BACKGROUNDThe ATM Industry Association estimates that there over 3 million ATMs worldwide as of 2015 – and that number is expected to grow significantly as financial service firms continue their march toward self-service customer enablement. While offering significant convenience to the customer, these ATMs represent a huge investment in physical infrastructure that involves both complex electronic components (screens, readers, boards, etc.) and physical cash balances that need to be maintained, purged, and replenished regularly for optimum ATM performance.

CHALLENGESTraditional methods of infrastructure management rely on replacing hardware components before they fail. Maintenance is handled on a strict hardware-refresh calendar with components cycled out-of-service before they are “scheduled to fail,” regardless of how well (or poorly) they are functioning. The difficulty with hardware-refresh calendars is that they rely on statistical models for the “average” component life, rather than actual data on each component. As a result, many perfectly good components, with years of functional life left, end up being replaced simply because the scheduled amount of time has passed. Conversely, failing components that haven’t reached their scheduled end of life are left in place.

Optimizing the life span of every component requires that components are monitored and alerts issued the instant anomalies or errors occur. The ultimate goal is to utilize component analytics data to predict failures.

It is hard to manage an extensive ATM network while looking only at historical data. Moving to a predictive model requires continuous moni-toring of the ATMs and immediate response to changes in the network.

STRIIM SOLUTIONThe Striim platform allows users to view their entire network of ATMs in real time to inform both cash management and infrastructure teams of current and predicted anomalies and issues. Each component in every ATM streams data into the Striim platform, creating a holistic, correlated, and aggregated view. Instant visibility into all ATM data across the network is correlated with history and context.

OVERVIEW

Use Case: ATM Predictive Maintenance

www.striim.com

Industry: Financial Services

Real-time Use Cases

• Continuous device health monitoring

• Cash management insights

Business Impact

• More efficient cash management

• Increased ATM efficiency

• Reduction of hardware and infrastructure downtime

Data Sources

• ATM health pingbacks

• Enterprise ERP systems

• IT infrastructure management systems

Instantaneous predictive and prescriptive insights ensure that scheduled maintenance work is now focused on machines that are showing likely signs of failure. Distressed components are now automatically scheduled for repair based on based on the criteria of the financial institution such as ATM productivity and risk of downtime.

The Striim solution doesn’t just monitor hardware health. Real-time cash balances are also correlated with current, scheduled, and predicted events that will impact ATM activity. Holidays and unexpected events (e.g. the hometown underdog team making it to the Final 4 in March Madness... the bars are suddenly full and the ATMs empty) are correlated with real-time ATM events to efficiently manage the cash demands in the network.

BENEFITSThe Striim platform provides increased ATM efficiency through the reduction of hardware and infrastructure downtime, as well as more efficient cash management (leading to lower treasury borrowing costs). Additionally, maintenance routes are optimized and components are no longer replaced on an arbitrary calendar. They are only replaced when they show signs of distress, ultimately saving money across the network by keeping functional components in service as long as possible.

Every aspect of an entire ATM network’s health can now be visualized in a single real-time dashboard that further enables users to drill down deeply into actionable intelligence with virtually unlimited detail options.

Striim offers real-time transparency like never before.

Use Case: ATM Predictive Maintenance

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