| Foreword |
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xvii | |
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| Acknowledgments |
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xix | |
| About the Author |
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xxi | |
| About the Contributors |
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xxiii | |
| Introduction |
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xxvii | |
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Chapter 1 Introduction to Industry 4.0 and Smart Manufacturing |
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1 | (20) |
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1 | (2) |
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The First Industrial Revolution |
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3 | (1) |
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The Second Industrial Revolution |
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4 | (2) |
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The Third Industrial Revolution |
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6 | (1) |
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The Fourth Industrial Revolution |
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7 | (1) |
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The Major Components of Smart Manufacturing |
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8 | (8) |
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Lean and Six Sigma in the Age of Smart Manufacturing |
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9 | (1) |
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Improving Cybersecurity Using Smart Technology |
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10 | (1) |
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11 | (1) |
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Big Data for Small, Midsize, and Large Enterprises |
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11 | (1) |
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11 | (1) |
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Artificial Intelligence Machine Learning and Computer Vision |
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12 | (1) |
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Networking for Mobile-Edge Computing |
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13 | (1) |
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Additive Manufacturing and 3D Printing |
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14 | (1) |
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15 | (1) |
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Smart Technology to Improve Life on the Factory Floor |
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16 | (1) |
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Summary: The Advantages of Smart Manufacturing |
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16 | (1) |
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Improved Quality and Safety |
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16 | (1) |
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17 | (1) |
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17 | (1) |
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High Efficiency with Well-Defined Smart Factory Processes |
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17 | (1) |
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17 | (1) |
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18 | (3) |
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Chapter 2 Lean Six Sigma in the Age of Smart Manufacturing |
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21 | (22) |
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21 | (1) |
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The History of Lean -- American Assembly Lines |
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22 | (1) |
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The History of Lean -- Toyota Embraces Deming and Piggly Wiggly |
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23 | (1) |
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The Toyota Production System: The Birthplace of Lean |
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24 | (4) |
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Lean Empowers Employees, Treating Them with Respect |
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28 | (1) |
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Resilient Supply Chain Management: How Toyota Fared During the COVID-19 Pandemic |
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28 | (1) |
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The History of Six Sigma: Bill Smith and Jack Welch |
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29 | (1) |
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Six Sigma's DMAIC Framework to Fix an Existing Process |
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30 | (1) |
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The DMAIC Framework Using Smart Technologies |
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30 | (1) |
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Six Sigma's DMADV Framework to Design a New Process |
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31 | (1) |
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The Statistics Behind Six Sigma |
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32 | (3) |
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Six Sigma Professionals in the Age of Smart Manufacturing |
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35 | (1) |
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Six Sigma Project Charters and SMART Goals |
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36 | (2) |
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Lean and Six Sigma Uses of the Scientific Method |
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38 | (1) |
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Summary: Six Sigma's Marriage to Lean |
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38 | (1) |
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39 | (2) |
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41 | (2) |
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Chapter 3 Continuous Improvement Tools for Smart Manufacturing |
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43 | (66) |
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43 | (1) |
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Voice of the Customer in the Age of Smart Manufacturing |
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44 | (2) |
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Voice of the Customer Using Net Promoter Score |
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46 | (2) |
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Voice of the Customer Using the Delphi Technique |
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48 | (2) |
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Voice of the Customer Using the Kano Model |
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50 | (2) |
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Affinity Diagrams to Organize Many Ideas into Common Themes |
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52 | (3) |
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Critical to Quality to Convert the VOC to Measurable Objectives |
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55 | (1) |
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56 | (1) |
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57 | (2) |
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59 | (1) |
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60 | (1) |
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61 | (1) |
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Process Maps with Decision Points |
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62 | (1) |
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Process Maps with Swim Lanes |
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62 | (1) |
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Limited Data Collection and the Hawthorne Effect Impacting Process Mapping |
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63 | (2) |
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Value Stream Maps to Eliminate Waste |
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65 | (1) |
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Value-Added Activity versus Non-Value-Added Activity |
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66 | (1) |
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Root Cause Analysis Using a Fishbone Diagram and Risk Matrix |
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67 | (2) |
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Root Cause Analysis Using the Five Whys |
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69 | (2) |
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Changes Coming to Root Cause Analysis with Smart Technologies |
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71 | (1) |
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71 | (3) |
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74 | (2) |
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Poka-Yoke to Error-Proof Processes and Products |
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76 | (1) |
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77 | (3) |
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80 | (1) |
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81 | (3) |
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84 | (1) |
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Setup Time Reduction Using Single Minute Exchange of Dies |
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85 | (2) |
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Gage Repeatability and Reproducibility (Gage R&R) |
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87 | (2) |
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Failure Modes and Effects Analysis (FMEA) to Solve Complex Problems |
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89 | (3) |
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Pugh Matrix to Design New Processes and Products |
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92 | (4) |
