KR100635433B1 - How to group automation devices - Google Patents

Abstract

The present invention relates to a grouping method for correcting data that is not predicted by an Optimum Cash Manager server in accurate demand prediction of an automated device such as an automated teller machine (ATM) or a cash dispenser (CD). For example, device grouping for devices with the same cash consumption pattern by region and region, and period grouping for periods with unusual cash consumption patterns for specific days such as holidays or public holidays can be used to predict demand for automated devices. The present invention relates to a grouping method of an automated device that enables more accurate operation.

It can maximize operational efficiency by appropriately distributing limited operating funds, reducing the number of operations required for in-service planning, and reducing unnecessary work and increasing the number of transactions through accurate demand forecasting through grouping. Furthermore, it can be extended not only to grouping of automation devices but also to grouping of office, team, and branch demand forecasts.

Demand forecasting, device grouping, period grouping, automated devices, optimal cash management

Description

Grouping method of automated devices {Grouping of ATM}             

1 is a schematic overall workflow of a general Optimum Cash Manager system.

2 is a schematic configuration diagram of an internal module of a general OCM server.

3 is a block diagram illustrating device grouping according to the present invention.

4 is a block diagram illustrating period grouping according to the present invention.

5 is a diagram illustrating a process of assigning weights by integrating device grouping and period grouping used in the present invention.

6 is a flowchart illustrating a process of performing grouping used in the present invention.

* Explanation of symbols for the main parts of the drawings

10: optimal cash management (OCM) server 20: bank server

30: automation device server 40: user terminal

50: current company 60: grouping

70: data correction module 80: demand prediction module

90: optimization module 100: device grouping

102: regional characteristics 104: regional characteristics

106: environmental characteristics 108: period grouping

110: integration group code 112: integration group name

114: period group name 116: device group name

118: weight 120: integration group description

The present invention relates to a grouping method for correcting data that is not predicted by an Optimum Cash Manager server in accurate demand forecasting of automated devices such as ATMs and CD machines, and more particularly reflects regional and regional characteristics. By classifying the input data, device grouping for devices that are expected to show the same cash consumption pattern by region, and period grouping for periods showing unusual cash consumption patterns for specific days such as holidays or public holidays By enabling accurate demand forecasting of automated devices, it is possible to allocate limited operating funds appropriately, reduce the number of operations required for in-service planning, increase the number of transactions by maximizing the profit of the device, and maximize the operational efficiency. A method of grouping devices.

After the financial crisis in late 1997, the insolvency of financial institutions was the cause. In Korea's banking business, overbanking is a serious problem, and McKenzie Consulting's assertion that the number of banks and stores should be reduced, along with academic consensus, has led many banks Merged. As a result, the number of banks, which were more than 30 at the end of 1997, has now fallen to less than 18, and only a few banks survive. The surviving banks are struggling to increase their competitiveness and improve work efficiency through restructuring, which has resulted in a significant reduction in bank branches and employees, resulting in an increase in the workload of individual employees.

In recent years, most companies have implemented a five-day week, and more people use automated devices such as ATMs and CDs, rather than going directly to banks. Accordingly, the cycle of cash reimbursement taking into account holidays and holidays, and the estimation of cash, are other factors that increase the workload of employees due to automated device management.

Hereinafter, a general cash transportation management system will be described with reference to the accompanying drawings.

1 is a schematic overall workflow of a general Optimum Cash Manager system.

Referring to this, a general cash transportation (hereinafter referred to as 'current delivery') procedure is the optimal cash management (Optimum Cash Manager, hereinafter referred to as OCM) server 10 from the bank server 20, the transaction performance and the automatic work close information After receiving the device information and fault information from the automated device server 30, such as ATM or CD machine, and estimate the amount of cash and the number of times per device through the data correction, demand prediction, optimization process, The person in charge of the automation device uses the user terminal 40 to determine the current amount of money estimated by the OCM server 10 through a user program such as a web page or a pop-up window.

The determined current amount of money is notified to the current carrier 50 by the OCM server 10, and each current carrier 50 that has been informed of the information is sent to the device by the amount of the given amount, and then the input amount. And reporting the result of the collected amount and the like to the OCM server 10 can complete the current dispatch.

FIG. 2 is a schematic configuration diagram of a general internal module of the OCM server of the current transmission management system described with reference to FIG. 1.

