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US University Sensor Pilot Program

Clean university building with trees.

20,000

Student enrollment

$266

Monthly ROI per bin

$89,519

Yearly potential savings

8 weeks

Pilot length

University Pilot Results

Project and customer description

Sensoneo recently released the results of a successful US university sensor pilot program.  The pilot snapshot shown here offers 8 weeks of data. A public university with a student enrollment of over 20,000 was pleased with the real-time data captured which gave it insights into how full their bins are.

The fill-level data collected showed the university opportunities to right-size and where the hauler could reduce pick-ups.  The data provided the university the ability to track the hauler, and that the hauler is picking up on schedule.

Furthermore, the university was interested in the amount of waste generated and more importantly, the amount of waste diverted.  Before data was available, the university was forced to assume the bins were 100% full, therefore under reporting the waste diverted.

Challenge

The university was looking to determine the number of pickups needed and to right-size the containers to save costs and increase efficiency.

Solution

Single Sensors installed on both general and recycling containers.

Impact

The university chose to roll out campus-wide.  The university found value in the service verification component of the Sensoneo sensors. It also saw how the data captured will show the waste diverted into recycling and looks forward to getting the rest of the bins online.

The university used the sensor data to not only increase the efficiency of their waste operations but also increase the cost savings from right-sizing pick-ups.  They will achieve increased efficiency and cost savings after the rollout.

Key achievements

  • 18% of waste collections were missed
  • 62% average fill level at the time of collection
  • $266 estimated ROI per container per month
  • $89,519 yearly potential hauling savings on 28 bins
  • 40.5% diversion rate using actual data

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