The Monetization of Technical Data: Innovations from Industry and Research

Greetings from Minister Prof. Dr. Andreas Pinkwart
Greetings from Dorothee Bär
Monetization of data—Foreword
Contents
1 Monetization of Data at the Example of Manufacturing Machines
Abstract
1.1 Introduction
1.2 Basics
1.2.1 What is Data Monetization?
1.2.2 Company-centered, Internal Added Value
1.2.3 Customer-centered, External Value
1.3 Challenges and Solutions
1.4 The Path to Data Monetization
1.4.1 Framework for Data Monetization
1.4.2 Use Cases
1.4.2.1 Use Case 1: Trading of Machine Data
1.4.2.2 Use Case 2: Data-driven Assistant System
1.4.2.3 Use Case 3: Transform Business Model
1.5 Conclusion
References
Part I Legal aspects of Data Monetization
2 Data Monetization in Law
Abstract
2.1 Classification of the Observed Monetization Effects
2.2 Current Importance
2.3 Guide for the Commission for the Joint Use of Data from the Private Sector
2.3.1 Recorded Variants
2.3.2 Contractual Basis
2.3.3 Legal Principles—Connected with Competition Law
2.3.3.1 Transparency
2.3.3.2 Joint Value Creation
2.3.3.3 Protection of the Business Interests of All Parties
2.3.3.4 Unadulterated Competition
2.3.3.5 Data Independence
2.3.4 Extensions
2.4 “Property” of the Data
2.4.1 Previous Indications
2.4.2 Sampling Judgment of the European Court of Justice
2.4.2.1 Connection with Digitization
2.5 Competition Law Access Claims
2.5.1 Platform Companies
2.5.2 Modification Due to Climate Protection?
2.6 Obligation to Pay a Reasonable Fee
2.6.1 Assessment According to the Innovative Character of the Performance
2.6.2 Prohibition of Discount Systems
2.6.3 Injunction Under the Ruling Huawei
2.7 Standardization by Associations
2.8 Corporate Cooperation
2.9 Information Exchange
2.10 Summary
References
3 Monetization of Technical Data using the Example of Technical Employee Data
Abstract
3.1 Introduction
3.2 Basics and Methods
3.2.1 Employer’s Monitoring Right
3.2.2 Works Agreement
3.2.3 Consent
3.2.4 Interim Result
3.3 Challenges and Solutions
3.3.1 Prohibition of Coupling
3.3.1.1 Applicability of the Prohibition of Coupling
3.3.1.2 Violation of the Prohibition of Coupling
3.3.1.3 Consequence of the Violation
3.3.1.4 Interim Result
3.3.2 Excessive Incentives
3.4 Examples
3.4.1 Examples of Technical Employee Data
3.4.2 Examples of Economic Advantages
3.5 Summary
References
4 The enforcement and bankruptcy of blockchain-based assets (Crypto-Assets)
Abstract
4.1 Introduction
4.2 Basics of blockchain-based values
4.2.1 Blockchain technology at a glance
4.2.1.1 Basic structure of a blockchain
4.2.1.2 Continuation of the blockchain
4.2.1.3 Types of blockchain applications
4.2.2 Crypto-assets as a new form of digital assets
4.3 Enforceability of the law
4.3.1 Individual enforcement according to the ZPO
4.3.1.1 Enforcement in token credits due to a money claim
4.3.1.2 Enforcement of claims for transfer of crypto assets
4.3.1.3 Enforcement of claims for transfer of crypto assets
4.3.1.4 Jurisdiction of German enforcement authorities
4.3.1.5 Practical challenges within nforcement
4.3.1.6 Enforcement protection
4.3.2 Bankcrupcy according to the InsO
4.3.2.1 Current state of research
4.3.2.2 Insolvency procedure
4.3.2.3 Exclusion and segregation rights
4.3.2.4 Practical challenges within the scope of enforcement
4.3.2.5 Result of a more detailed insolvency law examination
4.4 Summary and outlook
References
Part II Business aspects of Data Monetization
5 Silence is Silver, Speech is Gold: The Benefits of Machine Learning and Text Analysis in the Financial Sector
Abstract
5.1 Current Relevance of Machine Learning in Finance
5.2 Applications Within the Financial Sector
5.2.1 Research
5.2.2 Insurance Companies
5.2.3 Banks
5.3 Text Analysis and Machine Learning in the Financial Sector
5.3.1 Procedure and Methodology
5.3.2 Benefits for Practice
5.4 Conclusion
References
6 Monetization of Machine-generated Online Data — Cross-industry Opportunities and Challenges
Abstract
6.1 Introduction
6.2 Basics and Methods
6.2.1 Machine-generated Online Data: Definition and Relevance
6.2.2 Data Sources and Data Usage
6.3 Opportunities Through the Monetization of Machine-generated Online Data
6.4 Trends and Outlook
6.5 Summary
References
7 Monetary Valuation of Data in the Context of Accounting
Abstract
7.1 Introduction
7.2 Basics and Methods
7.2.1 Properties of Data
7.2.2 Valuation Approaches
7.3 Challenges and Solutions
7.4 Application to Technical Data Sets
7.5 Discussion and Outlook
References
8 How Can the German Mittelstand Embrace Digital Transformation?—Considerations on Data Products, Business Models and Platform Economics
