In today’s hyper-connected world, the emergence of AIoT holds the potential to transform industries at an unprecedented pace. The number of active IoT (Internet of Things) devices globally has been growing steadily over the past number of years, with more than 40 billion forecasted to be in circulation by 2034, according to the Global IoT Forecast Report (Transforma Insights). With this volume of sensors reporting on a host of data points, effective analytics becomes crucial to unlocking valuable, actionable insights. Quality data analytics drives better decision-making and avoids cognitive overload among workforces. Many of these devices are deployed for the role of remote monitoring in the supply chain, making it crucial for those in the industry to understand the implications and potential that AIoT holds for the sector. In this article, we will explore everything you need to know about AIoT and its real-world applications for the supply chain, from the container to the port and at sea.
What is AIoT?
Artificial Intelligence of Things (AIoT) refers to the integration of IoT devices with artificial intelligence (AI), adding a layer of knowledge processing to the data communicated from IoT systems. This convergence of technologies is enabling us to not only collect and communicate data, but also interpret it. This allows us to detect patterns, predict outcomes and automate processes (Rohaninejad & Nozari). The fusion of these complementary fields in the context of the supply chain covers a vast range of use cases and benefits, overall leading to faster response times and scalable optimisation of operations.
Cloud-based versus Edge-based IoT
There are two distinct set ups for AIoT as it pertains to the supply chain. Cloud-based AIoT involves the processing, management and storage of data created by IoT devices on cloud-computing platforms (in the Cloud). Edge-based AIoT means processing the data as close to the device as possible to minimise the bandwidth needed to move the data. In this case, a layer is removed and data is carried locally over connectivity gateways in the field (TechTarget).
Both methods are used in particular instances in the supply chain, with edge-based devices allowing for quick responses to safety or security alerts on-site, while cloud-based AIoT allows additional people to securely access and analyse the data in the Cloud. For example, edge computing allows crews on a container vessel to detect alarms instantly from smart reefers, benefitting from reduced latency. In the case of unusual temperature hikes this can be instrumental to preventing runaway fires, cargo losses and protecting personnel.
On the other hand, this data can also be stored in the Cloud, where different parties, such as the cargo or vessel owner can use it in a less urgent, more strategic manner. In the smart container example, this might be the operational teams of shipping companies using location trends over time in order to optimise routes taken by their vessels.
AI Technologies used in AIoT
There are several key types of AI relevant for supply chain application. Two such examples are:
- Machine Learning: Machine Learning (ML) involves systems learning from and improving their performance over time, allowing for increased efficiency gains and predictive maintenance. On-device Machine Learning is gaining traction, primarily because it allows AI models to operate with reduced reliance on cloud-based infrastructure and associated computing resources (Viso.ai). AI model compression is used to implement low-latency and energy-efficient model inference at the edge. Smaller and more efficient “lightweight” ML models can run on low-power devices like mobile phones, SoC, or embedded computers, expanding the use cases for ML in the field.
- Agentic AI: Agentic AI is a newer class of artificial intelligence which goes a step further than the traditional functionality of data analytics and Machine Learning, to implement higher level tasks with minimal human intervention. This is a major breakthrough for supply chain AIoT, as we can reduce delays and errors associated with highly manual logistics operations through the assistance of AI Agents. For example, digitising and automating documentation involved in the customs process, such as eBOLs (electronic Bills of Lading).
Benefits of Supply Chain AIoT
The supply chain is complex, involving many moving parts and players. It faces an increasingly uncertain environment due to geopolitical instability and climate insecurity in a highly globalised industry. This causes fluctuations in demand, supply disruptions and inefficiencies due to existing information silos.
