Hear the stories of two Rivigo warriors as they describe about how they are making an impact and building new India.
Work on cutting edge technologies and build IoT systems. Rivigo is the place to be!
Global trucking revenue pool is close to USD 2 trillion dollars which is about 20X the cab market revenue pool. Even in the developed market such as US, it is a highly fragmented and antiquated business which lacks use of technology and data.
If you are an aspiring leader in technology and data, this is the place to be for the next 5-10 years for the following 3 reasons:
Rivigo is leading the wave of disruption in trucking through a combination of the following factors
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 is going to be exciting and enriching – some of the sample problems Rivigo tech and data teams work on:
Network relay model
The driver relay model needs sophisticated technology to ensure that millions of trucks can run smoothly every month with several millions pilot changeovers. The underpinning of this technology is a network model that can predict estimated time of arrival, simulation models to predict vehicle arrivals, wait time optimization and driver performance and behavior. This model brings everything together from the network and creates a coherent stream of output to make the pit stop changeover process seamless and scalable
Fuel analytics and optimization
Fuel is one of the biggest operating cost in logistics and fuel pilferage is a rampant problem for any trucking company having fleet of vehicles. However, reliable technology solutions are not available at present to prevent pilferages as the values fluctuate and the data has to be processed real time for even small reduction in fuel value. A fuel graph is a volatile time series graph, very similar to some of 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 optimization
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
Human behavior 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 current has to 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 make immediate corrective actions. Is the driver in control of the vehicle? Is the driver driving carefully? Is the driver driving cautiously? These are just some of questions that needs to answered to convert a qualitative system via quantitative model.
Geo analytics
All the trucks at Rivigo are fitted with several different sensors and IoTs. These IoTs generate massive amount of data that needs to be processed, consumed and analysed. The analysis and data science on this data turns Rivigo trucks into smart trucks. The smart trucks run on a geo-grid and we are building very advanced location analytics engine for constant monitoring and simulating intelligent events. We are 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 visualization
Rivigo is building a time continuum of its key resources that will allow to predict and create performant and efficient logistic system. A time continuum is analysis and visualization of all that is happening during the lifecycle of the resource and is a solution that gets built after applying algorithms, intelligence and predictive behaviour on a time-series on huge quantities of data. This needs scalable real time and batch processing over big data.
Line haul planning
Line haul planning optimizes the plan based on historical demand, volumes and service time commitments. The planning model determines the number of vehicles required on each route and network in an optimized way such that the shipments can be routes in the most efficient way. This planning can also be used for processing center capacity planning and building sales strategy to optimize the entire network. This problem is inherently an LP problem with multiple optimization and requires very sophisticated approximation and heuristics to solve it.
Tech platform
One of our over-arching goals is 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 efficiency, lower costs and data led optimization 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 less and less 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 the marginal efficiency to make the world of logistics as automated, efficient and safer as possible.
Please do reach out at hemant@rivigo.com if you have common interests.
A convoy of truck recently traveled in Europe as part of the European Truck Platooning Challenge. One of the convoys traveled more than 2,000 KMs platooning where ever possible with all driverless trucks following the lead truck with a driver inside.
The driverless trucks open up several possibilities including higher utilization of inventory, lower fuel consumption (which is a bug cost) and less human errors leading to overall safety and better efficiency in the system.
This week as part of Kista Mobility Week Program, Ericsson is offering test rides of driverless buses from and to Kista Galleria in Sweden.This is a very interesting demonstration of what is possible around future transport.
The transportation industry is huge both in volume and financial terms. And it is witnessing massive disruption that will change the way people and goods get transported. I am very excited about the kind of technology, intelligence and innovation that it will require to build such a system and operate at a scale.
On November 11, Alibaba posted a record $14.3 billion in sales on Singles’ day passing every record that any company have ever posted. And this is just the beginning of what it means for future of logistics.
According to the Bloomberg post, Alibaba quoted
“Alibaba estimated that 1.7 million deliverymen, 400,000 vehicles and 200 airplanes would be deployed to handle packages holding everything from iPhones to underwear. Mobile devices accounted for 69 percent of Wednesday’s transactions.”
