This book is essential for anyone looking to understand how hyperautomation can revolutionize businesses by simplifying operations, reducing errors, and creating more intelligent and adaptable workplaces through the use of automation technologies such as artificial intelligence, machine learning, and robotic process automation.
The use of automation technologies to simplify any and every activity conceivable in a business, allowing repeated operations to operate without manual intervention, is known as hyperautomation. Hyperautomation transforms current and old processes and equipment by utilizing artificial intelligence, machine learning, and robotic process automation. This digital transformation may assist a business in gaining cost and resource efficiency, allowing it to prosper in a more competitive environment. With the advancement of automation technologies, hyperautomation is becoming more prevalent. Companies are shifting their methods to create more human-centered and intelligent workplaces. This change has ushered in a new era for organizations that rely on technology and automation tools to stay competitive. Businesses may move beyond technology’s distinct advantages to genuine digital agility and scale adaptability when all forms of automation operate together in close partnership.
Automation tools must be simple to incorporate into the current technological stack while not requiring too much effort from IT. A platform must be able to plug and play with a wide range of technologies to achieve hyperautomation. The interdependence of automation technologies is a property that is connected to hyperautomation. Hyperautomation saves individuals time and money by reducing errors. Hyperautomation has the potential to create a workplace that is intelligent, adaptable, and capable of making quick, accurate decisions based on data and insights. Model recognition is used to determine what to do next and to optimize processes with the least amount of human engagement possible.
