Table of Contents

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Preface
13
What Is the Impact of Machine Learning?
13
What Ethical Aspects Will Be Considered?
14
What Is the Objective of This Book?
16
What Is the Target Audience for This book?
18
1 Introduction to Predictive Intelligence
19
1.1 The Intelligent Enterprise
19
1.2 How Predictive Intelligence Is Evolving at SAP
22
1.3 Connected End-to-End Scenarios
24
1.3.1 Modular Approach
25
1.3.2 Example Scenarios
25
1.4 Analytics of the Future
32
1.4.1 Unified Analytics
33
1.4.2 Business Problems and Business Needs
34
1.4.3 Trends and Technologies
35
1.4.4 User Roles with Analytics
35
1.5 Summary
37
2 The Evolution of Predictive Analytics and Machine Learning at SAP
39
2.1 Predictive Analytics and Machine Learning before SAP S/4HANA
39
2.2 Technologies and Methodologies
40
2.2.1 Automated Analytics
41
2.2.2 Expert Analytics
44
2.3 Best Practices
45
2.4 Summary
48
3 Tools, Technologies, and Services
49
3.1 Machine Learning and Predictive Analytics Approaches
49
3.2 Embedded Machine Learning and Predictive Analytics
51
3.2.1 Overview
51
3.2.2 Embedded Machine Learning with the SAP HANA Automated Predictive Library
53
3.2.3 Embedded Machine Learning with the SAP HANA Predictive Analysis Library
55
3.3 SAP Cloud Platform
57
3.3.1 SAP Data Intelligence
57
3.3.2 Hybrid Machine Learning Models
62
3.4 SAP Analytics Cloud
63
3.4.1 Overview
63
3.4.2 Smart Assist Services
65
3.4.3 Smart Predict Services
67
3.5 SAP Intelligent Robotic Process Automation
70
3.6 SAP Internet of Things
76
3.7 Summary
80
4 Architecture
83
4.1 Introduction
83
4.1.1 Technical Challenges in SAP S/4HANA
83
4.1.2 How to Operationalize Intelligence
84
4.2 Architecture Overview
90
4.2.1 Machine Learning Application Patterns
90
4.2.2 Guiding Principles for Solution Architecture
94
4.2.3 Solution Architecture
95
4.3 Embedded Machine Learning
97
4.4 Side-by-Side Machine Learning
102
4.5 Side-by-Side Predictive Analytics
114
4.6 Summary
118
5 Technical Implementation
119
5.1 Approach Comparison
119
5.2 Implementing Embedded Machine Learning Applications
122
5.2.1 Generated Approach Based on the SAP HANA Automated Predictive Library
123
5.2.2 Coded Approach Based on the SAP HANA Predictive Analysis Library
131
5.3 Implementing Side-by-Side Machine Learning Applications
137
5.3.1 Required Development in SAP Data Intelligence
137
5.3.2 Required Development in ABAP
144
5.4 Implementing Side-by-Side Predictive Analytics Applications
148
5.5 Application Management Processes
155
5.5.1 Lifecycle Management
155
5.5.2 Data Integration
169
5.5.3 Data Protection and Privacy
183
5.5.4 Configuration
194
5.5.5 Extensibility
200
5.5.6 Model Degradation
215
5.5.7 Explanation of Results
221
5.5.8 Workload Management and Performance
229
5.5.9 Legal Auditing
237
5.5.10 Model Validations
244
5.5.11 User Interface Design
252
5.6 Summary
260
6 Business Implementation
261
6.1 Overview of Intelligent Scenarios
261
6.1.1 Creating a Purchase Requisition as an Employee
262
6.1.2 Processing a Purchase Requisition as an Operational Purchaser
263
6.1.3 Monitoring the Spend as a Strategic Purchaser
264
6.1.4 Creating Sales Inquiries as a Sales Manager
264
6.1.5 Recording Financial Transactions as an Accounts Receivable Manager
265
6.2 Configuration Basics
266
6.2.1 SAP Best Practices Explorer
267
6.2.2 SAP Help Portal
271
6.3 Finance
272
6.3.1 SAP Cash Application
273
6.3.2 Accounting and Financial Close
280
6.3.3 Financial Planning and Analysis
285
6.3.4 Governance, Risk, and Compliance
289
6.3.5 Detect Abnormal Liquidity Items
293
6.4 Sourcing and Procurement
295
6.4.1 Contract Consumption
296
6.4.2 Propose Resolution for Invoice Payment Block
297
6.4.3 Supplier Delivery Prediction
299
6.4.4 Proposal of New Catalog Item
300
6.4.5 Proposal of Material Group
301
6.4.6 Proposal of Options for Materials without Purchase Contract
303
6.4.7 Image-Based Buying
305
6.4.8 Intelligent Approval Workflow
306
6.4.9 Blockchain-Verified RFQ Processing
307
6.5 Inventory and Supply Chain
308
6.5.1 Stock in Transit
309
6.5.2 Demand-Driven Replenishment
311
6.5.3 Defect Code Proposal with Text Recognition
313
6.5.4 Early Detection of Slow and Nonmoving Stock
315
6.6 Sales
317
6.6.1 Predict Conversion of Sales Quotations to Sales Orders
318
6.6.2 Predict Sales Forecasts
319
6.6.3 Predict Delivery Delay
322
6.7 Research and Development/Engineering
323
6.7.1 Project Cost Forecasting
324
6.7.2 Digital Content Processing
325
6.8 Industries
327
6.8.1 Professional Services
327
6.8.2 Component Manufacturing
336
6.8.3 Retail
342
6.8.4 Utilities
352
6.8.5 Consumer Products
357
6.8.6 Insurance
360
6.8.7 Telecommunications
364
6.8.8 Banking
368
6.8.9 High-Tech
370
6.8.10 Sports and Entertainment
372
6.8.11 Public Services
373
6.9 Summary
374
7 Services on SAP Cloud Platform
375
7.1 Key Trends and Capabilities
375
7.2 SAP Data Intelligence
382
7.3 Machine Learning
383
7.4 Internet of Things
386
7.5 Blockchain
387
7.6 Summary
388
8 The Road Ahead and Further Learning
389
8.1 Upcoming Features and Functionality
389
8.1.1 Embedded Predictive Models
389
8.1.2 Machine Learning Models on SAP Cloud Platform
390
8.1.3 SAP Analytics Cloud
391
8.1.4 Extensions of Existing Approaches
392
8.2 Blogs for Continuous Information
392
8.3 Summary
393
The Authors
395
Index
397