Referring to this, the data correction module 70 corrects the cash transportation data of each device by receiving the data of the past and grouping 60 information distinguished by performance and the information from the automation device and the bank server 20. And a demand prediction module 80 that receives the corrected data and estimates the number of withdrawals or the amount of withdrawals for each device, and optimizes the cash flow and the number of times by receiving the predicted amount, so that the current delivery date and the amount of money for each device are optimized. It consists of an optimization module 90 to determine.

The grouping 60 receives a past withdrawal amount of each device and serves to group them by performance. The grouping operation by the above performance is performed based on the past experience of the people in charge of the present.

The data correction module 60 receives failure information such as device information and failure time from the automated device management server 30 and input / output transaction data from the bank server 20 to correct cash transportation data of each device. Play a role.

The demand prediction module 80 calculates the daily withdrawal amount or the number of withdrawals per device by calculating statistics based on general statistical techniques or past performances, and predicts the average for each recent date. The demand forecast is performed once a week to predict the current amount of the week for each device.

In addition, the optimization module 90 is provided to receive the data predicted by the demand prediction module 80 to optimize the current funding and the number of times of delivery to determine the current date and current amount of money corresponding to each device code. The optimization module optimizes the withdrawal amount of the corresponding devices only for the devices for which the demand forecast is completed, and is automatically executed immediately after the demand forecast is completed once a week.

In the conventional method, the grouping for correcting data that the OCM server cannot predict is determined by the experience of the people in charge, and thus, accurate prediction is not possible. The reliability is poor. As a result, it is impossible to predict the withdrawal amount of the equipment necessary for the right place, and the dissatisfaction of customers who do not find cash when necessary increases the need for a solution.

Moreover, oversupply of oversupply will result in a loss of interest income, and oversupply more frequently will require a greater cost of maintenance. In addition, the method of predicting the demand of the automation equipment is not diverse, and the range of selection is not wide, and even if it is present, there is a lack of a system that can be driven by combining with an existing system.

Therefore, there is a growing need for a system that can be compatible with the current system, improve customer satisfaction by predicting the demand of customers, and reduce the banking business by performing appropriate on-site operations.

In order to solve the above problems, the present invention proposes a device grouping that reflects the local characteristics of an automated device and a period grouping that reflects information on a specific day, thereby correcting a special event that the OCM server does not predict and accurately By enabling demand forecasting, you can distribute the current shipments appropriately, maximize the profits of your devices, reduce the labor required for planning your shipments, reduce unnecessary work, and increase the number of transactions to maximize operational efficiency. For the purpose of It is also aimed at minimizing the cost of cash loading of automated devices.

By using the system as described above, it is possible to accurately predict the amount of cash flow through grouping, thereby reducing the loss of interest income on cash that is unnecessarily supplied to the automated equipment, safe management of cash, and It aims to improve the bank's competitiveness by reducing the workload of employees due to the adjustment, and to improve customer satisfaction by supplying appropriately when needed.

In order to achieve the above object, the present invention proposes a method for grouping devices and period grouping of an automated device, and proposes a method for setting weights by integrating them.

For grouping of automated devices, the present invention includes the steps of: performing device groupings including regional and regional characteristics for devices that are expected to exhibit the same cash consumption pattern; Performing period grouping on devices that are expected to exhibit the same cash consumption pattern for a particular date or period, such as holidays, holidays, and the like; Integrating the results of the device grouping and the period grouping into an integrated grouping, and assigning a weight to the integrated grouping; Calculating the actual amount of money using the predicted amount of money and the weights described above.

Preferably, the device grouping includes a field of a device group code, a device group name, a device number, and a device group description, and the period grouping includes a period group code, a period group name, an application range, and a classification (positive / negative). ), A set period, a field of a period group description, and the unified grouping includes a field of a unified group code, a unified group name, a device group name, a period group name, a weight, and a unified group description. . In addition, the region-specific characteristics in the device grouping includes information on the location characteristics of the divided devices according to where the automation equipment is located, such as university, entertainment, and shopping malls, and the region-specific characteristics include Gangnam Station, Bang Bang Intersection, and Sillim Intersection. It contains information about the geographical characteristics of the equipment divided according to the region of the automation equipment, and includes information on the characteristics of the surrounding environment such as the number of peripheral automation equipment, day / night trade, fixed / floating trade zone, etc. Characterized in that.

3 is a block diagram illustrating device grouping according to the present invention.

