection Using Computer Vision
Introduction
Conclusion
Notes
CASE STUDY 2: Bar Coding, the Most Ubiquitous and Most Critical IIoT Technology
Introduction
Barcode Technology
Mobile Barcodes: Radio-Frequency Identification (RFID)
Summary
Notes
CASE STUDY 3: Improving Safety with Computer Vision
Introduction
The Deep Learning Revolution
Examples of Computer Vision's Role in Improving Safety
Conclusion
Notes
CASE STUDY 4: COVID-19 Accelerates the Adoption of 3D Printing
Introduction
3D Printing During the COVID-19 Pandemic
3D Printing in a Post-COVID-19 World
Summary
Notes
CASE STUDY 5: How Mobile Apps Benefit Small to Midsize Enterprises
Introduction
Mobile Apps for All SMEs
Mobile Apps for Manufacturing and Distribution
Summary
Notes
CASE STUDY 6: Using Factory-Floor Touch Screens to Improve Operations
Introduction
Problem Definition
Solution Description
Solution Choices
Adjacent Applications
The Future and Conclusions
CASE STUDY 7: Edge Computing to Improve Operations
Edge Computing Deployment Use Case: Food and Beverage
Edge Computing Deployment Use Case: Automotive
CASE STUDY 8: Five Highly Dangerous Jobs That Robots Can Do Safely
Notes
Answers to Sample Questions
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Links to Continuous Improvement Templates
Index
End User License Agreement
List of Exhibits
Chapter 1
EXHIBIT 1.1 Digital twin of a car engine and wheels
EXHIBIT 1.2 A blacksmith shop in the Middle Ages
EXHIBIT 1.3 A painting of an 1800s textile mill
EXHIBIT 1.4 A 1930s auto assembly line
EXHIBIT 1.5 Lillian Gilbreth
EXHIBIT 1.6 Managing the factory floor with a personal computer
EXHIBIT 1.7 Collecting data traditionally
EXHIBIT 1.8 Military Standard 105e
EXHIBIT 1.9 Internet of Things (IoT) data analytic concept
EXHIBIT 1.10 Edge computing
EXHIBIT 1.11 A 3D printer at work
EXHIBIT 1.12 Robotic arm with mechanical hand
Chapter 2
EXHIBIT 2.1 A car assembly line
EXHIBIT 2.2 An early Piggly Wiggly store
EXHIBIT 2.3 Toyota senior engineer Taiichi Ohno
EXHIBIT 2.4 Push versus pull systems
EXHIBIT 2.5 The Eight Wastes of Lean
EXHIBIT 2.6 Using computer vision to monitor machine status
EXHIBIT 2.7 Three kanban bins
EXHIBIT 2.8 Monitoring kanban bin inventory levels and pallets
EXHIBIT 2.9 Smart Manufacturing technologies change how Six Sigma projects a...
EXHIBIT 2.10 Choosing between DMAIC and Design for Six Sigma
EXHIBIT 2.11 Probability of defects at different sigma levels
EXHIBIT 2.12 A normal distribution curve
EXHIBIT 2.13 Normal distribution curves with three different standard deviat...
EXHIBIT 2.14 Distribution of observations
EXHIBIT 2.15 Two questions about sigma levels
EXHIBIT 2.16 Six Sigma belts
EXHIBIT 2.17 Example project charter
Chapter 3
EXHIBIT 3.1 VOC converted into needs and requirements
EXHIBIT 3.2 The NPS scale
EXHIBIT 3.3 The 11 emoji in an NPS scoring system
EXHIBIT 3.4 License renewal user experience survey results
EXHIBIT 3.5 First-round survey results
EXHIBIT 3.6 Second-round survey results
EXHIBIT 3.7 Example Kano Model
EXHIBIT 3.8 Kano Model of a car
EXHIBIT 3.9 Affinity diagram grouping
EXHIBIT 3.10 An affinity diagram list of challenges
EXHIBIT 3.11 Challenges organized into themes
EXHIBIT 3.12 An affinity chart that reduces pain points to affinity groups
EXHIBIT 3.13 Flowcvhart for VOC to CTQ
EXHIBIT 3.14 CTQ tree
EXHIBIT 3.15 Benchmarking example
EXHIBIT 3.16 Hiring process map
EXHIBIT 3.17 Types of process maps
EXHIBIT 3.18 SIPOC steps and definitions
EXHIBIT 3.19 The five steps of a SIPOC
EXHIBIT 3.20 Process map with a decision point
EXHIBIT 3.21 Process map using swim lanes
EXHIBIT 3.22 Process map with nine decision points
EXHIBIT 3.23 Traditional versus automated process mapping
EXHIBIT 3.24 Simple value stream map
EXHIBIT 3.25 Fishbone diagram
EXHIBIT 3.26 Basic structure of a fishbone diagram
EXHIBIT 3.27 Risk matrix as a heat map
EXHIBIT 3.28 Example of a fishbone exercise
EXHIBIT 3.29 Complex fishbone exercise
EXHIBIT 3.30 A simple Five Whys
EXHIBIT 3.31 A complex Five Whys
EXHIBIT 3.32 Pareto chart of late arrivals by reported cause
EXHIBIT 3.33 Bank service call Pareto chart
EXHIBIT 3.34 Manufacturing defect Pareto chart
