Transport service providers, particularly small and medium-sized ones, face challenges in efficiently planning and adjusting vehicle routes. The aim is to minimize the number of kilometers traveled without a load. Currently, it takes human forwarders several hours to find the perfect load for a driver that is both close enough for pickup and ensures the mileage rate that aligns with company standards.
Thus, Product Challenge participants needed to come up with a SaaS solution that would incorporate AI and API of transport exchanges, aiming to reduce the time taken to find the perfect load for drivers, ensuring they don't have to travel far for a pickup, and that the mileage rate meets the company's standards.
Our solution is called Spedibot and is meant to transform the logistics landscape through a comprehensive SaaS platform, automating forwarding processes while providing unparalleled management of document flow and vehicle fleet operations. The Forwarding Assistant stands at its core, offering a seamless Google Maps integration, real-time vehicle tracking, cargo planning, and automated notifications on truck arrivals. Additionally, integration with external cargo markets, coupled with our in-app cargo market, ensures optimal utilization of resources, reducing idle time, and boosting revenue potential for companies using Spedibot.
The platform boasts a suite of advanced features including document management, chat live translation, innovative cargo sharing for small shipments, and cargo optimization for non-full truckloads.
Underpinning this robust platform is a tech stack comprising Django Admin panel for backend operations, React SPA for frontend, and proven algorithms like Dijkstra Shortest Path and Travelling Salesman Problem for efficient route planning. Our dedicated development team is set to bring this solution to life within an 8-week timeframe, ensuring a swift yet thorough transformation of your transport operations.
Our ideation journey began with an intense ideation process, aiming to create a robust model capable of calculating quantifiable components of transport data. Parallelly, we invested our efforts in crafting an application prototype specifically designed for the forwarding departments. It served as a tangible representation of our vision, allowing us to experiment and iterate on our ideas rapidly.
API Integration was crucial for accessing real-time data and insights, and also acquiring data from the external cargo markets. We ventured into creating our own transport exchange, aiming to provide a more tailored and efficient experience for our users.
Another integral part of the prototype was an internal application dedicated to document management, supervision of the vehicle fleet, and planning.
Our project development was meticulously focused on individual drivers, striving to understand the ins and outs of their daily operations. We also delved deep into comprehending how our product could function within small and medium-sized enterprises, ensuring that our solution is versatile and adaptable to various business scales.
Embarking on the MVP phase, there's a need to navigate through the complexities of routing algorithms, understanding the dependancies between routes, cargo, and fuel consumption.
It's also important to weigh the benefits of web versus mobile, all while considering the potential advantages of AI integration. Financial foresight would be a final chapter, ensuring the full application development and server maintenance are both feasible and sustainable.
In this compact yet intense journey, we’ve not just developed a product concept; we've crafted a solution, ready to revolutionize transport management and adapt to the evolving needs of companies across the spectrum.