Inhaltsverzeichnis
Preface xv
1 Journey To Hyperautomation: The Pathway of Today’s Industries to Next Generation Industries 1
T. Kavitha, S. Saraswathi and G. Senbagavalli
1.1 Introduction: What is Hyperautomation (HA)? 2
1.1.1 What Makes Hyperautomation Quite Crucial? 4
1.1.2 Benefits of Hyperautomation for Employees and Businesses 4
1.1.3 How Does It Work? 6
1.2 Technologies Associated with HA 8
1.2.1 Robotic Process Automation (RPA) 9
1.2.2 Artificial Intelligence/Machine Learning (AI/ML) 11
1.2.3 Optical Character Recognition (OCR) 11
1.2.4 Natural Language Processing (NLP) 12
1.2.5 Digital Twin of an Organization (DTO) 13
1.2.6 Process Mining 13
1.2.7 RPA Bot Example 15
1.3 Potential Benefits/Abilities of HA 18
1.4 Challenges in Hyperautomation 19
1.5 Applications 21
1.6 Case Studies 24
1.6.1 Touchless Accounts Reconciliation 24
1.6.2 AI-Enabled Healthcare Claims Processing 25
1.6.3 Investment Consultant 26
1.6.4 AI-Enabled Vacation Rental Site 27
1.6.5 Detection of Forged Signatures 28
1.6.6 IT Automation 28
1.6.7 Customer Service Based on Social Media Marketing and Sentiment Analysis 29
1.6.8 Data Center Migration 30
1.7 Conclusion 31
References 31
2 Software Robot for Next Generation Industries Using RPA 35
S.V. Juno Bella Gracia, J. Godwin Ponsam, S. Sheeba Rachel and R. Gayathiri
2.1 Introduction 36
2.2 Evolution of Industries 37
2.2.1 Industry 4.0 37
2.2.2 Industry 5.0: The Future 37
2.2.2.1 What is the Purpose of Industry 5.0? 38
2.2.2.2 How to Make It Happen? 38
2.2.3 RPA and Industry 4.0 39
2.2.4 RPA and Industry 5.0 39
2.3 Robotic Process Automation 39
2.3.1 Features of Robotic Process Operation 40
2.3.2 Types of Robotic Process Automation 41
2.3.3 The Key Performance Indicators of RPA 41
2.3.4 Benefits of Robotic Process Automation 42
2.3.5 Business Automation Using RPA 43
2.3.6 Features and Capabilities of RPA 44
2.3.7 RPA: The Fastest Growing Enterprise Software in the World 44
2.3.8 The Role of API Integration in Process Automation 44
2.4 Case Studies 45
2.4.1 Enhancing the Platform for End-to-End Automation 45
2.4.2 RPA for Finance and Accounting 46
2.4.3 Intelligent Document Processing 47
2.4.4 A Three-Way Model for Automation 48
2.4.5 A Three-Stage, Basic Model 48
2.4.6 The Essential Elements 49
2.5 Debunking RPA Rumors 50
2.6 Conclusion 52
References 53
3 Artificial Intelligence-Based Hyperautomation for Smart Factory Process Automation 55
Balasubramaniam S., A. Prasanth, K. Satheesh Kumar and Seifedine Kadry
3.1 Introduction 56
3.1.1 Smart Factory 57
3.1.2 Advantages of Smart Factory 58
3.1.3 Smart Factory Levels 58
3.1.3.1 Simple Access to Information 58
3.1.3.2 Proactively Analyzing Data 58
3.1.3.3 Present Data 58
3.1.3.4 Actionable Information 58
3.1.4 Technologies Used in Smart Factory 59
3.1.4.1 Sensors 59
3.1.4.2 Cloud Computing 59
3.1.4.3 Big Data Analytics 59
3.1.4.4 Virtual and Augmented Reality 59
3.1.4.5 Digital Twins 59
3.1.4.6 Io Ts 59
3.1.5 The Fundamentals of a Smart Factory 60
3.1.6 Challenges 60
3.1.7 Industry 5.0 60
3.2 Hyperautomation 60
3.2.1 What is Hyperautomation? 62
3.2.2 Basic Components of Hyperautomation 62
3.2.3 RPA 63
3.2.3.1 Selecting the Appropriate Business Processes for Automation 63
3.2.3.2 Scaling Up Operations and Programs for Digital Process Automation 64
3.2.3.3 Considering Regulatory and Corporate Restrictions, i.e., Competing Initiatives 64
3.2.3.4 Controlling and Observing Automation in an Efficient Manner 64
3.2.3.5 Prior to Automating Business Processes, Improve Them 64
3.2.4 Hyperautomation’s Emergence 65
3.2.5 Advanced Technologies 65
3.2.5.1 Artificial Intelligence (AI) 65
3.2.5.2 Advanced Analytics 65
3.2.5.3 Intelligent Automation 66
3.2.5.4 Administration of Information 66
3.2.6 Hyperautomation Advantages 66
3.2.6.1 Employee Empowerment 66
3.2.6.2 Employee Training 67
3.2.6.3 Integration of Systems 67
3.2.6.4 Digital Nimbleness 67
3.2.6.5 Return on Investment 67
3.2.7 Increasing Trends in Hyperautomation 67
3.2.8 Hyperautomation Roadmap 68
3.2.9 Hyperautomation Work Flow Methodology 68
3.3 Hyperautomation-Based Industrial Ecosystem 69
3.3.1 Robotic Process Automation 69
3.3.2 Process Mining 70
3.3.3 AI 70
3.3.4 i BPMSs 71
3.3.5 Advanced Analytics 71
3.3.6 Approach to Delivery for Hyperautomation 71
3.3.6.1 Imagine 71
3.3.6.2 Deliver 71
3.3.6.3 Run 71
3.3.6.4 Benefits of Delivery Approach for Hyperautomation 71
3.4 Artificial Intelligence 72
3.4.1 Concepts of Artificial Intelligence 73
3.4.2 Shallow Learning 74
3.4.2.1 Support Vector Machine (SVM) 74
3.4.2.2 Random Forest RF 74
3.4.2.3 KNN 74
3.4.3 Deep Learning 74
3.4.4 Deep Reinforcement Learning 75
3.4.5 NLP 75
3.4.5.1 Practical Applications for NLP in RPA 76
