In 2017, supply chains started lagging as China production slowed down due to the tariff war, then they ground to a halt as the world experiences a pandemic. When production gears up again, and it has already in some regions of China, logistics teams can expect to be strained as goods, vehicles and data start flowing. Its likely workforces will be reduced for factories, ports, carriers and warehouses. Shipments might be delayed due to bans or additional screenings, and vessels could be held offshore for unknown periods. Nevertheless, disruptions will occur, and communication between service providers is likely to be spotty.
We routinely hear from logistics service provider (LSP) and freight forwarding companies that a key priority is to keep customers top of mind. Customers want to know one thing, “Where’s my stuff?” and they expect you to have a solid answer even though the carrier isn’t giving quality data and regular updates.
The average cycle time of a trans-ocean shipment is 21 days, with six days of variability. International shipments also require the involvement of many more trading partners than domestic deliveries. Hence, variability is a significant factor, leaving room for each to be off schedule a bit until small delays build up into a substantial amount of time. With every shipment critical to fill increased demand, it’s the role of the LSP to know how in-transit shipments are progressing, provide real-time updates to customers and centralize that essential information and data.
Tracking ocean shipments is the most challenging due to a lack of status updates between ports. Shippers often learn of delays only when a container arrives late at the port. This results in missed deliveries and a frantic scramble to make alternate transportation plans, all culminating in poor customer service and costly penalties or fees. Robust ocean container shipment visibility capabilities that are part of logistics visibility or control tower technology addresses this issue. With artificial intelligence and machine learning-based predictive ETA competencies, you can identify exceptions and updates to critical transportation schedules and provide your customers with essential information about their shipments.
Predictive ETAs are vitally important because they provide more accurate insight into a shipment’s arrival, leading to improved customer service, lower transportation costs and less margin erosion. Historically, uncommitted ETA timeframes and ranges were acceptable. But the need for tighter hand-offs between modes that save money and time has led to the need for accurate and more predictive information.
To generate accurate predictive ETAs, technology providers harvest big data from multiple systems and sources. Providers with experience developing machine learning capabilities and predictive algorithms in other areas of supply chain management (e.g., demand planning, demand sensing and supply chain planning) can provide better decision-making capabilities. Based on this expertise, technology can employ machine learning-enabled algorithms to process and utilize information—including container events, schedules, historical data and real-time automatic identification system (AIS) data—to provide dynamic, real-time arrival updates and predictive ETAs.
Shippers can proactively adjust downstream supply chain activities when they have accurate arrival information. In return, shippers avoid demurrage and detention fees, dock appointment charges and customer penalties.
When shipment ETAs deviate from the plan, ocean shipment visibility capabilities help logistics service providers, freight forwarders and shippers elevate the level of service customers receive—armed with this information, downstream container movements, personnel staffing and inventory planning is more effective and cost savings are realized.