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The Power Behind Wise Systems.

Q: How does Wise Systems’ Machine-Learned Service Times help my fleet?

Accurate service times -- the amount of time a driver takes to complete a delivery -- are the key to maximizing fleet efficiency, performance and utilization. However, most fleets operate on estimated service times, which don’t accurately reflect the reality of the drivers’ days. With a pioneering application of machine learning for last-mile fleets, Wise Systems introduced Machine-Learned Service Times to address this issue. Wise Systems’ proprietary model analyzes historical data and builds much more accurate estimates which form the foundation of much tighter route plans.

At the core of Wise Systems’ platform is the AI-driven Dynamic Optimization Engine (DOE) that continuously ingests data from your operations and makes real-time decisions. The DOE contains the route optimization algorithms that make intelligent routing decisions for your fleet. It also houses the machine learning models which provide fleet performance improvements over time – benefits that were not previously available with traditional platforms.

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DOE MLST

Machine learning for the win.

DOE

Our engine takes in continuous inputs and gives you updates, analytics, and an ever-improving fleet.

Route Optimization

Scheduler from Wise Systems creates routes powered by the DOE, making recommendations based on your business constraints and past fleet performance data.

Real-time Optimization

Receive real-time recommendations such as alternative routes for drivers based on traffic, time of day, weather, and more, prioritizing on-time deliveries.

Machine Learning

We leverage machine learning to close the gap between your planned and actual routes so that you can make better-informed decisions about your routing operation.

On-Demand Dispatch

As on-demand orders come in, the DOE will recommend the best drivers to take those orders with minimal impact on your miles driven, on-time arrival, or time on the road.

MLST

Machine Learned Service Times: The Key to Continuous Improvement

Our breakthrough application of machine learning to driver service times has helped last-mile fleets significantly improve efficiency and utilization. Service time describes the full duration of a driver’s visit to a customer. It includes parking time, unloading, transporting goods, performing a service, completing a transaction, and returning to the vehicle. Most companies use a rough time estimate for every stop on a route. In reality, service times are highly variable. Treating them as an average leads to inefficiencies, inaccurate route times, and negatively impacts both on-time arrivals and utilization.

With Wise Systems’ machine-learned service times, more accurate data and service time predictions drives future route plans to improve route performance and fleet utilization.

Frequently asked questions

Service time describes the amount of time a driver or technician spends at a customer’s stop. It includes the amount of time spent parking, unloading, merchandising, transporting goods, performing a service, completing the transaction, and returning to the vehicle.

Accurate service times — the amount of time a driver takes to complete a delivery — are the key to maximizing fleet efficiency, performance and utilization. However, most fleets operate on estimated service times, which don’t accurately reflect the reality of the drivers’ days. With a pioneering application of machine learning for last-mile fleets, Wise Systems introduced Machine-Learned Service Times to address this issue. Wise Systems’ proprietary model analyzes historical data and builds much more accurate estimates which form the foundation of much tighter route plans.

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We’d love to learn more about your routing challenges and see where we can help.

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Current routing challenges?

The Power Behind Wise Systems.

Machine learning for the win.

Machine Learned Service Times: The Key to Continuous Improvement

Frequently asked questions

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