According to Tradlinx research, ports worldwide saw a 300% increase in delays due to congestion in June 2025. In some locations, vessels had to wait for 10 days or more.
Unfortunately, this isn’t a new development. Since the pandemic, only 58.7% of ships have arrived “on time”.
The reasons range from natural disasters and geopolitical tensions to labor shortages and disputes. And this is just at the ports. Other modes of transport, such as rail, road, and air, also face the same struggle.
The ramifications of these delays and port congestion are inevitably felt by both companies and customers.
For example, companies face cash-flow issues due to shipping and product delivery delays. There is also a risk of perishable products spoiling due to temperature fluctuations. Perhaps even more importantly, delayed shipments and poor-quality products can damage a company’s reputation.
Customers, on the other hand, have to wait longer to receive the product, leading to a poor experience and, in some cases, additional costs.
Given the mounting instability across global supply chains, companies must adopt strategic approaches to build more resilient operations.
AI can help companies achieve this resiliency. Let’s find out more.
How AI Builds a Resilient Supply Chain
Supply chain disruptions have become the new normal over the past few years. In 2024 alone, companies faced a 119% increase in extreme weather events, a 101% increase in typhoons, and a 38% rise in overall supply chain disruptions.
Traditional systems such as Enterprise Resource Planning (ERP), Transportation Management Systems (TMS), and Warehouse Management Systems (WMS) are not designed to predict extreme events. They are built to process historical data — not predict future events. So, while companies can plan for predictable circumstances and seasons, they cannot prepare for uncertainties. This forces companies into a reactive approach.
However, this can be changed with AI.
With AI, companies can integrate ERP, TMS, WMS, and other legacy systems. AI then ingests data from these sources and provides comprehensive, real-time insights that allows decision-makers to make better decisions.
Additionally, AI can process large datasets within seconds. Thus, it can identify and read patterns across datasets, understand correlations among various factors, and provide actionable insights within minutes.
AI can also simulate various scenarios—such as demand spikes or supplier failures—and use the results to develop contingency plans.
For example, if a typhoon is expected in an area, AI will automatically analyze production and shipment schedules that may be at risk. It will recommend alternatives, such as dock rescheduling or rerouting to other ports, to ship the products before the storm arrives. This allows companies to avoid major disruptions and deliver products on time.
Similarly, AI can predict the likelihood of labor disputes in a particular geography and automatically identify alternative suppliers who can accept additional orders and deliver on time. This reduces reliance on a single supplier and ensures timely delivery despite challenges.
Unlike traditional systems that rely on manual updates, AI self-learns from these events and updates its models automatically. It enables AI to adapt to various real-time situations without extensive human training.
How CSCS Helps Companies Build Resilient Supply Chains
CSCS’s AI-powered platform unifies the company’s existing TMS, WMS, ERP, IoT, and other systems into a single interface using custom APIs. It is environment-agnostic. Hence, it supports both cloud systems and on-premise applications.
From pickup and dwell times to loading and delivery—the CSCS platform pulls real-time data from all sources and provides end-to-end supply chain visibility to decision-makers.
This eliminates data silos—often a major cause of slow decision-making—and allows companies to quickly adjust strategies to unfolding situations. For example, if the WMS indicates the inventory has fallen below a threshold, the platform automatically triggers the replenishment workflow to prevent stockouts. Similarly, if the TMS indicates a shipment delay, the platform can auto-adjust routes, alert the WMS, and update the ERP with the new delivery status.
The platform’s predictive and prescriptive capabilities enable companies to respond proactively to potential disruptions and build a robust supply chain.
Conclusion
Over the past few years, companies have experienced unprecedented volatility and widespread supply chain disruptions. From port congestion and shipment delays to geopolitical tensions and labor shortages, on-time deliveries have become challenging for companies. This has led to significant financial losses for many organizations.
Unfortunately, traditional systems operate in silos and lack real-time insights. They rely on historical data to make decisions. That’s why most companies have been more reactive than proactive in responding to these fluctuations.
To avoid delays and meet customer expectations on time, companies must stop depending on historical data. They must integrate their existing systems, such as TMS, WMS, ERP, and others, to get real-time insights.
AI-powered platforms, such as CSCS, unify these systems, ingest data from all sources, and provide end-to-end visibility into the supply chain. It helps companies predict risks, simulate scenarios, and design preventive strategies to avoid disruptions. From rerouting ships to finding alternative suppliers in risky areas to resetting dock schedules, CSCS’s AI-powered platform predictive capabilities help companies build a resilient supply chain.
Frequently Asked Questions (FAQ)
Q. What happens when the supply chain is not resilient?
Companies that lack a resilient supply chain struggle with long wait times, delayed deliveries, and other operational inefficiencies. This could lead to spoiled products, loss of reputation, and revenue losses.
Q. Why are traditional systems inefficient in building a resilient supply chain?
Resilient supply chains respond and adapt quickly to real-time disruptions such as weather fluctuations, labor shortages, supplier problems, or geopolitical tensions. Because traditional systems operate in silos and rely on historical data, decision-makers lack complete visibility and often take a reactive approach.
Q. How do AI’s predictive capabilities protect supply chains from disruption?
Besides integrating the TMS, WMS, and other systems and ingesting real-time data from all sources, AI uses its predictive capabilities to analyze data, identify patterns, and recommend solutions to help companies avoid disruptions.
