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.