Abstract
8.1 Digital Challenges for Industry
8.2 How Can Data be Monetized?
8.3 Development of Smart Services
8.4 Innovation Through Business Models
8.5 Research Competition for AI Solutions
8.6 AI-based Solution Approach
8.7 Use Cases in the AI Service-Meister Project
8.7.1 Monitoring Remote States
8.7.2 Accelerating Service Processes
8.7.3 Recognizing Anomalies
8.7.4 Diagnosing Problems Automatically, Reducing Maintenance Costs
8.7.5 Monitoring Production Processes, Preventing Downtime
8.7.6 Planning Deployments, Procuring Spare Parts
8.8 What Changes can be Expected in the Area of Industry 4.0 Through Gaia-X?
8.9 What is the Significance of Gaia-X for the Service-Meister Project?
8.10 A Solution, Not Only for Technical Service
References
9 Data Monetization Strategies for Manufacturing Companies
Abstract
9.1 Classification of the Observed Monetization Effects
9.2 Introduction
9.3 Difficulties of Data Monetization
9.4 Strategies for Data Monetization
9.5 Practical Example
9.6 Discussion and Outlook
References
Part III Information technology aspects of Data Monetization
10 End-to-End Architectures for Data Monetization in the Industrial Internet of Things (IIoT)
Abstract
10.1 Introduction
10.2 Architecture
10.2.1 Problem Overview
10.2.2 Solution Approaches
10.2.3 End-to-end-IoT
10.3 Information Processing
10.3.1 IoT Platform
10.3.1.1 Definition
10.3.1.2 Functions and Capabilities
10.3.1.3 Support for IoT Applications
10.3.2 Information Processing at the Edge
10.3.2.1 Drivers
10.3.2.2 The Edge Continuum
10.3.2.3 Use Case 1—IoT Gateways
10.3.2.4 Use Case 2—Edge-Analytics
10.3.3 Non-functional Requirements
10.3.3.1 Scalability
10.3.3.2 Multi-tenancy
10.3.3.3 Deployment Options ()
10.4 Information Acquisition
10.4.1 IoT Gateways and Other Architecture Patterns
10.4.2 Low Power Devices
10.5 Information Allocation and Distribution
10.5.1 Lambda Architecture
10.5.2 API Management
10.6 Discussion
10.7 Summary
References
11 The Technology IOTA as an Open Infrastructure for Micro-Payments, IoT Communication, and Global Digital Security
Abstract
11.1 Classification of the Observed Monetization Effects
11.2 Introduction
11.3 What is IOTA?
11.4 The Disadvantages of Traditional Blockchain
11.5 IOTA as an Infrastructure for People, Organizations and the Internet of Things
11.6 Micro-Payments for Data
11.7 Microtransactions and Data Sharing as the Basis for New Business Models
11.8 Requirements for the Capture and Monetization of Technical Data
11.9 Data Management in IOTA—IOTA Streams
11.10 Summary
References
12 Data Notary—Auditable Data as a Basis for Monetization
Abstract
12.1 Introduction
12.1.1 Digital Seal for Data
12.1.2 Data Notary as a Driver for Data Monetization
12.2 Technical Basics and Requirements
12.2.1 Security ≠ Checking by Evidence
12.2.2 Data Authenticity
12.2.3 Data Integrity
12.2.4 Raw Data vs. Auditable Data Assets
12.2.5 Secure Storage of Evidence
12.3 Data Notary Architecture
12.3.1 Pillars of a Data Notary Service
12.3.1.1 Source
12.3.1.2 Service Source
12.3.1.3 Data Storage Source
12.3.1.4 Service Consumer
12.3.1.5 Data Storage Consumer
12.3.1.6 Consumer
12.3.1.7 Memory
12.4 Data Notary in Practical Use
12.4.1 Company Profiles
12.4.2 Problem Description
12.4.3 Solution Approach—Auditable Machine Data
12.4.4 Integration as an Extension to the Industrial IoT Platform
12.4.5 Data Provision
12.4.6 Audit and Use of Data
12.5 Conclusion
References
13 Secure, Verifiable Object Identities as Enablerfor Value Creation in Distributed Networks
Abstract
13.1 Classification of the Observed Monetization Effects