AIoT tackles many of these challenges, making information sharing between supply chain actors easier and resulting in a more resilient and sustainable supply chain. From intelligent warehousing to predictive maintenance, port automation and smart fleet management, there are endless applications of AIoT for the industry. Some of the key advantages of AIoT are:
- Increased operational efficiency: AIoT allows businesses to achieve new levels of efficiency by automating tasks, improving data analytics and decision making. By applying ML, greater efficiency gains are made over time, e.g. monitoring port equipment and assets across their lifetime to predict when maintenance is needed, or patterns in operations in order to improve turnaround times. Overall, these leads to less traffic and congestion around transport hubs.
- Real-time asset visibility: AIoT devices provide continuous, real-time monitoring of supply chain assets e.g. reefers, dry containers, container chassis and cranes. A multitude of factors such as location, temperature, door security, etc. can be viewed in real-time, which it can then be used to detect anomalies or predict outcomes. This leads to faster response times to potential incidents, empowering personnel to act in real-time and prevent unwanted outcomes e.g. Real-time door opening alerts from dry boxes can prevent security risks such as smuggling and cargo theft from taking place along trucking routes and assess which routes are typically safer based on incident patterns over time.
- Reduced risk: The supply chain faces a diverse range of risks which are costly to manage. AIoT can help to minimise risks such as environmental impact, fire incidents at sea, theft and cargo spoilage. This helps stakeholders involved, including shippers, carriers and intermediaries like freight forwarders, to reduce insurance and risk management costs, while also leading to emissions reductions and enhanced crew safety. This improves the social and environmental performance of the sector as a whole.
- Reduced costs: AIoT is easily scalable, with the ability to handle increasingly complex data analytics as more devices are added. This makes it a great solution for fleet-wide adoption in the supply chain across assets, leading to maximum cost savings and value creation for clear and measurable ROI (Return on Investment) to the user e.g. port operator, shipping company, cargo owner. It also allows for flexibility, which adds to its longevity as it can grow with the user’s evolving needs and strategies.
AIoT for the Supply Chain
Net Feasa’s IoTPASS™ is an AI-enabled edge IoT device for dry box visibility. It goes beyond traditional track and trace to provide real-time location services and geofencing, even in remote areas, for unparalleled fire safety and cargo security as it moves through the supply chain. The device uses built-in Machine Learning to effectively time its smart alerts based on its stage in the supply chain, for energy optimization and appropriate information levels to the user for more strategic use. The device connects to Net Feasa’s Cloud platform enabling reporting and analytics, accessible to shipping lines, logistics companies and cargo owners where required.
The company’s latest release is a step-change for logistics, a platform powered by AI Agents which creates a new marketplace for connected containers. Agentic Control Tower™ collects the data from the container via the IoTPASS device, allowing for automated booking processes for the container’s next trip while still in transit on the previous job. The result is less trucks on the road, less traffic congestion around ports and less empty containers is circulation, which will have a huge impact on supply chain optimization and sustainability.
Conclusion
AIoT is a gamechanger for the supply chain, unlocking new levels of efficiency, resilience and sustainability that were not before possible. This convergence of technologies eliminates data silos, bridging the gaps in visibility across all links of the supply chain and empowering its stakeholders to optimize their operations.
Have More Questions About AIoT?
We at Net Feasa are looking for industry collaborators to be among the first to experience the capabilities of our groundbreaking AI toolbox, Agentic Control Tower™ , designed to optimize container freight logistics. We gather your requirements and deliver AI Agents to carry out those tasks. Get in touch to get early access and learn more.
About Net Feasa
Net Feasa is a pioneering digital transformation partner and trusted IoT service provider to the global supply chain. The company specializes in delivering end-to-end visibility, safety and security to the intermodal industry, for a seamless transition to a smarter, more efficient and sustainable system. From the container to the vessel and the port, the company’s vision is to connect every link in the supply chain for true transparency and unparalleled insights. Through decades of rich technical experience, a proven track record in deploying wireless connectivity, and strong global partnerships, Net Feasa empowers the world’s largest transportation companies to improve operations and fleet management at scale, leveraging the power of AI, intelligent connectivity and advanced data analytics for measurable cost savings and maximized asset revenue generation.
Visit www.netfeasa.com for more information.