How Shipium's Platform-Wide Data Modeling Powers More Efficient, Scalable Operations

To truly modernize shipping operations and optimize for costs and performance, you need to look at all available data. This means both gaining leverage over your own data (historical transit times, carrier contracts & cost structures etc.) as well as considering outside data that impacts your network (weather patterns, unexpected carrier changes, macroeconomic factors like fuel prices.)

Shipium’s platform-wide data modeling enables you to make smarter process decisions by leveraging our wealth of available data in conjunction with your own. It’s also worth mentioning that we’ve tested 100+ models, have 10+ in production, and generated proven results for our customers — if you’re looking to make more data-driven ops decisions, you can use Shipium to do it.

Let’s take a look at how.

Understanding Shipium modeling

First, it’s worth explaining how Shipium’s models make predictions and the data that’s taken into account. This can broadly be broken up into two categories — Dynamic Time-in-Transit (TnT) modeling, and fully-loaded carrier rate modeling. The former primarily helps to optimize transit performance (though it does also assist with cost reduction through intelligent service downgrades). The latter helps to make the most informed cost decisions possible by presenting fully-loaded rates that account for surcharges and accessorials. 

Let’s explore each in detail.

Dynamic Time-in-Transit modeling

Untitled presentation

Put simply, Shipium’s Dynamic TnT estimates are designed to generate more accurate, data-driven EDDs so that you don’t need to rely on static values like historical shipping times or carrier SLAs. There are many different types of data used to generate these predictions. 

First, it’s important to understand that while brands and LSPs using Shipium benefit from anonymized, encrypted historical data, a big piece of the puzzle is gaining leverage over your own. Uploading historical data is fast and easy, and from there, our model will take relevant factors into account when generating TnT predictions. Some of these include:

  • Origins throughout your network including FCs, warehouses, stores etc.
  • Historical carrier performance and cost data including OTD and contract details
  • Warehouse performance data, for example, pick and pack times

Next up, anonymized, aggregated network data enables you to leverage similar data, but in this case, it’s generated by other large brands and 3PLs. Historical data spans the entire Shipium platform and can help with everything from accurate EDDs to fulfillment and carrier selection — filling any gaps you may have within your own datasets.

The next area is critical, and something that many shipping ops teams have trouble accounting for — data related to external variables that impact shipping times and costs. Shipium’s modes are built to account for this, including criteria like weather disruptions, industry changes, macroeconomic factors (ex. Rising fuel prices) and unexpected carrier service changes.

Lastly, it’s important to account for changes in demand and volume. The “right” carrier decision on a Friday at 7 PM is different than a Monday at 7 AM, which is different from Cyber Monday at 7 AM. Point being: if your EDDs aren’t accounting for factors like time of day, day of week and holidays/seasonality, they’re likely not very accurate.

Note: You can upload historical data and carrier contracts to Shipium directly, and/or integrate with systems of record for key processes such as your PDPs, OMS, WMS, TMS, BI tools and more.

Carrier rate modeling

Untitled presentation (1)

Carrier charges are among the most significant and variable costs for any large shipping operation. To ensure that you can hit your desired delivery dates at the lowest possible cost, you need to have an accurate understanding of complete rates up front.

While many operators look to carrier base rates to compare costs, in reality, those costs don’t tell the whole story. And using legacy rate shopping systems is no longer a viable option, as they’re unable to keep up with how frequently carriers shift rates, limiting their value. We’ve found that on average, companies overpay for carrier services by an average of 6%. Even at $5 million in annual parcel spend, that’s $300K left on the table for a completely avoidable reason.

Meanwhile, Shipium’s carrier rate modeling powers fully-loaded rate shopping, which means that operators can account for accessorials and surcharges when choosing between carriers. Rather than choosing Carrier A over B because their base rate is $.25 cheaper, you may opt for option B when you account for the $.50 in accessorials in surcharges not presented by Carrier A up front. 

Here are the factors the model is taking into account. 

Your negotiated base rates and modifications — simply upload your carrier contracts using a self-serve console, and Shipium will model out their structure and account for your unique business considerations when rate shopping.

Published surcharges, including fuel, Dimweights, special handling etc. are also factored into Shipium’s ML models, ensuring that you’re keeping an eye on these highly variable costs. Our in-house TransOps team keeps all carrier charges up-to-date — for example, we make daily updates to fuel surcharges to keep up with changes.

Peak and demand-based surcharges are also accounted for. When a carrier relays extra costs to cover their own fluctuating operating expenses, Shipium factors that information into carrier rate modeling to ensure that your understanding of costs is complete. For example, you can account for the variance in different carriers’ peak season surcharges when making a rate shopping decision.

Wrapping up

Today’s most effective shipping operations are completely data-driven. To provide accurate delivery dates and meet them in the most cost-effective way possible, you need to account for the underlying factors that contribute to transit times and carrier costs. This means getting more value from your existing historical data on carrier and FC performance, as well as costs. It also means accounting for outside, constantly changing factors like macroeconomic conditions and carrier surcharge changes. 

Shipium’s platform-wide data modeling helps you do just that. By accounting for your unique business data, network properties, and carrier contracts in combination with aggregated data. Shipium can help you offer more accurate EDDs and make more informed rate-shopping decisions.

If gaining data leverage and improving decision accuracy is a priority, feel free to reach out to our team of experts here.