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Quality Function Deployment (House of Quality) |
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96 | (1) |
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96 | (1) |
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Structure of the House of Quality Used in QFD |
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96 | (2) |
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Building a House of Quality |
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98 | (3) |
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101 | (1) |
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Using QFD in Combination with the Pugh Matrix |
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102 | (1) |
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Smart Technologies to Automate QFD |
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103 | (1) |
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103 | (1) |
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104 | (1) |
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105 | (4) |
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Chapter 4 Improving Supply Chain Resiliency Using Smart Technologies |
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109 | (22) |
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109 | (1) |
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110 | (2) |
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Supply Chain Risk Heat Maps |
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112 | (1) |
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Supply Chain Mapping at a Macro and Micro Level |
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113 | (4) |
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Preferred Supplier Programs |
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117 | (1) |
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Bill of Material Risk Grading Tools |
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117 | (2) |
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Environmental Risk Solutions |
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119 | (1) |
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The Global Driver Shortage and Poor Utilization |
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120 | (1) |
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121 | (1) |
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Computer Vision Systems Using Smart Cameras |
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121 | (1) |
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122 | (1) |
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Supply Chain Resilency in a Post-COVID World |
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123 | (1) |
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Criticism and Defense of Lean Inventory Management |
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124 | (1) |
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125 | (1) |
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Supply Chain Stress Testing |
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125 | (1) |
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126 | (1) |
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126 | (2) |
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128 | (3) |
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Chapter 5 Improving Cybersecurity Using Smart Technology |
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131 | (18) |
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131 | (1) |
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Trends Increasing the Risk of Manufacturing and Supply Chain Cyberattacks |
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132 | (2) |
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Globalization and Specialization |
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132 | (1) |
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Improved Security Within the Corporate Network |
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133 | (1) |
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Artificial Intelligence and the Internet of Things |
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133 | (1) |
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Software Supply Chain Compromises |
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133 | (1) |
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The Emergence of Cloud Computing and the Public Cloud |
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133 | (1) |
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Targeting Small Companies |
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134 | (1) |
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So Why Is Manufacturing and the Supply Chain an Attractive Target? |
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134 | (1) |
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Primary Motives Behind Manufacturing and Supply Chain Attacks |
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135 | (2) |
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Stealing Proprietary Information and Intellectual Property |
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135 | (1) |
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Financial Gain from Ransomware |
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136 | (1) |
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136 | (1) |
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137 | (1) |
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Methods Used to Breach Target Systems |
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137 | (1) |
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Using a Third-Party Connection as a Means to Get to Your Network |
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137 | (1) |
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Using a Third-Party Connection as a Means to Get to Your Customers' Information |
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137 | (1) |
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Tampering with Components or Products in the Manufacturing Process |
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138 | (1) |
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Tampering with Manufacturing Process Equipment |
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138 | (1) |
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What Are the Potential Costs of a Cyberattack? |
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138 | (1) |
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Protecting Against Cyberattacks |
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139 | (6) |
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Approaches to Mitigating Risk |
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140 | (2) |
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Developing Internal Processes and Controls |
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142 | (1) |
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Developing Secure Third-Party Relationships |
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143 | (2) |
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145 | (1) |
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146 | (1) |
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147 | (2) |
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Chapter 6 Improving Logistics Using Smart Technology |
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149 | (18) |
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Introduction: Why Logistics? |
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149 | (1) |
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Megatrends in Logistics That Impact Brands/Manufacturers |
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150 | (1) |
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The Different Expectation of Your Customer-by-Customer Type |
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151 | (1) |
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The Cost of Not Paying Attention to Logistics |
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152 | (1) |
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The Benefits of Making Logistics a Strategic Competency |
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152 | (1) |
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Steps to Make Logistics Your Competitive Advantage |
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153 | (1) |
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Why Technology Is So Important to Logistics |
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154 | (1) |
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Area 1 Insight/Planning/Monitoring |
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155 | (2) |
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Area 1, Use Case 1 Logistics Insight via Data Analytics |
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155 | (1) |
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Area 1, Use Case 2 Advanced Forecasting via Machine Learning and AI-Based Prediction |
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156 | (1) |
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Area 1, Use Case 3 Dynamic Decision-Making via Machine Learning and AI-Based Prediction with Real-Time Data and Blockchain |
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156 | (1) |
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157 | (2) |
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Area 2, Use Case 1 Automation for Information Processing via Robotics Process Automation |
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157 | (1) |
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Area 2, Use Case 2 Automation for Physical Tasks via Robotics |
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158 | (1) |
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Area 2, Use Case 3 Autonomous Transportation via Autonomous Technologies |
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158 | (1) |
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Area 3 Exchanges and Collaborations |
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159 | (2) |