EXHIBIT 3.35 Customer order kanban
EXHIBIT 3.36 Example kanban board
EXHIBIT 3.37 Using smart cameras with kanbans
EXHIBIT 3.38 The 5Ss
EXHIBIT 3.39 Computer vision AI and 5S
EXHIBIT 3.40 Five levels of social distancing
EXHIBIT 3.41 Lean/JIT production line with and without leveling
EXHIBIT 3.42 This is
not
Heijunka
EXHIBIT 3.43 This is Heijunka
EXHIBIT 3.44 Heijunka kanban board
EXHIBIT 3.45 Steps of PDCA
EXHIBIT 3.46 EOQ formula
EXHIBIT 3.47 Gage R&R example
EXHIBIT 3.48 FMEA process flow
EXHIBIT 3.49 FMEA analysis sheet
EXHIBIT 3.50 FMEA worksheet
EXHIBIT 3.51 A manufacturing FMEA
EXHIBIT 3.52
EXHIBIT 3.53 Simple Pugh Matrix exercise
EXHIBIT 3.54 The House of Quality
EXHIBIT 3.55 A QFD example
EXHIBIT 3.56 Customer needs and ratings
EXHIBIT 3.57 Smartphone design requirements
EXHIBIT 3.58 Importance symbols and requirement scoring
EXHIBIT 3.59 Correlation matrix
EXHIBIT 3.60 Arrows in the correlation matrix
EXHIBIT 3.61 QFD House of Quality
EXHIBIT 3.62 Neural network model
Chapter 4
EXHIBIT 4.1 Tiers in supply chains
EXHIBIT 4.2 Typical heat map
EXHIBIT 4.3 Construction project heat map
EXHIBIT 4.4 Global heat map
EXHIBIT 4.5 World Bank logistics performance
EXHIBIT 4.6 Comparing the control of corruption
EXHIBIT 4.7 Sample BOM report card
EXHIBIT 4.8 Changes in supply chain disruptions, 2019–2020
EXHIBIT 4.9
EXHIBIT 4.10 Real-time monitoring of a truck terminal
EXHIBIT 4.11 Using cameras to navigate trucks
EXHIBIT 4.12 The need for change
Chapter 5
EXHIBIT 5.1 Balance between technologies
EXHIBIT 5.2 Cloud storage
EXHIBIT 5.3 Threats from APT groups in China
EXHIBIT 5.4 Determining a cyber risk strategy
EXHIBIT 5.5 Risk management crossword
Chapter 6
EXHIBIT 6.1 Typical cost–benefit analysis
EXHIBIT 6.2 Machine learning pipeline
Chapter 7
EXHIBIT 7.1 Growth in unstructured data, 2010–2025
EXHIBIT 7.2 Four levels of data analytics
Chapter 8
EXHIBIT 8.1 An early PLC
EXHIBIT 8.2 Sample webcam images
EXHIBIT 8.3 Dr. Paul Jarderzky's Philips camera
EXHIBIT 8.4 The final coffeepot image
EXHIBIT 8.5 Four layers of IIoT
EXHIBIT 8.6 Specifications for a low-cost temperature sensor switch
EXHIBIT 8.7 Temperature sensor specifications
EXHIBIT 8.8 Common transmission interfaces
Chapter 9
EXHIBIT 9.1 The relationship among artificial intelligence, machine learning...
EXHIBIT 9.2 Learning to differentiate
EXHIBIT 9.3 Image classification
EXHIBIT 9.4 Object detection
EXHIBIT 9.5 Instance and object segmentation
Chapter 10
EXHIBIT 10.1 Simplest network
EXHIBIT 10.2 Network with a node
EXHIBIT 10.3 Network with a ring topology
EXHIBIT 10.4 Mesh topology
EXHIBIT 10.5 Six-byte MAC addresses
EXHIBIT 10.6 Initial Ethernet installations
EXHIBIT 10.7 Simple configuration with hub
EXHIBIT 10.8 Internetwork topology
EXHIBIT 10.9 LAN connected to the Cloud
EXHIBIT 10.10 IPv4 addresses
EXHIBIT 10.11 Definitions of classes
EXHIBIT 10.12 Common IP addresses
EXHIBIT 10.13 Subnetted network diagram
EXHIBIT 10.14 NAT router
EXHIBIT 10.15 IP addresses
EXHIBIT 10.16 IANA port addresses
EXHIBIT 10.17 Predefined domain labels
EXHIBIT 10.18 Layers of the OSI model
EXHIBIT 10.19 Features of Wi-Fi generations
EXHIBIT 10.20 Typical Wi-Fi LAN
EXHIBIT 10.21 Mobile generations
Chapter 11
EXHIBIT 11.1 The rise of industrial connectivity (2018–2024)
EXHIBIT 11.2 Intelligent Edge computing
EXHIBIT 11.3 Technology stack with Edge and Cloud infrastructure
EXHIBIT 11.4 Technology stack with Edge infrastructure detail
EXHIBIT 11.5 Deployment steps
Chapter 12
EXHIBIT 12.1 CAD model and slicing: (a) CAD model; a finished part using (b)...
EXHIBIT 12.2 Example of a general AM process cycle
EXHIBIT 12.3 Schematic diagrams showing (a) the Munz system, (b) the Swainso...
EXHIBIT 12.4 3D patterns using the Housholder, Kodama, and Herbert systems
EXHIBIT 12.5 Summary of the ASTM and ISO standards in 2020
EXHIBIT 12.6 A schematic diagram of VPP
EXHIBIT 12.7 Examples of printed microstructures by VPP
EXHIBIT 12.8 Schematic diagram of material extrusion
EXHIBIT 12.9 Schematic drawing of material jetting
EXHIBIT 12.10 Droplet formation and expulsion. (a) Schematic of continuous s...
