AI in the Transportation Industry: Switching to Predictive Maintenance
By anticipating when components will fail based on performance data and other information, predictive maintenance has the potential to assist fleets by avoiding vehicle failures while lowering maintenance costs.
However, this data-driven maintenance strategy is still in its early stages, and problems regarding warranties and other issues must be addressed before a major shift in industry norms can take place.
The transportation business is already significantly investing in artificial intelligence (AI). The worldwide AI market for this industry was $1.4 billion in 2017, and it is anticipated to grow to $3.5 billion by 2023. It’s also heartening to see that transportation and logistics is one of the areas that benefits the most from AI and machine learning (ML) technologies.
Predictive Maintenance vs. Traditional Approach
The old method to fleet management was to pull cars out of rotation for routine maintenance every 4,000 miles (roughly), which was often unnecessary. Owners would inspect their gear at pre-scheduled repair sessions based on manufacturer recommendations, rather than equipment status, if they followed the traditional approach to maintenance. Even if the old components were still functional, they would be replaced. Age-related maintenance, on the other hand, isn’t always the greatest option.
The issue with this method is that it ignores other important aspects. Operating circumstances, for example, may differ from those predicted by the manufacturer.
As a result, some parts may be destroyed more quickly or may last longer than the manufacturer intended. Only 18% of assets fail due to age-related concerns, according to research by ARC Advisory Group. The remaining 82 percent of problems are unpredictably caused.
Predictive maintenance (PdM) enables owners to make repair decisions based on the vehicle’s actual state rather than predetermined time periods. AI-enabled algorithms aid in determining a machine’s “health” status and if it requires maintenance. The maintenance crew adds sensors to detect a variety of factors, such as vibration and temperature, in order to thoroughly check the equipment. Predictive maintenance is becoming increasingly popular in the transportation business, which is not shocking.
Predictive Maintenance is Booming
Predictive maintenance, as we know it now, arose with the fourth industrial revolution. It is based on data-driven choices and machine monitoring in real time. PdM adoption is mostly facilitated by the following factors:
- The proliferation of wireless connection, which benefits the Internet of Things.
- AI advancements.
- Safe, dependable, and affordable computing abilities.
IoT sensors relay data from devices in real time. This data is analyzed by AI systems which detect aberrant activities, indicating that a component will break shortly. Detecting problems early allows equipment owners to schedule maintenance and stock replacement parts.
According to McKinsey, this type of predictive maintenance strategy decreases equipment downtime by 30–50 percent while increasing its lifetime by 20–40 percent.
How Does the Transportation Industry Benefit from AI-Based Predictive Maintenance?
AI integration in the logistics business will aid specialists in generating prescriptive maintenance plans. PdM implementation provides the following advantages:
Analytics is automated.
AI relieves fleet operators of the load of data processing and provides helpful insights. AI integrated into dynamic dashboards, for example, may detect recurrent difficulties and drivers with the most troublesome “style of driving.” It will also allow the maintenance staff to prioritize which equipment needs to be serviced.
Minimized vehicle downtime
Vehicle downtime costs some fleet businesses $448–760 in lost income per day. Organizations may drastically reduce this by integrating artificial intelligence into their transportation systems and monitoring truck data to detect possible problems. This will completely remove mileage-based maintenance.
Reduces fuel waste
Vehicle maintenance that is done correctly saves gas. An artificial intelligence logistics system can assist in detecting pressure variations between the input and exit, which can occur when the filter is blocked. As a result, the vehicle owner will have an opportunity to change the filter before the vehicle’s monitoring system generates Diagnostic Trouble Codes (DTC).
Predictive Maintenance in Action
Despite the fact that predictive maintenance is still in its early phases, there are numerous convincing examples of AI in logistics and transportation.
Autonomous Vehicles (Use Case 1)
Self-driving cars are getting a lot of attention these days, and for good reasons. The deployment of autonomous vehicles in the logistics and transportation sector has the potential to save time and money while also lowering accident rates.
Smart Roads (Use Case 2)
Smart highways are another example of AI use in logistics. Highways with solar-powered LED lights are one type of this technology being used today. Solar panels help to generate power, while LED lights warn vehicles about road conditions. In addition, solar panels keep the road from becoming icy in the winter.
Demand Forecasting (Use Case 3)
One essential business necessity that most organizations face is the need to forecast the amount of supply and commodities they will require in the future. Running out of inventory leads to missed sales and income, and, in many cases, might encourage consumers to defect to competitors.
AI provides a variety of algorithms that can forecast trends. According to Deloitte, in many situations, computer algorithms may outperform human specialists in predicting outcomes.
5 Things to Consider When Starting Predictive Maintenance
If you are confident that adding artificial intelligence-based predictive maintenance into your logistics system would save you time and money, follow these steps:
- Collect many sorts of data.
Begin by putting sensors on the board of your car to collect telematics data such as hard braking or acceleration, fuel usage, idle time, and so on. It should be noted that contextual data, such as weather conditions, will be required for full analytics. Keep track of the maintenance you’ve previously done, as well as the cost profiles.
- Use adaptable cloud technology for data storage.
Trucks are made up of several components supplied by various manufacturers, and they create around four gigabytes of data every day, which is difficult to store and retrieve. If necessary, flexible cloud architecture can make it easier to integrate third-party services, while cloud computing can help analyze huge datasets and access data from anywhere, at any time.
- Connect this new system to the rest of your corporate apps.
Integrate the new tool with your existing enterprise asset management (EAM) and data warehouse to generate a unified picture of your affairs and to initiate a pipeline of related actions.
- Include user-friendly dashboards.
Because predictive maintenance systems generally store massive quantities of data, adding a visual depiction will be very beneficial. Fleet managers may examine the data in which they are most interested at any given time by using a dashboard with customized capabilities. Consider a cross-platform representation so that workers may view the data from both their work PCs and mobile devices.
- Publicize the new culture.
The transportation sector will transform as a result of predictive maintenance, and managers must ensure that everyone is on board. Drivers have followed the same routine since the early 1900s, when the first diesel engine was mounted in a truck: Whenever the vehicle malfunctions, they tow it in and fix it at a repair shop. Their attitude will have to shift in order for them to embrace the notion of proactively fixing a part that has yet to fail.
Maintenance Is Better than Repair
The transportation sector has numerous opportunities to enhance its operations as the Internet and IoT technologies become more widely available. Predictive maintenance is one such possibility. It may significantly reduce vehicle downtime, stock spare parts more effectively, and even guide professionals through the repair procedure.
You will need to make some internal adjustments in order to fully profit from PdM. However, it does not end there. The entire transportation ecology will have to change. Manufacturers, for example, may have to alter their warranty standards to allow replacing damaged parts that haven’t yet failed.
It will take time for these modifications to take effect. Meanwhile, skipping the warranty to repair a part that will ultimately fail may be less expensive than putting the truck out of service later on. Maintenance is already preferable than repair.
