TMS Modernization: AI-Powered Route Optimization and Delivery Forecasting

The global logistics market keeps growing at a high pace despite the overall economic recession. In particular, researchers say the last-mile delivery market could be worth $258.7 billion by 2030 (showing 8.8% CAGR). However, increased customer expectations, both in B2B and B2C markets, are cutting into profits.

Inefficiencies in logistics handovers and operational interactions can also become a bottomless pit of waste. For example, 13-19% of logistics costs stem from inefficient operations, indicating the true need for improvement. This is where AI in delivery can win the day.

The Devox Software developers have carried out several cases of TMS modernization throughout our legacy modernization services, and we’re ready to share the tips and tricks of when, how, and what to implement for the ultimate ROI for your business.

AI-Powered Route Optimization and Delivery Forecasting Benefits

Why do companies opt for AI-powered TMS improvements in the first place? Here are the impressive enhancements AI in delivery brings:

• Lower Mileage: Better clustering and sequencing cuts down on wasted driving and helps fleets deal with empty-mile dynamics.
• Less Overloaded Routes: The system learns how long it really takes to serve each stop based on location type, time of day, driver, and SKU mix, so it can plan routes that drivers can actually finish.
• More Accurate ETAs: Combining real-time and historical signals with traffic behavior modeling makes predictions more accurate.
• Faster Emergency Replanning: Late orders, missed deliveries, and traffic accidents require quick automatic recalculation of routes with no manual intervention.
• Better SLA Performance: Instead of waiting until something goes wrong, predictive ETAs let you react early: reassign, notify, and reschedule according to the particular challenge.
• Less Stress for Dispatchers: It enables a transition from manually putting out fires to managing exceptions.
• Cleaner Integration Story: Updating your systems ensures that APIs and events are consistent across TMS, WMS/ERP, telematics, and customer notifications.

AI doesn’t “make routes shorter” at all. AI delivery service continuously optimizes routing, which is why the ROI continues to increase.

How Does AI-Powered Route Optimization Work?

Route optimization often means a solution to a Vehicle Routing Problem (VRP), giving vehicles scheduled stops and telling them to do them in the least amount of time, miles, and penalties while following rules like capacity, time windows, driver hours, and priorities.

What Changes when You Use AI instead of Traditional Methods?

AI-powered routing improves three levels at once:

• Better Inputs: So service time turns from a constant to a case-sensitive prediction,
• Intelligent Business KPIs: You optimize for SLA probability, overtime risk, priority customers, and cost instead of “shortest miles.”
• Closed Loop Learning: Every route you finish makes tomorrow’s plan better thanks to machine learning.

But how is it implemented in a workflow? Despite the need for TMS software modernization, businesses have to update their infrastructure and beyond, including:

1. GPS and IoT sensors that record how fast a car is moving, where it is, how long it has been sitting still, and how hard it is braking.
2. Modules of AI-powered analytics that tell you when you might be late because of volatile environments, such as traffic problems, stop complexity, warehouse departure drift, weather effects, and more.
3. New features: To keep deliveries on schedule, routes are recalculated in real time via dynamic insertion, reassignments, and exception handling.

All these become clearer when explained with examples. For instance, if the system thinks that Vehicle #12 will be late by 35 minutes because of traffic and a long service time at Stop #4, it has several options:

• Change two stops with Vehicle #9, which has some extra time, or
• Change the order of the remaining stops to protect time windows,
• Let customers know automatically when their ETAs change.

Depending on the situation, the system will find the most fitting and cost-effective solution without a manual update. So you can see that AI delivery opens the window for real impact for your business.

Furthermore, to sum up, we’ve compared AI route optimization and traditional methods.

Now, it’s time to move to AI-powered delivery forecasting.

How Does Delivery Forecasting Work?

Delivery forecasting, as a part of AI in delivery, is making predictions about delivery timelines, stops and their ETAs, risks of failures, and more. Predicting the estimated time of delivery (ETA) in logistics stresses the need to use real-time and historical data, feature engineering, and traffic behavior modeling together to make predictions more reliable.

Traditional vs. AI-Powered Forecasting

“ETA” stands for “estimated time of arrival,” which is based on how much time is needed for transportation. AI adds up here, too. Here is a brief list of improvements that AI in delivery offers:

• Analyzed travel patterns over time and by segment (Monday 8–10 a.m. is not the same as Friday 8–10 a.m.),
• Managed complexity (number of SKUs, type of building, signature needed, access to floors, returns),
• Minimized drift in warehouse departures (late dispatches build up over the day),
• Nurturing positive driver behavior (speed changes, how long they stay),
• Better external context consideration (traffic, weather, and events).

In particular, last-mile delivery reaches 30–35% of the total transportation cost, so even small changes quickly increase profits. Here’s an example:

Let’s say it’s a medium-sized business: 120 cars, 140 stops a day per vehicle on average (16,800 stops a day), cost per stop (when full): $6.50, days worked per year: 260. The cost of delivery each year is about $28.4 million, or 16,800 times $6.50 times 260.

If the AI delivery service cuts the cost per stop by only 8%, we have the savings reach up to $28.4 million × 0.08 = $2.27 million per year.

This short overview shows why businesses strive for updates. They do not get “AI.” Businesses purchase capacity, dependability, and a favorable cost curve.

How to Implement AI-Powered Route Optimization and Delivery Forecasting: Simple Steps

Several cases in our practice of AI delivery service implementation have crystallized one comprehensive approach that infinitely streamlines the development process. Here it goes for you:

1. Explain what “better” means in the KPI contract: Choose 3–5 key performance indicators, such as cost per stop, on-time percentage, dispatch time, or failed delivery rate, and build on them. The requirements gathering phase is the most crucial for the impactful outcome, so don’t underestimate it.
2. Check the audit data’s readiness (2–4 weeks, quick and harsh): Orders, stops, timestamps, GPS pings, POD events, exceptions, and driver hours. Identify the gaps and “lying fields.”
3. Modernize integration (events and APIs): Make a clean event stream with these events. This approach is what makes an AI delivery service work.
4. Start with a baseline optimizer and modeling constraints: Use time windows, capacity, and driver limits with Vehicle Routing Problems (VRP) (OR-Tools is a common choice for this).
5. Add forecasting models (ETA + risk) as a second track: Train the ETA prediction on past runs and obtain not only the ETA but also the confidence (p50/p90). Use this model to find “routes that will fail” before they start.
6. Close the loop: dynamic replanning and handling exceptions. Add triggers for real-time inserts, reassignments, and customer notifications.
7. Run an A/B rollout by region, depot, or dispatcher team: Compare KPI changes to the baseline and prepare the ground to go back as soon as needed.
8. Scale in a safe way: Add advanced goals only after stability. It could be reducing CO₂, setting priority tiers, using micro-fulfillment logic, optimizing multiple depots, and more.

As a result, you’ll move from try-and-check methods to proven practices from experts in the field.

Final Thoughts

An AI delivery service is useful when it’s built into your TMS as a learning system instead of just a one-time planning tool. With modern routing and delivery forecasting, you get more benefits: lower cost per stop, better SLA performance, and fewer manual interventions.

If you’re updating an old TMS and need a clear plan for how to build and implement the architecture (and what to build first), Devox Software will help you design and deliver legacy modernization services with the AI-powered route optimization and delivery forecasting layer as a production-grade feature that is integrated, measurable, and scalable. Let’s talk.