Analytical Components

Transcription

1 Executive Summary Router Banks wants to ensure that its network of ATMs provide superb customer service at the lowest possible cost. In pursuance of this, Owl Research (OR) has been asked to implement a system which will schedule ATM replenishments. The schedule must ensure that ATMs have sufficient inventory, so that customers are not frustrated by an out of order message when they need access to their funds. However, since each ATM replenishment entails a fixed fee to a third party vendor, Router does not want more replenishments than necessary to meet its customer service goals. Owl Research has evaluated the business challenge and outlined a method for producing replenishment schedules which will achieve the duals goals of ensuring sufficient ATM inventory while minimizing the replenishments. OR has done this while keeping in mind the other operational constraints, as specified in the optimization section of this SOW. To ensure the schedule meets Router s needs, we plan to send weekly reports on a number of key performance indicators, including current and historical cash-outs. Further, OR recommends at least a one week period when the current scheduling system remains in use, but when the new schedules are sent to Router on a daily basis. This will allow Router to verify the benefits of the new schedules before using them in its operations. System Environment An installation of SAS Forecast Server will be used to generate forecasts. This has two substantial benefits. First the ability to forecast demand for cash for all ATMs and for particular geographic zones and to reconcile these results with forecasts for individual ATMs. This will help particularly in forecasting for ATMs with less historical data the aggregated historical data from the zone ATM is in can improve the forecast. Second, the ability to run in batch mode, and hence to allow for the quick turn-around which Router requires. In addition to Forecast Server, the environment must include an installation of SAS/OR. This will allow Owl Research to create a replenishment schedule which is optimized according to Router s preferences. Project Description Information Flow Router Banks will be responsible for sending a log of withdrawals and replenishment events for the previous 24 hours (from 5 AM to 5 AM) sent to us by 6 AM each morning. Files can also be sent via a secure FTP connection or database connection. Having the most current data will greatly increase our ability to determine the need for replenishment. When new ATMs are installed or ATMs are removed, OR should be notified by Router. The data will be imported to SAS Forecast Server using the Base SAS import procedure and converted into correct format. OR will incorporate the data in our forecasts and the replenishment schedules will be available through the web site by 8 AM daily. If desired, s or text message alerts can be sent when the schedules are complete. Analytical Components

2 After receiving the daily transaction log update from Router, OR will forecast demand for the coming day and, using that forecast, create a replenishment schedule optimized to reduce cashouts and replenishment fees. Details are explained in the sections below. Forecasting Hourly Demand for Cash Router Banks currently forecasts based on last year s demand with a trend factor. It has been tested against actuals, and found to be very inaccurate. This inaccuracy prevents Router from optimizing replenishments. When actual demand is substantially less than forecasted, and the ATM is replenished before it needs to be, leading to unnecessary fees. On the other hand, when demand exceeds forecasted demand, the ATM is replenished later than it should be, risking a cash-out. Improving forecast accuracy is therefore crucial to the goal of optimizing replenishments. The revised forecasts must be more granular, predicting demand by rather than by day. Demand for cash will nearly always be higher at noon than at 5 AM, so expect substantial improvement simply by changing to hourly forecasts. Some ATMs are in stores which are only open during certain hours, so after-hours ATM use should virtually cease. Hourly forecast will allow us to account for this variation. Further, a more reliable model should have the flexibility to handle the following: trend, shifts in demand due to opening or closing of businesses or due to installation of nearby banks or ATMs, variation based on day of the week, on the season, and on holidays. An ATM near a bar may have more transactions on late evenings or weekends, while another near an office may see most use during work week lunch hours. One paper on ATM forecasting explains: For example, people tend to draw relatively large sums of cash at the beginning of each month. Before Christmas, drawing rates soar, whereas in August, during the summer holidays, rates tend to drop considerably. ATMs that are located in shopping centres, for example, are most heaped on Fridays and Saturdays. (CASH DEMAND FORECASTING FOR ATM USING NEURAL NETWORKS, 2008 p. 417) OR will use SAS Forecast Server to generate individualized hourly forecasts for each ATM, as well as forecasts aggregated by zone and for the entire Router Banks ATM network. Holidays, including those which do not occur on the same calendar day each year (such as Black Friday), can be included in the forecast. Forecast Server will automate much of the forecasting process, but inaccurate forecasts for particular ATMs will be analyzed and adjusted on an as-needed basis. Forecast Server has another feature hierarchical forecasting - which will aid in developing accurate forecasts. Hierarchical forecasting means that forecasts can be generate for the entire network of ATMs, for ATMs in a given zone, and for individual ATMs. These three levels (overall, zone, and individual ATM) can then be reconciled. This reconciliation process should improve the accuracy of individual ATM forecasts, particularly in cases where the ATM has been installed recently and therefore has less historical data available. The forecast output will not only include a point estimate by hour, but 80, 95, and 99% prediction intervals. In addition, Forecast Server will produce prediction intervals for cumulative

