Project Description

About our Customer

The customer is one of the largest refrigerated storage specialists in the United States. The company provides sales, service, leasing, and support for thousands of refrigerators trailers, providing full lifecycle support for products requiring cold storage.

Systems + Devices Addressed

System Devices Addressed Info

Key Challenges

The company had collected a large amount of data about the service records and operating conditions of their assets, but had been unable to deploy a truly predictive model to identify maintenance activities before they happened.

Korcomptenz’s Solution

Korcomptenz was selected as the partner of choice to deploy an agile predictive maintenance model, starting with building the appropriate infrastructure, selecting and training the model, testing it for accuracy, and ultimately deploying it to support operations:

We collaborated with the customer to identify their short and long term needs for the initial predictive analytics program followed by the phase one implementation and future plans. The predictive analytics program includes the following key features:

Korcomptenz Solutions

Comprehensive, Innovative Approach to Machine Learning

Korcomptenz approach combines plug-and-play Microsoft Azure services with in-depth data science and analytics knowledge. Quickly deploy predictive solutions leveraging Microsoft technologies and pre-built code:

  • Detecting anomalies in equipment
  • Predicting whether an asset may fail in the near future
  • Estimating the remaining useful life of an asset (RUL)
Data Science Life Cycle

Azure Predictive Maintenance

  • Predict the remaining useful life for key components of heavy equipment
  • Reduce downtime and cost by responding proactively to incidents
Azure Predictive Maintenance
Azure Predictive Maintenance
Azure Predictive Maintenance

Proven Machine Learning Workflow

Proven Machine Learning Workflow

Real World Benefits

The predictive analytics model had the following, measurable real world benefits:

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