Outcome: logistics control platform using Artificial Intelligence
Tools: NodeJS, AdonisJS, GraphQL, React, React-native, Python
Project Schedule: feb/20; MVP: apr/20 - aug/20
"We chose ateliware based on it’s high market reference, the visibility we had of the projects they developed and also for being a company known for working on disruptive and innovative solutions. In summary, we had a great cultural fit, which was confirmed throughout the execution of the project"
In Brazil, almost 65% of all the freight transportation is done by trucks. To optimize the time on the road and reduce costs of operation, the sector is going through a real Digital Transformation by investing in high-end technologies, such as Artificial Intelligence and Machine Learning. With these tools, decisions, once made by humans, are now being done by machines that can analyze thousands of data in seconds.
In 2020, the Cargolift Group, in partnership with MatrixCargo, a startup dedicated to innovate the brazilian logistics market, accelerated it’s technological transformation process.
MatrixCargo's objective was to develop a “robot” capable of sequencing freight services to find the most economical and efficient solution possible. This sequencing would be done using logistics data and on vehicle positionings.
After previous failed attempts with other technology suppliers, MatrixCargo’s demand reached ateliware and we started our partnership to develop a digital solution that was 100% customized.
We started this journey with a discovery phase, where we collected data about our market, the technology that would be used and the product's users. This step is crucial to develop successful digital products.
With this analysis, we realized that before developing the requested equipment, we needed to structure the data that would be used in Machine Learning. In other words, before developing a ML solution, we needed to meet the system's initial demand, which was to capture and manage vehicle position information, then to transmit these appointments to truck drivers in a mobile app.
To make this possible, the first step was to create an Artificial Intelligence feature to calculate the lowest vehicle cost vs. demand, considering fixed expenses, toll taxes, vehicle distance, driver's working hours and truck’s transportation capacity.
Therefore, we worked on two major fronts: the capture of structured data for a future Machine Learning solution, and a digital product created to optimize fleet logistics with Artificial Intelligence.
The co-created solution was named OTIMIZADOR MATRIX (Matrix Optimizer). It’s main objective is to fit, in the best way possible, the carrier's demands, reducing costs and improving companies' financial results. To perform this task, the optimizer needed an Artificial Intelligence component, which based on business rules and heuristic algorithms, allows for better decision-making in a timely manner. Its secondary objective was to capture data so that, in the near future, we can implement Machine Learning to the solution.
The OTIMIZADOR MATRIX consists of:
We have also developed a web application and a mobile app that provide full connectivity between the carrier and the drivers, as well as real-time information and greater supply chain integration, three essential pillars of Logistics 4.0. In addition, we carry out several integrations with the "TMS" systems so that the solution includes the entire freight transportation process.
OTIMIZADOR MATRIX is highly innovative, as it automates the calculation of the best service scenario on a daily basis, also allowing the scalability of the volume of vehicles and demands to be served. Furthermore, it avoids human error while it processes data with greater assertiveness and speed.
“The optimizations provided by the system are on the rise throughout the operation. Our optimizer has an assertiveness of allocations close to 95%, reducing planning time and operational cost to fulfill the routes. Our application also generates great operational agility, digitizing the entire process with the drivers”.
Rodrigo Fávero | CTO MatrixCargo
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