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--- en:safeav:as:suppluchain [2025/10/17 09:53] – created agrisnik
+++ en:safeav:as:suppluchain [2025/10/17 10:00] (current) – [Possible Solutions and Best Practices] agrisnik
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 Autonomous systems depend heavily on specialised components such as LiDARs, high-density batteries, and embedded processors. Many of these have limited global suppliers, creating vulnerability to shortages or geopolitical disruptions ((Kumar, S., Panda, A., & Veloso, F. (2021). Managing global supply chain disruption: Lessons from the semiconductor crisis. MIT Sloan Management Review, 63(1), 38–45)).
+^ Challenge ^ Description ^ Impact ^
+| Component Scarcity | Limited suppliers for high-performance chips or sensors. | Production delays, increased cost. |
+| Globalisation Risks | Dependence on international logistics and trade. | Exposure to geopolitical instability. |
+| Quality Variability | Inconsistent supplier quality control. | Rework and testing overhead. |
+| Cybersecurity Threats | Counterfeit or tampered components. | System failure or security breaches. |
+| Data Supply Issues | Dependence on labelled datasets or simulation platforms. | Delayed AI development or bias introduction. |
+**The Semiconductor Bottleneck**
+The semiconductor supply chain crisis (2020–2023) revealed how fragile technology manufacturing can be. Autonomous vehicles and drones rely on advanced microprocessors and GPUs fabricated using sub-10nm processes available only in a few facilities globally (TSMC, Samsung, Intel). Disruptions in this sector ripple across the entire autonomy industry ((Veloso, F. (2021). Global semiconductor bottlenecks and resilience. Nature Electronics, 4, 394–396)).
+**Environmental and Ethical Constraints**
+Supply chains for autonomy-related technologies often rely on materials such as lithium, cobalt, and rare earth metals used in sensors and batteries. Ethical sourcing, sustainability, and carbon accountability are now critical supply chain dimensions ((Dutta, P., Ge, J., & Yang, R. (2022). Sustainable supply chains for electronics manufacturing. Journal of Cleaner Production, 356, 131862)).
+**The Rise of Supply Chain Cybersecurity**
+As hardware and software become interconnected, supply chain cybersecurity has emerged as a critical risk domain. Compromised firmware or cloned microcontrollers can introduce vulnerabilities deep within a system’s hardware root of trust ((Boyens, J., Paulsen, C., Bartol, N., Shankles, S., & Moorthy, R. (2020). NIST SP 800-161: Supply Chain Risk Management Practices for Federal Information Systems and Organizations. National Institute of Standards and Technology)). Security frameworks such as NIST SP 800-161, ISO/IEC 27036, and Cybersecurity Maturity Model Certification (CMMC) are being applied to mitigate these threats.
+===== Challenges Specific to Autonomous Systems =====
+Autonomous systems add several unique layers of complexity to both hardware integration and supply chain management:
+Multi-Vendor Dependency
+A single autonomous platform may use components from dozens of vendors — from AI accelerators to GNSS modules. Managing version control, firmware updates, and hardware compatibility across this ecosystem requires multi-tier coordination and continuous configuration tracking ((Raj, A., & Saxena, P. (2022). Emerging trends in autonomous systems hardware integration and supply chain management. IEEE Access, 10, 54321–54345.)).
+**Safety-Critical Certification**
+Hardware must meet safety and regulatory certifications, such as:
+  * ISO 26262 (automotive functional safety)
+  * DO-254 (aerospace hardware design assurance)
+  * IEC 61508 (industrial functional safety)
+Each certification adds cost, time, and documentation requirements.
+**Real-Time and Deterministic Performance**
+Integration must guarantee low-latency, deterministic behaviour — meaning that sensors, processors, and actuators must communicate within microsecond precision. This influences hardware selection and network design ((Kopetz, H. (2011). Real-Time Systems: Design Principles for Distributed Embedded Applications (2nd ed.). Springer)).
+**Rapid Technology Obsolescence**
+AI and embedded computing evolve faster than mechanical systems. Components become obsolete before the platform’s lifecycle ends, forcing supply chains to manage technology refresh cycles and long-term component availability planning ((Handfield, R. B., & Nichols, E. L. (2020). Introduction to Supply Chain Management (2nd ed.). Pearson)).
+===== Possible Solutions and Best Practices =====
+The most important challenges and possible solutions are summarised in the following table:
+^ Challenge ^ Solution / Mitigation Strategy ^
+| Component Shortages | Multi-sourcing strategies and localised fabrication partnerships. EU's Chip Act is a good example of securing future supplies. |
+| Supplier Quality Variance | Supplier qualification programs and continuous audit loops. |
+| Cybersecurity Risks | Hardware attestation, firmware signing, and supply chain transparency tools (e.g., SBOMs). |
+| Ethical Sourcing | Traceable material chains via blockchain and sustainability certification. |
+| Obsolescence | Lifecycle management databases (e.g., Siemens Teamcenter, Windchill). |
+| Integration Complexity | Use of standardised hardware interfaces (CAN-FD, Ethernet TSN, PCIe). |

en/safeav/as/suppluchain.1760694829.txt.gz · Last modified: 2025/10/17 09:53 by agrisnik