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Jan 02, 2024

What Foresee™ Machine Learning can do for your Supply Chain

Shippabo's next-gen predictive system ensures highly accurate intelligence, reduces risk, enables faster response times, and improves forecasting. Learn how Foresee™ can revolutionize your shipping performance!

The notion of applying Machine Learning (ML) to address everyday business challenges has gained huge momentum in recent quarters. Touted as the essential tool to deliver targeted insights and solutions for almost every technical process imaginable, ML has indeed proven useful in many businesses that deal with large amounts of data.


Given the dizzying array of data inputs in shipping, integrating ML into daily operations seems to be a natural fit for the industry – helping shippers predict arrivals, deliveries, and identifying potential exceptions. However, given the complexities of shipping management and the multitude of software used in a given organization, shippers are often left scratching their heads as to where best to integrate ML into the process.

At Shippabo, we’ve been working with ML for years – and have delivered our next-generation predictive system, Foresee™, into customers' hands earlier this year. We have learned first-hand how ML brings value to shippers, and how Foresee™ impacts the real-world customer experience. Key benefits include:

1. Highly-Accurate, Actionable Intelligence

An ongoing challenge faced by shippers is the irregularity of data availability. Every combination of origin, carrier, port, yard, inland port, and drayage can yield varying levels of data resolution and reliability – often resulting in a frustrating array of incomplete results or inaccurate delivery predictions.  Making matters worse, is that some of the biggest shipment problems occur at transition points – transloading, transshipping, or in a railyard – where gaining visibility is often difficult since no localized tracking data is available. 
This is where the power of Machine Learning comes in. Using a variety of historical data inputs, coupled with real-time transit data, a well-designed ML system can predict the likely outcome based on known sequences of carriers, ports, and transportation partners.

Shippabo’s Foresee™ technology is proven to handle this sort of challenge with grace – averaging over 90% accuracy to within 48 hours on transoceanic shipments. Foresee™integrates historical data (transit times, patterns, routes, disruptions, etc. with real-time data and factors in external events to progressively calculate delivery timelines and provide a highly-accurate ETA.  
Not only does this allow for clear understanding of the current and upcoming shipment status, but also lets you plan actions to mitigate downstream issues with confidence.

2. Reduced Risk 

Risk is a constant companion throughout the shipment process, often increasing throughout. Where, unlike an early delay that can be communicated - and mitigated through creative adjustments to inland transport choices - unexpected last-minute delay can wreak havoc. 
Thus, there’s significant value in ML’s ability to provide consistent, reliable prediction data for the milestones preceding delivery, helping shippers understand if and how their risk is increasing, and if so, due to which factors. 

With Shippabo Foresee, data is delivered dynamically in real-time, and results can be seen at a glance via the Health Score (0-100 ranking of shipment health) and Shipment Condition (on-time, delayed, etc.). This delivers both an update to milestone-level risk and overall shipment health every time new information is received, ensuring a reliable and current view of shipment status relevant both to Logistics management and executives.

This predictive capability serves to reduce net risk by delivering a wealth of information to shippers before critical points are reached, thus enabling them to alert and set expectations with both internal and  external partners

3. Faster Response Times

Visibility tools have become a critical technology for logistics teams. The ability to track and trace a shipment or container at any point in time is now recognized as an essential business requirement.

Coupling this tracking with ML-generated predictive insights at each milestone in the supply chain not only helps reduce risk, but also enables faster response by shippers. Systems such as Shippabo Foresee™ provide real-time updates as new information arrives – and dynamically generate notifications, updated reports, and other data outputs that help logistics teams (and vendors) understand status, and quickly take action.

4. Improved Forecasting

The accuracy of most ML systems improves over time, as the system monitors an increasing number of shipments on a given shipping lane or string. This translates to improved accuracy at the shipment level, but also offers shippers a clear view of what to expect in the future – making it immeasurably easier to predict shipping performance in context of seasonal or market trends. 

These data insights are centrally available in the Shippabo Platform, and can also be easily exported to a separate company ERP system for deeper analysis. These insights improve both short and long-term forecasting, and also help shippers balance inventory and capital expenditures.


Machine Learning is a powerful tool that delivers the insights importers need to effectively manage shipping performance – letting them understand the current state, quickly pinpoint risk factors and enabling them to take quick action to mitigate unavoidable exceptions.

Shippabo’s Foresee™ is an industry-leading predictive ML system that provides enabling insights at every stage of shipment planning and tracking – and delivers these insights in intelligent, straightforward ways that let you execute corrective actions effectively. With the upcoming release of Foresee™v. 1.5, Shippabo is extending their industry lead for both accuracy and performance – helping optimize routing decisions, reduce transportation costs, identifying demand patterns, and ultimately helping shippers predict demand for materials and supplies.

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