Finding the Real Problem
The real challenge then is to determine the actual condition of each individual in-service equipment to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance because tasks are performed only when warranted. The key then is to access insights into the state of the machine at the right time. By knowing precisely which equipment needs maintenance, machine upkeep can be better planned.
We Juxtaposed data from machines across multiple sites, over months of usage, with near real-time data from individual machines that must be maintained. This helped us to derive insights that provide warning signals and alert managers to potential machine failure.
We leveraged our Insights-as-a-Service offering to curate information from machines like the one that must be maintained – from across a wider landscape. For example, we curated four million failure ticket records from over 8,500 ATMs to develop, train, and test a machine learning model for predicting ATM failure in North America.
The color-coded dashboard helps maintenance teams review notifications, service calls and failure patterns over time periods, cities/states, and model types.
- Service-level guarantees with up to 99% machine availability.
- Up to 18% reduction in costs of unwarranted preventive maintenance and repair.
- The solution delivered a 14.3% increase in operational efficiency and reduced daily average service calls per technician to three from four.
- Insights derived from an exhaustive study of maintenance logs can help refine the design of the next generation of the machine.