Modern supply chains are extremely complex, complicated and expansive, including many parties (such as brokers, forwarders and warehouses), which must communicate and act in a timely and organized manner. Like any ecosystem, one small disturbance can affect a larger environment in an unexpected and ruin way. Therefore, many enterprises have included systems and applications in the scope of artificial intelligence (AI) propulsion to more easily facilitate their constantly developing supply chains.
AI had an extraordinary impact on supply chains and logistics. At the beginning, AI systems can analyze real -time data and compare these observations with historical data much faster than people. This unprecedented speed and accuracy allow managers of supply chain to make data based on data and engage in forecasting, planning demand and management of a predictive warehouse. AI can also help automatize documentation and other data entering tasks, saving time for short -term teams. AI can even examine weather forecasts and traffic patterns to optimize routes for drivers.
Experts expect that global artificial intelligence in the size of the logistics market will increase exponently. In fact, Precedent tests He estimates that it will increase from USD 26.35 billion in 2025 to around 707.75 billion USD by 2034, accelerating CAGR 44.40% from 2025 to 2034, although enterprises are necessary for enterprises to implement AI to their logistics and supply chain processes to remain competitive, they could not read the network resistance.
Consequences of a failure and an unexpected risk of increased use of artificial intelligence
Supply chains require a resistant network underlying applications that support AI-AI to ensure business continuity, even during interference. Without such a network, unexpected breaks, incorrect configuration and security gaps can threaten AI performance. If AI religious logistics systems stop working, companies will have to encounter consequences, from minor inconvenience to significant interference and financial losses. For example, if the key AI tools do not work, demand forecasts will be inaccurate, which means that the resources will be incorrectly assigned, which will cause delayed deliveries and ultimately dissatisfied customers.
It is worth paying attention to the increased use of artificial intelligence in supply chains, is that because AI-AI-Ai-AI-Functionaries' support systems become more complex, they also become more delicate, which increases the potential of failure. Something as simple as a single incorrect configuration or unintentional interaction between automatic security gates can lead to network failures, preventing access to access chain staff to access critical AI applications. During the failure of the AI cluster (connected GPU/TPU nodes used for training and inference) may also become inaccessible. Worst of all, administrators could be closed from the network and cannot solve the problem.
Another challenge is that AI load requires specialized network considerations. In contrast to traditional loads for enterprises, the AI movement includes a large volume data transfer, movement patterns and frequent synchronization. AI movement is also sensitive to delays, which means that even small delays can significantly affect performance. Without a resistant network, traffic from the AI application, especially those requiring real -time processing and large data transmission, can overload the network infrastructure, causing bottlenecks, delay and even failures.
Strengthening network immunity without team management
Companies must increase network resistance to ensure that their supply chain and logistics teams always have access to key AI applications, even during network failures and other interference. One of the approaches which companies can take to strengthen network immunity is the implementation of a specially built infrastructure, such as management outside the band (OOB).
Thanks to OOB management, network administrators can separate and contain the functions of the management plane, enabling it to operate freely from the main network of another. This secondary network acts as always available, independent, dedicated channel from which administrators can use for remote access, management and solving problems with network infrastructure. Even if the basic network has a failure (whether from intense AI, cyber attacks or improper configuration), OOB management allows administrators to access infrastructure for management purposes, maximizing the time of critical AI applications.
Organizations can further increase OOB management, connecting it with network technology, such as an emergency, with a cellular backup connection (3G, 4G or 5G), it automatically activates if the original connection fails. As another protection of business continuity, emergency to cellular switching helps administrators maintain the visibility of the entire network, enabling them to be managed and access to the entire infrastructure remotely.
In addition to invaluable to solve problems during a failure, OOB management can proactively prevent problems by constantly monitoring, registering and supervising security. OOB management is also extremely useful for administrators who work with dispersed networks, as well as the nature of today's extensive supply chains. In particular, the management of OOB enables administrators to perform remote updates of the firmware, reset the system and enforce safety rules without disturbing AI loads. These remote possibilities save time because companies do not have to send technicians to visit each device in the field.
The need for network resistance in the light of digital transformation
Because supply chains are becoming more and more sophisticated thanks to artificial intelligence and other digital transformation technologies, such as machine learning, IOT, Cloud and Blockchain, the most important thing is that companies protect their systems from interference through solutions, such as OOB management. Enterprises must plan outside the initial implementation and focus on operations for two days, including remote problem solving, diagnostics and data collection when problems arise.