EXHIBIT 12.11 Schematic diagram of a binder jetting system
EXHIBIT 12.12 Schematic diagram of powder bed fusion
EXHIBIT 12.13 Schematic diagram of sheet lamination
EXHIBIT 12.14 Schematic diagram of directed energy deposition
EXHIBIT 12.15 Schematics showing (a) top view of the 3D model, (b) side view...
EXHIBIT 12.16 Examples of structural and nonstructural applications of 3D pr...
EXHIBIT 12.17 3D bioprinting vascular model
EXHIBIT 12.18 Schematic of four dimensions
EXHIBIT 12.19 4D printing applications
Chapter 13
EXHIBIT 13.1 Simple industrial robot system
EXHIBIT 13.2 A manipulator
EXHIBIT 13.3 End-of-arm tooling
EXHIBIT 13.4 Different types of robots
Chapter 14
EXHIBIT 14.1 Nineteenth-century factory workers.
EXHIBIT 14.2 BMW's modern assembly line
EXHIBIT 14.3 Examples of consumables used in electronics assembly
EXHIBIT 14.4 Advanced vending machine
EXHIBIT 14.5 A variety of cutting tools
EXHIBIT 14.6 A zero-gravity mechanical arm
Chapter 15
EXHIBIT 15.1 J. Howard Miller's iconic poster
EXHIBIT 15.2 BiC pen “for Her”
EXHIBIT 15.3 Overall Leadership Effectiveness by Gender by Position (Percent...
EXHIBIT 15.4 The Top 16 Competencies Top Leaders Exemplify Most
EXHIBIT 15.5 Earnings gap by race (median weekly earnings in 2020)
Case Study 1
EXHIBIT CS1.1 Factors impacting physical inspection
EXHIBIT CS1.2 Automated visual inspection factors
EXHIBIT CS1.3 Bottling line with automated visual inspection
EXHIBIT CS1.4 Automated visual inspection adoption rates
EXHIBIT CS1.5 Parts classification on a printed board assembly
EXHIBIT CS1.6 Verification of model numbers
EXHIBIT CS1.7 Verification of a car door assembly
EXHIBIT CS1.8 Discovery of fabric defects
EXHIBIT CS1.9 Detection of bottle cap defects
Case Study 2
EXHIBIT CS2.1 Handheld wireless barcode scanner
EXHIBIT CS2.2 Components of a 1D barcode
EXHIBIT CS2.3 Examples of RFID tags
EXHIBIT CS2.4 RFID system and RFID tag circuit
Case Study 3
EXHIBIT CS3.1 Using a forklift to provide a work platform
EXHIBIT CS3.2 Computer vision capturing the distance between pedestrians and...
EXHIBIT CS3.3 Computer vision using thermal imaging to detect a fever
EXHIBIT CS3.4 Computer vision ensuring compliance in wearing hard hats
EXHIBIT CS3.5 Computer vision detecting a major safety violation
Case Study 4
EXHIBIT CS4.1 3D printing applications
EXHIBIT CS4.2 Additive manufacturing as an investment priority
EXHIBIT CS4.3 How 3D printing is used
EXHIBIT CS4.4 3D printing and product lifecycle stage
EXHIBIT CS4.5 Expected increases in using 3D printing
Case Study 5
EXHIBIT CS5.1 Regional growth in mobile payments (in millions)
EXHIBIT CS5.2 Examples of mobile accounting app screens
EXHIBIT CS5.3 QuickBooks’ mobile accounting screens
EXHIBIT CS5.4 WorkflowMax's Leads dashboard
EXHIBIT CS5.5 WorkflowMax's project quote screen
EXHIBIT CS5.6 Collaborative Gantt chart
EXHIBIT CS5.7 An individual timesheet
EXHIBIT CS5.8 An individual calendar for tracking work time
EXHIBIT CS5.9 An individual expense report
EXHIBIT CS5.10 A receipt and its transcription into an expense report
EXHIBIT CS5.11 Inventory summary dashboard
EXHIBIT CS5.12 Phone camera scanning a barcode and sales order
EXHIBIT CS5.13 Screens to update inventory levels and to fulfill orders
EXHIBIT CS5.14 Screens showing filters and stock
Case Study 7
EXHIBIT CS7.1 Litmus architecture created for food and beverage client
EXHIBIT CS7.2 Litmus architecture created for automotive client
Guide
Cover Page
Title Page
Copyright
Dedication
Foreword
Acknowledgments
About the Author
About the Contributors
Introduction
Table of Contents
Begin Reading
CASE STUDY 1: Automating Visual Inspection Using Computer Vision
CASE STUDY 2: Bar Coding, the Most Ubiquitous and Most Critical IIoT Technology
CASE STUDY 3: Improving Safety with Computer Vision
CASE STUDY 4: COVID-19 Accelerates the Adoption of 3D Printing
CASE STUDY 5: How Mobile Apps Benefit Small to Midsize Enterprises
CASE STUDY 6: Using Factory-Floor Touch Screens to Improve Operations
CASE STUDY 7: Edge Computing to Improve Operations
CASE STUDY 8: Five Highly Dangerous Jobs That Robots Can Do Safely
Answers to Sample Questions
Links to Continuous Improvement Templates
Index
WILEY END USER LICENSE AGREEMENT
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Additional Praise for Smart Manufacturing: The Lean Six Sigma Way
“China is no longer the low-labor cost manufacturer of the past and must adopt smart manufacturing to remain viable. In the design process and implementation of smart manufacturing, our company fully draws on the knowledge of this book, especially on how to combine lean six sigma tools with smart technologies. It is a rare book that fully and effectively combines production management concepts and practices. This book can be very effective in helping to realize smart manufacturing in the factory to lower cost, improve customer satisfaction, and improve employee morale.”
