The Digital Playbook : : A Practitioner's Guide to Smart, Connected Products and Solutions with AIoT.

• Author: Slama, Dirk.
• TeilnehmendeR: Rückert, Tanja.; Thrun, Sebastian.; Homann, Ulrich.; Lasi, Heiner.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2023.; ©2023.
• Year of Publication: 2023
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (413 pages)

Table of Contents

• Intro
• Preface
• Digital Playbook and the AIoT User Group
• How to Get Involved
• Vision
• About This Book
• Structure of the Digital Playbook
• Key Plays of the Digital Playbook
• How to Read This Book
• Contents
• Part I: Introduction
• Chapter 1: AIoT 101: What, Why, How, Who
• 1.1 What: Smart, Connected Products and Solutions with AIoT
• 1.2 Why: Purpose and AIoT-Enabled Business Outcomes
• 1.3 How: Getting Things (and AI) Done
• 1.4 Who: AIoT Roles and Responsibilities
• Chapter 2: Artificial Intelligence 101
• 2.1 Introduction
• 2.2 Supervised Learning
• 2.3 Unsupervised Learning
• 2.4 Reinforcement Learning
• 2.5 Deep Learning and Artificial Neural Networks
• 2.6 Summary: AI &amp
• Data Analytics
• Chapter 3: Data 101
• 3.1 Enterprise Data
• 3.2 Data Management
• 3.3 Analytics Platforms
• 3.4 Data Engineering
• 3.4.1 Data Pipeline
• 3.4.2 Edge Vs. Cloud
• 3.4.3 The Big Loop
• 3.5 Data Science
• 3.5.1 Understanding AIoT Data Categories and Matching AI Methods
• 3.5.2 Data Sets
• 3.5.3 Data Labeling
• 3.6 Domain Knowledge
• 3.7 Chicken Vs. Egg
• Chapter 4: Digital Twin 101
• 4.1 Introduction
• 4.2 Example
• 4.3 Digital Twin and AIoT
• 4.3.1 Example 1: Electric Vehicle
• 4.3.2 Example 2: Particle Collider
• 4.4 DT Resolution and Update Frequency
• 4.5 Advanced Digital Twins: Physics Simulation and Virtual Sensors
• Chapter 5: Internet of Things 101
• 5.1 Introduction
• 5.2 IoT Architecture
• 5.3 IoT Sensors and Actuators
• 5.4 IoT Protocol Layers
• 5.5 IoT Connectivity
• 5.6 Over-the-Air Updates
• 5.6.1 Distribution
• 5.6.2 Deployment
• 5.7 AIoT AppStores
• 5.7.1 Example 1: OEM with Closed AppStore
• 5.7.2 Example 2: OEM with Open AppStore
• 5.8 Expert Opinion: Nik Willetts, President &amp
• CEO of TM Forum
• Chapter 6: Hardware 101
• 6.1 Smart, Connected Products.
• 6.2 Smart, Connected (Retrofit) Solutions
• 6.3 Edge Node Platforms
• 6.4 Sensor Edge Nodes
• 6.5 AI Edge Nodes
• 6.6 Putting It All Together
• Part II: Business Strategy
• Chapter 7: Digital OEM
• 7.1 WHY
• 7.1.1 Digital OEMs: Business Models
• 7.1.2 Incumbent OEMs: Business Improvements
• 7.2 WHAT
• 7.2.1 Smart, Connected Products: Enabled by AIoT
• 7.2.2 Example: Robot Vacuum Cleaner
• 7.2.3 Example: Kitchen Appliance
• 7.2.4 Example: Automatic Wiper Control
• 7.2.5 Example: Physical Product Design Improvements
• 7.2.6 Example: Smart Tightening Tool
• 7.3 WHY Revisited
• 7.3.1 Aligning the Product Lifecycle with the Customer Journey
• 7.3.2 Benefits
• 7.4 HOW
• 7.4.1 Key Design Decisions
• 7.4.2 Considerations for Execution and Delivery
• Chapter 8: Digital Equipment Operator
• 8.1 WHY
• 8.2 WHAT
• 8.2.1 Example: Escalator Operator (Railway Company)
• 8.2.2 Example: School Bus Fleet Operator
• 8.2.3 Example: Aircraft Fleet Operations Planning Using a Flight Path Optimizer
• 8.3 HOW
• 8.3.1 Solution Lifecycle
• 8.3.2 Considerations for Execution and Delivery
• Chapter 9: Platforms
• 9.1 WHY
• 9.2 WHAT
• 9.3 HOW
• 9.4 Example: Parking Spot Detection (Multi-Sided Business Platform)
• 9.5 Challenges
• Chapter 10: Hybrid Models
• 10.1 WHY
• 10.2 WHAT
• 10.2.1 Example: Predictive-Maintenance-as-a-Service
• 10.2.2 Example: Drone-based Building Facade Inspection
• 10.3 HOW
• Chapter 11: Scalability
• 11.1 Understand Strategy Requirements
• 11.1.1 Digital OEM: Strategy for Smart, Connected Products
• 11.1.2 Digital Equipment Operator: Strategy for Smart, Connected Solutions
• 11.2 Clearly Define Your Focus Areas
• 11.3 Take a Holistic View of Product, Marketing and Commercialization
• 11.4 Ensure Product/Market Fit (or Solution/Internal Demand Fit)
• 11.5 Ensure Efficient Exploration.