3.5 Hyperautomation Use Cases and Examples in Industry/ Factory Processes 76
3.5.1 Payables Accounts 77
3.5.2 Journey and Expense 78
3.5.3 Handling Claims 78
3.5.3.1 Insurance Occurrences 78
3.5.3.2 Occupational Claims 79
3.5.4 Cash Order (O2C) 79
3.5.5 Additional Document Handling 79
3.5.6 Operations for Customer Service 79
3.5.7 Obtaining Leads From Anonymous Website Visitors 79
3.5.8 Processing for Underwriting 80
3.5.9 Redaction to Protect Privacy 80
3.5.10 Anti-Money Laundering (AML) 80
3.5.11 Underwriting a Loan 80
3.5.12 Customer Onboarding for Banks 80
3.6 Artificial Intelligence-Based Hyperautomation for Smart Factory Process Automation 81
3.6.1 Industry 4.0 Intelligent Automation Solution Based on the Io Ts and Machine Learning 81
3.6.2 Soft Sensors Powered by Deep Learning to Increase Industrial Automation’s Adaptability 82
3.6.3 Analysis of Robot Control System Optimization Design Using Artificial Intelligence 83
3.6.4 Automation of the Power Distribution Network Using AI 83
3.6.5 Modular Deep RL and Policy Transfer Allow for Flexible Automation 84
3.6.6 Hyperautomation in the Auto Industry 84
3.6.7 Hyperautomation in Transforming Under Writing Operation in the Life Insurance Industry 85
3.7 Conclusion 85
References 87
4 Intelligent Assistants Using Natural Language Processing for Hyperautomation 91
M. Nalini, Rajesh Kumar Dhanraj, Balamurugan Balusamy, Abirami, V. and Kavya, K.
4.1 Introduction 92
4.1.1 Hyperautomation 92
4.1.2 Automation vs Hyperautomation 94
4.1.3 Natural Language Processing (NLP) 94
4.1.4 Components of Natural Language Processing 95
4.1.4.1 Natural Language Generation (NLG) 95
4.1.4.2 Natural Language Understanding (NLU) 95
4.1.5 NLP in Intelligent Automation 96
4.1.6 NLP in Robotic Process Automation (RPA) 96
4.2 Phases of NLP 97
4.2.1 Morphological Analysis 99
4.2.2 Syntactic Analysis 101
4.2.2.1 Parsing 102
4.2.2.2 Derivation 103
4.2.3 Semantics Analysis 104
4.2.4 Discourse Integration 106
4.2.5 Pragmatic Analysis 107
4.3 NLP Application in Web/Android Services 108
4.3.1 Chatbox 109
4.3.2 AI Assistant 109
4.3.3 Search Result 110
4.3.4 Digital Phone Call 111
4.3.5 Machine Translation 112
4.4 Role of NLP in Internet Protocol 114
4.4.1 Market Intelligent 114
4.4.2 Text Analytics 116
4.4.2.1 Benefits of Text Analytics 117
4.4.3 Data Analysis 117
4.4.4 E-mail Analysis 119
4.4.4.1 Dataset 119
4.4.4.2 ETL Pipeline 119
4.4.4.3 AI Pipeline 120
4.4.5 Predictive Text 120
4.4.6 Auto Correct 121
References 123
5 Digital Twins for Hyperautomation for Next Generation 127
V. Divya, A. Prasanth, K. K. Devi Sowndarya and Chien Thang Pham
5.1 Introduction 128
5.2 Hyperautomation Requirement 129
5.3 Literature Review 129
5.4 Hyperautomation Methodology 131
5.5 Background of Hyperautomation 132
5.6 Hyperautomation Enhancement 133
5.7 Association of Versatile Technologies with Hyperautomation 134
5.8 Hyperautomation Workflow 136
5.9 Hyperautomation Domains 138
5.10 Path to Hyperautomation 140
5.11 Automation Process Categories 140
5.12 Sophistication of the Automation 141
5.13 Technologies in Hyperautomation 143
5.14 Technological Ecosystem of Hyperautomation 146
5.15 Future Scope of Hyperautomation 147
5.16 Conclusion 147
References 148
6 IQ Bot for Intelligent Document Process and Mail Processing 153
Vinora A., Lloyds E., Nancy Deborah R., Sivakarthi G. and Mohanad Alfiras
6.1 Introduction to IQ Bot 154
6.2 Understanding the Internal Operations of the IQ Bot 155
References 173
7 Bot-Based Process Triggering by Incoming E-mails and Documents 177
M. Nalini, Rajesh Kumar Dhanraj, Balamurugan Balusamy, Abirami, V., Kavya, K. and Aishwaryalakshmi, G.
7.1 Introduction of Bot 178
7.1.1 What are Bots? 178
7.1.2 Chat Bots and their Influence in the Industry 178
7.1.3 Bot Based on RPA 180
7.2 Bot Triggering by Incoming E-mail and Incoming Document 182
7.2.1 The Technologies Behind E-mail Bots 182
7.2.2 E-mail Bots and Other Systems 183
7.2.3 E-mail Bot Expected Results 184
7.2.4 Automate Handling of Incoming Documents with Documentbot 185
7.3 Types of Bots 186
7.3.1 Malicious and Non-Malicious Bot Activity 186
7.3.2 Scraper Bots 187
7.3.3 Spam Bots 188
7.3.4 Social Media Bots 189
7.3.5 Spider Bots 191
7.3.6 Ticketing Bots 191
7.3.7 Download Bots 193
7.4 Various Other Bot Triggers 195
7.4.1 Add a Hotkey Trigger 195
7.4.2 Add an Interface Trigger 195
7.4.3 Add a Process Trigger 197
7.4.4 Add a Service Trigger 197
7.5 Applications of Bot Trigger 198
7.5.1 Software Application for Particular Task—Performance 198
7.5.2 Instant Messenger Applications 200
7.5.3 Bots Used on Applications 201
References 202
8 Hyperautomation for Automating the Customer Service Operations 207
Nancy Deborah R., Alwyn Rajiv S., Vinora A. and Sivakarthi G.