—Jianfeng Du, Founder and CEO, Millennium Power, China's Leading Hybrid Energy and Battery Storage Solution Innovator
“This book covers topics that are at the heart of our firm's investment thesis. Modern supply chains will have to become fully digitized and required to be resilient and efficient. Just like software has changed the world, smart technologies will change how goods and services are manufactured and delivered swiftly in a fully automated way. The author covers all of the building blocks that will be at the core of the smart technologies wave that unfolds in the next few years. The book is a great reference to have and I strongly encourage you to read it!”
—Najib Khouri-Haddad, General Partner, Sway Ventures
“Dr. Tarantino's newest tome, Smart Manufacturing: The Lean Six Sigma Way, is a tour de force and comprehensive work that will appeal to both readers who are new to the field as well as accomplished experts. In addition to providing fresh perspectives on the latest smart manufacturing approaches, he and his chapter co-authors also expand on several of the most pressing challenges and important issues facing the United States and the global manufacturing economy, including supply chain resiliency, cybersecurity, big data, as well as the rapid adoption of game-changing technologies including artificial intelligence, machine learning, and edge computing. This encompassing volume is highly recommended reading for anyone interested in understanding the state-of-the-art in the rapidly evolving advanced and smart manufacturing landscape.”
—Daniel Dirk, PhD, Interim Dean of Engineering, Florida Institute of Technology
“Smart Manufacturing: The Lean Six Sigma Way is a comprehensive and accessible overview of the technologies that are transforming industry. Relevant to both students and practitioners, the book places smart manufacturing in its historical context while clearly bringing across the powerful disruptive potential of Industry 4.0. This is already being felt in the aerospace sector, where a combination of the approaches and technologies outlined in Smart Manufacturing are bringing down development costs and time to market, while reducing entry barriers and enabling a new generation of start-ups with innovative business models. Anthony Tarantino's book provides insight into this emerging paradigm that will be of huge benefit to the reader.”
—Harry Malins, Chief Innovation Officer, Aerospace Technology Institute
“Industry 4.0 is underway. Data analytics, augmented reality, artificial intelligence, collaborative robots, additive manufacturing, and other technologies are already helping manufacturers increase efficiency, reduce downtime, lower prices, and improve service, delivery, and quality. And there's more to come. These technologies are not science fiction. They are being applied right now by manufacturers, large and small, in a variety of industries. However, Industry 4.0 is not merely a matter of connecting machines to the Internet. Industry 4.0 will inevitably lead to new types of work and new ways of working. It will require changes to company structures and relationships between companies. Businesses must understand what they want to achieve and then develop an implementation strategy. This book will help you get there.”
—John Sprovieri, chief editor, ASSEMBLY magazine
“A wonderful book to introduce undergraduate students to a career in operations or manufacturing, a long-overlooked field. The book is easy to read and will allow students to understand the challenges facing those implementing Industry 4.0. Particularly enlightening in describing how smart manufacturing will open up opportunities for women who choose a STEM field for a career.”
—Deborah Cernauskas, PhD, Professor of Business Analytics and Finance, Chair Undergraduate Business (retired), Benedictine University
SMART MANUFACTURING
The Lean Six Sigma Way
Anthony Tarantino
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To my beloved wife, Shirley, whose continued encouragement and support have guided my writing and teaching efforts over the past 15 years in creating five tomes for John Wiley & Sons and in teaching at Santa Clara University.
Foreword
Narendra Agrawal, PhD
Benjamin and Mae Swig Professor of Supply Chain AnalyticsLeavey School of BusinessSanta Clara University
It is with great pleasure that I write this foreword for Smart Manufacturing: The Lean Six Sigma Way. I want to congratulate the editor, Dr. Anthony Tarantino, for compiling this impressive volume. To the best of my knowledge, this is the first book that discusses applications of the well-known Lean and Six Sigma (LSS) concepts in the new and emerging world of Smart Manufacturing, or Industry 4.0. I have no doubt that this book will turn out to be a great resource for practitioners, and hope that it will inspire academics to embark on new research opportunities in this sector.
What is distinct about the manufacturing environment is the potential for vast amounts of data that can be generated, stored, and analyzed. This data can relate to production processes as well as to the broader ecosystem of which the manufacturing process is a part. When I first started conducting research on issues related to the design of production systems and supply chains nearly three decades ago, my colleagues and I would often find the timely availability of sufficient data at the right level of granularity to be a major constraint. Consequently, we would have to rely on limited data sets, and extrapolate implications based on these results. However, the fantastic developments in our ability to generate, store, and access vast amounts of (big) data at unprecedented levels of granularity, optimize large-scale mathematical models of such manufacturing and supply chain systems at incredible speeds, and leverage cloud computing infrastructure have fueled the convergence of physical and digital systems. The various technologies underlying such developments form the core of Smart Manufacturing/Industry 4.0. Therefore, deployment of these technologies can lead to improvements in process flexibility, speed, cost, quality, scale, customizability, and responsiveness in unimaginable ways. Since such improvements are fundamental goals of the LSS methodologies, it is imperative for academics and practitioners alike to explore its applications in this emerging world of Smart Manufacturing/Industry 4.0.
