Gopison’s Predictive Maintenance Solution
Overview: Gopison aimed to leverage data analytics and predictive maintenance technologies to optimize vehicle maintenance schedules, reduce downtime, and enhance the reliability of its vehicles. Challenges: Unplanned Downtime: Unscheduled maintenance and repairs led to unexpected downtime for vehicle owners, impacting their productivity and satisfaction. Reactive Maintenance: The traditional approach to maintenance was reactive, with repairs being performed only after a breakdown occurred, leading to higher repair costs and reduced vehicle reliability. Limited Visibility: Gopison lacked visibility into the health and performance of its vehicles in real time, making it difficult to proactively identify and address maintenance issues. Solution: Gopison implemented a predictive maintenance solution that leveraged IoT sensors, machine learning algorithms, and predictive analytics to monitor vehicle health in real time and forecast potential maintenance issues before they occurred. The solution included: IoT Sensor Integration: Gopison installed IoT sensors on its vehicles to collect real-time data on various parameters such […]
