City planners need a wealth of data to plan snowplow routes, identify problem areas, or determine how to best spread salt and other chemicals. And that’s where predictive monitoring comes in. Predictive monitoring uses data recorded by intelligent sensors on machines to monitor their behavior over time and then evaluates that data using big data analytics in the cloud. This approach identifies abnormalities before they become downtime, increasing production reliability and schedulability.
Identifying Problem Areas
Snow removal is a critical service for cities, and optimizing efficiency with predictive monitoring is an essential part of the equation. Cities rely on GPS-based mapping software and real-time data from plow trucks to keep track of where each vehicle is operating and where it is not, enabling city planners to proactively address problem areas by adjusting truck routes as needed. Predictive analytics also help fleet managers anticipate when vehicle repairs are needed, which can help reduce downtime during storms and minimize the time that equipment is idle.
Identifying Problem Drivers
Some parts of the country sought to improve its snow removal operation by leaps and bounds. The solution was to automate the calling and tracking of their fleet of vehicles, which would save the foreman a significant amount of time during each shift. A specialized dispatching system will streamline this process while maximizing the company’s efficiency. One of the most important aspects was to identify problem drivers in a timely fashion to prevent accidents and possible injury or property damage to both the driver and others on the road. Fortunately, this was made easier with the introduction of predictive technology and a little common sense. Many data points were collected to inform the department’s decision-making better. They subsequently used these results to train better, retain drivers, and minimize the company’s liability exposure.
Identifying Problem Vehicles
If a problem vehicle is affecting the operation of your plant, it is important to identify it and take steps to fix it quickly. Having a way to track down the root causes of issues in real time can save money and increase production efficiency. One method to identify troubled vehicles is to look for patterns in the system’s data. These patterns may be indicative of past errors. This can help IT teams isolate the problem and work to correct it before it affects the entire operation. The Department of Transportation is experimenting with intelligent vehicle technology to give snowplow operators information about the roadway they cannot see. The system projects the road’s outline onto a flip-down flat glass screen like a sun visor. As the team works on the system, they listen to drivers’ reactions and make changes based on their feedback. For example, some snowplow operators requested the display be colored to distinguish white lines from yellow-marked ones.
Identifying Problem Routes
Predictive monitoring enables businesses to spot even small changes in data – the type of data that would otherwise be invisible – that could significantly impact plant performance and efficiency. This is often done by combining predictive analytics with big data and AI technology to produce early alerts for any anomalies. It’s also possible to use machine learning to determine the most suitable time to carry out a given action. In a snow removal operation, many decisions need to be made to ensure safe and efficient plowing of roads – including where to route vehicles and how best to transport and dispose of snow. The right combination of decision-making and technological innovation can help maximize efficiency, resulting in improved production and safety. The most effective approach to route optimization is utilizing a combination of mathematical algorithms and heuristic techniques to get the job done in the most cost-effective way possible. This includes the best way to route vehicles for snow and ice management and the most cost-effective methods to transport, dispose and recycle snow.