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Area 3, Use Case 1 Digital Freight Brokerage via Cloud Technologies |
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159 | (1) |
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Area 3, Use Case 2 Supply Chain Collaboration via Cloud Technologies |
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160 | (1) |
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Area 4 Safety, Security, and Compliance |
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161 | (1) |
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Area 4, Use Case 1 Global Trade Compliance via Cloud Technologies |
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161 | (1) |
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162 | (1) |
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163 | (1) |
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164 | (3) |
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Chapter 7 Big Data for Small, Midsize, and Large Operations |
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167 | (18) |
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167 | (1) |
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Structured Data and Relational Databases |
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168 | (1) |
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169 | (1) |
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Why Manufacturing Needs Big Data Analytics |
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170 | (1) |
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The Four Levels of Data Analytics |
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170 | (1) |
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Descriptive Analytics -- What Happened? |
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171 | (1) |
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The Five Phases of Descriptive Analytics |
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171 | (1) |
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The Value of Descriptive Analytics |
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172 | (1) |
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Diagnostic Analytics -- Why Did It Happen? |
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172 | (1) |
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Predictive Analytics -- What May Have Happened? |
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173 | (1) |
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Prescriptive Analytics -- What Is the Best Next Step? |
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174 | (1) |
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Future of Big Data Analytics |
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174 | (2) |
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176 | (1) |
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177 | (1) |
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177 | (1) |
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The Benefits of Big Data for SMEs |
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178 | (1) |
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179 | (1) |
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Problems SMEs Face in Adopting Big Data Analytics |
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180 | (1) |
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Best Practices in Data Analytics for SMEs |
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180 | (1) |
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181 | (1) |
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181 | (2) |
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183 | (2) |
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Chapter 8 Industrial Internet of Things (IIoT) Sensors |
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185 | (20) |
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185 | (1) |
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186 | (2) |
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188 | (1) |
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188 | (1) |
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188 | (2) |
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IIoT-Enabling Technologies |
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190 | (2) |
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IIoT Platform Building Blocks |
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192 | (1) |
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192 | (6) |
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Application Areas for IIoT |
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198 | (1) |
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Industries Where IIoT Can and Does Play a Role |
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199 | (2) |
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201 | (1) |
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202 | (1) |
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202 | (1) |
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203 | (2) |
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Chapter 9 Artificial Intelligence, Machine Learning, and Computer Vision |
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205 | (14) |
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205 | (2) |
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History of AI and Computer Vision |
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207 | (1) |
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Understanding Machine Learning and Computer Vision |
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208 | (5) |
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Types of Machine Learning |
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208 | (1) |
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Common Computer Vision Tasks |
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209 | (2) |
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211 | (2) |
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Machine Learning Pipelines |
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213 | (1) |
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Issues with Artificial Intelligence |
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213 | (2) |
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215 | (1) |
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215 | (1) |
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216 | (3) |
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Chapter 10 Networking for Mobile Edge Computing |
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219 | (34) |
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219 | (1) |
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Brief History of Networking |
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219 | (2) |
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Basic Networking Concepts, Architecture, and Capabilities |
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221 | (9) |
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Network Address Management |
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227 | (3) |
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230 | (7) |
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Network Address Translation |
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230 | (2) |
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232 | (1) |
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Autoconfiguration of Networks |
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233 | (1) |
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234 | (1) |
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Introduction to the OSI Model |
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235 | (2) |
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Basic Wi-Fi Concepts, Architecture, and Capabilities |
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237 | (3) |
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Mobile Cell Phone Concepts, Architecture, and Capabilities |
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240 | (4) |
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240 | (1) |
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Cell Architectural Concepts |
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241 | (1) |
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Mobile Networking Security and Reliability |
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242 | (2) |
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Future Evolution of Mobile Networking |
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244 | (1) |
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IT and Telecommunications Networking Convergence |
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244 | (2) |
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Convergence of the Internet and Telephony |
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244 | (1) |
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Capabilities and Benefits of Mobile Edge Networking |
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245 | (1) |
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246 | (1) |
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246 | (2) |
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248 | (1) |
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Popular Acronyms Used in Networking and Mobile Computing |
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249 | (3) |
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252 | (1) |
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Chapter 11 Edge Computing |
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253 | (14) |
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Introduction: What Is Edge Computing? |
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253 | (1) |
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Benefits of Edge Computing |
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254 | (1) |
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Top Use Cases for the Edge in Smart Manufacturing |
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255 | (1) |
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255 | (1) |
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256 | (1) |
