The services trinity: three is the magic number
For years, the services industry has been preoccupied with predictive capabilities. In many areas it’s a present-day reality: the internet of things (IoT), the cloud and artificial intelligence (AI) are finally beginning to work together in meaningful ways, correlating data and turning it into actionable insights.
While predictive maintenance capabilities has often been perceived as the holy grail of services excellence, I believe the real answer is more complex and nuanced. Truly, strictly predictive maintenance would mean taking a machine down to fix it before it breaks. In the self-service environment, that’s not always the most prudent move.
Using the “Three Ps” to streamline and drive better uptime
ATMs are not all equal. The positioning, usage, prioritisation – none of it is equal. Servicing ATM fleets requires a dynamic approach that pairs predictive capabilities with prescriptive and preventative measures. Here’s what that looks like at our organisation.
How we’re shifting the service model from reactive to proactive
DN’s global service team manages more than 30,000 connected devices 24/7, through DN AllConnect Data Engine, a one-way data feed that delivers information about the health of each individual terminal. We aggregate that data to get a better understanding of how a terminal functions within various environments – with such a large pool of data, we gain a better understanding of what “good” really looks like in the real world.
That information enables us to make educated predictions about how or when a self-service device might fail, because we’re seeing anomalies, modules that are operating in unusual ways, patterns in behavior and use that are harbingers of issues to come.
In parallel, where those connected devices are monitored through DN Vynamic Software, we have a clearer picture of exactly what’s happened to the terminal, in near-real-time.
In the field, we use that information in a few key ways. When we flag a potential issue, we can work with a financial institution proactively to discuss their options: is the potential failure serious enough that a tech needs to visit the terminal immediately? Would it be more advisable to wait until a first-line maintenance issue occurs, and turn that call into a second-line visit that addresses both issues? Should we schedule a visit for off-peak hours?
Pairing the low-level module data with high-level monitoring information enables us to cut out unnecessary steps. In a traditional, reactive model, if a terminal failed the monitoring team (whether DN or another vendor) might receive an extremely superficial error message. That could result in a first-line visit in which the engineer checks all the typical issues – is it a cash jam, is it out of paper etc – all the things that would bring the machine back up in about 80% of the cases.
Once they’ve conducted those fixes, if the terminal still isn’t up and running, a second-line visit needs to be scheduled. At this point, the engineer may still only have limited information about what’s wrong. When they complete their on-site diagnosis, they may or may not have the parts on hand to perform the fix. So they’ll be forced to leave, order the parts and return the next day. And during the entire time that the terminal is down, the monitoring teams are spending extra time managing the network’s imbalance, ensuring that terminals nearby can handle increased visits and that cash levels stay constant. Whew!
Using the “Three Ps” to streamline and drive better uptime
Rather than reacting, our team and the FI’s team can effectively turn the table on the issue, and work thoughtfully, rather than in a fire-drill mode of racing to get a machine back up without knowing all the information. Because we have a more complete picture of the terminal’s issue, we can plan the service work, avoid a first-line call altogether and ensure the tech has the right parts on hand. Even if the team decides not to do a predictive intervention, when the machine is serviced next, we have everything we need to send the right tech out, with the right parts, at the right time – or, we can simply fix it remotely.
Delivering faster fixes, conducted in off-peak hours, and often before there’s an issue, we ensure the highest levels of uptime, which translates directly to optimised consumer experiences. In this day and age, when your consumers have raised the bar on customer experience higher than ever, it’s critical that you use every tool at your disposal to meet and exceed their needs.
If your service partner isn’t already operating with a model that uses prescriptive, preventative and predictive capabilities, your network could be unnecessarily operating at sub-par levels.
The right monitoring, management and analytics experts working on your behalf behind the scenes can move the needle in a material way – so what are you waiting for?
Learn more about how we’re shifting the service model at DieboldNixdorf.com/AllConnect
About the author
Dom Hasler, vice-president and head of services banking portfolio at Diebold Nixdorf, leads a team of solution managers that design, launch, manage and eventually retire all of Diebold Nixdorf’s services delivered to banking customers. Keeping a watchful eye on changing customer requirements the services portfolio team ensures rapid response to Diebold Nixdorf’s offering in the wake of connected commerce, digitisation and other market interruptions.
Prior to joining Diebold Nixdorf, Dom acquired his detailed understanding of service delivery in many different markets across Europe, Africa and Asia with the ATM fleet servicing teams at G4S and then Brinks. His service delivery and customer-focused mindset was then further refined upon joining Diebold Nixdorf where as head of field maintenance operations in APAC Region where he was responsible for over 2,000 Engineers across 11 countries, servicing close to 200,000 ATMs.
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