University Housing Streamlines Work Order Management with CMMS
It can be challenging to keep a university housing system working properly with effective work order management and forecasting abilities of future maintenance requirements.
One university housing office began searching for an efficient way to automate work order and asset management with a computerized maintenance management software (CMMS) system.
The univeristy’s housing assets include five complexes that total about 6,000 student rooms, four dining halls and five convenience stores. This adds up to about 2.5 to 3 million square feet and around 20,000 individual assets to be maintained.
They had previously managed asset maintenance and tracking with a combination of paper tickets and spreadsheets. Due to growth, the housing office team needed a better solution.
After researching and exploring several options, the housing office chose eMaint CMMS in 2016. This CMMS provided asset management features and customization. It could also be integrated with Star Rez, software they used mainly to handle room assignments, contracts and billing.
As the assistant director built the asset database, eMaint implementation specialists developed an integration piece to automatically integrate work requests from Star Rez into the CMMS’s work order system. This enabled students to enter work requests through Star Rez that automatically flow into the system.
Training began on the work order module by fall 2017. They started with one complex, and based on feedback from the staff at the first complex, the housing team customized a few screens and trained the rest of the housing facilities’ staff as it moved one complex at a time to adopt the new CMMS system. By the end of January 2018, they were live with all complexes.
The new system allows the university housing facility managers to assign work orders to their mechanics in the CMMS system from a mobile device or PC with just with a few clicks.
Read the full case study to learn how the CMMS has expedited work order processing and improved asset life-cycle forecasting.