8.1 Introduction 207
8.2 Advantages of Hyperautomation 211
8.3 Issues With Hyperautomation 212
8.4 Use Cases of Hyperautomation 213
8.5 Customer Service Hyperautomation 214
8.6 Customer Support Hyperautomation: Where We are Now and Where We are Headed 216
8.7 Benefits of Customer Service Hyperautomation 220
8.8 Reforming the Future of Customer Service Operation through Hyperautomation 222
8.9 Case Study: Hyperautomation in Customer Onboarding 224
8.10 Conclusion 227
References 228
9 Applications of Hyperautomation in Finance and Banking Industries 229
S. Arunarani, A. Prasanth, N. Pushpalatha and Mariya Ouaissa
9.1 Introduction 230
9.2 History of Hyperautomation 231
9.3 Literature Review 233
9.3.1 RPA vs. Intelligent Automation 234
9.3.2 Tools and Techniques for Hyperautomation 235
9.3.3 Major Applications of Hyperautomation Across Industries 239
9.4 The Role of Hyperautomation in Banking and Finance 243
9.4.1 Benefits of Using Hyperautomation in Banking and Financial Sector 244
9.4.2 Challenges Associated with Hyperautomation in Banking and Finance Industry 245
9.4.3 Issues that Banks and Financial Institutions Face in the Absence of Hyperautomation 246
9.5 Dedicated Workflow Process for Hyperautomation 247
9.6 Case Study 249
9.6.1 Exploring the Case Study 251
9.7 Future of Hyperautomation 252
9.8 Conclusion 253
References 253
10 Application of Hyperautomation in COVID-19 Analysis and Management 255
P. Ashok, Pon Bharathi A., S. Rathika and Venkatesh Ramamurthy
10.1 Introduction 256
10.2 Literature Review 257
10.3 Summary 274
References 275
11 Application of Hyperautomation in Insurance and Retail Industries 277
A. Vinora, E. Lloyds, R. Nancy Deborah and Sivakarthi G.
Introduction 277
Conclusions 297
References 298
12 Application of Hyperautomation in Predictive Maintenance— A Technical Analysis 299
Sunith Babu L., Hemanth Kumar J., Madhusudhan B., Nitish Kumar V. and Sujitha R.
12.1 Introduction to Hyperautomation 300
12.2 Predictive Maintenance: An Overview 302
12.3 Application of Hyperautomation in Predictive Maintenance 306
12.4 Conclusion 316
References 318
Index 325
Über den Autor
Rajesh Kumar Dhanaraj, Ph D, is a professor at the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed more than 25 books on various technologies, 21 patents, and 53 articles and papers in various refereed journals and international conferences, as well as contributed chapters to books. He is a senior member of the Institute of Electrical and Electronics Engineers and is a member of the Computer Science Teacher Association and International Association of Engineers. He is also an expert advisory panel member of Texas Instruments Inc.
M. Nalini, Ph D, is a professor at the Sri Sairam Engineering College, Chennai, Tamil Nadu, India. She has more than 14 years of experience working in teaching and research. Dr. Nalini is the author of more than two books and over 25 international journals and conferences. She has also received invitations to address international conferences as a keynote speaker and session chair and is a member of the Institute of Electrical and Electronics Engineers and a life member of the Indian Society for Technical Education.
A. Daniel, Ph D, is an associate professor at the School of Computing Science and Engineering in Galgotias University, Greater Noida, Uttar Pradesh, India. He has published several articles in reputed international journals and is a member of the Institute of Electrical and Electronics Engineers, Association of Computing Machinery, Institute for Educational Research and Publication, International Association of Engineers, and the Computer Science Teachers Association.
Ali Kashif Bashir, Ph D, is affiliated with the School of Computing and Mathematics, Manchester Metropolitan University, United Kingdom. Additionally, he is an adjunct professor for the School of Electrical Engineering and Computer Science, National University of Science and Technology, Islamabad, an honorary professor at the School of Information and Communication Engineering, University of Electronics Science and Technology of China, and a chief advisor at the Visual Intelligence Research Center, UESTC. He is the author of over 100 peer-reviewed articles and has served as a chair for several conferences and workshops, delivering several invited and keynote talks.
Balamurugan Balusamy, Ph D, is an associate dean to students at Shiv Nadar University at the Delhi-NCR Campus in Noida, India. He has authored/edited more than 80 books, as well as over 200 contributions to international journals and conferences.
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