Case Study: Logistics
What It Takes to Achieve Predictive Precision in Cold-Chain Logistics
It takes a team of experts of IoT engineers, data scientists, and Azure architects to harness Azure’s cloud-scale analytics and IoT telemetry.
About the Client
The client is a leading U.S. refrigerated-storage specialist supporting major food and pharmaceutical supply chains. They manage a nationwide fleet of IoT-equipped refrigerated trailers, generating vast streams of unstructured telemetry and service-record data.
The Challenges
- Fragmented Data: Data was unstructured, dispersed across multiple platforms.
- Integration Complexity: Difficulty integrating diverse data from IoT devices and applications.
- Limited Analytics Resources: Insufficient internal expertise for machine learning.
- Past Implementation Failures: Previous predictive analytics efforts were unsuccessful.
The Solutions
- Advanced Infrastructure: Azure Synapse, Data Factory, and Power BI to streamline data.
- Machine Learning Models:Azure IoT and Machine Learning Studio for predictive insights.
- IoT Device Integration: Unified data collection from fleet IoT and telematics.
- Predictive Maintenance Tools: Anomaly detection and Remaining Useful Life (RUL) estimation.
The Benefits
- Proactive Maintenance: Early issue detection prevented breakdowns.
- Reduced Costs: Lower downtime and minimized repair expenses.
- Better Customer Satisfaction: Reliable services increased customer trust.
- New Revenue Opportunities: Offering predictive maintenance services.
- Rapid Digital Transformation: Quickly modernized operational processes.
What the Client Said
“Thanks to Korcomptenz’s embedded Power BI dashboards, we now see live profitability by job at the click of a button — no more waiting days for spreadsheet consolidations. Their change-management support ensured our staff adopted the new workflows overnight.”