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case study

SaaS Route Planning Tool for Fleet Management Optimization

SaaS route planning software development to accommodate the growing delivery volumes. Integration of ML algorithms for real-time traffic data analysis and automated route optimization. Incorporating fleet management features to track driver performance and ensure real-time oversight of vehicle locations for better visibility.

client

NDA

Sweden
60 employees

A medium-sized transportation provider specializing in local and regional transportation faced a lack of resources to handle the ever-growing number of deliveries. The company manages a varied fleet, including trucks, vans, and drivers, serving e-commerce platforms and retail brands. As they grew, they required a more efficient, automated, and scalable solution for optimizing route planning and managing their fleet.

SaaS routing optimization software development case study

request background

SaaS Routing Software with Real-Time Fleet Management Capabilities

Our client had been managing its logistics and fleet operations with minimal software, which led to increasing inefficiencies as the business grew. Over time, their delivery volume expanded significantly, but the lack of a sophisticated route optimization system started to severely impact operations.

Without the proper tools that could accommodate expansion, their fleet management resulted in delays, higher fuel costs, and underutilized resources. This inefficiency blocked the way to scale and compete in a saturated market that demands fast, reliable deliveries.

Plus, the absence of real-time data and tracking capabilities made it difficult to make informed decisions on the fly. With customer expectations rising, the client recognized the need for a more robust technological solution.

Thus, the company addressed its challenges to Acropolium, requesting a SaaS route optimization software development to streamline operations, reduce costs, and enhance fleet performance.

SaaS route optimisation software development for better fleet management

challenge

A SaaS Route Planning Tool to Deal with Manual Process Errors

First off, the company faced multiple challenges due to outdated and inefficient systems. Manual route planning was a major issue, with routes being calculated through time-consuming processes that often led to errors. These inefficiencies resulted in higher fuel usage, longer delivery times, and increased operational costs.

Our client also lacked the ability to factor in critical data from the roads and weather conditions. Since drivers couldn’t adapt routes on the go, it often led to missing delivery windows and hence affected customer satisfaction.

Fleet scalability was one of the foremost challenges. As the company evolved, its minimal software tools couldn’t handle the increasing complexity of managing a larger fleet. Poor visibility into fleet operations also made it difficult for managers to track vehicles, monitor driver performance, or respond to issues in real-time.

At some point, it became impossible to gain meaningful insights from the data. With all the roadblocks in mind, we set the goal to develop SaaS software for route planning as a centralized, automated system.

goals

Automate and improve route planning by using machine learning to analyze both historical and real-time data.
Generate optimal delivery routes that minimize manual errors.
Continuously refine route efficiency through machine learning algorithms to reduce travel time and fuel consumption.
Utilize cloud computing to create scalable SaaS software for route optimization capable of handling increasing data volumes and operational complexity with time.
Enable real-time integration of critical data to allow dynamic adjustments to delivery routes.
Improve fleet visibility and control through a centralized cloud-based platform that tracks vehicle locations, driver performance, and load statuses in real-time.
Provide accurate delivery estimates and reduce delays to boost customer satisfaction and strengthen the company’s reputation.

SaaS software for route optimization and fleet visibility

solution

SaaS Route Optimization Software Built with ML & Cloud Best Practices

Amazon Web Services (AWS), TensorFlow, Google Maps API, WebSockets, OpenStreetMap, MongoDB, PostgreSQL, Docker and Kubernetes, Node.js, GraphQL, React.js, OAuth 2.0
21 months
7 specialists

We began the project by conducting an in-depth discovery phase to thoroughly understand the client’s operations, challenges, and future goals.

This comprehensive assessment allowed us to identify key areas where improvements were necessary and provided the foundation for building a tailored solution. Based on these findings, we tailored SaaS route planning and optimization software.

To power the system, we utilized Amazon Web Services (AWS), ensuring the infrastructure is scalable and able to handle increasing data loads as the business grows. Our developers integrated TensorFlow for machine learning, allowing the platform to analyze both historical and real-time data, improving routes automatically. For real-time data integration, we employed Google Maps API and OpenStreetMap, offering precise traffic and location data to update the routes based on real-world conditions.

The backend is built with Node.js, leveraging GraphQL for efficient communication between the client and server, enabling faster queries and data management. Additionally, we used WebSockets to facilitate real-time updates between the fleet and central management, ensuring instantaneous adjustments to routes.

On the data management side, our team employed MongoDB to store unstructured data like driver logs. Simultaneously, PostgreSQL with PostGIS would handle structured and geospatial queries, enhancing route calculations and fleet tracking.

For containerization and seamless scalability, we implemented Docker and Kubernetes, allowing the application to manage fluctuating workloads effectively. Finally, OAuth 2.0 is integrated for secure user authentication, ensuring data privacy and protecting user accounts.

Ultimately, the SaaS software for route planning now offers several key features:

Automated route planning powered by machine learning algorithms that continuously improve based on performance data.
Real-time data integration from traffic, weather, and other sources to enable prompt adjustments to routes.
Scalability through cloud infrastructure, allowing seamless expansion as the business grows.
Centralized fleet visibility, offering real-time tracking of vehicle locations, driver performance, and delivery statuses.
Comprehensive reporting and analytics, helping fleet managers make data-driven decisions to enhance operational efficiency.

outcome

Streamlined Logistics with Tailored SaaS Routing Software

The client experienced a 15% boost in profit margins, driven by reduced fuel consumption, lower operational costs, and overall efficiency improvements.
Customer retention increased by 25% thanks to more accurate delivery estimates and reliable service, enhanced by real-time data integration and dynamic route adjustments.
The company saw a 25% improvement in logistics efficiency, with smoother coordination of delivery schedules, real-time vehicle tracking, and optimized resource allocation.

SaaS routing software case study outcomes

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