The global trucking revenue pool is close to USD 2 trillion dollars which is about 20X the cab market revenue pool. However, even in developed markets such as the US, it is a highly fragmented and antiquated business which suffers from a lack of use of technology and data.
If you are an aspiring leader in the technology and data space, this is the place to be in the next 5-10 years for the following three reasons:
- It is the highest growth market to create impact, second only to goods commerce. More the internet commerce, higher will be the need for logistics and trucking to move goods. The next Amazon and Alibaba will come from supply chain technology and data disruption.
- Technology and data play has only just begun. Data availability is exponentially increasing through the use of GPS, smartphones and IoT sensors.
- The problems in this sector are far more challenging and futuristic. It requires interplay of automation using IoT/driver assist systems, advanced mathematics/algorithms and high quality UI/UX to exponentially increase adoption. Many other sectors don’t offer such a wide range and depth of problems.
Rivigo is leading the wave of disruption in trucking through a combination of the following factors:
- A global-first unique operational ideas based on driver and network relay.
- An outstanding leadership team across business, operations and technology
- A strong and unflinching belief in the power of data
Rivigo has already attained a high-quality business scale in India and aspires to build solutions which are applicable globally. In the truest sense, it has the potential to do what Amazon and Alibaba have done to commerce, Uber has done to cabs and several other disruptors have done to large global markets. The next 5-10 years are, therefore, going to be exciting and enriching with Rivigo tech and data teams working on some of the critical problems in this industry:
Network relay model
The driver relay model needs sophisticated technology to ensure that millions of trucks can run smoothly every month with several million pilot changeovers. The underpinning of this technology is a network model that can predict estimated time of arrival, optimize wait time and carry out driver performance and behaviour management. This model brings everything together from the network under one umbrella and creates a coherent stream of output to make the pit stop changeover process seamless and scalable.
Fuel analytics and optimisation
Fuel is one of the biggest operating costs in logistics and fuel pilferage is a rampant problem for any trucking company having its own fleet of vehicles. Currently, no reliable technology solutions are available to prevent pilferages as the values fluctuate and the data must be processed in real time for even small reduction in fuel value. A fuel graph is a volatile time series graph, very similar to financial time series models and requires both predictive and heuristic problem-solving approach. We are building patented fuel technology involving many complex algorithms and data science models to improve fuel efficiency.
Resource allocation and optimisation
In trucking, any idle capacity – truck or the driver, is a fungible capacity. You cannot keep less or more of the capacity at any point in the network. This is a massive problem and requires queuing theory, linear programming and advanced mathematical modelling to ensure the system is optimised and balanced.
Human behaviour analysis
Good driving is at the core of making logistics successful. This means that every minute of driving across the network has to be monitored and analysed. The big data from past and present must be constantly evaluated to determine and predict the driver's behaviour. This needs to be done in real time to know how a driver is driving to take immediate corrective actions. Is the driver in control of the vehicle? Is the driver driving cautiously? These are just some of the questions that need to be answered to create a qualitative system via a quantitative model.
All the trucks at Rivigo are fitted with several different sensors and IoTs. These IoTs generate massive amount of data that need to be processed, consumed and analysed. The analysis and data science carried out on this data turns Rivigo trucks into smart trucks. These smart trucks run on a geo-grid; and we are building an advanced location analytics engine for constant monitoring and simulating intelligent events. We are also building an artificial intelligence layer based on machine learning and deep learning approach for simulation such as demand-supply matching, traffic maps (imagine Google Maps for logistics), hotspot and density analysis.
Time continuum and visualisation
Rivigo is building a time continuum of its key resources that will allow the prediction and creation of a performant and efficient logistics system. A time continuum is the analysis and visualisation of all that is happening during the lifecycle of a resource and is a solution that gets built after applying algorithms, intelligence and predictive behaviour on a time series of huge quantities of data. This needs scalable real time batch processing of big data.
Line haul planning
Line haul planning optimises the plan based on historic demand, volume and service time commitments. The planning model determines the number of vehicles required on each route and network in an optimised way such that the shipments can be routed in the most efficient manner. This planning can also be used for processing centre capacity planning and building sales strategy to optimise the entire network. Line haul planning is inherently a linear programming problem with multiple levels of optimisation and requires very sophisticated approximation and heuristics to solve it.
One of our overarching goals is to bring 2 million trucks in India online in the next 3-4 years. We are building a high-quality tech and data platform to bring the entire trucking commerce (fuel, service, brokerage, resale, financing) online to ensure higher efficiencies, lower costs and data-led optimisation for individual truckers. This is an immensely exciting project being led by world-class engineers.
The future will be better if we waste less and use fewer resources for more and more output. Rivigo’s core operating philosophy is based on this approach – through use of data we want to further gain marginal efficiencies to make the world of logistics as automated, efficient and safe as possible.