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

This open access book is a practitioner's guide to smart, connected products and solutions. As a hands-on playbook, it combines the business and technical execution perspectives to help product companies, OEMs, manufacturers and equipment operators utilize the full potential of digital enablers...

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Main Authors: Slama, Dirk, Rückert, Tanja, Thrun, Sebastian, Homann, Ulrich, Lasi, Heiner
Format: eBook
Language:English
Published: Cham Springer Nature 2023
Springer International Publishing AG
Springer
Edition:1
Subjects:
agile cloud development
AIoT DevOps
Artificial intelligence
Artificial Intelligence of Things
Automatic control engineering
Computer science
Computer Technology
Computing and Information Technology
Data management
Digital OEM
Electrical engineering
Electronics and communications engineering
Electronics engineering
Energy technology and engineering
Engineering
Information technology
Management
Nonfiction
Product AI
Technology
Technology, Engineering, Agriculture, Industrial processes
TJFM1 Robotics
ISBN:3030882217, 3030882209, 9783030882211, 9783030882204, 9783030000, 9789783030008
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Abstract This open access book is a practitioner's guide to smart, connected products and solutions. As a hands-on playbook, it combines the business and technical execution perspectives to help product companies, OEMs, manufacturers and equipment operators utilize the full potential of digital enablers, especially Artificial Intelligence (AI), Internet of Things (IoT) and Digital Twins. The Digital Playbook provides comprehensive and actionable guidance, helping to address the challenges of creating sustainable and scalable digital business models, managing cocreation and sourcing, setting up the digital organization, and handling the legal aspects. For the technical execution perspective, the playbook includes the AIoT Framework, which explains how to combine data science and AI engineering with Digital Twins, as well as software development for cloud and edge. The integration with physical product development and retrofit integration of existing equipment is included as well. A pragmatic, agile approach is introduced that takes common agile inhibitors into consideration. A holistic AIoT DevOps approach is described, which combines key elements of DevOps for cloud, edge and AI. Enterprise readiness is ensured by looking at trust and security as well as reliability and resilience for AIoT. A large number of real-world examples and case studies help ensure practical relevance. Readers should have a previous, general understanding of digital strategies and technologies. This book offers readers a clear understanding of the opportunities, as well as the challenges related to building and operating smart, connected products and solutions. They are given a set of tools and blueprints, which they can apply to their practical work in this space. ;
AbstractList This open access book is a practitioner's guide to smart, connected products and solutions. As a hands-on playbook, it combines the business and technical execution perspectives to help product companies, OEMs, manufacturers and equipment operators utilize the full potential of digital enablers, especially Artificial Intelligence (AI), Internet of Things (IoT) and Digital Twins. The Digital Playbook provides comprehensive and actionable guidance, helping to address the challenges of creating sustainable and scalable digital business models, managing cocreation and sourcing, setting up the digital organization, and handling the legal aspects. For the technical execution perspective, the playbook includes the AIoT Framework, which explains how to combine data science and AI engineering with Digital Twins, as well as software development for cloud and edge. The integration with physical product development and retrofit integration of existing equipment is included as well. A pragmatic, agile approach is introduced that takes common agile inhibitors into consideration. A holistic AIoT DevOps approach is described, which combines key elements of DevOps for cloud, edge and AI. Enterprise readiness is ensured by looking at trust and security as well as reliability and resilience for AIoT. A large number of real-world examples and case studies help ensure practical relevance. Readers should have a previous, general understanding of digital strategies and technologies. This book offers readers a clear understanding of the opportunities, as well as the challenges related to building and operating smart, connected products and solutions. They are given a set of tools and blueprints, which they can apply to their practical work in this space. ;
This open access book is a practitioner's guide to smart, connected products and solutions.As a hands-on playbook, it combines the business and technical execution perspectives to help product companies, OEMs, manufacturers and equipment operators utilize the full potential of digital enablers, especially Artificial Intelligence (AI), Internet of.
This open access book is a practitioner's guide to smart, connected products and solutions. As a hands-on playbook, it combines the business and technical execution perspectives to help product companies, OEMs, manufacturers and equipment operators utilize the full potential of digital enablers, especially Artificial Intelligence (AI), Internet of Things (IoT) and Digital Twins. --
Author Thrun, Sebastian
Homann, Ulrich
Lasi, Heiner
Slama, Dirk
Rückert, Tanja
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Notes Electronic reproduction. Dordrecht: Springer, 2023. Requires the Libby app or a modern web browser.
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Snippet This open access book is a practitioner's guide to smart, connected products and solutions. As a hands-on playbook, it combines the business and technical...
This open access book is a practitioner's guide to smart, connected products and solutions.As a hands-on playbook, it combines the business and technical...
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SubjectTerms agile cloud development
AIoT DevOps
Artificial intelligence
Artificial Intelligence of Things
Automatic control engineering
Computer science
Computer Technology
Computing and Information Technology
Data management
Digital OEM
Electrical engineering
Electronics and communications engineering
Electronics engineering
Energy technology and engineering
Engineering
Information technology
Management
Nonfiction
Product AI
Technology
Technology, Engineering, Agriculture, Industrial processes
TJFM1 Robotics
SubjectTermsDisplay Computer Technology.
Electronic books.
Engineering.
Information technology -- Management.
Nonfiction.
Technology.
Subtitle A Practitioner’s Guide to Smart, Connected Products and Solutions with AIoT
TableOfContents 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
22.5.5 Mockups/Wireframes/Prototypes
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
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
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
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)
Title The Digital Playbook
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