3 demand by hour for each ATM. Since hourly demand is correlated, forecast error is likely to cascade, so the cumulative demand will likely require a wider interval. When new ATMs come online, the forecast will have wider prediction intervals, and as a result replenishment and replenishment fees will start high. However, this is necessary to reduce risk of cash-outs. Before the revised forecasts are used in Router s operations, OR will produce a report showing the increased accuracy. The current moving average forecasts and the revised forecasts will be used to forecast at least one week of ATM demand, and the results will be compared. Optimizing Schedule to Reduce Cash-outs and Replenishment Fees Defining the Problem As explained in the previous section, more accurate forecasts will help to prevent cash-outs and avoid unnecessary replenishment fees. However, even a much improved forecast will not perfectly predict demand. Therefore, it is also necessary to balance the risk that unexpected demand will cause a cash-out against the fee associated with replenishment. Prerequisite to finding this balance, it is necessary to clearly define the cash-out event. Router defines a cash-out as an ATM having less than four hours of inventory. However, the forecasting techniques currently in use are not accurate enough to be used in determining cash-outs, so Router reports a cash-out to have occurred four hours prior to an ATM running out of money. However, after making the forecasting improvements explained in the section above, OR will switch to defining a cash-out as an ATM having less than four hours of forecasted inventory. OR will forecast demand by hour by ATM, so for each ATM and for each hour of the day, the forecast for the next four hours will sum up to the required inventory. Next, we must define how to balance the fees associated with replenishments against the risk of cash-outs. If the costs to the business of cash-outs could be precisely quantified, OR could create a schedule which would minimize the combination of the cash-out cost and the cost of replenishment. However, estimating cost to the business of cash-outs is complex. Some insight into this can be gained using surveys of current and potential customers, to see which features of Router s service are motivating consumers to bank with Router or a competitor. In addition to the direct impact on the customer, there is the secondary impact of brand reputation. Customers share frustrating experiences with friends and coworkers, magnifying the impact of poor customer service. OR has the capacity to analyze these factors to estimate the cost to the business of ATM outages, however doing this is beyond the scope of the current project. Therefore, until the survey and behavioral data necessary to estimate the cost to the business of a cash-out, we recommend creating a schedule which minimizes replenishments while seeking to reduce cash-outs by some specified threshold. OR would suggest seeking a 50% reduction from the current average level of cash-outs, but there is a complication we will be modifying the definition of cash-outs, and the updated method of determining cash-outs will consider ATMs as cashed-out more frequently than the current method (holding all else constant). Because of this complication, OR will work with Router to evaluate the impact of different cash-out thresholds. Other constraints must be handled while designing the schedule:

4 1. Not all ATMs are available 24-hours a day. Those which are not must be replenished during business hours. 2. The third-party vendor the trucks has a limited fleet to use for replenishments. Hence, the number of replenishments in a given hour is limited. The number of replenishments per truck per hour will vary depending on the distance between ATMs. The time of day will also impact capacity, since a truck will travel more slowly during rush hour. Finding an Optimal Schedule First we must calculate the most recently known inventory, based on replenishment history and transactions, for each ATM. Subtracting the cumulative hourly forecasts by ATM give us expected inventory by hour. If replenishments were done at most only once a day, a mixed integer linear programming solver can be used to create a schedule which meets the constraints while minimizing replenishments. However, because replenishments can be done multiple times a day, a replenishment at one hour will effect inventory for the rest of the day. Because of this complexity, OR plans to begin by using a greedy algorithm first scheduling a truck at the time most likely to reduce risk of cash out (while satisfying other constraints), then scheduling the next truck until the number of cash-outs fall below the specified threshold. In addition, OR will try the genetic algorithm for schedule selection, though the run time for a good solution from this method may take too long to be useful given the schedule. In addition, the variation between the forecasted demand and the actual demand means that simply using the expected values of the forecast will sometimes result in more cash-outs than expected. More complex methods of stochastic optimization could be used to handle this, but alternatively one of the upper bounds of the cumulative hourly forecasts can be used in place of the expected value. This, combined with the four-hour buffer, will ensure that variation from the forecast does not result excessive out-of-service ATMs and poor customer service. Input Data Structure Daily extract of transaction logs and replenishment events for all ATMs for the previous 24 hours period (5 AM to 5 AM), received by 6 AM each day. Data file with information about ATMs: MACHINE_ID, ZONE, ADDRESS, TYPE, LOC_ID, OPEN24HRS. Router Banks will send updates on an as-needed basis. Output Data Structure Forecast of expected demand, showing number and value of expected transactions by machine by hour as well as cumulative transactions and cumulative dollar amounts by hour. In addition, OR will provide aggregated views of the same information by day and by ATM. A schedule for each available truck, listing the ATMs by hour, from 9 AM till 9 AM the following day. For example: Truck Time ATM Zone Truck 7 9 AM ATM # 311, located at address 31 Main Street A Truck 7 9 AM ATM # 315, located at address 5 Shire Line A Truck 7 9 AM ATM # 325, located at address 11 Fayette Street A Truck 7 10 AM ATM # 355, located at address 161 Gorman Street C

5 A web interface will allow for manual adjustments to the schedule, with warning messages additions to the schedule exceed truck capacity, printing schedules for particular trucks, and exporting the schedule as an Excel file. In addition, on a weekly basis, OR will also send a report to Router which will include the following: Cash-outs as currently defined by Router Cash-outs based on new definition: insufficient inventory for four hours of forecasteddemand Length of time that ATMs have been out-of-order due to lack of inventory Total number of replenishments The amount of cash returned to Router by the third party vendor Third party vendor deviations from replenishment schedule (if any) These reports will include historical data and will show both absolute numbers and values which are adjusted based on the aggregate daily demand for cash. The latter will help in making more useful comparisons over time. Bar or line charts will help to visualize trends. Assumptions We assume that all replenishments are complete replenishments. Cash in ATMs does not count toward capital requirements, so partially replenishing less trafficked ATMs has the potential to allow the capital to be used more profitably. However, the problem description did not mention the option of partial replenishments. We assume that replenishing an ATM takes very little time, so as not to disrupt customer service. If this assumption is incorrect and replenishing means decommissioning the ATM for twenty minutes, it would be reasonable to consider scheduling replenishments during off-peak hours. We also assume that cash deposits to an ATM cannot be used as a source of funds for future withdrawals. We assume that the third party vendor charges a flat fee for replenishment, rather than a permile fee.