• 11.6 Understand How Best to Cross the AIoT Chasm
• 11.7 Understand Implications of AIoT Short Tail vs. Long Tail
• 11.8 Ensure Organizational Scalability
• 11.9 Deal with Repeatability, Capacity and Marginal Costs
• Part III: Business Execution
• Chapter 12: Business Model Design
• 12.1 AIoT-Enabled Business Models
• 12.1.1 AI Business Model Patterns
• 12.1.2 IoT Business Model Patterns
• 12.2 Ignite AIoT Business Model Templates
• 12.2.1 The Smart Kitchen Example
• 12.2.2 AIoT Business Model Canvas
• 12.2.3 AIoT Solution Sketch
• 12.2.4 AIoT Use Case Mapping
• 12.2.5 AIoT Customer Journey Map
• 12.2.6 Commercial Model
• 12.2.7 KPIs
• 12.2.8 AIoT Business Case
• 12.2.9 AIoT Business Case Validation
• 12.3 Proof of Concept
• 12.4 Investment Decision
• Chapter 13: Product/Solution Design
• 13.1 From Business Model to Implementation
• 13.2 The Agile Approach
• 13.2.1 Story Maps
• 13.2.2 Example: AIoT Story Map &amp
• User Stories
• 13.3 Non-Functional Requirements
• 13.4 AIoT System Design
• 13.4.1 AIoT Design Viewpoints
• 13.4.2 AIoT Viewpoint Details
• 13.5 From Requirements and Design to Implementation and Validation
• 13.6 Design vs. Co-creation &amp
• Sourcing
• Chapter 14: Co-Creation and Sourcing Intro
• 14.1 Co-Creation
• 14.1.1 Why AIoT &amp
• Co-Creation?
• 14.1.2 AIoT Co-Creation Options
• 14.1.3 Expert Opinions
• 14.1.4 Tradeoffs
• 14.2 Sourcing
• 14.2.1 Challenges
• 14.2.2 AIoT Sourcing Process
• 14.2.3 AIoT Sourcing Strategy
• 14.2.3.1 Strategic Options: Make vs. Buy vs. Partner
• 14.2.3.2 The AIoT Bill of Materials
• 14.2.3.3 Example: ACME Smart Shuttle
• 14.2.3.4 Creating the AIoT BOM
• 14.2.3.5 Make vs. Buy Breakdown
• 14.2.3.6 ACME Smart Shuttle: Outsourcing AI?
• 14.2.3.7 AIoT Sourcing BOM
• AI-specific Sourcing BOM Elements
• IoT-specific Sourcing BOM Elements.
• 14.2.3.8 Schedule Alignment
• 14.2.4 General Considerations
• 14.2.4.1 SLAs and Warranties
• 14.2.4.2 ACME Smart Shuttle: SLAs for AI?