13.2 Introduction
13.3 Basics and Methods
13.3.1 Attribute-Based Identity Model
13.3.2 Zero-Knowledge Proof
13.3.3 Digital Object Memory
13.4 Architecture and Attack Description
13.4.1 Architectural Description of a Zero-Knowledge Proof for Attributes of Objects
13.4.2 Attacks on the System
13.5 Discussion and Case Studies
13.6 Summary
References
Part IV Data Monetization in the Manufacturing Industry
14 Putting a Price Tag on Data
Abstract
14.1 Classification of the Observed Monetization Effects
14.2 Introduction
14.3 Vision of MyDataEconomy
14.4 Architecture of MyDataEconomy
14.4.1 Marketplace
14.4.2 Network Nodes
14.4.3 Client
14.4.4 Fundamentals of the Decentralized Service Landscape
14.4.5 Scalability of a Global Service and Data Alliance
14.5 Examples of Applications
14.5.1 Condition Monitoring
14.5.1.1 Process Monitoring During Fineblanking
14.5.1.2 Environmental monitoring during fineblanking
14.5.2 Advanced Analytics
14.5.2.1 Predictive Maintenance with the aid of structure-borne ound
14.5.3 Monetization of Assets and Services
14.5.3.1 Asset Monetization
14.5.3.2 Service Monetization
14.6 Summary
References
15 The Internet of Production as the Foundation of Data Utilization in Production
Abstract
15.1 Classification of the Observed Monetization Effects
15.2 Introduction
15.3 Basics
15.3.1 Internet of Production
15.3.2 Digital Twin
15.3.3 Digital Shadow
15.4 Challenges and Solutions
15.4.1 Challenges of Data-Driven Modeling
15.4.2 Infrastructural Challenges
15.5 Examples of the Digital Material Shadow
15.5.1 In-Situ Material Classification using Artificial Neural Networks
15.5.2 Interaction Effects as Digital Material Shadow in Process Chains
15.6 Conclusion
References
16 Graphic Approach to Energy Optimization through Artificial Intelligence
Abstract
16.1 Classification of the Observed Monetization Effects
16.2 Introduction
16.3 Basics and Methods
16.3.1 Artificial Intelligence and Machine Learning
16.3.2 Optimization
16.3.3 Graphic Methods for Optimization
16.4 Challenges and Solutions
16.5 Optimization through Artificial Intelligence
16.5.1 Process Understanding
16.5.1.1 The Plant
16.5.1.2 Retrofitting
16.5.1.3 Variables and Process Diagrams
16.5.1.4 Network
16.5.2 Data acquisition and cleansing
16.5.3 Creating an Optimization Strategy
16.5.4 Optimization and Integration
16.6 Results
16.7 Outlook
16.8 Conclusion
References
17 Efficiency Increase through Data-Based Modeling of Quality and Production Cost Factors in the Nonwoven Industry
Abstract
17.1 Classification of the Observed Monetization Effects
17.2 Introduction
17.3 Basics and Methods
17.3.1 Multi-Dimensional Optimization Methods
17.3.2 Modeling Methods for Product Quality and Production Costs
17.4 Challenges and Solutions
17.4.1 Determination of the Influencing and Target Variables
17.4.1.1 Target Variables
17.4.1.1.1 Quality Parameters of the Carded Web
17.4.1.1.2 Production Cost Parameters
17.4.1.2 Influencing Factors
17.4.2 Measuring the Parameters in Operation
17.4.3 Data Preparation
17.4.3.1 Labeling of Usable Data Sets During Production
17.4.3.2 Removal of Unusable and Redundant Data Sets
17.4.3.2.1 Generation of Training Data
17.5 Results
17.5.1 Modeling of Target Values
17.5.2 Simulation and Optimization
17.6 Monetary Consideration of Optimization
17.6.1 Amortization Period Calculation
17.6.2 Further Monetization Effects through Scrap Reduction
17.7 Summary
References
18 Added Value through Linking of Product and Process Data on the Example of a Textile Process Chain
Abstract
