Ambition:
A leading heavy lift and construction equipment brand recently deployed a connected telematics ecosystem, generating vast amounts of data on equipment usage patterns, loads, service hours, and more. Despite this influx of data, the brand faced challenges in translating it into actionable insights and monetizable opportunities, relying on unstructured Excel sheets that limited their ability to leverage the data effectively.
Action:
To tackle these challenges, we implemented a robust data modeling and analytics tool—leveraging industry-leading solutions such as Microsoft Power BI, Tableau, and Qlik Sense. We transformed raw data from Excel sheets into structured, actionable insights, creating opportunities to enhance equipment management and operational efficiency:
- Structured Data Integration: We consolidated data from Excel sheets into a centralized analytics platform, ensuring seamless integration and accessibility. Using Microsoft Power BI, we created a unified dashboard that provided real-time visibility into equipment usage, location, and status, allowing for comprehensive monitoring and better decision-making.
- Data Segmentation and Analysis: The unstructured data limited the brand’s ability to identify key patterns and trends. With Tableau, we segmented the data into critical categories—vehicle maintenance issues, usage patterns, fuel consumption, rapid acceleration, hard braking, hard cornering, idling, and speeding. This segmentation enabled detailed analysis and identification of actionable insights.
- Actionable Use Cases Development: Raw data lacked context, making it difficult to derive actionable use cases. Leveraging Qlik Sense, we developed actionable use cases for each data segment, turning insights into practical applications such as:
- Track Location and Usage: Monitoring application to track equipment location, utilization rates, and status.
- Improve Safety: Identifying equipment abuse and operational safety risks.
- Schedule Maintenance: Scheduling maintenance based on real-time usage patterns.
- Prevent Breakdowns: Diagnosing machinery health to prevent major breakdowns.
- Optimize Asset Allocation: Assessing equipment deployment to reduce idle time and ensure optimal resource availability.
- Improve Fuel Efficiency: Reducing unnecessary fuel consumption by tracking equipment utilization.
- Identify Bad Habits: Improving operator efficiency by identifying and correcting bad habits like prolonged idling.
- Predict Maintenance Requirements: Predicting maintenance needs to reduce operating expenses and last-minute repairs.
- Create Maintenance and Inspection Schedules: Developing accurate maintenance and inspection schedules from telematics data.
- Plan Routes: Planning efficient routes based on telematics data insights.
Ambition Realized:
By implementing advanced data modeling and analytics tools like Microsoft Power BI, Tableau, and Qlik Sense, we transformed the brand’s telematics data from Excel sheets into a structured, actionable framework. This enabled the brand to leverage real-time insights for improved equipment management, enhanced safety, optimized fuel consumption, and predictive and preventive maintenance scheduling, do soft-recalls and save a ton of money. The structured approach not only boosted operational efficiency but also empowered the brand to monetize their telematics ecosystem effectively, converting data into valuable business opportunities.