Latest Articles Most Popular Articles
AI data visualization with digital analytics, neural networks, and global data network elements
#Cash Cycle

Keeping the heart of a cash center beating

Feature
5 Mins.

From reactive repairs to a proactive, data-driven service approach, this article explores how predictive maintenance is transforming cash operations.

Key takeaways

  • Unplanned downtime costs cash cycle stakeholders thousands of dollars per hour in lost productivity, idle staff, and delayed processing – making the shift from reactive to predictive maintenance a financial imperative.
  • For central banks, the stakes go beyond economics – public trust in the cash cycle depends on reliable operations.
  • Predictive maintenance shifts cash operations from reactive repairs to a proactive, data-driven service model – reducing unplanned downtime, increasing reliability, and extending system lifespan.
  • Successful implementation requires secure connectivity, high-quality data, and a service partner with both advanced analytics and deep industry expertise.

Table of contents

Banknote processing systems are the backbone of modern cash centers. When these systems fail, the consequences extend far beyond technical inconvenience. Upstream, cash waits to be counted, sorted, and authenticated. Downstream, cash delivery schedules are delayed, affecting the entire cash cycle. Operators, meanwhile, are left idle – highlighting that downtime is more than just a logistical setback; it is a strategic vulnerability that demands proactive management and innovative solutions.

Avoiding idle time is a top priority for the industry. Rising personnel costs and increasing difficulty in finding qualified staff make it essential to keep operations running smoothly. Every minute of unplanned downtime not only disrupts workflows but also leads to significant financial losses. According to the 2024 State of Industrial Maintenance Report by MaintainX, the average cost of unplanned downtime across industries reaches $25,000 per hour – and can exceed $500,000 for larger organizations.1 In cash operations, where central banks must guarantee the availability of cash and commercial players depend on throughput, these numbers are critical.

“In our discussions with customers, we consistently hear that rising personnel costs make it even more important to keep staff engaged in productive work,” says Stephan Wunderle, Head of Strategy and Digital Excellence at G+D’s Strategic Business Segment Service. “Idle time is expensive – both operationally and in terms of productivity. Minimizing unplanned downtime is therefore essential for efficient and resilient cash operations.”

As a result, predictive maintenance is increasingly recognized as essential for maintaining reliable cash operations. To understand its impact, it’s helpful to look at how maintenance practices have evolved.

From reactive to predictive: how maintenance practices have evolved

Traditionally, cash processing systems were maintained like any other industrial machinery: fix it when it breaks. This reactive approach – calling a technician only after a failure – may be simple to manage but is costly in practice. Every unexpected repair leads to unplanned downtime, idle staff, and increased operational costs.

The first step away from this model is preventive maintenance, which involves scheduling check-ups at fixed intervals, regardless of whether the system actually needs attention. While this reduces surprises, it introduces a new issue: systems running well are still serviced. Faults that occur between service windows often go undetected until they cause a breakdown.

Condition-based maintenance improves on this by using sensors to track system performance in real time.2 When a specific indicator – such as vibration, temperature, or pressure – crosses a defined threshold, a technician is called in. Here, the actual condition of each individual system dictates the maintenance schedule, rather than arbitrary calendar dates.

Predictive maintenance takes this a step further. Instead of waiting for a threshold to be breached, predictive maintenance uses pattern recognition, data analytics, and, increasingly, artificial intelligence to spot anomalies before they become faults – sometimes days or weeks in advance. 

The evolution of maintenance practices (Copyright: Giesecke+Devrient)

Across industries, 65% of maintenance professionals consider predictive maintenance the most effective way to reduce unplanned downtime.3 Yet only 30% of facilities currently use it.4 While the stakes vary by industry, the incentive for early adoption is clear – especially in cash processing.

How cash operations benefit from predictive maintenance

In most industries, a machine going offline is an inconvenience with commercial consequences. In cash operations, however it’s not just money at stake – it’s public trust.

Central banks and national printworks are responsible for ensuring the reliable circulation of cash. A system failure during peak processing – when millions of banknotes must be handled in a single shift – threatens their ability to fulfill this core mandate. Resilience is essential, and predictive maintenance plays a key role in supporting it.

