A Detailed Look at Shipium's Time-in-Transit Modeling

Today’s customers have come to expect transparency and speed, which means that delivery dates based on static criteria (ex. Carrier SLAs) often fall short. 

At Shipium, we believe the solution for this lies in leveraging the power of data and AI. This article explores how our sophisticated Time-in-Transit (TNT) model provides the intelligence needed to optimize your fulfillment strategy, giving you an edge by balancing customer expectations with financial goals.

The pitfalls of static transit data

Static time-in-transit (TNT) data — like a carrier's published service level agreement (SLA) or a table you configure in-house — is a one-size-fits-all approach that fails to account for the real-world variables that impact delivery. This forces shippers into a difficult scenario — they must either risk missing an aggressive delivery date or provide a padded, conservative window that underwhelms customers.

The problem with static SLAs

Carrier SLAs often don't reflect daily or seasonal fluctuations. For example, a 3-day ground service might only take 2 days during low volume periods, while a major weather event could extend it to 5. Static data can't capture this type of variability. As a result, shippers either pay for a faster, more expensive service than they need or miss a delivery date and disappoint the customer.

The financial and operational impact

The reliance on static transit data has significant financial and operational consequences.

• Increased Costs: To mitigate the risk of missing dates, shippers often upgrade to a more expensive service (like 2-Day Air) when a cheaper ground service may have been just as fast. This unnecessary spending erodes margins.
• Customer Dissatisfaction: Conversely, packages that arrive later than their promise dates lead to frustration, a higher volume of WISMO calls, and a negative impact to customer loyalty.

This is why an approach that can account for real-world criteria is no longer a “nice-to-have”.

How Shipium’s modeling elevates your fulfillment strategy

Shipium's modeling gives you a powerful advantage by using your data (and millions of platform-wide data points) to optimize fulfillment workflows like routing, carrier selection, and scenario planning. Core benefits include:

• Boost Delivery Accuracy and Reliability: Switch from static carrier SLAs or static custom tables to our modeling for highly specific and reliable delivery estimates that are generated in real time to fight the variability of transit networks, giving you a competitive edge with a significant boost in accuracy. This ensures your delivery dates are consistently met, building customer trust and loyalty.
• Intelligently Balance Speed and Cost: You don’t always have to sacrifice speed for margins. Our models give you the flexibility to choose dates that align with your specific business goals, whether you want to be more aggressive or conservative.
• Gain Control and Optimize Costs Achieve your desired customer experience while balancing speed with costs. By accurately predicting transit times, Shipium helps you confidently select more cost-effective carrier services without compromising on delivery speed. This is a primary use case, turning what was once a tradeoff into a strategic advantage.

Now, let’s take a closer look at how our Time-in-Transit Model works.

How our Time-in-Transit model works

Our TNT model is one of the main engines powering delivery predictions and downstream decisions. The video below illustrates the outcomes of leveraging data and AI to make transit predictions, rather than static values.

Here's a look at how it operates.

The purpose of the TNT model

The primary goal of our model is to predict the exact time from ship to delivery as accurately as possible. By analyzing a wide variety of data — including weather, seasonal trends, and route adjustments — it provides a dynamic time-in-transit estimate. This empowers shippers to select the best carrier service based on their specific needs (how they prioritize speed, cost, and accuracy). 

Factors that influence the model

Our TNT model uses a comprehensive set of inputs to generate predictions:

• Carrier and Service Data: The model considers the specific carrier and service level (e.g., FedEx Ground) being used for the shipment.
• Geographic Data: This includes the distance between origin and destination, as well as other unique geographic characteristics of the route.
• Temporal Data: The model accounts for the time of day, day of the week, and other time-related factors that impact transit times
• Package Data: It analyzes key package attributes, such as dims and weights.

Data sources used for training

To ensure a high degree of accuracy, our models are trained on a wide range of aggregated data from various sources:

• Customer Data: Customers provide historical shipment data to train models based on their specific performance.
• Shipium Data: We anonymize and encrypt data sourced from shipments executed by our platform to drive further accuracy.
• Third-Party and Public Data: The model is supplemented with data from external sources, such as geographic or weather data.
• Engineered Data: We create our own data points, such as calculated distances, to further enhance the model's predictive power.

How model efficacy is evaluated

We rigorously evaluate our Time in Transit model both before and after it's deployed. Prior to release, we compare new predictions against actual delivery times, which act as a validation set. Once live, we continually monitor KPIs like prediction accuracy and invocation error rates to guarantee reliability and consistency.

How the model is retrained

To maintain accuracy and account for recent market and seasonal shifts, our model is consistently retrained to incorporate new data and maintain accuracy. Due to a number of factors, retraining frequency is variable — more on this below.

More frequent retraining is not always better; it is only beneficial when there is a sufficient volume of new, diverse, and representative data to ensure meaningful updates and avoid noise in predictions. When data drift and performance issues are minimal, a regular training schedule (ex. monthly) may be preferable. Conversely, for dynamic environments (ex. Peak season), more frequent retraining can help to capture ongoing shifts to shipping patterns.

A note on variability within the same ZIPs

Our model's strength is its ability to go beyond simple, fixed zip-to-zip time estimates. While zip codes are a core factor, they are just one of many inputs. Predictions are highly nuanced because they also account for the carrier, service level, package characteristics, and seasonality, as mentioned above. Because of this, two packages shipped between the same origin and destination zips may have different transit time predictions. 

This complexity allows for a unique prediction tailored to each individual shipment, providing a level of accuracy that a generic, fixed time-in-transit table simply couldn’t match.

Wrapping up

Shipium’s Time in Transit model provides the dynamic, AI-driven intelligence that modern shippers need to thrive. By moving beyond static estimates, our model gives you the power to confidently offer and meet exact delivery dates for every single order. This not only elevates your customer experience, but also unlocks new levels of control over your costs. In short, you can profitably deliver on your promises which an unmatched degree of accuracy.

If leveraging AI to improve your fulfillment outcomes is a priority, reach out to our team here.