• 14.2.4.3 Pricing Models
• 14.2.4.4 AIoT Vendor Selection Criteria
• 14.2.5 RFP Management
• 14.2.5.1 RFP Document Creation
• 14.2.5.2 RFP Document Distribution and Q&amp
• A Process
• 14.2.5.3 AIoT Vendor Selection
• 14.2.6 Legal Perspective
• Chapter 15: Rollout and Go-to-Market
• 15.1 Smart, Connected Solutions: Rollout
• 15.2 Smart, Connected Products: Go-to-Market
• 15.2.1 Example: Physical-Feature-on-Demand
• 15.2.2 Continuously Improve Commercialization
• Chapter 16: Operations
• 16.1 Digital OEM (Fig. 16.1)
• 16.1.1 Sales
• 16.1.2 Support
• 16.1.3 DevOps
• 16.2 Digital Equipment Operator (Fig. 16.3)
• 16.2.1 Field Service Management
• 16.2.2 IT Service Management
• 16.2.3 Option 1: Separate Systems
• 16.2.4 Option 2: Integrated System
• 16.2.5 Supplier Management
• Chapter 17: Organization
• 17.1 Digital OEM (Fig. 17.1)
• 17.1.1 Product Organization
• 17.1.2 Product Lifecycle Perspective
• 17.1.3 Traditional Project Organization
• 17.1.4 Toward the AIoT Product Organisation
• 17.1.5 Organizational Culture
• 17.2 Digital Equipment Operator (Fig. 17.6)
• 17.2.1 Solution Provisioning
• 17.2.2 Solution Retrofit
• 17.2.3 Solution Utilization
• Part IV: Technical Execution - AIoT Framework
• Chapter 18: Development Life-Cycle Perspective
• 18.1 Smart, Connected Products
• 18.2 Smart, Connected Solutions
• Chapter 19: Designing Smart Connected Products and Solutions with AIoT
• Chapter 20: AIoT Pipelines
• 20.1 Definition
• 20.2 Pipeline Aggregations
• 20.3 AIoT Pipelines &amp
• Feature-Driven Development
• 20.4 Holistic AIoT DevOps
• 20.5 Managing Different Speeds of Development
• Chapter 21: AIoT.exe
• 21.1 AI.exe (Fig. 21.2).
• 21.1.1 Understanding the Bigger Picture
• 21.1.2 The AIoT Magic Triangle
• 21.1.3 Managing the AIoT Magic Triangle
• 21.1.4 First: Project Blueprint
• 21.1.5 Second: Freeze IoT Sensor Selection
• 21.1.6 Third: Freeze AIoT System Architecture
• 21.1.7 Fourth: Acquisition of Training Data
• 21.1.8 Fifth: Productize the AI Approach
• 21.1.9 Sixth: Release MVP
• 21.1.10 Required Skills and Resources
• 21.1.11 Model Design and Testing
• 21.1.12 Building and Integrating the AI Microservices
• 21.1.13 Setting Up MLOps
• 21.1.14 Managing the AIoT Long Tail: AI Collaboration Platforms
• 21.2 Data.exe (Fig. 21.16)
• 21.2.1 Overview
• 21.2.2 Business Alignment &amp
• Prioritization
• 21.2.3 Data Pipeline: Implementation &amp
• Data Lifecycle Management
• 21.2.4 Data Capabilities and Resource Availability
• 21.2.5 Data Governance
• 21.3 Digital Twin.exe (Fig. 21.18)
• 21.3.1 Is a Digital Twin Needed?
• 21.3.2 If So, What Kind of Digital Twin?
• 21.3.3 Examples
• 21.3.4 Digital Twin Roadmap
• 21.3.5 Expert Opinion
• 21.4 IoT.exe (Fig. 21.28)
• 21.4.1 Digital OEM: Product Perspective
• 21.4.2 Digital Equipment Operator: Solution Perspective
• 21.5 Hardware.exe (Fig. 21.31)
• 21.5.1 A Multidisciplinary Perspective
• 21.5.2 Embedded Hardware Design and Manufacturing
• 21.5.3 Minimizing Hardware Costs vs. Planning for Digital Growth
• 21.5.4 Managing System Evolution
• Chapter 22: AIoT Product/Solution Design
• 22.1 AIoT Design Viewpoints and Templates
• 22.2 Important Design Considerations
• 22.3 ACME:Vac Example
• 22.4 Business Viewpoint (Fig. 22.3)
• 22.4.1 Business Model
• 22.4.2 Key Performance Indicators
• 22.4.3 Quantitative Planning
• 22.4.4 Milestones/Timeline
• 22.5 Usage Viewpoint (Fig. 22.8)
• 22.5.1 Site Surveys and Stakeholder Interviews
• 22.5.2 Personas
• 22.5.3 User Journeys
• 22.5.4 UX/HMI Strategy.
• 22.5.5 Mockups/Wireframes/Prototypes.