For commercial operators, such as cash-in-transit companies, the motivation is primarily economic. Every hour of downtime means lost throughput and lost revenue. Additionally, the significant upfront investment in cash processing systems must be justified to stakeholders. Shifting from reactive to predictive maintenance not only boosts productivity and reduces unplanned downtime, but also extends the lifespan of these systems.

Personnel and logistics costs are also significant. Planning maintenance around shift changes or off-peak hours, rather than responding to unexpected alerts, minimizes operator idle time. Technicians can prepare in advance, reducing last-minute travel and guesswork about required parts. This leads to fewer unnecessary trips and a smaller carbon footprint. Systems are serviced during idle periods, ensuring they are ready for the next shift.

To fully realize these benefits, organizations need a clear path from understanding the relevance of predictive maintenance to putting it into practice.

How to successfully implement predictive maintenance in cash operations

Successfully implementing predictive maintenance in cash operations requires high-quality data collection, robust prediction models, and deep industry expertise to distinguish early warning signals from normal operational fluctuations. Cash processing environments are, by design, high-security facilities – often isolated from external networks – making systems integration with cloud-based monitoring platforms a legitimate cybersecurity concern.

These challenges are real, but cash cycle stakeholders do not have to address them alone. Partnering with a trusted technology provider– with proven infrastructure and security credentials – can make the transition both practical and secure.

G+D’s Smart Maintenance platform, for example, enables 24/7 system health monitoring across connected systems worldwide. Service experts in centralized care centers can monitor system performance in real time, diagnose issues remotely, and collaborate with on-site technicians to plan and execute interventions. This shortens repair times and lowers downtime costs.

The core principle is to connect on-site service technicians with remote experts leveraging advanced analytics and real-time data monitoring to guide and support them. This enables proactive service that identifies and resolves potential issues early, before they escalate. This approach is part of a broader shift toward digital service solutions that enhance – rather than replace – the expertise of on-site teams.

A service technician uses a tablet to check the health of a banknote processing system

The same logic applies to AI. While concerns about AI integration are common, in cash operations, AI should augment human expertise. For instance, AI can generate knowledge articles from fault data, which are then reviewed by experts and shared with service teams globally. This creates a continuously expanding knowledge base, grounded in real-world data and validated by experienced professionals.

“As data accumulates, so does our collective knowledge,” says Stephan Wunderle. “Over time, this enables us to move toward the next evolution: prescriptive maintenance – where the platform not only predicts problems but also recommends solutions. Importantly, a human must remain involved at every step.”

This vision for the future of maintenance is promising, but it requires cash cycle stakeholders to take the first step. 

Idle time is expensive – both operationally and in terms of productivity. Minimizing unplanned downtime is therefore essential for efficient and resilient cash operations.

Stephan Wunderle
Head of Strategy and Digital Excellence, Strategic Business Segment Service at G+D

A shift that rewards the early movers

Predictive maintenance is poised to become the standard in cash operations. Paradoxically, the better a system is maintained, the fewer failures occur – resulting in less data to refine prediction models. However, as more systems become connected and actively used, the volume of data grows, enabling more accurate and comprehensive forecasts.

With advancing AI capabilities and decreasing data handling costs, today’s data will become even more valuable in the future. Delaying adoption means missing out on these benefits and falling behind those already leveraging predictive maintenance.

Cash centers that have implemented predictive maintenance are already seeing clear advantages: fewer emergency interventions, reduced unplanned downtime, and a smaller carbon footprint. These improvements lead to lower operational costs, greater efficiency, and more reliable cash operations. Most importantly, operators regain their most valuable asset: time.

  1. State of Industrial Maintenance Report, MaintainX, 2024 

  2. What is a maintenance strategy?, IBM, accessed 2026

  3. State of Industrial Maintenance Report, MaintainX, 2024 

  4. Ibid., MaintainX, 2024

Published: 19/05/2026

Explore our products & solutions

Explore similar topics

#Transportation #Logistics #CITs #Central Banks #Commercial Banks

Share this article

Read similar articles
Subscribe to our newsletter

Don’t miss out on the latest articles in G+D SPOTLIGHT: by subscribing to our newsletter, you’ll be kept up to date on latest trends, ideas, and technical innovations – straight to your inbox every month.

Please supply your details: