ATM Replenishment: Forecasting and Optimization

Why do people steal ATMs? Because that’s where the money is!!!

Why do people steal ATMs? Because that’s where the money is!!!

While the old “smash-n-grab” remains a favorite modus operandi of would-be ATM thieves, the biggest brains on the planet typically aren’t engaged in such endeavors (see Thieves Steal Empty ATM, Chain Breaks Dragging Stolen ATM, An A for Effort).

And of course, as we learned in Breaking Bad, successfully stealing an ATM (but then insulting your crime partner), can have unfortunate mind-numbing consequences.

The ATM Replenishment Problem

Suppose you operate hundreds of ATMs, processing millions of customer transactions a month. You want to keep your customers happy (no out-of-cash or other down time situations), yet minimize the cost of restocking the machines.

It turns out that managing ATMs is even more difficult than stealing one, and this was the challenge faced by DBS Bank in Singapore.  With a network of 1100 ATMs, there is an ever-present threat of inconveniencing customers any time an ATM runs out of cash, or is otherwise out of service. Replenishment trips are costly (can you imagine the gas mileage on those armored trucks, even with oil under $50/barrel?). And when you reload an ATM that isn’t running low on cash, you lose in two ways (wasting resources on an unnecessary trip, and temporarily making the ATM unavailable to customers while being reloaded.)

Fortunately there are bigger brains than the criminals thinking about the ATM replenishment problem. With the help of my colleagues from SAS Advanced Analytics R&D, DBS solved their problem and received top honors from the Singapore government for Most Innovative Use of Infocomm Technology. (See this write-up from Analytics magazine.)

Forecasting + Optimization

ATM replenishment is a perfect example of combining two areas of advanced analytics, forecasting and optimization. For DBS Bank, the first step was to understand withdrawal activity. Withdrawal rate is impacted by many factors, such as location, day of week, day of month, and time of day, and can be dramatically impacted by holidays or other special events.

Once you have a reasonably reliable forecast of customer activity at each ATM location, the next step (which helped DBS win the honors) is to convert the forecast into a daily execution plan for optimal reloading at just the right time. Since implementing the solution, DBS has been able to reduce cash-outs by 90%, reduce the number of customers impacted by the reloading process by 350,000 versus prior year, reduce the amount of returned cash (that was leftover in the ATM when it was reloaded) by 30%, and reduce the number of costly replenishment trips by 10%!

There are plenty of applications of forecasting + optimization outside ATM replenishment. For example, any company operating multiple production or distribution sites (or considering opening new ones) could benefit from a similar approach. First, get a good understanding of the timing and geographical location of customer demand. Then, optimize the placement of facilities or production lines. Revenue management, used by airlines and hotels to dynamically adjust pricing, is another example.