
The Future of Airplane Factory
Description
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Complete factory enterprise visibility requires a higher order of decision capability that current operating systems do not have. A highly visible factory collects and displays data and information as it happens-at a rate beyond the ability of humans and current systems to analyze, process, decide, and act upon. Expert systems are constructed to present humans with right decisions in the form of optimal choices for right actions by incorporating the knowledge of experts into the logic for the decision. Structured Knowledge-Based Expert Systems (SKBES) are incorporated in this book and defined as a critical component for full enterprise actionable visibility.
The power of the Digitally Optimized Intelligent Airplane Factory not only is found in its ability to unify the factory, reduce touch labor, improve quality, and streamline throughput but it also enables a significant reduction in above-the-shop-floor support and management. Such an ecosystem frees the human to focus on the complexity of interpersonal responsibilities.
If the use of a DOIAF can be viewed as a holistic mechanism, then the human can be the agent engaging with that mechanism, improving negotiations for pricing, contracts, or other person-to-person events that require instinct and relationship.
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Content
- Cover
- Table of contents
- Prologue
- Preface
- Acknowledgments
- CHAPTER 1 Introduction
- 1.1 Introduction
- 1.2 Myopic View
- 1.2.1 Case Example
- 1.3 Enterprise Visibility
- 1.4 Game Theory
- 1.4.1 Case Study: Game Theory
- 1.5 First Steps
- 1.6 Summary
- References
- Additional Reading
- CHAPTER 2 Augmented Reality
- 2.1 Introduction
- 2.2 Time to Market
- 2.3 Complexity
- 2.4 New Design Intervals
- 2.5 Augmented Reality
- 2.6 Extension to Manufacturing
- 2.6.1 The Process for Automation
- 2.6.2 New Tools
- 2.6.3 The Highly Immersive Virtual Environment (HIVE)
- 2.6.4 Virtual Commissioning
- 2.6.5 Virtual Commissioning Test-Bed (VET)
- 2.7 Mobility
- 2.8 Future State
- 2.8.1 Understanding of the Nervous System
- 2.8.2 Ability to Manipulate the Central Nervous System
- 2.8.3 Computer Hardware/Software to Process Inputs/Outputs
- 2.9 Summary
- References
- CHAPTER 3 Data Fusion and Neural Networks
- 3.1 Introduction
- 3.2 Data Fusion
- 3.2.1 Simple Case Example: Drill and Fill
- 3.2.2 Intermediate Case Example: Data Fusion across Dissimilar Machine Platforms
- 3.2.3 Macro-Example: Data Fusion across the Enterprise
- 3.3 Data Analytics (Advanced Analytics)
- 3.4 Neural Networks
- 3.5 Summary
- References
- CHAPTER 4 Cyber Threats: Cyber Security
- 4.1 Introduction
- 4.2 Cyber Threats
- 4.2.1 Cyber Threat Case Example
- 4.3 Art-to-Part
- 4.4 Protection from the Threat
- 4.4.1 Product Security Case Example: Mattel Toy Company
- 4.4.2 Exclusion
- 4.4.3 Segmentation
- 4.4.4 Isolation
- 4.4.5 Deflection
- 4.4.6 Control
- 4.5 The Cyber Security Conundrum
- 4.6 Mitigating Cyber and Physical Threats to Security
- 4.7 Summary
- References
- CHAPTER 5 Automation
- 5.1 Introduction
- 5.2 What Is Automation?
- 5.3 Trusting the System and Risk
- 5.4 Expert Systems
- 5.5 Types of Aerospace Automation
- 5.6 International Complications (Offset and Countertrade)
- 5.7 Flex to Rate (Flexible Automation for Multiple Platforms)
- 5.8 Determinant Assembly
- 5.9 Engineering Release
- 5.10 ROI
- 5.11 Collision
- 5.12 Automation versus Mechanization
- 5.13 Variability (Taming the Beast)
- 5.14 Case Example: Jewels on the Ground
- 5.15 Evolution of Specialized Suppliers
- 5.15.1 Limited Integrators Number of Integrators: Risk
- 5.15.2 Integrator Firewall Placement
- 5.15.3 Controlling Your Destiny
- 5.15.4 Coatings
- 5.16 Summary
- 5.16.1 Strengths
- 5.16.2 Weakness
- 5.16.3 Opportunities
- 5.16.4 Threats
- 5.17 One Future State Possibility
- References
- CHAPTER 6 Change
- 6.1 Introduction
- 6.2 Fear
- 6.3 Shop Floor to Top Floor
- 6.3.1 Driving Change to the Top
- 6.4 Awareness
- 6.4.1 Case Example: Oxford Mini
- 6.5 Demonstrating a Vision
- 6.6 Communicating Change (Top to Shop)
- 6.6.1 Personalizing Change
- 6.7 Summary
- One Final Note
- 6.8 Ten Questions to Ponder
- 6.8.1 Definition of Terms
- References
- CHAPTER 7 Taming Variability
- 7.1 Introduction
- 7.2 Materials (Black Metal)
- 7.3 Variability Accommodation
- 7.4 Composite Material Genesis
- 7.4.1 Controlling Fiber Distribution and Fuzzing
- 7.5 Compounding Variance (Unique Part DNA)
- 7.6 Spring-Back Control
- 7.6.1 Dimension on Day-One: Future State
- 7.6.2 Methodology
- 7.6.3 Benefits
- 7.7 The Digital Twin
- 7.8 Summary
- Case Study: Automation and Variability
- References
- CHAPTER 8 Visibility
- 8.1 Introduction
- 8.2 Tactile Dexterity with Cognition
- 8.3 COBOTS
- 8.3.1 Historical Background
- 8.3.2 Need and Benefits
- 8.3.3 Technical Concept for Development
- 8.3.4 Execution Approach
- 8.3.5 Conclusion
- 8.4 RFID
- 8.4.1 Basics
- 8.4.2 Security
- 8.5 Digital Twin
- 8.6 Global Enterprise Visibility
- 8.6.1 Empowering the Organization
- 8.7 Summary
- 8.8 Case Example: RFID for Chemical Tracking and Hazardous Waste Management
- References
- CHAPTER 9 Lessons from the Field
- 9.1 Introduction
- 9.2 Case Example
- 9.3 Further Sensitivity
- 9.4 Automation
- 9.5 Data Mining
- 9.6 Variability
- 9.7 Visibility
- 9.8 Restructuring the Enterprise Image
- 9.9 Some Final Thoughts
- 9.9.1 Design for Manufacture and Affordability
- 9.9.2 Brand Loyalty and Association
- 9.9.3 Sustainability (F-35 Example)
- 9.9.4 Collectivized Solutions
- 9.9.5 Cyber Security
- 9.10 Finally
- References
- Additional Reading
- Ending
- About the Author
- Index
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