Machines often form the backbone of businesses, and machinery breakdowns are inevitably costly. In response to the challenge, companies have explored a variety of strategies and tools – even preventive maintenance programs – to increase machine uptime. But mostly these tools only generate a wealth of data which is not often aggregated, unified, analyzed, or acted upon.
The real challenge for our client, a large ATM manufacturer, was to determine the actual condition of each individual in-service equipment to predict when maintenance should be performed? When Infosys was asked to provide a solution, 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.
Find out more about how we can offer these insights for your machines too.
machine availability guaranteed
reduction in costs of unwarranted preventive maintenance and repair
increase in operational efficiency
is the time our algorithms take to predict machine fault with 80% accuracy