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<\/p>\nFleet management companies have many expenses, but even seemingly insignificant maintenance issues can start a costly domino effect if not addressed properly and in time. Sometimes, timely detection of an upstream problem such as a turbo failure can prevent more expensive downstream issues, such as an aftertreatment failure.<\/p>\n
Waiting a week or two for the next scheduled maintenance kills precious time. Real-time monitoring and data-driven vehicle fleet maintenance software can warn a fleet manager about an entire chain of troubles even a minor problem can cause.<\/p>\n
The cost of trucking<\/b>
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The difference between preventive and predictive maintenance all comes down to the technologies behind software for vehicle maintenance. Artificial intelligence (AI) and Internet of Things (IoT) technologies can save fleet companies a fortune on vehicle repairs.<\/p>\n
In this article, you\u2019ll find:<\/strong><\/p>\n Fleet predictive analytics is used to forecast fleet maintenance based on available fleet management data and past experience. Businesses use fleet predictive analytics to reveal both explicit and implicit dependencies between particular events as well as to identify recurring patterns and trends.<\/p>\n Predictive analytics in fleet management helps to improve both fleet management modeling and the decision-making process. Fleet predictive analytics may rely on diverse types of input data, tools, and approaches. The essential components needed for effective and accurate predictive analytics include:<\/p>\n Simply put, historical data serves as the raw material for fleet predictive analytics. In turn, a blend of statistical analysis approaches is used to process the data, while ML- and AI-based solutions help to scale the process, enabling accurate and timely analysis of billions of data points, with no risk of human mistakes.<\/p>\n Fleet managers have a long journey from using preventive vehicle maintenance system that identifies existing problems to using predictive maintenance in automotive software that can detect issues before they manifest. Telematics data, artificial intelligence<\/a>, and cloud technologies<\/a> play a growing role in the development of predictive truck fleet maintenance software.<\/p>\n The most illustrative difference between preventive maintenance software for fleets and predictive maintenance is a shift from assumptions and strict scheduling to data-driven decisions and real-time equipment monitoring.<\/p>\n The old-fashioned approach was based on taking a vehicle out of rotation every 3,000 to 4,000 miles or every 10,000 to 12,000 hours for prescheduled and regular maintenance. On the contrary, trucking maintenance software can collect measurements on separate parts of a vehicle that influence maintenance \u2014 like oil and tire pressure \u2014 to schedule service of equipment only when conditions reach a threshold and most likely will cause problems.<\/p>\n Software for vehicle maintenance can build predictions based on direct measurements of equipment as well as contextual data like weather conditions, traffic, road quality, and a\u202fdriver\u2019s behavior<\/a>. Machine learning in fleet management can be trained on collected data to detect specific failure scenarios, taking into account the condition of separate vehicle parts and the conditions of vehicle exploitation.<\/p>\n Moreover, when integrated with MLOps services<\/a>, predictive analytics in fleet management software ensures continuous optimization, enabling efficient decision-making for maintenance, route planning, and resource allocation.<\/p>\n Time is the most valuable asset when it comes to detecting potential issues with fleets. It\u2019s pointless to detect a problem minutes before it causes a road incident or cargo damage. More time is needed to act in order to minimize costs and hazards.<\/p>\n This is why predicting equipment issues and planning relevant maintenance becomes so important for fleet management businesses. And predictive vehicle fleet maintenance software can provide alerts on potential issues several days to months in advance.<\/p>\n A smart alert system integrated within fleet maintenance software for trucking can accelerate fleet managers\u2019 and drivers\u2019 reactions in the field. It can also send alerts to make fast decisions on taking a truck off the road, visiting a nearby repair shop, or finishing the route and addressing the issue upon the vehicle\u2019s return.<\/p>\n Fleet Management Software Development Services<\/p>\n Tech-driven fleet maintenance is powered by decisions fueled with telematics data. The combination of large volumes of fleet management data captured with IoT devices and advanced machine learning for fleet management enable businesses to proactively identify fleet issues.<\/p>\n Using fleet predictive analytics software, managers can identify trends, forecast problems before they occur, and ensure that the fleet is managed sustainably. Simply put, the work of fleet predictive analytics can be divided into three major phases:<\/p>\n Let\u2019s take route optimization as an example. A route optimization system puts together and analyzes both current and historical traffic data along with GPS information and logistics schedules. Then, it generates the most efficient route.<\/p>\n While processing the data, an algorithm scores the likelihood of a particular event, such as traffic, a delay, or an accident. These predictions are then taken into account to generate an optimal route.