Predictive analytics insights for better vehicle maintenance and customer service

See how we helped a refrigerated storage logistics pioneer efficiently plan their maintenance efforts using predictive analytics insights from a host of Microsoft Business Solutions.

Keeping the #FocusOnInsights to predict problems in a time-sensitive industry


The refrigerated transport industry is redefining its standards through cutting-edge technology. Temperature monitoring systems, automated security platforms, rule-based alarms, time-based reporting, and more have helped overcome many logistical problems. But maintaining a fleet of these vehicles is still a Herculean task. It is not enough to detect problems in real-time anymore. Proactive maintenance of these trucks is essential to prevent problems like temperature loss and equipment failure even before they might occur. For refrigerated storage specialists, ensuring they are one step ahead is also crucial in offering excellent customer service every time. This is where predictive analytics comes in.

The deluge of data coming in from the many intelligent systems running on these trailers can be sorted and processed to uncover insights.

Case Study Predictive Analytics Insights for better Vehicle Maintenance and Customer Service

The deluge of data coming in from the many intelligent systems running on trailers can be processed to uncover insights on the day-to-day functioning of the fleet and to schedule maintenance on time.



The customer, one of the largest refrigerated storage specialists in the US, provides full life cycle support for products requiring cold storage. They had collected a vast amount of data from the service and operation records of their assets and wanted to implement a robust predictive analytics solution. They wanted to use data-driven insights to proactively schedule maintenance efforts and therefore, offer better customer service.

Nevertheless, the deluge of data on their hands had a number of problems:

Case Study 1 to 4
Case Study 1

The data generated by their Field Service application and telematics devices was siloed and unstructured

Case Study 2
Plan to increase efficiency by scheduling key repairs, to enhance customer service
Case Study 3

Difficulty in finding resources for applying machine learning techniques to data integration and analysis

Case Study 4

Unfavorable experiences in the past with implementing a prediction model, resulting in delays to market and limited success

Predictive Analytics Challenges



After analyzing the customer’s challenges and requirements, we put together a cutting-edge solution for the client:

Leveraging our expertise in Azure services such as Synapse, Data Factory, Power BI, etc., to build a best-in-class infrastructure for predictive analytics
Selecting and training a model using Microsoft Azure IoT and Machine Learning Studio to take advantage of its code bases for a quick and efficient start
Integrating telematics and IoT devices across the client’s refrigerated storage fleet to help them with better data management
Quickly deploying Microsoft Azure’s plug-and-play services to detect anomalies in equipment, predict if it may fail in the future, and estimate its remaining useful life (RUL)

Our proven machine learning workflow streamlined data processing and analytics for the customer:




The fully trained and operational model that we developed for the customers’ predictive maintenance needs held a very high correlation to actual maintenance events, delivering a variety of benefits:

Case Study Predictive Analytics Insights for better Vehicle Maintenance and Customer Service

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