Big Data & Real-time Analytics in Transportation
Logistics is a particularly data-heavy industry that is very reliant on optimization for a profitable and sustainable business model. If that weren’t enough, it is also disproportionately risk sensitive as any unscheduled downtime is a direct and irrevocable loss. DataReady’s platform can widen the profit margin and ensure that assets are exposed to fewer risk factors.
Predictive and Preventative Applications
The same ATM card is used within 10 minutes in two separate locations, triggering a high-probability pattern suggesting card fraud. The second location is immediately supplemented with additional verification measures, potentially preventing loss of revenue to the bank.
Vehicle operators are a primary failure point and, until road legal automated vehicles become practical, one must still contend with human nature which can be unpredictable. By plugging vehicle telemetry and machine data into DataReady for real-time analysis, it is possible to immediately identify driver behavior that isn’t normative. Either by matching pre-determined parameters or by comparison to a generalized model of driver behavior built on pre-existing logs of machine data.
Keeping your vehicles operational is a top priority, a vehicle that isn’t delivering a service is not only failing to generate revenue, it is actively draining it. Scheduled maintenance is of course necessary and goes a long way to preventing mechanical failure, but even scheduled maintenance may miss an issue that could lead to a vehicle-stopping failure. Using DataReady’s platform enables predictive analytics based on a model built from machine data logs (depending on the type of data captured), real-time vehicle data can be compared and when it becomes apparent that a vehicle will need maintenance before going out on its next trip you will be notified by the system and appropriate measures can be taken to mitigate the disruption
Foreseeing the unforeseen is the holy grail of transportation, a sustained run of unfortunate events can seriously affect capital reserves and put a strain on liquidity. Analytics cannot predict complete accidents, but it can take events that are very hard to predict and make them feasible to predict as a normal part of business.
Route planning is the key to profitability in the transport sector. The need to optimize multiple variables such as fuel consumption, time, road safety and many others has led to impressively sophisticated planning methods in this industry. In the face of modern Big Data analytics and predictive algorithms however, using these techniques is akin to using a blunt instrument in place of a scalpel to perform surgery. Routes can now be optimized using a massive number of variables, which can include real-time traffic conditions, weather data or any other data source of sufficient quality that will be relevant to the model used to plan routes. This is also one of the easiest applications in which to measure effectiveness, as one can quickly see whether costs are dropping, performance is increasing or profits are rising.
Are your prices really competitive? Are you actually making money or are there hidden variables shrinking the profit margin? Powerful analysis of pricing patterns, trends and cost factors can illuminate these questions and many more. Pricing is especially complex in transport and once the other usage cases mentioned in this section are achieved it may become necessary to substantively review pricing to take new levels of efficiency into account.
Revenue Generating Applications
Directly generating revenue using DataReady’s platform in the transport sector is one of the most clearly powerful applications of the technology.