<\/p>\n To process data analytics and deliver fast alerts, advanced predictive vehicle maintenance systems usually rely on cloud computing power and a flexible microservices architecture to integrate additional services for contextual data on things like weather and traffic. In the end, service and fleet managers receive easy-to-use reports with all the tools needed to plan relevant equipment maintenance.<\/p>\n<\/div>\n Let\u2019s<\/span><\/span>\u00a0<\/span><\/span>take a look on how\u00a0<\/span><\/span>artificial intelligence impacts fleet management<\/span><\/span>.<\/span><\/span>\u00a0<\/span><\/span>AI<\/span><\/span>\u00a0plays a critical role in building predictions based on collected telematics data. To process data analytics and deliver fast alerts, predictive\u00a0<\/span><\/span>vehicle maintenance system<\/span><\/span>\u00a0<\/span><\/span>should rely on cloud computing power and a flexible microservices architecture to integrate additional services for contextual data on things like weather and traffic. Finally, service and fleet managers should receive easy-to-use reports and have all the tools at hand to plan relevant equipment maintenance.<\/span><\/span>\u00a0<\/span><\/span><\/p>\n By turning to telematics data, fleet companies can shift from reactive to preventive measures. Doing so helps them address small problems before they become big. Connected IoT sensors can provide real-time data on vehicle parts and send Diagnostic Trouble Codes (DTCs) to track mechanical failures in real time while\u00a0<\/span><\/span>fleet maintenance management system<\/span><\/span>\u00a0uses this data for analytics and predictive maintenance planning.<\/span><\/span>\u00a0<\/span><\/p>\n A practical case study on how to apply telematics to fleet maintenance for tracking and analyzing DTCs collected from vehicles in the field<\/p>\n Taking into account<\/span>\u00a0the amount of data from the many sensors and vehicles,\u00a0<\/span>trucking maintenance software<\/span>\u00a0requires a flexible architecture that allows for easy integration of third-party services for additional contextual data. Cloud computing power allows for processing big data sets and provides access to critical data from everywhere while allowing businesses to shift to customer-oriented SaaS business models.<\/span>\u00a0<\/span><\/p>\n Example of a predictive\u00a0<\/span><\/b>vehicle fleet maintenance software<\/span><\/b>\u00a0<\/span><\/b>built on Google Cloud Platform<\/span><\/b><\/p>\n AI and machine learning algorithms<\/span><\/b>\u00a0<\/span><\/p>\n Service managers and fleet companies can use telematics data beyond DTC information to ensure smart analytics of all historical and collected data points on separate vehicle parts. Diving deeper into the data\u00a0<\/span>using\u00a0<\/span>ai for fleet maintenance software<\/span>,\u202f<\/span>sensor fusion algorithms for autonomous driving<\/span><\/a>, fleet companies can detect early warning signs of potential equipment failure.<\/span>\u00a0<\/span><\/p>\n Simply gathering all the data coming from sensors is not enough. Making effective machine learning predictions for fleet maintenance requires you to follow a certain flow:<\/span>\u00a0<\/span><\/p>\n So,\u00a0<\/span>how artificial intelligence impacts fleet management<\/span>?\u00a0<\/span>For electric trucks, for example,\u202f<\/span>predictive maintenance software<\/span><\/a>\u202fcan regularly check the battery status, transfer data to the cloud, apply AI models to predict how a vehicle will consume energy under the current conditions, and notify the driver to avoid issues like a dead battery as well as to plan maintenance when the battery\u2019s capacity goes lower than what\u2019s specified by the OEM.<\/span>\u00a0<\/span><\/p>\n User-friendly and comprehensive dashboards<\/span><\/b>\u00a0<\/span><\/p>\n When running AI data analytics for failure prediction,\u00a0<\/span><\/span>truck fleet maintenance software<\/span><\/span>\u00a0<\/span><\/span>should provide a visual representation of data in a consumable form. The best way to do this is to offer dashboards with customizable functionality so fleet managers can choose what data is the most critical to show. Cross-platform accessibility ensures fleet managers are always connected and can react to potential issues, monitoring fleets from mobile devices.<\/span><\/span>\u00a0<\/span><\/p>\n\n
\n\t\t\t![\"Digitally](\"https:\/\/intellias.com\/wp-content\/uploads\/2022\/07\/Digitally-Mature-Logistics.jpg\")
<\/div>\n\t\t\t\n\t\t\t\t\n\t\t\t\t\tDIGITALLY MATURE LOGISTICS WHITEPAPER<\/div>\n\t\t\t\t\tConquering the Waves of Technological Disruption<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t Download now <\/span>\n\t\t\t<\/div>\n\t\t<\/a><\/div>\nWhat is fleet predictive analytics?<\/h2>\n
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What is the difference between preventive and predictive software for fleet maintenance?<\/h2>\n
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<\/p>\n\nWhy should you use <\/span>predictive<\/span> analytics in fleet management software?<\/span><\/h2>\n<\/div>\n
\n \n \n Apply advanced IoT and AI technologies to telematics data for increased efficiency, insight-driven decisions, and transparent control over vehicle fleets <\/div>\n <\/div>\n <\/div>\n Learn more<\/span>\n\t\t <\/a><\/div>\n\nHow does predictive analytics for fleet maintenance work?<\/span><\/h2>\n
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What advanced technologies are behind predictive fleet maintenance?<\/h2>\n
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<\/p>\nTelematics data collection<\/h3>\n
\n \n \n <\/div>\n <\/div>\n <\/div>\n Read more<\/span>\n\t\t <\/a><\/div>\nFlexible cloud architecture<\/h3>\n
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