Key Takeaways
- The dynamic routing algorithm changes routes in real-time, depending on traffic and weather conditions, and avoids any unnecessary delays.
- The machine learning model forecasts congestion before it happens and drastically decreases engine idling times.
- Optimized dispatching cuts down fuel costs and boosts profit margins for businesses-to-business logistics companies.
- Automated routing prevents mistakes and saves many hours each day from administrative tasks.
What is AI route optimization?
AI-based routing optimization is the application of artificial intelligence for the identification of optimal route for making deliveries. The system takes into account several variables like traffic situation, weather conditions, carrying capacity of the vehicle, and delivery time frame in order to optimize fuel consumption and distance travelled.
The Financial Drain of Inefficient Routing
Cost of fuel is the largest variable cost when it comes to fleet management and logistics. Traditional routes operate through fixed plans based on previous experiences. The dispatcher prepares a plan according to the distances covered and the speed at which the driver needs to go. The plan is then handed over to the driver. However, the minute the truck rolls out of the depot, the truth becomes clear. Traffic hitches, accidents that occur unexpectedly and the climatic conditions put an end to any form of static routing. A vehicle idling because of traffic is a huge loss to the company.
This is the reason why some of the top performing supply chains have started leaning toward automation in their systems. Artificial Intelligence in fleet management turns the business strategy from a reactive one to a proactive one. Rather than the driver calling the dispatch center for assistance in case of delays, the software can anticipate such problems.
Implementing Dynamic Routing
Dynamic Routing serves as the core of the development. As opposed to traditional GPS navigation, which would find the optimal way from point A to point B, the Dynamic Routing is responsible for the finding of the most appropriate route while driving. It collects huge amounts of information in seconds from the municipal traffic surveillance cameras, weather satellites and other vehicles.
In case of incoming weather disturbances on the highway, the Dynamic Routing system is to identify whether there is an alternative route. It is necessary to calculate the fuel costs during using the alternative route and compare it with the fuel costs during being stuck in traffic due to the weather conditions. The weight and fuel consumption of the truck should be taken into account as well.
The Mechanics of Machine Learning Logistics
Dynamic routing deals with the present, while logistics by means of machine learning deals with the future. Advanced delivery algorithms are not merely responding to immediate needs; they are learning from them. Each successful route provides new information to be fed into the main algorithm. Eventually, the artificial intelligence recognizes patterns which escape human analysis.
The algorithm will discover that a particular junction is affected by a 15-minute delay on Tuesdays from 2:00 pm to 3:00 pm because of municipal waste removal operations. The computer program makes necessary adjustments to the manifest in order to steer trucks away from that point in time. This kind of fine-tuning enables the company to save on fuel costs.
Real-World B2B Use Case: Slashing Fuel Costs by 15%
Take, for instance, a medium-sized business-to-business logistics firm that owns a fleet of 200 trucks traveling around the Pacific Northwest region. They were faced with the problem of inconsistent weather conditions during winter and traffic jams, which often resulted in higher fuel costs and missed delivery schedules for their important industrial customers.
The logistics firm upgraded their old dispatch system to a state-of-the-art artificial intelligence route planning tool. This AI solution was seamlessly compatible with their current telematics system. On the first day of snowfall during the winter season, a road accident involving several vehicles occurred, blocking the main highway route.
In this case, however, the AI solution was able to instantly identify the problem using its live traffic APIs. It applied dynamic routing protocols almost instantaneously to guide 14 trucks around the closure site. Based on the data about the amount of snowfall along the alternative routes, the delivery algorithms safely routed the trucks. They reached their destinations in compliance with their SLAs.
Following six months of operating on the machine learning logistics system, the company carried out an accounting review. As a result of reducing the amount of unnecessary idling time and optimizing mileage for each day, there was a 15% decrease in overall fuel usage. Also, the dispatching team managed to gain 20 hours weekly from solving routing issues manually.
Integrating Delivery Algorithms into Existing Workflows
Using the AI tool for improving delivery routing does not mean that the entire IT system of the company must be renewed. Modern algorithms can be easily connected to the current system using interfaces. The AI will get information about the list of deliveries from the ERP database, find the best loading plan and routing, and deliver the information to the driver’s phone.
The introduction of the new system will also improve the transparency for the customer. B2B companies will receive highly precise estimates of time arrival. If there are some delays in the process, the notification will be delivered to the warehouse, where workers will have enough time to change plans of working at the docks.
FAQ
What is the speed of ROI of AI routing optimization?
It takes a return on the investment between three and six months for the medium and large fleet. It happens due to the immediate savings in terms of fuel costs, as well as reduced expenses on overtime pay and penalties for late deliveries.
Do I need to purchase any trucks or hardware for my company to use dynamic routing?
Not at all. Modern AI-based routing system works via the cloud and automatically integrates with the already installed telematics/GPS system on your trucks. Usually, the drivers receive updates of their routes with the help of provided by the company tablets/smartphones.
Are AI-based algorithms able to deal with multi-stop B2B delivery restrictions?
Certainly, they are. The algorithms are specifically designed for solving such difficult problems. The algorithms consider specific time of loading/unloading cargo, weight limitations, height restrictions, refrigeration and HOS of drivers.