We rely on our frontline workers to surface and flag at-risk assets in our EAM.
We pull data from our EAM to manually create reports and identify at-risk assets.
We have an automated solution in place to identify risky assets and alert managers.
When we choose to maintain, repair, and/or replace assets it often feels like a guessing game that is more art than science.
Our maintenance schedules mostly live in spreadsheets and rely on a blend of art and science.
Our maintenance strategy leverages an automated solution that is built on a reliable data model.
Our teams are siloed and the information regarding assets is often difficult to find.
Our teams are somewhat aligned and rely on offline conversations to share information.
Our teams are aligned and have complete visibility into all asset statuses, asset history, and maintenance activities as they happen.
We estimate maintenance productivity by periodically asking technicians how they spend their time.
We measure technician productivity by analyzing reports of activity data pulled from our EAM.
We capture maintenance productivity in real time through an automated solution that analyzes structured and unstructured data from work order activity reports.
After onboarding, training is done on an as-needed basis at the discretion of managers.
We have standardized coaching requirements with formal training occurring at a regular cadence.
We've developed a coaching culture and provide managers with dynamic tips and insights to inform their team’s ongoing development.
We rely on the intuition of top performers to understand what is driving their success and organically share their knowledge throughout the organization.
Our top performers share best practices formally during meetings or write notes and documents for others to use.
Our entire team has access to view and learn from best-in-class maintenance activities exactly as they happened.
We solely rely on our operators and technicians to surface when failures occur at irregular rates.
We conduct ad-hoc failure and root cause analyses to understand our failures and failure rates.
We have a solution in place to flag when assets fail at irregular rates and surface them in the context of maintenance schedules.
We rely on third-party, OEM data to understand our assets.
We use limited EAM data to understand our assets and their historical lifecycles.
We capture every piece of asset data to understand what each asset needs in order to be available, reliable, and to perform at optimal levels.
We have no scalable way to measure the success and adoption of PMs.
We measure the adoption of PMs but lack visibility into how valuable the PMs are.
We track every maintenance activity to measure PM adoption, its impact on unplanned downtime, and how we can optimize our PMs.
