An Overview of Shipium’s ML-Powered Time-in-Transit Modeling

With the bar for digital customer experience now higher than ever, retailers are increasingly focused on creating a competitive edge through faster, more transparent, lower-cost shipping. But without the right understanding of transit performance, it’s impossible to actually make promises you know you can keep — which leaves many shippers in the position of having to decide between optimizing for customer experience at the expense of margins, or vice-versa. 

By gaining a deeper understanding of shipping performance, you can build a shipping experience that you know your network is built to support — and enhance the overall shopping experience in the process. Current options for this are limited. Companies often rely too much on some combination of inventory positioning, static carrier SLAs, and historical performance analysis to predict transit performance. 

Shipium’s dynamic time-in-transit modeling — a core element of the platform — helps to solve this problem by connecting relevant process data and using industry-leading machine learning to create highly accurate, reliable transit time predictions. Our customers leverage this to predict and manage shipping operations with unprecedented accuracy, and make more informed delivery promises that they know they can keep. 

In this post, we’ll provide a breakdown of the benefits of using Shipium’s time-in-transit modeling, common use cases, and how predictions are made.

Why Customers Use Shipium’s ML-Powered Transit Times

Understanding the balance between delivery speed, accuracy, and cost is critical for shippers looking to maintain competitive advantage. Shipium’s ML-powered transit times equip operators with insights to find the balance that works for their business.

Better Accuracy

With 99.1% OTD during 2023 peak, Shipium’s dynamic time-in-transit provides unprecedented confidence by combining anonymized platform-wide data sourced from millions of shipments with the unique parameters of users’ operations. By tailoring predictions based on specific business needs and constraints, Shipium predicts transit times with extreme precision, enabling more reliable decision-making across the board.

Major Cost Savings

By leveraging Shipium’s ability to model shipping speed and costs accuracy across the parcel fulfillment process, businesses can optimize their shipping cost structures in ways that would have previously not been possible (we’ll cover this in more detail when exploring use cases below). As a result, our customers realize a 12% reduction in parcel spend on average, making a significant impact on their bottom line while improving operational efficiency.

More Revenue Through Cart Conversion and Loyalty

Leveraging Shipium’s transit predictions enables businesses to make more aggressive and accurate delivery promises. This leads to increased trust from shoppers, which in turn leads to improved conversion rates at checkout. Shipium’s customers see an average 4% improvement in checkout conversion rates, and create a competitive and transparent experience that encourages repeat purchases.

Seamless Integration with Existing Systems

It’s worth noting that Shipium’s ML-powered optimization platform is API-based, and built to compliment your existing systems rather than replace them. The platform seamlessly integrates with order management systems (OMS), warehouse management systems (WMS), transportation management system (TMS), and other systems of record to coordinate and optimize critical shipping decisions from end to end.

Common Use Cases

Now that we’ve established the value of using Shipium’s Dynamic Time-in-Transit modeling, let’s explore how shippers are using the platform to realize that value.

Customer Experience Improvements

Businesses using Shipium can elevate customer satisfaction and improve key metrics like cart conversion, average order value, and customer lifetime value.

Confident Delivery Promises

As mentioned above, the ability to make reliable, real-time delivery promises builds trust and increases brand loyalty. Shipium enables organizations to move from vague, carrier-provided delivery ranges to those precise, data-driven delivery promises. By offering customers accurate, reliable predictions, you can strike the perfect balance between what’s possible and what’s best for your business.

Faster Deliveries

Shipium’s platform enhances every stage of the parcel fulfillment process, from order routing to cartonization to carrier selection. This enables organizations to make highly competitive delivery promises at a low shipping cost for shoppers, which can often be the difference in who they decide to purchase a desired product from.

Demand Forecasting

By pairing time-in-transit data with your demand forecasting systems, Shipium enables more accurate sourcing of products, helping to prevent costly stockouts or overstock situations. Our models help you place the right products in the right locations at the right times.

Network Optimization

Shipium’s ML-powered insights allow for a smarter, more efficient use of your shipping network.

Dynamic Order Routing

Integrate Shipium’s time-in-transit models with your order routing system to predict the fastest and most cost-effective delivery routes. With accurate delivery window predictions, you can optimize fulfillment from multiple origin points to enhance order routing strategy.

Inventory Allocation

Shipium’s models also help with stronger inventory allocation. By integrating with your system of record for inventory, you can strategically position products based on predicted delivery times, ensuring that they’re as close as possible to the customers who are most likely to purchase them.

Shipment Execution

Businesses can leverage Shipium to optimize shipping execution in several ways.

Intelligent Upgrades/Downgrades

Shipium’s platform and TNT predictions can be used to consistently identify opportunities to downgrade to less expensive shipping methods while still meeting delivery promises (and set up rules to automate the selection of those methods whenever possible). Shipium’s customers have saved millions by choosing more economical ground shipping methods without compromising on speed or customer satisfaction.

Split Reduction

Minimize costly order splits by using Shipium’s speed and cost models to assess when it’s more cost-effective to consolidate shipments versus split them. This approach both saves money and improves the overall customer experience by reducing the number of shipments customers receive for multi-item orders.

Subscription Timing

For subscription-based models, timing is everything. With Shipium, businesses can predict the optimal fulfillment windows, ensuring that subscription shipments arrive precisely when needed while choosing the most cost-effective shipping methods.

Simulating the Future

Shipium Simulation allows businesses to simulate potential changes to their carrier or network strategy before implementing those changes.

Carrier Expansion Analysis

Adding new carriers to your network is a big decision. Shipium’s models allow you to simulate the cost and performance impacts of incorporating new carriers, helping you make more informed decisions about your multi-carrier strategy.

Network Expansion Analysis

When considering the addition of new warehouses, distribution centers, or even third-party logistics (3PL) providers, our platform can model the impacts on your delivery network.

Smarter Rate Negotiation

Armed with dynamic time-in-transit data, you can negotiate better rates with carriers based on actual performance rather than SLAs, including a deep understanding of the performance of ground methods, which aren’t typically attached to an SLA. This leads to more favorable discount tiers and optimized surcharge adjustments.

How Shipium’s Machine Learning Works

Shipium’s ML models are powered by differentiated data and fine-tuned for each customer’s unique network, providing predictive accuracy that’s hard to match.

Leverage Differentiated Data

Accurate transit time predictions require vast amounts of data, and gathering this data independently is a challenging (if not impossible) task. Shipium bridges that gap by factoring in real-world carrier performance across millions of shipments, as well as macro conditions (weather, carrier bulletins etc.) across your network.

Configure and Tune to Your Operations

Shipium’s models are carefully tuned to reflect your network’s unique properties and specific needs. From carrier pickup times to volume limits, our models consider all available parameters, ensuring precise, customized predictions.

Optimize for Desired Outcomes

When you’re aiming to reach your desired speed-to-cost ratio, Shipium’s ML-powered transit predictions help you adjust your network strategy to meet your unique business goals.

Supported by a Dedicated Data Science Team

Shipium’s commitment to ML goes beyond algorithms — we have a dedicated data science team that continually tests and refines our models using the latest applicable techniques and most relevant data possible. In the first half of 2024 alone, we ran over 100 new model tests to ensure our technology remains cutting-edge and highly reliable.

Wrapping Up

Shipium’s advanced ML capabilities offer significant advantages for businesses navigating today’s logistics landscape. With reliable, highly accurate transit predictions and seamless integration into existing systems, Shipium’s machine learning helps modern enterprises drive efficiency, cost savings, and customer satisfaction.

If you’re looking for a more data-driven approach to supply chain management, book a demo with our team to discuss your use case.