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For companies operating major vehicle fleets, up to half of all their expenditures on traditional maintenance are wasted. That’s right – only half of the money spent on maintenance is money well spent. Moving from either a reactive (responding to emergencies) or prescriptive (simply following established maintenance schedules) model to an AI-driven predictive maintenance model can save money, reduce vehicle downtime, and substantially increase driver and customer satisfaction.
Traditionally, fleet management used a combination of reactive and prescriptive or schedule-based maintenance. For example, when managing trucks, vans, or other heavy vehicles, it was a common practice to take the machine out of the fleet rotation every 4,000 miles or so to perform a full regular maintenance protocol whether or not it seemed necessary. This same conventional approach dictates that owners or service staff check machinery during predetermined repair sessions according to the manufacturer’s recommendations and without considering equipment status unless there is a breakdown. Again, following this model, service staff would replace old or out-of-spec parts even if they were still working and showing no signs of critical wear. This prescriptive and schedule-based maintenance approach helps minimize downtime, but it isn’t the best or most efficient way to allocate scarce resources and get the maximum value from your fleet.
This approach might focus on the wrong things, and a fleet’s operating conditions may differ markedly from a manufacturer’s expected operating conditions. This can mean some parts wear out faster (or slower) than the manufacturer’s service recommendations. ARC Advisory Group has assessed that less than one-fifth of all component failures are because of age, while fully 82% of such failures are random.
A predictive maintenance (PdM) model allows fleet operators to repair vehicles according to their condition rather than pre-determined and unresponsive maintenance schedules and replacement timeframes. Under this model, sensors gather data about vibration, pressure, temperature, and unexpected shock forces. The sensors then pass the data off for AI-enabled algorithmic analysis to understand the machine’s overall level of “health” and performance and then make informed decisions about when and what replacements should occur. Given the dramatically enhanced efficiency and reduction in waste, it’s no surprise that the transportation industry is embracing AI-enabled predictive maintenance.
The practice of predictive maintenance, as we now understand it, took shape during the 4th industrial revolution. This set of techniques and technologies leverages real-time machine monitoring to make informed, data-driven maintenance decisions that maximize the components’ usable lifespan and seek to prevent unexpected downtime. Here are a few of the factors that have led to the proliferation and adoption of PdM techniques and strategies:
In a predictive maintenance regime, fleet-installed IoT sensors continuously monitor and transmit equipment data in real-time to the cloud. Specialized AI algorithms analyze this data to spot anomalies or behaviors suggesting imminent equipment failure. As a result, operators have time to acquire spare parts, plan to take the machinery out of rotation, and schedule maintenance.
Global consultancy McKinsey estimates that predictive approaches to maintenance can offer a reduction in equipment downtime of as much as 30-50% while extending the lifespan of the same equipment by at least 20% and as much as 40%.
The incorporation of advanced, AI-driven analysis in the transportation and logistics industry will enable maintenance personnel to establish cost-cutting predictive maintenance programs. PdM implementation in the transportation industry can bring a wide range of benefits, including:
AI can directly lift the burden of analyzing fleet data from fleet operators and managers. An AI-based predictive maintenance system can also offer better insights than human analysis. For example, dynamic dashboards with AI analysis can identify and disincentivize the most damaging driving styles, encouraging drivers to accelerate and decelerate more slowly to preserve parts and reduce fuel consumption. The AI-based predictive maintenance system will also let your maintenance team prioritize the most critical machinery for maintenance.
The costs of vehicle downtime can be substantial. Some fleet operators face direct losses of $448-$760 per day in lost revenue due to an individual vehicle’s unexpected downtime. If you incorporate artificial intelligence and an enhanced sensor suite into your fleet, you can start to analyze truck data to identify declining or deteriorating components before the telltale signs of failure emerge. This approach does away with time or mileage-based maintenance altogether.
It is well-known that properly maintaining vehicles lowers fuel consumption. An AI-based predictive maintenance system might detect pressure differences between a machine’s fuel inlet and fuel outlet, indicating that your fuel filter may be clogged. This means you can replace the component before it fails, protect the machine from the upstream and downstream effects of a plugged filter, and stop replacing filters with plenty of life. This is just one example of an AI-based predictive maintenance system being vastly superior to waiting for the vehicle to send a Diagnostic Trouble Code (DTC) over a failed part.
You may have already decided that artificial intelligence-based predictive maintenance makes sense in your transportation and logistics fleet. If you have decided that this technology is likely to save you time and money, here are your next steps:
This will typically include installing appropriate sensors in your vehicles to gather and transmit analytics data on driving patterns (such as sudden acceleration or deceleration, time spent idling, temperature, and fuel consumption over time)
Trucks and other fleet vehicles include many components from various manufacturers. A full suite of sensors in these machines will generate around four gigabytes of telemetry data per day, a considerable quantity that creates significant challenges for transmission and storage. Implementing a scalable cloud storage system can enable you to integrate or migrate to a third-party service provider if or when you require it. In addition, a cloud computing platform can help you securely process the enormous amount of data your fleet generates. These virtualized storage and processing systems can collectively guarantee that you can access your data and insights drawn from it anywhere and at any time.
Depending on your existing fleet management system, this could take various forms. At the very least, you’ll want to integrate your new storage, transmission, and predictive analytics tools into your current enterprise asset management (EAM) and data systems so that you can gain a holistic overview of your fleet’s condition and use these insights to activate a variety of workflows.
Like any large data project, a predictive maintenance program will contain more information than an average human being can ever make sense of at a glance. This means that data visualization techniques will be hugely beneficial to help you and the rest of your organization understand the most relevant data and insights at a given moment. In addition, customizability and flexibility of layout are key considerations to ensure that all staff can arrange the dashboards to suit their needs, and cross-platform functionality will ensure that everyone can access from their choice of device, whether that be a smartphone, tablet, or personal computer.
A change this big will inevitably usher in further changes. It is the job of organizational leaders to keep everyone together and help them see the bigger picture of how these changes will help. Bear in mind that there may be some resistance, given that the industry has stuck mainly to the scheduled, prescriptive model of maintenance since the early 1900s when manufacturers deployed the first commercial diesel engine in a truck. For predictive maintenance to deliver its full value, the mindset of drivers and service technicians will have to change to accommodate the idea of replacing a part that has not failed yet and which the AI systems expect to fail imminently.
With the widespread proliferation of high-speed internet connections and the increasing mainstreaming of iot software development company, the transportation and logistics industry now faces a menu of options for improving operational efficiency. The most immediately accessible for many organizations will be predictive maintenance as a one-stop solution to enhance almost every aspect of fleet management and maintenance. Few other solutions can promise to simultaneously increase vehicle uptime, improve spare parts inventory management, reduce costs, and improve technician performance on repairs.
To maximize the benefits of PdM for your fleet, you will need to make some internal and mindset changes. However, this shift will bring further changes across the industry and beyond. In fact, the entire transportation and logistics ecosystem will have to adapt to the changes brought about by predictive maintenance, AI, and IoT within the transportation industry. One immediate example is that this shift in mindset will require manufacturers to adjust their warranties to cover the replacement of parts that have not failed yet but are forecast to do so. While it may seem like an increased risk and cost initially, the overall reduction in repairs and associated costs makes the change positive for fleet operators and manufacturers alike.
Change is hard, but most fleets will save money by replacing components with predicted failures, even if it means bypassing the manufacturer’s warranty, and covering the cost directly. Increasing uptime and avoiding unplanned withdrawals from fleet rotation can be the cheapest choice. We know that maintenance is better than repair, and an AI-based predictive maintenance system allows us to get there comprehensively and quickly.
