How to maximize fleet efficiency with smart logistics?
- Dataphi Solutions
- Jan 13
- 2 min read
If your logistics team spends hours, perhaps days, poring over spreadsheets and maps to plan the week's routes, you're witnessing the symptoms of an operation that has reached its manual limit: high costs, lengthy planning times, underutilized vehicles, and a frustrating inability to scale the business in an organized manner.
Many managers, faced with this chaotic scenario, believe that the solution lies in some complex and nebulous Artificial Intelligence system. But the truth is more precise, more powerful and, we dare say, more elegant.
The solution isn't AI, it's mathematics.
The real challenge
What the logistics team tries to solve manually every day is a classic and complex challenge known as the "Capacity Constrained Vehicle Routing Problem" (CVRP).
In simple terms, it's a giant puzzle where you need to:
Visiting dozens or hundreds of addresses.
Respect each client's time slots.
Utilize a fleet with different load capacities.
Determine the ideal sequence of stops to minimize distance and time.
Doing this manually is not only time-consuming, it's humanly impossible to find the best solution, and the result is what you already see: longer journeys, extra hours, and wasted vehicle capacity, which, at the end of the day, increases the cost per stop.
Transforming chaos into efficiency.
In a recent project, we faced exactly this scenario. A partner with a logistics operation at its limit, dependent on manual route planning, which was hindering business growth.
Our approach was not to apply an AI "black box," but rather to act as the brain of the logistics operation, using an advanced optimization core to solve the CVRP problem. This powerful algorithm simultaneously processes all the variables and constraints of the business to calculate the most efficient route.
This transformation process took place in three simple steps:
We delved into the operation to map all the critical variables: vehicle capacity, time windows, addresses, and other constraints.
With the problem modeled, we applied the algorithm, which was directly connected to the client's management system, automating data capture and eliminating manual spreadsheets.
The end result was a practical and actionable tool. We started generating a weekly schedule of optimized routes, a clear plan for each driver to follow.
If your operation is stuck in manual processes, suffering from inefficiency, and viewing growth as a chaotic threat, perhaps it's time to stop looking for generic AI solutions and focus on the true power of applied mathematics.
Is your company ready to transform logistical complexity into a competitive advantage?
Comments