Case Study

Municipal Waste Management

Intangles’ AI-powered predictive analytics fleet intelligence system reduced costs and increased fuel efficiency for a large regional municipality in the United States.


  • The fleet manager operates a large municipal fleet of over 1,400 vehicles traveling almost one million miles a month, including 90 refuse trucks manufactured by three major OEMs.
  • The fleet manager’s objectives include reducing turnaround time on repairs, decreasing comebacks and completing repairs in one day.
  • The fleet manager wants to predict potential issues with his vehicles well in advance of a breakdown, so his team can initiate preventative maintenance measures.
  • In addition, the fleet manager is seeking to increase the fuel efficiency of the municipality’s vehicles and/or increase the availability of the assets.


The fleet manager installed Intangles’ AI-powered predictive fleet maintenance system in his CNG refuse vehicles as part of a pilot program.


  • Intangles detected engine faults in 30% of the trucks in the pilot and alerted the fleet manager in real time. Intangles identified the faults before the vehicles’ DTC codes were triggered.
  • During the pilot, major DTC fault code
alerts were detected in real time, including
an accelerator pedal sensor malfunction, engine coolant temperature issues and engine misfires involving multiple cylinders.
  • Intangles’ predictive alerts saved the municipality an estimated $4,200 in repairs, parts and
towing costs during the pilot through preventative maintenance and helped to keep the vehicles on the road – and out of the shop.
  • The municipality saved over $500 per vehicle per month during the course of the pilot. Furthermore, the municipality believes it will no longer be required to keep five vehicles in reserve and is considering selling three trucks due to the increase in asset availability.


After a successful pilot, the municipality installed Intangles in the remaining refuse trucks and is expanding the pilot to new fleets in the region.

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