In this book, Anthony and a group of amazing academics and practitioners with deep domain expertise provide insightful illustrations of how LSS principles can leverage a variety of Smart Manufacturing/Industry 4.0 technologies in a wide range of contexts. I had the pleasure of working closely with Anthony when we jointly advised a major cloud infrastructure provider on several LSS projects, which led to demonstrable and compelling cost savings and process improvements. It is gratifying to see him bring his unique perspective and deep knowledge of LSS honed over a nearly 40-year career in the high-technology industry to this volume. The applications, insights, and lessons contained in this volume are relevant to manufacturing and service industries alike. I am sure that readers will share my great enthusiasm for this book.
Acknowledgments
I wish to acknowledge the exceptional efforts of my proofreaders and editors. Besides writing their own great chapters, Deborah Walkup and Jeff Little made valuable suggestions to chapters in related fields based on their subject matter expertise. Alexander Tarantino and Apollo Peng proofed and edited several chapters, making critical revisions to the final content. Angelina Feng is our 13-year-old middle school student with a remarkable mastery of the English language. She spent hours reviewing each chapter, making hundreds of suggested changes. Most remarkable is that her grammatical suggestions were spot on. I believe she has a great career ahead of her as a journalist or author if she chooses to pursue it.
I also wish to acknowledge the support and guidance from my Wiley editors: Sheck Cho, executive editor; Susan Cerra, managing editor; and Samantha Enders and Samantha Wu, assistant editors.
About the Author
Anthony Tarantino received his bachelor's degree from the University of California, Santa Cruz, and his PhD in organizational communications from the University of California, Irvine. He started his manufacturing and supply chain career working first in small and then in large domestic manufacturers, including running Masco's supply chain for the world's largest lockset manufacturing facility. He was certified in purchasing management (ISM), materials management (APICS), and Lean in the 1980s. During the same period he began implementing ERP systems and Lean programs for divisions of Masco Corporation at several facilities. After 25 years in industry, he moved into consulting, becoming a supply chain practice lead for KPMG Consulting (BearingPoint) and later IBM. In the 2010s he led 30-plus Lean Six Sigma projects as a Master Black Belt for Cisco Systems Supply Chain and trained over 1,000 employees in their lunch-and-learn programs. He leveraged his consulting experience to create and deliver executive-level seminars in supply chain and risk management in Europe, Asia, Australia, New Zealand, and the United States.
He began as an adjunct faculty member at Santa Clara University in 2010, teaching risk management in finance and supply chain. More recently, he created a Lean Six Sigma Yellow Belt training program that introduced students to continuous improvement tools and techniques. Working with Professor Narendra Agrawal, he created and delivered an accelerated Lean Six Sigma Green Belt program. The most recent program was for a leading corporate client of the university. The five live projects in that program generated an estimated annual savings of $3 million.
Over the past five years he has supported Smart Manufacturing startups focused on computer vision identifying the most attractive industry verticals and accounts to pursue. He has also acted as a client-facing advocate for the new technologies to improve operations, safety, and competitiveness. His work with these startups was the inspiration for Smart Manufacturing: The Lean Six Sigma Way, his fifth book for John Wiley & Sons over the past 15 years.
About the Contributors
Omar Abdon is a product-focused growth-hacker working with successful startups in Silicon Valley with 15-plus years of experience in building and growing B2B4C products. He founded, grew, and successfully exited three startups in mobile software and digital growth marketing spaces across a wide range of industries like manufacturing, banks, telecom, financial institutions, and more. Currently, Omar is the head of innovation and customer success at Atollogy Inc., a platform to connect, collect, and leverage valuable enterprise big data through machine vision (MV) and to utilize artificial intelligence (AI) to digitize business operations and achieve the highest possible efficiency and end-user experience.
Narendra Agrawal is the Benjamin and Mae Swig professor of supply chain management and analytics in the department of information systems and analytics of the Leavey School of Business at Santa Clara University. He has conducted extensive research on problems related to supply chain management in the retail and high-technology industries and conducted numerous management development seminars on these topics internationally. His research has been published in leading academic and practitioner-oriented journals. Previously, he served as the interim dean as well as the associate dean of faculty at the Leavey School. Naren holds an undergraduate degree in mechanical engineering from the Institute of Technology, BHU, India, where he received the Prince of Wales Gold Medal; an MS in management science from the University of Texas at Dallas; and an MA and PhD in operations and information management from The Wharton School of the University of Pennsylvania.
Jae-Won Choi received his BS, MS, and PhD in mechanical engineering from Pusan National University, Busan, Korea, in 1999, 2001, and 2007, respectively. He is an associate professor in the department of mechanical engineering at The University of Akron. He has authored more than 50 articles and secured five patents. His research interests include additive manufacturing, 3D-printed smart structures including sensors, actuators, and electronics; 3D-printed rubbers for insoles and tires; and bio fabrication and low-cost binder-coated metal/ceramic for 3D printing. He is currently serving as an associate editor of the journal Additive Manufacturing and editorial board member of the International Journal of Precision Engineering and Manufacturing – Green Technology.
Steven Herman builds useful artificial intelligence to solve real-world problems. He is currently a software engineer at Atollogy Inc., leading the development and deployment of novel computer vision models to solve problems in manufacturing and yard management. He holds a BS in computer engineering from Santa Clara University.
Rui Huang received her BS and MS in mechanical engineering from the North China University of Technology and Syracuse University in 2014 and 2016, respectively. She is currently a PhD candidate in the department of mechanical engineering at The University of Akron. Her research interests include additive manufacturing, 3D printing of ceramic materials, conformal printing, and 3D printing of proximity sensor packaging for harsh environments.
