Caring for Your Mission-Critical Taskforce: Your Machines

When it comes to machines, there’s never really a good time for them to break down. Machinery breakdowns are inevitably costly. This is true for manufacturers of equipment, for their customers, and also for providers of managed machine maintenance services. In response to the challenge, companies have explored a variety of strategies – even preventive maintenance programs – to increase machine uptime. The goals have always been to ensure high availability of machines, rationalize service and repair costs across the network, and incorporate learning into the manufacturing and application of new models of machines. But every strategy – even a combination of strategies – has had only limited success.

Preventive maintenance relies on average machine-life

These statistics often fail to predict when exactly maintenance will be required for a particular machine. And thus deliver expected machine uptime results.

Too many tools

There are sensors and there are alignment tools and there are other health monitoring systems to monitor and maintain machines. But mostly these tools only generate a wealth of data which is not often aggregated, unified, analyzed or acted upon.

Every machine is special

While manufacturers often look at machines in batches or networks, predictive maintenance programs often fail to recognize the weak links in the ecosystem. Often, if you leave out certain key pieces of equipment data in any preventive maintenance program, it could result in downtime for the entire network.

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.

The Outcomes

  • 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.