18.1 Classification of the Observed Monetization Effects
18.2 Introduction
18.3 Basics and Methods
18.3.1 Aachen Textile Production Theory
18.3.2 Digital Twin and Digital Shadow
18.3.3 Data Warehousing
18.3.4 Optimization with Regression Models
18.4 Challenges and our Approach
18.4.1 Challenges
18.4.2 Solution Approaches
18.4.2.1 Building a Machine Network
18.4.2.2 Structure of the Data Warehouse
18.4.2.3 Selection of Analysis Methods for Data Analysis
18.4.2.4 Validation of our Method
18.4.2.5 Concept for the Transfer of Results into Industry
18.5 Results
18.6 Discussion
18.7 Summary
References
19 Data-Based Knowledge Gain from the Perspective of Surface Technology
Abstract
19.1 Classification of the Observed Monetization Effects
19.2 Introduction
19.3 Fundamentals and Challenges
19.4 Methods and Solutions
19.5 Case Studies and Outlook
19.6 Summary
References
20 Digitization in the Plastics Processing
Abstract
20.1 Classification of the Observed Monetization Effects
20.2 Introduction
20.3 Monetization Through Digital Services
20.4 Monetization Through Digital Services
20.5 Monetization Through Improvement of Internal Processes
20.6 Monetization of Anonymized and Generalized Production Data
20.7 Conclusion
References
21 Monetization of Data in Joining Technology
Abstract
21.1 Classification of the Observed Monetization Effects
21.2 Introduction
21.3 Basics and Methods
21.4 Challenges and Solutions
21.4.1 Use of Key Performance Indicators
21.4.2 Settings for Welding
21.4.3 Predictive Quality
21.5 Discussion and Case Studies
21.5.1 Cycle Time Optimization in Spot Welding
21.5.2 Inverse Search of Parameters for given Weld Geometry in Gas Metal Arc Welding
21.5.3 Process Stability Consideration during GMA Welding
21.5.4 Systemic Optimization through Analysis and Interpretation of Welding Production Data
21.6 Summary
References
22 With Transparency to Zero-Defect Production and Added Values for the Customer
Abstract
22.1 Classification of the Observed Monetization Effects
22.2 Introduction
22.3 Background
22.4 Solution Approaches
22.4.1 Development of the Error
22.4.2 Prevent known Defects
22.4.3 Discovering Unknown
22.5 Implementation
22.5.1 Cost Reduction
22.6 An Example from Practice
22.6.1 Secure Implementation with IOTA Tangle
22.6.2 Traceability
22.6.3 Monetization
22.6.4 Changes to Business Models
22.7 Outlook
22.8 Conclusion
References
23 Decentralized Marketplace Structures as Protection against Information Asymmetries
Abstract
23.1 Introduction
23.2 Basics
23.2.1 Blockchain and DLT
23.2.2 Internet of Things
23.3 Challenges and Solutions
23.3.1 Internet of Things
23.3.2 Trade Secrets vs. Data Monetization
23.4 Solution: A Decentralized IoT Marketplace
23.4.1 Properties
23.4.2 Who will be Affected by these Markets?
23.4.3 Emerging Business Models and Processes
23.4.3.1 Data-Driven Flexibility: New data Sources Effectively used to Expand Business Models
23.4.3.2 Data Discovery as a Service
23.5 Summary
References
24 As-a-Service Models for Manufacturing Technology
Abstract
24.1 Classification of the Observed Monetization Effects
24.2 Introduction
24.3 Basics and Methods
24.3.1 FE-Simulation of Fineblanking
24.3.2 Data-driven Modeling of Fineblanking
24.3.2.1 Modeling of Die Roll by Means of Artificial Neural Networks
24.3.2.2 Modeling of the Die Roll by Means of Support Vector Machines
24.3.2.3 Modeling of Die Roll with Statistical Regression Methods
24.3.2.4 Validation of the Implemented Models
24.4 Challenges and Solutions
24.5 Cloud Computing Platform for the Digital Twin of Fineblanking as a Service
24.5.1 Use Cases of the Platform
24.5.1.1 Finite-Element Method as-a-Service
24.5.1.2 Data-driven Modeling as-a-service
24.5.2 Requirements Analysis
24.5.3 Platform Architecture
24.6 Summary
References
25 Using Data from Agricultural and Construction Machinery profitably
Abstract
25.1 Classification of the Examined Monetization Effects
25.2 Introduction
25.3 Fundamentals
25.4 Mechanisms and Obstacles to Data Monetization According to the State of the Art
25.4.1 Mobile Machinery in the Construction Industry
25.4.1.1 Data Value of Digital Solutions
25.4.1.2 Interim Conclusion
25.4.2 Mobile Machinery in Agriculture
25.4.2.1 Data Value of Digital Solutions
25.4.2.2 Interim Conclusion
25.4.3 Conclusion on the Current Data Monetization in the Construction Industry and in Agriculture
25.5 Solution Approach: Guideline for Developing Data-Driven Business Models
25.6 Case Studies
25.6.1 Construction Industry: Case Study Data-Driven Operator Model
25.6.2 Construction Industry: Case Study Construction Project as a System
25.6.3 Agriculture: Example Case of Trade in Product Information
25.7 Summary
References
26 Sustainable Production through Predictive Quality and Sustainability Analytics along the Supply Chain
Abstract
26.1 Classification of the Observed Monetization Effects
26.2 Introduction
26.3 Basics and Methods
26.3.1 Predictive Quality and Sustainability Analytics
26.3.2 Supplier and Customer Data
26.3.3 Data Quality
26.4 Monetization of Technical Data along the Supply Chain
26.5 Examples of Applications
26.5.1 Predictive Quality in the Automotive Industry
26.5.2 Sustainability Analytics in the Production of Household Appliances
26.6 Conclusion
References
27 The Data Lifecycle from Data Capture to Insight
Abstract
27.1 Classification of the Observed Monetization Effects
27.2 Perfect Processes in a Real World
27.3 Generate and Provide Data
27.3.1 From the Sensor into the Digital World
27.3.2 Knowledge Creation through Consideration of Different Sources
27.3.3 Expansion of the Data Basis through Artificial Data
27.4 Enrich and Refine Data
27.4.1 Raw Data
27.4.2 Annotated Data
27.4.3 Models and Information
27.4.4 Enrichment with Knowledge
27.5 Discussion and Outlook
27.5.1 Technical Trends
27.5.1.1 Synthetic Generation of Data
27.5.1.2 Edge AI and Distributed AI
27.5.1.3 Transfer Learning
27.5.1.4 Quantum Machine Learning
27.5.2 Legal Basis and Perspectives
27.6 Summary
References
28 “ReLIFE”: Business Models for Data-Based Remanufacturing
Abstract
28.1 Classification of the Observed Monetization Effects
28.2 Introduction
28.3 Basics of Maintenance
28.4 Development of Data and Knowledge Sources
28.4.1 The Capital Good as a Data Source
28.4.2 Remanufacturing Measures as an Action Framework
28.5 Adaptive Remanufacturing as an Individual Maintenance Strategy
28.6 Business Models for Adaptive Remanufacturing
28.6.1 Product-Service-System
28.6.2 Monetization Scenarios for Technical Data from Capital Goods
28.6.3 Validation of Business Models
28.7 Summary
References
29 Data Utilization and Data Reduction in Laser Material Processing
Abstract
29.1 Classification of the Observed Monetization Effects
29.2 Introduction
29.3 Basics and Methods
29.3.1 Data Acquisition and Expectations
29.3.2 Data Processing and Reduction
29.4 Challenges and Solutions
29.5 Discussion and Examples
29.5.1 Sequence of Seams for Autonomous Laser Beam Welding
29.5.2 Laser Beam Welding of Battery Cells
29.5.3 Laser Beam Drilling of Filters for Water Treatment
29.6 Summary
References
30 Transparency and Value of Data in Construction
Abstract
30.1 Introduction