Jeff Little is an electrical engineer with 40-plus years’ experience in design, engineering management, and technical program management. His areas of experience and expertise include CPU design, voice and network telecommunications, software, microcode, power engineering, compliance, systems engineering, and highly reliable systems design.
Companies and organizations he has been involved with over the years include major corporations such as Intersil, AMD, IBM, Siemens, ROLM, Cisco Systems, and Tandem Computers as well as startups such as Procket Networks, Maple Networks, S-Vision, and RGB Labs. He is currently enjoying retirement while occasionally consulting.
Craig Martin is a seasoned operations and supply chain leader with more than 30 years’ experience in the technology sector as the senior executive (VP/SVP) driving global initiatives through all stages of corporate growth. He is currently a senior consultant for On Tap Consulting and an adjunct professor at the Leavey School of Business: at Santa Clara University.
Craig helped establish a new company as cofounder, ramped global operations for a private security firm from startup to a successful IPO, scaling to $800 million, and managed global operations for two multibillion-dollar industry leaders. He has extensive experience in supply chain design and operations, hardware development and manufacturing, managing multiple international factories, commodity management, global facilities, and real estate. Technologies he supports range from simple, high-volume electronics to full cabinets with infinite combinations of highly complex electrical and electromechanical assemblies.
Alex Owen-Hill works with business owners and technology companies that want to stand out in their industries, helping them to create a unique voice for their business that feels authentic to them and attracts the people they most want to work with. He earned his PhD in robotics from the Universidad Politécnica de Madrid with a project investigating the use of telerobotics for the maintenance of particle accelerators at CERN and other large scientific facilities. His regular blog articles on the use of robotics in industrial settings are often shared throughout the online robotics community. Details of his work can be found at CreateClarifyArticulate.com.
Thomas Paral received his doctorate in mechanical engineering and applied computer science from the University of Karlsruhe in 2003. His career began in 2003 as director of R&D engineering for electromechanics at Aichele GROUP GmbH & Co.KG. After various functions in Germany, China, and the United States, he developed as CTO the Aichele GROUP into a global market and technology leader in its rail and automotive markets. From 2014 to 2018, as director of technology of industry solutions at TE Connectivity, he was responsible for new markets and smart factory technologies with a focus on industrial robotics.
From 2018 to 2020, as executive vice president of strategy and business development and GM of cobots and new markets at Schunk he was responsible for the reorganization and realignment of structures including the robotic gripping components and gripping solutions business units. He successfully established and managed the new business unit cobots and new markets. Since 2020 he has been chief business development officer at OnRobot, the leading robotic end-of arm solutions provider for collaborative robotic applications.
Aaron Pompey received his PhD from the University of California at Los Angeles. With several years’ experience in executive management across both the corporate and public sectors, he has leveraged smart technologies to achieve efficiency, satisfaction, and growth with major brands across multiple industries, including education, government, healthcare, manufacturing, quick-service restaurants, and transportation. Aaron is based in the Bay Area and currently leads the Pan America region of AOPEN Inc., a global technology company specializing in small form factor hardware solutions for commercial, industrial, and medical environments.
Frank Poon is an enterprising and intuitive business and product leader with over 20 years of experience in growing both multinational companies as well as startups with successful exits. His focus is on business strategy, growth hacking, general management, product strategy, business transformation, operations strategy, and supply chain management. He has an MBA from the University of Chicago and master's and bachelor's degrees in industrial and operations engineering from the University of Michigan.
Miles Schofield is a professional engineer, dancer, musician, speaker, teacher, designer, artist, entrepreneur, and IT specialist with 10 years of experience in application engineering for the semiconductor industry in metrology, where he wrote qualification and control procedures for a number of processes in addition to integrating unique optical and phase imaging tools into global production flow. He has 10 years of application engineering experience in global hardware and IoT computing solutions for leading brands in retail, healthcare, hospitality, and transportation.
Vatsal Shah leads the management and engineering team as co-founder and chief executive officer of Litmus. He has extensive experience with industrial engineering, electronics system design, enterprise platforms, and IT ecosystems. Vatsal earned his master's degree in global entrepreneurship from Em-Lyon (France), Zhejiang University (China), and Purdue University (United States) jointly and his bachelor's degree in electronics engineering from Nirma University in India.
Bowen Shi, aka Randy, from Santa Clara University received dual BS degrees in Mathematics and Sociology in 2016 and a MS degree in Business Analytics in 2019. In 2016, he spoke at the 43rd Annual Western Undergraduate Research Conference with his Witold Krassowski Sociology Award winning research Success of Digital Activism: Roles of Structures and Media Strategies. Published in Silicon Valley Notebook Volume 14, 2016, the data analytical research investigated how different forms and purposes of digital campaigns affected their success. His expertise is analytics in IT, finance, and manufacture world. He initiated a series of successful analytic projects as the Sr. Data Analyst at Atollogy, Inc. and he is currently a Business Intelligence Analyst at Intuitive Surgical, Inc as of 2021.
Bahareh Tavousi Tabatabaei received her BS in biomedical engineering from Azad University, Isfahan, Iran, in 2014. She is now a PhD student in the Department of Mechanical Engineering at The University of Akron. Her research interests include additive manufacturing, 3D-printed sensors, and biomedical application.