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Solving Deployment Challenges with an Edge Computing Platform |
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257 | (2) |
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The Edge Computing Platform Landscape |
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259 | (1) |
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259 | (3) |
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How a Successful Edge Computing Rollout Works |
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262 | (2) |
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263 | (1) |
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263 | (1) |
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264 | (1) |
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264 | (1) |
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264 | (1) |
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264 | (2) |
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266 | (1) |
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Chapter 12 3D Printing and Additive Manufacturing |
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267 | (44) |
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Bahareh Tavousi Tabatabaei |
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267 | (3) |
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270 | (5) |
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Early Stages of Additive Manufacturing |
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270 | (2) |
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1980s -- The Emergence of the First AM Technologies |
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272 | (1) |
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1990s -- Process and Innovation |
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273 | (1) |
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2000s -- Development of New Applications |
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273 | (1) |
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274 | (1) |
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Additive Manufacturing Process |
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275 | (16) |
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275 | (3) |
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278 | (2) |
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280 | (3) |
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283 | (3) |
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286 | (3) |
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289 | (1) |
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Directed Energy Deposition |
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290 | (1) |
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291 | (8) |
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3D-Printed Electronic Devices |
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291 | (2) |
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3D Printing in Construction |
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293 | (1) |
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294 | (2) |
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296 | (3) |
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299 | (1) |
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300 | (1) |
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301 | (10) |
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311 | (20) |
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311 | (1) |
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311 | (1) |
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312 | (1) |
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313 | (1) |
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314 | (1) |
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315 | (1) |
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315 | (2) |
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Robotics Timeline: 1961 to 2011 |
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317 | (6) |
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323 | (3) |
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326 | (1) |
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327 | (1) |
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328 | (1) |
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329 | (2) |
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Chapter 14 Improving Life on the Factory Floor with Smart Technology |
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331 | (14) |
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331 | (1) |
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Life on the Factory Floor from 1700 to Today |
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331 | (3) |
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The Smart Manufacturing Factory Floor |
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334 | (1) |
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How AI Is Powering Smart Manufacturing |
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334 | (1) |
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Smart Manufacturing Is Optimizing Factory Processes |
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335 | (1) |
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Hurdles Faced in Implementing Smart Technologies |
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336 | (1) |
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Three Essential Job Types in Smart Manufacturing |
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337 | (3) |
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337 | (2) |
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339 | (1) |
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340 | (1) |
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Three Types of Tools Needed in Smart Manufacturing |
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340 | (2) |
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Smart Manufacturing Design Choices |
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342 | (1) |
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342 | (1) |
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343 | (1) |
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344 | (1) |
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Chapter 15 Growing the Roles for Women in Smart Manufacturing |
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345 | (20) |
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346 | (1) |
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347 | (1) |
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Women Hold the Answers (Skills Where Women Excel) |
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348 | (2) |
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350 | (1) |
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Barrier One Building a Math Identity |
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350 | (1) |
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Barrier Two The Question of Race and Class |
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350 | (1) |
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Barrier Three It's Not Just Content; It's Context, Too |
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351 | (1) |
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Companies Working to Overcome Barriers to Women's Entry |
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351 | (1) |
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Programs to Develop STEM Skills for Women |
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352 | (2) |
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Growing the Role of Women in Smart Manufacturing |
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354 | (1) |
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354 | (1) |
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355 | (1) |
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355 | (3) |
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How I Got into Manufacturing |
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355 | (2) |
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357 | (1) |
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WET's Use of Smart Technologies |
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357 | (1) |
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358 | (1) |
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359 | (1) |
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360 | (1) |
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361 | (4) |
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Case Study 1 Automating Visual Inspection Using Computer Vision |
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365 | (8) |
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Case Study 2 Bar Coding, the Most Ubiquitous and Most Critical IIoT Technology |
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373 | (6) |
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Case Study 3 Improving Safety with Computer Vision |
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379 | (6) |
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Case Study 4 COVID-19 Accelerates the Adoption of 3D Printing |
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385 | (6) |
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Case Study 5 How Mobile Apps Benefit Small to Midsize Enterprises |
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391 | (14) |
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Case Study 6 Using Factory-Floor Touch Screens to Improve Operations |
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405 | (6) |
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Case Study 7 Edge Computing to Improve Operations |
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411 | (4) |
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Case Study 8 Five Highly Dangerous Jobs That Robots Can Do Safely |
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415 | (4) |
| Answers to Sample Questions |
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419 | (2) |
| Links to Continuous Improvement Templates |
|
421 | (2) |
| Index |
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423 | |
