Sensor Data Analysis for Predictive CBM+

Customer Challenge

A large grocery chain required a big data and predictive analytics modeling framework to expose information contained in streaming compressor and refrigeration sensor data.

Innovative Solution

ILW implemented an IoT software solution by collecting refrigerator sensor data and pairing with other related datasets to perform real-time, predictive CBM+ analytics and determine the optimal time to replace/repair refrigeration units.

Benefits/Outcomes

  • Created an advanced analytics modeling framework to anticipate corrective maintenance requirements
  • Identified usage and performance parameters to determine the optimal point to replace/repair to preempt component and system failures
  • Reduced facility energy consumption and maintenance costs, saving the retailer an estimated $20M

Toolbox

  • Hadoop, IoT Sensors, Python, Custom Analytics Modeling Framework, CBM+ Analytics
  • Data Profiling, Data Movement, Data Federation, Big Data/Hadoop, Web Services, Internet of Things

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