Our all-embracing goal is to bring 50% of the entire intra-city trucking needs online in the next 3-5 years. In line with this we have built a sophisticated tech and data platform to change the way intra-city trucking commerce is currently executed in India.
We are building a high-quality tech and data platform to eventually bring the entire trucking commerce (services, payments, fuel, brokerage, financing) online to ensure higher efficiencies, lower costs and data led optimization for individual truckers. The over-arching philosophy is to make world a better place to live by reducing waste, using less resources and in turn producing more output. We want to gain marginal efficiencies using data to make intra-city logistics as automated, efficient, safe and green as possible.
For us this is an immensely exciting project being led by our world class engineers. Every day we come to work with exciting and interesting challenges at hand. We solve these using technology writing sophisticated algorithms and designing intuitive products. We believe we are set to create disruption and modernize an otherwise antiquated logistics industry.
Please do reach out at email@example.com if you wish to brainstorm new ideas, explore a career in the logistics domain or want to work in our world class technology team.
Technology led platform and fully automated delivery platform, unlike other operations led platforms allow accurate, efficient and consistent logistics support.
Instavans uses real-time data to show demand and supply side KPIs, heat maps and predictive analyses during expansion process, allowing shippers efficiency and flexibility in their operations.
Integrated approach to handling a variety of customer segments to enable efficiency gains combined with increased utilization of assets and open APIs to enable easy onboarding of customers, pre-populating locations, importing pricing/rate cards and order information, as opposed to non-integrated approach to different customer segments.
Trips scheduled based on the time of the day, real-time supply of contracted vs. aggregated carriers, truck types, home location and the number of trips made by carrier. Continuous monitoring of supply and demand are carried out to maximize asset utilization and efficiency.
The API uses algorithms to automatically convey right information to carriers and customers to notify them on the exact status of their deliveries.
Keep the customers abreast and actively engaged with real-time notifications whenever a vehicle/driver gets attached, estimated pickup and delivery times, real-time tracking of the vehicle, online availability of orders, invoices, bills, e-pods, e-signatures along with estimated and actual delivery times. Keep track of the entire history of the order right from the time the driver gets attached to the final drop.
At Instavans we are working on several projects to transform the outdated logistic industry and making it new age through process automation, driver analytics and data science. We are building end-to-end automation via a powerful notification center, intelligent recommendation engine, behavior analysis for our drivers, shippers and our internal operations. Some of these features will use leading edge tools in behavior analytic, artificl intelligence, IoT, and data science. S of the immediately planned features are around the following concepts:
automatically plan deliveries respecting constraints of capacity, distance and time. Get the minimum number of vehicles required along with their routes.
In trucking any idle capacity – truck or the driver is a fungible capacity. You cannot keep less or more of capacity at any point in the network. This is a massive problem and requires queuing theory, linear programming and advanced mathematical modeling to ensure the system is optimized and balanced.
A driver driving well and efficiently is key to making logistics successful. It is imperative that every minute of driving across the city must be monitored and analyzed. To predict the behavior of the driver, a lot of the driving data and driver behavior data must be continuously evaluated in real time throughout the lifecycle of a driver. The system needs to determine if the driver is driving safely and is in complete control of the vehicle and in case of abnormal driver behavior immediate corrective actions need to be taken. Data science needs to be applied to convert the qualitative information to a quantitative model for constant monitoring and simulation of intelligent events and the right triggers based on threshold values.
Based on historical demand, volumes, TAT and service time commitments optimized planning will be made. The planning model takes into consideration the number of vehicles required on each route and network and find an optimized way such that the shipments can be routed in the most efficient way. This planning also helps in keeping the supply (vehicles/drivers) and demand (shipments) capacities in equilibrium and helps to build sales strategy to optimize the entire network.