Asset tracking, condition visibility and sustainability using unmanned aerial systems in global logistics
Global logistics systems operate at large scales that make real-time visibility of individual assets difficult to maintain. A paper by a team of American researchers proposes the integration of an unmanned aerial system (UAS) in a global logistics system, highlighting best practices for its potential inclusion.
UAS have been introduced to logistics systems to support warehouse, freight, and logistics operations, cutting the time and labor costs of conducting inventory. Such systems may use image processing and deep learning algorithms to fly without collisions, and "swarm" to share power and resources.
- Establish consistent metrics to evaluate the success of the UAS deployment. The authors noted that current metrics exhibited significant gaps, which could be filled via integration of UAS-captured data. In particular, the authors described four categories of metrics:
- Material status metrics, which track and report individual assets. These should be used to gain insight into asset inventory, trends, and patterns, and may also be used to track real-time status of safety-critical items or systems.
- Warehouse management metrics, which assess asset management and security conditions. These should be used to inform decision-makers about the status of cases and their contents.
- Business impact metrics, which include case and item condition assessment data. These should be used to understand financial and logistical effects on supply chains.
- Sustainability metrics, which include logistics costs, loss or damage, and project acceptance. A UAS system may be used to capture process efficiency and waste.
- Include environmental and societal sustainability metrics. These points were typically overlooked in existing metric recommendation lists.
- Understand the challenges of integrating UAS data. UAS-based cameras and sensors generate large volumes of unstructured data, which may be difficult to integrate with existing structured datasets. Integratory and interoperability standards tend to lag behind technology development, so implementers should be confident in their ability to manipulate and parse the influx of data.