Predictive maintenance lowers cost, while improving performance.

A global supplier of security inks and related solutions provides service contracts on its equipment at client sites around the world. They adopted robust maintenance processes, with on-site technicians and a preventive maintenance schedule to meet stringent customer Service Level Agreement (SLA) targets for production volumes.

Their schedule-based preventive maintenance program achieved SLA targets, however it was not sustainable. The cost, time and effort to support customer SLA targets eroded margins; unplanned downtime adversely impacted operational efficiencies; and protocols were not easily scalable due to the dependency on human expertise at the customer sites.

QiO's Foresight Maintenance® application was deployed to build a digital twin of the equipment failure using predictive analytics generating insights and then turning them into actions. QiO's solution addressed:

  • How can we improve field technician efficiency and leverage field knowledge and expertise?
  • Without impacting customer satisfaction, reliability, safety and compliance?
  • And at what optimal total cost of ownership?

Using machine-learning techniques, the model was integrated with ServiceNow to improve Preventive Maintenance actions.



How can we move to a more proactive maintenance approach to efficiently scale, maintain quality and lower costs?


  • Able to proactively identify potential failures
  • Able to prioritise technician activities
  • Lower overall costs, while improving SLA compliance