Detecting pilferage and leaks for Oil & Gas Industry

About the Customer: Healthcare Provider ($5B+ market cap) Consistently ranked under top 15 providers in the US In North America with over 45 hospitals and >2+ million patients

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Compelling Event

Identify leakages in Gas stations.

The Pain point

- Handling massive  sensor data is problematic for many organizations and our client was no exception.

- To perform analytics on sensor data is a nightmare because of instrumentation errors, you can observe values changing haphazardly with in milliseconds.

- Loss due to False Alarms:

Type 1 error: Cost of investigation incurred because of  false alarm.

Type 2 error: Loss of fuel or accident  cost, if any,  for not carrying the investigation, assuming that alarm is false.

Tech stack

  • Tableau
  • Amazon Redshift
  • SQL Workbench
  • Predera- KAI

Predera Benefit

Optimization of Anomaly Detection technique

  • Solid Feature Engineering - Several heuristics pertinent to seasonality of data were added. Dimensionality reduction techniques were used  to come up with optimum set of features for ML model
  • Clear comprehensive process limits were set up to differentiate leakages from false alarms
  • Root Cause investigation is made easier by providing visualizations across various dimensions
  • Errors in existing summarization techniques have been identified and rectified, thereby improving the quality of sensor analytics

Business Impact

Avoiding the false alarms by optimizing anomaly detection technique using AI model has helped our client to identify losses at the earliest, thereby saving huge costs. Out of pocket cost of investigation on  false alarms was also avoided.