Theme: Digitalisation
Industry Adoption: ⚫ ⚫ ⚪ ⚪ Early Adopters
Impact: ⚫ ⚫ ⚪ Significant

Edge computing brings data processing closer to its source, enabling faster, more efficient operations and reducing dependency on centralised data centres. This innovation is crucial for logistics, supporting real-time capabilities for autonomous vehicles, robots, and dynamic supply chain processes. It ensures safety in human-machine interactions while optimising tasks like tracking, route planning, and inventory management. With 83% of executives considering edge computing critical for future competitiveness, the technology is emerging as a transformative force in logistics.

Adoption is currently limited to early adopters, with wider implementation expected as more use-cases are rolled out. Edge computing relies heavily on data from sensors, computer vision, and smart devices, particularly in autonomous transport and smart warehouses. C-Suite mentions have recently begun to rise, from none in 2022 to about five in 2024. The increase in C-Suite engagement in this case signals that the trend is in its early adopter stage. Since 2019, over 850 patents have been filed, with approximately 30 groundbreaking, reflecting early momentum. Academic focus is similarly limited, with around 100 publications to date. 

What are the opportunities?

Edge computing facilitates real-time monitoring, predictive analytics, and infrastructure management. It transforms logistics operations by enhancing efficiency, resilience, and adaptability in fast-paced environments.

What are the challenges?

This technology faces barriers such as high implementation costs and the need for operational restructuring. Limited innovations and cautious executive engagement further delay its broader adoption across the logistics industry.

Sources:

• Accenture (2023).
• Survey among 500+ global logistics decision makers across various industries, conducted by Statista for Maersk (Q4 2024).
• In-depth interviews with global industry experts, academia, and futurists.
• Unstructured web sources with more than 10,000 search term permutations using AI.
• Curated data from startup databases, patent databases, and analysis tools, as well as Semantic Scholar.