Maria Villamil has a bachelor of science degree in computer information systems from Woodbury University and is a Certified Scrum Master. As senior vice president of WET Design, she is responsible for the planning, construction, and maintenance of the multibuilding WET campus, which includes everything from science labs to state-of-the-art manufacturing facilities consisting of capabilities like sheet metal, welding (manual and robotics), CNC machining, vertical machining, precision machining, tube bending, metrology, vacuum forming, injection molding, surface mount technology manufacturing, additive manufacturing, and powder coating facilities to computer server farms. Maria is in charge of the acquisition, installation, and ongoing maintenance of WET's scientific and industrial manufacturing equipment.
Maria began her career at WET in IT (which she now leads), and which at WET includes high-performance computing, enterprise networking, software development, animation rendering farms, and support for computational engineering systems. She is WET's governmental liaison, in which role she deals with issues ranging from regulatory compliance to the hosting of community and state leaders for events at WET's campus. Maria has led the recent launch of WET's line of PPE products to help the world deal more safely with the COVID-19 pandemic.
Deborah Walkup holds a bachelor of science degree in mechanical engineering from Iowa State University. She began her career designing circuit boards and enclosures for military and space applications at Texas Instruments and Boeing. For the bulk of her career she has worked in solution engineering, teaming up with sales representatives for enterprise software companies in the supply chain space. Her sales career began with a reseller of HP Unix workstations and mechanical CAD software used to support design engineering. She works and lives in Silicon Valley and survived the internet bubble and bust of the early 2000s. Other companies she has worked for include i2, FreeMarkets, Ariba, E2Open, GTNexus, and Infor. Deborah is an avid traveler and scuba diver, having visited all continents except Antarctica, with over 400 hours in the water.
Allison Yrungaray has 20 years of experience in high-tech marketing and public relations. With a bachelor's degree in communications from Brigham Young University, she has written hundreds of articles and achieved media placements in the Wall Street Journal, the New York Times, Forbes, and many other leading publications. She currently leads marketing communications at Litmus, a company with an Industrial IoT Edge platform that unifies data collection and machine analytics with enterprise integration and application enablement.
Introduction
Naren Agrawal
Benjamin and Mae Swig Professor of Information Systems and Analytics, Santa Clara University
In Smart Manufacturing: The Lean Six Sigma Way, Dr. Anthony Tarantino and his collaborators deliver an insightful and eye-opening exploration of the ways the Fourth Industrial Revolution is dramatically changing the way we manufacture products across the world, and how it is revitalizing and reshoring American and European manufacturing for both large operations and small to midsize enterprises (SMEs).
Lean Six Sigma has been the mainstay driving continuous improvement efforts for over 20 years. Over time, some shortcomings have become apparent, one of which is that it requires labor-intensive data-gathering requirements. Because of the cost and time required to collect this data, only small sample sizes are created. Operators also behave differently while they are being monitored and tend to backslide into old habits once a project or initiative ends. By creating a digital twin of physical operations using unobtrusive, continuous monitoring devices, data gathering becomes relatively inexpensive, sample sizes grow to 100%, and all behavioral modes for all operators are captured.
This text profiles 23 popular Lean Six Sigma and continuous improvement tools and how Smart Manufacturing technologies supercharges each one of them. The author also explains why much of the criticism of Lean that arose during the COVID-19 pandemic is unfounded.
Dr. Tarantino explores technology's evolution from the start of the Industrial Revolution through today's Industry 4.0 and Smart Manufacturing. He next explores how Smart Manufacturing can improve supply chain's resilience to quickly adjust to sudden disruptive changes that negatively affect supply chain performance. Expert contributors highlight the role of Smart Technologies in making logistics and cybersecurity more effective, critical with the growing volatility of global supply chains and the sophistication of cyberattacks. Leading experts in individual chapters showcase the major tools of Industry 4.0 and Smart Manufacturing:
Modern networking technologies
Industrial Internet of Things (IIoT)
Mobile computing
Edge computing
Computer vision
Robotics
Additive manufacturing (3D printing)
Big data analytics
The text explores the contributions women can make in Smart Manufacturing, and how adding their perspective can enrich Smart Manufacturing initiatives. In this breakthrough analysis, the coauthors share their personal stories, providing practical advice on how they achieved success in the manufacturing world.
Finally, several case studies provide examples of Smart Manufacturing helping manufacturers and distributors address previously unsolvable issues. The focus is on SMEs highlighting tools that are affordable and easy to implement. Case studies explore the use of:
Barcoding to enable rapid inventory transactions
Computer vision to automate visual inspection and to improve safety
Mobile computing to replace legacy manufacturing systems
Robots to do dangerous and boring jobs
Factory touchscreens to improve shop-floor communications
Edge computing to collect data close to physical operations for immediate visualizations and business value
3D printing to provide vital medical equipment during the COVID-19 pandemic
This book is a must-read for anyone involved in manufacturing and distribution in the twenty-first century. Smart Manufacturing: The Lean Six Sigma Way belongs in the library of anyone interested in the intersection of smart technologies, physical manufacturing, and continuous improvement.
CHAPTER 1Introduction to Industry 4.0 and Smart Manufacturing
Anthony Tarantino, PhD
Introduction
The terms Industry 4.0 and Smart Manufacturing (SM) are widely used today in industry, academia, and the consulting world to describe a major industrial transition underway. This transition is truly revolutionary in that it is now possible to create a digital twin of physical operations to improve operational efficiency and safety while fostering the automation of repetitive, labor-intensive, and dangerous activities.
Exhibit 1.1 shows the digital twin of a car engine and wheels in an exploded image above the physical car.1
EXHIBIT 1.1 Digital twin of a car engine and wheels
Source: Digitaler Zwillig/Shutterstock.com.
