Further, Faster, Cheaper: How Data Analytics Helps Mobility Businesses Go an Extra Mile
A practical guide for mobility businesses on how to make the most out of their data and build data-centric solutions. … Read More
Picture this. You’re driving back home after a long day of hard work. Exhausted from a series of back-to-back calls, starving despite that quick, half-eaten sandwich you rushed through between one virtual meeting and another. Dying to finally get well-deserved rest.
But just then, a sharp squeal comes from under the hood. Is it an issue with brakes or a loose or worn fan belt? Maybe the steering system went down?
Either way, your dreams of a good evening’s rest are now ruined, and you’re up for towing or an unplanned inspection. Both of which are going to cost you dearly.
Now, what if all of that time and hassle could have been avoided?
We won’t keep you in suspense. It could, and predictive maintenance is the way to achieve it. And not only on an individual driver’s level, but also when it comes to maintaining entire fleets of vehicles in good shape and preventing unexpected failures.
Predictive maintenance generally refers to the combined powers of IoT sensors, data analytics, and artificial intelligence (AI) applied to forecast equipment failures before they occur and recommend repairs in time.
In the specific context of fleet management and vehicle maintenance, mobility predictive maintenance software uses:
to come up with recommendations on impending failures or suggested repairs.
Coming back to our traumatic road event, situations like this are common when the traditional approach to vehicle maintenance is applied. By traditional, we mean maintenance based on mileage or, worse—estimated mileage.
Since individual driving style heavily impacts a car’s tear and wear, it is extremely challenging to accurately predict the lifetime of car parts using this method, which considers the same, generic metric, regardless of how someone drives. As a result, many issues occur that impact individual drivers, courier and transport services, car rental and sharing operators, and fleet managers. These include:
As opposed to traditional preventive maintenance techniques, predictive maintenance software considers numerous factors to infer the time to the next maintenance and accurately assess vehicle wear.
Instead of focusing solely on time estimates and the number of miles, it factors in the vehicle, driver, and contextual information (weather conditions, traversed routes, road conditions, past events, etc.). The result is reliable, fact-based insights into the actual vehicle wear and the factors that affect it. As a result:
In addition to these “standard” benefits, vehicle predictive maintenance solutions offer an unlimited number and variety of use cases they serve. Their sophistication depends only on one’s imagination and the data sets available.
For example, a car manufacturer or a fleet rental company may leverage the insights from predictive analytics to build a customer-facing app showing the current condition of a vehicle. Or by integrating car maintenance data into the systems, road assistance companies can better prioritize interventions and provide support faster and more efficiently.
Here’s another way to look at predictive vs. preventive vehicle maintenance. Let’s take brake pads as an example. Traditionally, the owner or driver wouldn’t find out that they need changing until taking the vehicle to the inspection (or, worst-case scenario, when the brakes stop working properly en route). Meanwhile, predictive maintenance would allow them to learn exactly when they should schedule a part replacement based on the following key data:
Taking all of these factors together, the predictive maintenance software would apply AI algorithms to offer precise information about which parts need immediate attention. Depending on the needs, how that information is passed on to the owner and through what interface can be fully tailored.
For example, the status of brake pads can be visualized as a simple graph that provides the percentage value (where 100% means a part is brand new, and the closer to zero, the higher the wear and shorter the lifespan). The information presented in this way is clear, efficient, and easy to read, but other visualization methods can also be used for the user’s convenience.
Example for a truck mainenance state
Even though we’ve mainly discussed predictive maintenance in the context of an individual car, many sectors can take advantage of the technology’s capabilities. The specific benefits may slightly differ from one vertical to another (and if you want more details on that, get in touch with us regarding your needs).
Still, on the whole, they boil down to several key gains: proper maintenance, optimized car and parts lifetime, lower downtimes, safer trips, and lower costs.
Here are a few main beneficiaries:
Key takeaway: Tying the maintenance schedule solely to the driven miles is inefficient and outdated. Keeping vehicles in optimum operating condition relies on so much more than just the distance covered. Continued advancements in IoT, telematics, AI, and data science technologies have enabled a much more accurate and cost-efficient approach.
Let’s rewind to the moment you’re leaving the office and about to get in your car. With predictive maintenance systems under your hood, you can rest assured that your road runs smoothly, and you’ll get back home just in time for dinner. Safe, sound, and with savings in your pocket.
A practical guide for mobility businesses on how to make the most out of their data and build data-centric solutions. … Read More
Remember the early days of the pandemic? Cities became ghost towns, and public spaces were no longer public, as everyone locked in to avoid getting infected. COVID has affected most aspects of our lives, and mobility was no different. With work and school going remote and traveling severely limited or outright prohibited, roads emptied. As a result, traffic remained light—even though driving was the only way to get a little taste of the outside world for many. … Read More
The invention of the gas-powered car by Carl Benz in 1885 wasn’t just another step in the history of mobility—this time, humanity has made a real leap forward. By making transportation more efficient and accessible, internal combustion engine (ICE) vehicles have affected all industries in one way or another. Over time, we started to rely on them so heavily that we now design our routines, lifestyles, and even entire cities around them.
But no king rules forever. Fast-forward to today, and after almost 130 years, the reign of combustion engine cars is slowly but surely coming to an end. A new contender is on the rise—electric vehicles (EVs). … Read More
According to various sources, a new car loses 9–11% of its value the moment you drive off the dealer’s lot. Over 2-3 years, its price may diminish by more than a half! Although it might seem that the situation is helpless, data science can provide a cure. Learn how applying the right algorithms on telematics data can help you protect and boost the value of your vehicles.
… Read More
A practical guide for car insurers to build customer-engaging products, expand services, and boost profitability in the post-pandemic times.
… Read More
Picture this. You’re driving back home after a long day of hard work. Exhausted from a series of back-to-back calls, starving despite that quick, half-eaten sandwich you rushed through between one virtual meeting and another. Dying to finally get well-deserved rest.
But just then, a sharp squeal comes from under the hood. Is it an issue with brakes or a loose or worn fan belt? Maybe the steering system went down?
Either way, your dreams of a good evening’s rest are now ruined, and you’re up for towing or an unplanned inspection. Both of which are going to cost you dearly.
Now, what if all of that time and hassle could have been avoided?
… Read More
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