30.2 Development and Current State
30.2.1 Special Features of the Construction Industry and Construction Product
30.2.2 Data Basis in Construction
30.3 Exchange and Use of Building-Specific Data
30.3.1 Challenges in Data Exchange
30.3.2 Umbrella Format and Model Container as a Solution Approach
30.3.3 Current Development and Results
30.4 Data Evaluation in Construction
30.4.1 Parametric Design and Algorithms
30.4.2 Digital Models of Production in Construction
30.4.3 Data-Driven Feasibility Analysis
30.5 Summary
References
31 Data Monetization Potential in the Automotive Industry Value Chain and the Product Lifecycle
Abstract
31.1 Classification of the Observed Monetization Effects
31.2 Introduction
31.3 Basics and Methods
31.3.1 Value Chain Within the Automotive Industry
31.3.2 Product Lifecycle Management (PLM)
31.3.3 Industry 4.0, Data Analysis Methodology and Artificial Intelligence
31.4 Challenges and Solutions
31.5 Applications
31.5.1 Vehicle Value Chain
31.5.2 Life Cycle Management
31.5.3 Employee Motivation
31.6 Discussion and Case Studies
31.6.1 Product Lifecycle Management Tool
31.6.2 IoT Examples in Production
31.6.3 Logistics
31.7 Summary
References
Part V Data Monetization in Energy Technology
32 Technology Convergence in the Energy Sector
Abstract
32.1 Introduction
32.2 Progressive Application System
32.2.1 Technologies
32.2.2 Functions
32.2.3 Features and Potential
32.2.4 State of the Art and Market Adoption
32.3 Progressive Application System in the Energy Sector
32.3.1 Remote Read out of Consumption Metering Systems
32.3.2 Monetization Effects
32.4 Summary
References
33 Data Monetization in the Energy System and its Role in the Development of a Customer-Oriented Power Grid
Abstract
33.1 Classification of the Observed Monetization Effects
33.2 Introduction
33.2.1 Data Exchange Between Actors
33.3 The End Customer as an Active Participant in the Energy Market Two Possible Applications
33.3.1 Flexibility Markets
33.3.2 Local Energy Communities
33.4 Conclusion
References
Part VI Data Monetization in other emerging application fields
34 Monetization of Sensor Data in Commercial Real Estate
Abstract
34.1 Classification of the Observed Monetization Effects
34.2 Introduction
34.3 Basics
34.4 Monetization
34.4.1 Internal Data Monetization in Commercial Real Estate
34.4.2 External Data Monetization in Commercial Real Estate
34.5 Challenges and Solutions
34.5.1 Challenges
34.5.2 Solution Approach
34.6 Conclusion
References
35 Saving Costly Experiments and Simulations through Machine Learning
Abstract
35.1 Classification of the Observed Monetization Effects
35.2 Introduction
35.3 Fundamentals and Methods
35.3.1 Simplified Residual Neural Network (SimResNet)
35.3.2 Ensemble Kalman Filter (EnKF)
35.4 Challenges and Solutions
35.5 Applications and Results
35.5.1 Determination of Cutting Forces in Machining of Metallic Materials
35.5.2 Atmospheric Plasma Spraying
35.6 Discussion
35.7 Summary
References
36 PigConomy
Abstract
36.1 Classification of the Observed Monetization Effects
36.2 Introduction
36.3 Electronic Marketplaces
36.4 PigConomy—Generating and Marketing Knowledge in Animal Husbandry
36.4.1 Functional Components in Detail—Data as Raw Material of the PigConomy
36.4.2 Functional Components in Detail—Statistical Analysis as a Tool of PigConomy
36.4.3 Functional Components in Detail—Blockchain as a Clearing House for PigConomy
36.5 Core Results of Our Proof of Concept
36.6 From Proof of Concept to PigConomy Ecosystem
36.7 Findings for the Monetization of Data
36.8 Summary
References