The complexity of modern-day supply chains makes them highly vulnerable. One minor disruption can trigger a ripple effect that affects the entire system. In response, air freight, rail and trucking businesses continue to deploy new technologies — especially artificial intelligence — to overcome the supply chain challenges of visibility, agility and sustainability.
AI’s computing power brings unprecedented speed and accuracy, enabling supply chain managers across the transportation sector to tackle challenges, enhance efficiency and reduce waste. Following are some notable examples of AI in three transportation modes:
- Air freight. Traditional manual processes like flight scheduling and cargo booking are tedious and error-prone. With AI technologies, personnel can streamline these processes by automating routine tasks and optimizing workflows. Likewise, teams can use AI algorithms and machine learning to gather, organize and analyze historical data to forecast peak times for cargo volume, allowing airlines and freight forwarders to adjust their operations accordingly.
- Rail freight. AI-based algorithms enable teams to optimize load planning, improve capacity usage and minimize empty car movements. Specifically, an AI system will analyze vast amounts of data, including historical records, real-time demand and available resources, to determine the most efficient way to load trains, including the optimal positioning of containers and trailers concerning weight distribution and space utilization.
- Truck fleets. Medium and heavy-duty trucks are responsible for a considerable amount of CO2 emissions. To reduce their environmental impact, companies use AI to analyze historical data, such as traffic patterns and weather, and other relevant information, including delivery constraints, to generate route plans and schedules that are fuel and time efficient.
AI in a Network Outage
Network outages can have disastrous consequences, from financial losses and delayed shipments to lost productivity and deteriorated customer relationships. Perhaps the most serious consequence of outages is their impact on AI-enabled logistics and supply chain management solutions. Like any other always-connected technology, critical AI applications and services can become inaccessible during outages, jeopardizing the processes that depend on them.
When critical AI applications and services become unavailable, teams may not complete their jobs for the simple fact that they’ve become so accustomed to AI they don’t recall how to perform certain processes manually. Outside the organization, customers, suppliers and brokers could also lose access to AI tools, such as the ones they use to track their deliveries.
There are a variety of reasons why network outages occur. Most commonly, outages happen because of ISP carrier issues, human error (such as misconfigurations) and cyberattacks. Harsh environmental conditions and natural disasters can also cause network outages. Another culprit is AI itself.
The more complex AI-enabled systems become, the more susceptible they are to failures and downtime. Today, even the slightest misconfiguration or unintentional interaction between automated security gates can result in a network outage. Additionally, AI applications — particularly those that rely on real-time processing and transmit large volumes of data — can strain network infrastructure, potentially leading to bottlenecks, increased latency, and even system outages.
Out-of-Band Management
It’s paramount that businesses have a resilient network underpinning their critical AI applications to minimize the effects of disruptions and outages. One approach that companies can take to strengthen their networks is to implement a purpose-built infrastructure in the form of out-of-band (OOB) management.
When outages occur, IT and administrators often find themselves locked out of the primary in-band network, making it difficult for them to troubleshoot issues, ultimately extending the duration of downtime. They get locked out because they use the in-band network (the one used for normal operations and data transfer) to manage the network itself.
Alternatively, IT teams can use OOB management to separate the management plane from the primary network, allowing it to operate freely from the primary in-band network. OOB management creates an independent, dedicated network for teams to access, manage and troubleshoot critical infrastructure remotely. Importantly, this separation from the primary network guarantees uninterrupted administrative control, even during primary network congestion or outages.
OOB management bolsters network resilience, enables businesses to continue scaling AI while minimizing the threat of network outages and other disruptions. But the benefits of OOB management don’t stop there. Administrators can also use it to perform remote firmware updates, system resets and security policy enforcement, without interfering with AI workloads. These remote capabilities prevent companies from sending tech on-site, saving time and resources.
Network resilience is critical for organizations augmenting their supply chains with AI. As logistics becomes more digitized thanks to technologies like the internet of things, ML, and cloud computing, businesses must go beyond outage remediation to support the full network-management lifecycle.
Alan Stewart-Brown is vice president of sales, EMEA with Opengear.