This is significant in many ways. The technology needed for building such kind of reliable logistics has to provide intelligence at another level. Imagine a constant stream of geo-location data from half a million trucks.
How will you place such large number of trucks every day? What will be the placement algorithm that will be used?
How will the technology churn data at this large scale on a low latency system? How will you design technology for such low latency?
What about the memory and server farms that will be setup? What about the failure points in the system? The system cannot go down under any circumstances because there is no way to find something missing manually – a needle in haystack!
How will you monitor performance? Nobody can watch the normal performance of 400,000 trucks. Just imagine if looking at a truck takes 1 minute, you need 400,000 minutes or around 6,666 hours or cool 277 days to monitor these trucks. What kind of user interactivity that needs to be provided with the use of technology that will make 277 days job to a less than few minutes job.
There is a disruption in the logistic industry that requires another level of technology and it is inevitable!
At Rivigo, data meets logistics and magic follows. We are transforming the antiquated logistic industry and bringing it into the 21st century with process automation, driver analytics and data science.
Rivigo is re-envisioning the truck as a Internet of Things (IoT) platform with intelligent sensors that constantly interact with a real-time responsive logistics network. We use the IoT to assist in integration of communications, control, and information processing across logistics networks that focus on all elements including the vehicle, the infrastructure, and the driver.
The charter of Rivigo Labs is to create the next generation of data acquisition, processing and visualization tools that will drive change in the logistics industry. Some of the problems we work on includes network optimization, recommendations systems, end-to-end automation, human factor design, smart trucking systems and beautiful visualizations, all at tremendous scale. We are not only pushing the envelop in the logistics industry, but we are also generating cutting edge tools in IoT, data science and people analytics.
In nutshell, we are building next generation transportation data science!
Life was simple when cable television and mobile started prepaid model. All you have to do is to decide the right recharge amount to continue to use the service, go online and recharge. The only additional complexity was selecting the call rate for different recharge option in case of prepaid mobile.
It ain’t simple any longer. I was recently trying to recharge my cable television account and I was shown a variety of coupons that I can get *free*. And then you have services like paytm and freecharge that formalizes this business model.
While the idea of using coupons to market and get customer is great, there are few problems with this approach.
1. A simple recharge option has to go through a complex decision making process. The reason I am online is not to shop but to recharge the account. The decision making and hence the transaction is slowed down because there are just too many coupons to chose from.
2. Feeling of losing out on something. There will be a tendency to perhaps check all the coupons to avoid the fear of losing out on something free. After navigating to the third page, it is easy to forget that you logged in to recharge your prepaid account and not shopping.
3. Most options are not useful. At least that’s how they appeal to me. Maybe I was not looking to shop but to recharge or maybe I do not have need to use any of the options in near future.
What can be done
The current model needs to be flipped. When I am shopping, show me the option to recharge and not the other way round. The money for this recharge can still come from the mobile or cable provider. The benefit with this approach is that it will give me a sense of instant discount or benefit on the purchases that I am making. The coupons are suddenly useful. While I may accumulate more in my prepaid account but there is almost a certainty of using the extra recharge sooner or later.
Anyone who is using cable TV services from Tata Sky is familiar with this screen. When it rains, you lose the signal. In the world of internet connectivity of 4G and beyond, this is just unacceptable.
So why people are still hooked to cable services in India? Its cheaper. Monthly subscription is INR 500 ( < $10) with almost unlimited channels. The other one being low penetration of internet bandwidth and on-demand options.
However in a country like US, 7 in 10 TV households have a DVR, subscribe to Netflix, or use Video-on-demand (VOD) from a cable or telco provider, per new data from Leichtman Research Group (LRG), with about 1 in 10 using all three services. 61% of all cable subscribers have used VOD — compared to 43% in 2008, and 10% in 2004.
I consider areas where customers continue to use product and services despite poor quality of service as having the highest potential for disruptive opportunities.
I do not have current data on cable and VOD viewership in India but with services of existing cable service provides, on demand TV adoption for on demand TV can only accelerate.