The most expensive system in cash operations is the one standing still

When a banknote processing system fails, the impact reaches well beyond the technical fault. Cash queues up to be counted and authenticated. Delivery schedules slip. Operators remain idle while labor costs continue to accrue. Downtime in cash operations is not a logistics problem alone, it is an exposure that shows up in personnel costs, throughput and the reliability that central banks and commercial cash centers are expected to deliver.

The economics are unforgiving. Industry studies put the average cost of unplanned industrial downtime at around 25,000 dollars per hour, with significantly higher figures for larger operations. In cash centers, where shifts process millions of banknotes, unplanned downtime affects both operational performance and business continuity. Commercial operators lose productivity and throughput, while central banks face additional pressure in fulfilling their mandate to ensure a reliable cash supply. Every idle minute generates costs twice: through idle personnel and through lost processing capacity.

From "fix it when it breaks" to "fix it before it does"

The maintenance approaches used in cash operations are not new. Rather, predictive maintenance represents the latest step in a well-established evolution of maintenance practices used across asset-intensive industries.

Reactive maintenance calls a technician only after a failure. Simple to manage, expensive in practice. Preventive maintenance schedules service at fixed intervals regardless of system condition. It reduces surprises but services systems that do not need it and misses faults that develop between visits.

Condition-based maintenance moved the discipline forward by using sensor data to trigger interventions when defined thresholds are crossed. Predictive maintenance, increasingly seen as the benchmark, layers pattern recognition and analytics on top of sensor data to flag anomalies before they become faults. AI can further enhance those models, often identifying degradation weeks ahead of failure.

Across industries, surveys show that roughly two-thirds of maintenance professionals view proactive maintenance as the most effective way to reduce unplanned downtime. Yet the adoption of predictive maintenance remains considerably lower. 

Why cash operations face a sharper case

Cash handling sits in a different operational category. For central banks – and for printworks supporting them – keeping production and processing systems available is critical. A banknote processing system that fails during peak processing does not just slow throughput. It puts pressure on a mandate that cannot easily be deferred or rescheduled.

Commercial operators face a more direct economic calculation. Cash-in-transit companies lose throughput, miss delivery windows and watch the return on capital-intensive sorting and authentication systems decline with every hour offline. Predictive models stretch the useful life of those assets and shift maintenance into idle windows rather than emergency call-outs. Travel and parts logistics become planned activities, enabling maintenance work to be scheduled more efficiently and systems to be serviced at the most appropriate time.  

Implementation hurdles are real, but not insurmountable

Predictive maintenance is becoming increasingly accessible for cash operations. The combination of connected systems, advanced analytics and growing operational datasets enables cash cycle stakeholders to benefit from earlier issue detection and more informed maintenance decisions. To realize these benefits, however, predictive models require high-quality data and the ability to distinguish normal operational variation from genuine early-warning signals.

Cash centers also operate in highly secure environments, often with strict requirements around connectivity and data protection. As a result, the design of any monitoring architecture must give equal consideration to cybersecurity, operational requirements and predictive capabilities.

These considerations favor service partners with proven security credentials and sector-specific expertise over generic remote-monitoring offerings. The integration design is just as important as the predictive capabilities themselves.

Successful predictive maintenance depends not only on algorithms, but also on the expertise of service specialists who can interpret signals in an operational context. Data may indicate that something is changing; experienced professionals determine what matters, how urgent it is, and which intervention is appropriate.

Toward prescriptive operations

The direction is toward prescriptive maintenance, where systems do not just predict faults but recommend the appropriate response, with human experts validating each step. That progression depends on data volumes that accumulate only as more systems are connected and more operational events are recorded. Each additional data point sharpens the model.

Prescriptive maintenance builds on the data, experience and capabilities established through predictive maintenance. As predictive capabilities continue to evolve, cash cycle stakeholders benefit from increasingly accurate insights and recommendations derived from a growing body of operational data and expertise. Cash centers already running predictive programs report fewer emergency interventions, lower unplanned downtime, and longer asset lifespans. The operational dividend is straightforward: time returned to people and capacity returned to systems that were never meant to sit idle.