The first question most people ask is “What is the difference between Industry 4.0 and Smart Manufacturing?” The answer is that they are actually different phrases for the same thing. Klaus Schwab, president of the World Economic Forum, coined the phrase “Industry 4.0” in 2015.2 The argument for the name Industry 4.0 is that it captures the four phases of the Industrial Revolution dating back 400 years and highlighting the coming of cyber-physical systems. The advantage of the name Smart Manufacturing is that it is catchy and easy to remember. The first references to Smart Manufacturing date back to in 2014, so both names originated at about the same time.3
The two terms are now expanding and being applied to nonmanufacturing areas. For example, we now have Smart Quality, or Quality 4.0, and Smart Logistics, or Logistics 4.0. The important thing to remember is that they describe the same goal of creating a digital twin of physical operations. The digital twin is not restricted to equipment and includes people and how they interact with equipment, vehicles, and materials. Only by capturing the dynamic interaction of people, materials, and equipment is it possible to truly understand physical operations and the detailed processes that they use.
A more detailed definition of Smart Manufacturing is that it encompasses computer-integrated manufacturing, high levels of adaptability, rapid design changes, digital information technology, and more flexible technical workforce training.4 More popular tools include inexpensive Industrial Internet of Things (IIoT) devices, additive manufacturing (also known as 3D printing), machine learning, deep learning computer vision, mobile computing devices, Edge computing, robotics, and Big Data analytics. We will cover each of these tools and technologies in subsequent chapters.
Smart Manufacturing creates large volumes of data describing a digital twin, which in the past was not practical to create. The term Big Data has been used since the 1990s but has become central to the growth of Smart Manufacturing and Industry 4.0 in the past few years. By some estimates, the global per-capita capacity to store information has roughly doubled every 40 months since the 1980s.5 More recent estimates predict a doubling every two years. The good news is that Moore's Law applies to Big Data. (Intel's Gordon Moore predicted a doubling of technological capacity every two years while costs remain constant.) It can be argued that cheap and accessible data is the most critical pacing item to the use of Smart Technology.
The next question readers of this book may ask is “What is the connection between Smart Manufacturing or Industry 4.0 and Lean Six Sigma?” The answer is fairly straightforward. Six Sigma is a framework for complex, data-driven problem solving. Six Sigma practitioners excel at analyzing large volumes of data. Smart Manufacturing offers rich new sources of data. Traditionally Six Sigma practitioners would have to settle on taking small samples of data for their analysis. Now they can capture and analyze all data without the labor-intensive efforts of the past. I ran over 30 projects over a seven-year period for a global high-tech company and always feared that our sampling of data was merely a snapshot in time, regardless of how great the data gathering effort. Running those projects with Smart Technologies would yield a more accurate picture of the truth.
Lean also plays a critical role in Smart Manufacturing. Simply put, Lean is a philosophy for continuous improvement by eliminating all types of waste in operations. As envisioned by Taiichi Ohno, the founder of the Toyota Production System in the 1950s and 1960s, Lean also advocates empowering workers to make decisions on the production line. Smart Manufacturing will eliminate many low-skilled jobs in manufacturing. Smart factories and Smart distribution centers will require higher-skilled workers comfortable in utilizing the many new sources of data to drive continuous improvement efforts.
The First Industrial Revolution
Manufacturing before the Industrial Revolution was typically a cottage enterprise with small shops producing leather goods, clothing, harnesses, and so on. The labor was all manual, that is, people-powered. Beginning in the mid-1700s, the First Industrial Revolution introduced machines that used water or steam power. Factories using steam and water power were larger and more centralized than earlier cottage industries. Factory workers did not require the high skill levels of cottage industry craftsmen and artisans. Women and children were used as a cheap source of labor.
Exhibit 1.2 shows what a blacksmith shop may have looked like in the Middle Ages.6
EXHIBIT 1.2 A blacksmith shop in the Middle Ages
Source: O. Denker, Shutterstock.com.
The First Industrial Revolution began in England, Europe, and the American colonies. Textiles and iron industries were the first to adopt power. The major changes from cottage industries of the Middle Ages to the First Industrial Revolution can be summarized as follows:
Steam- and water-powered production centralized in one factory
Factories replace cottage industry (e.g., the village blacksmith or leather shop)
Specialization with the division of labor – workers and machines arranged to increased efficiency
Harsh and dangerous work environment – primarily using women and children as mechanical power eliminated the need for most heavy labor performed by men
Exhibit 1.3 is a painting of a textile mill powered with either steam or water and a labor force primarily made of children and women.7
EXHIBIT 1.3 A painting of an 1800s textile mill
Source: Everett Collection/Shutterstock.com.
The Second Industrial Revolution
The Second Industrial Revolution began in the United States, England, and Europe with the introduction of electrical power over a grid, real-time communication over telegraph, and people and freight transportation over a network of railroads. The railroad and telegraph also increased the spread of new ideas and the mobility of people. Travel times of days using horsepower were reduced to travel times of hours.
The introduction of electric power to factories made the modern mass-production assembly line a reality. The number of people migrating from farms to cities increased dramatically in the early twentieth century. Electric power made possible great economic growth and created a major divide between the industrial world and the poorer nonindustrial world. The rise of the middle class and the migration to cities may be the most visible manifestations of the Second Industrial Revolution. At the time of the American Civil War, only 20% of Americans lived in urban areas. By 1920 that number had risen to over 50% and to over 70% by 1970.8
Exhibit 1.4 shows workers on an auto assembly line in the 1930s.9
EXHIBIT 1.4 A 1930s auto assembly line
Source: Everett Collection/Shutterstock.com.