Referring to FIG. 3, the device grouping 100 performs grouping on devices that are expected to show the same cash consumption pattern locally, which is a regional characteristic 102, a regional characteristic 104, and an environmental characteristic 106. Can be divided into In addition, the field included in the device grouping 100 may include a device group code, a device group name and group description according thereto, and the number of devices installed in the group.

The region-specific characteristics 102 include information on the location characteristics of the divided devices according to where the automation equipment is located, such as in the university street-school, around the college street-housing, shopping malls, department stores, beaches, tourist attractions, and race tracks. The regional characteristics 104 may be information on the geographical characteristics of the devices divided according to the region of the automation equipment, such as Apgujeong Galleria, Gangnam Station, Bang Bang Intersection, Sillim Intersection, and Jongno, and the Environmental Characteristics 106. The information may be divided according to the surrounding environment, such as the number of peripheral automation equipment, daytime business, nighttime business, fixed business, and floating business.

The device grouping 100 may assign a device group code and a device group name using the above information, and write down the number of devices corresponding thereto. For example, one field may be created with the device group code '007', the device group name 'region-university-school', and the number of devices '3'.

4 is a block diagram illustrating period grouping according to the present invention.

The period grouping 108 performs grouping for periods showing a unique cash consumption pattern for a specific day such as a holiday or a holiday, and includes a period group code and a period group name and group description according to the field. It may be an application range indicating whether the period to which the group is applied every month and the set period, the division indicating whether the solar or lunar calendar.

Referring to FIG. 4, grouping is performed for a period in which transactions near the mall increase due to the influence of the Lunar New Year holidays before the Lunar New Year, or periods in which transactions around department stores, universities, florists, and stations increase on special days such as Valentine's Day or graduation. As a result, during the Lunar New Year holidays, the influence of the Lunar New Year holidays reduced the traffic in downtown areas such as Seoul, and during the holidays and weekends, when the traffic in the office-dense areas such as Yeoksam-dong and Samseong-dong decreased, or during the vacation period. You can see the results of grouping for periods of decreasing trading. The period grouping 108 may assign a period group code, a period group name, and a group description using the above information, and write down the applicable range, classification, and setting period corresponding thereto.

In more detail, FIG. 5 is a diagram for explaining a process of assigning weight by integrating device grouping and period grouping used in the present invention. The device grouping and the period grouping are integrated to form an integrated grouping. The unified grouping includes the unified group code 110, the unified group name 112 and the group description 120 accordingly, and the device group name 114 of the device grouping 100 and the period grouping 108 referenced in the unifying. ), A period group name 116, and finally a weight 118 that can be calculated from the combined grouping above. The weight may be calculated by comparing average cash withdrawal amounts of general devices based on historical data on past device grouping and period grouping, and using the estimated value of the comparison result.

Referring to FIG. 5, first, an unified group code 110 is given based on the device grouping and period grouping, and an unified group name 112 is set accordingly. The device name of each grouping referred to in the integrated grouping is set to the device group name 114 and the period group name 116. Finally, weights 118 estimated based on the average cash withdrawals calculated in the past consolidated grouping are given, and if necessary, a description 120 of the integrated group code is added.

6 is a flowchart illustrating a process of performing grouping used in the present invention.

First, device grouping is performed for devices that are considered to exhibit the same cash consumption pattern locally (S102). Device grouping assigns a device group code, and sets the device group name according to the region and region according to the device group code. The number of devices corresponding to the group is written down, and if necessary, the description of the group is added. For example, the period group code '008', the period group name 'summer vacation period', the coverage 'yearly', the division 'quantity', the set period '06 / 20 ~ 8/31 ', etc. You can create fields.

Next, period grouping is performed for periods showing an unusual cash consumption pattern for a specific day such as a holiday or a holiday (S104). Period grouping is likewise given a period group code and a group name, and each year, The scope of application, such as monthly or daily, and whether it is the lunar calendar or the solar calendar and the corresponding period are set, and if necessary, the description of the group is added. For example, integrated group code '001', integrated group name 'college summer vacation', device group name 'region-university-school', period group name 'summer vacation', weight percentage '-30', etc. You can create one field.

Subsequently, an integrated group code and an integrated group name are set based on the device grouping and period grouping, and the device group name and period group name referred to above are input (106). In addition, a weight estimated based on the average cash withdrawal amount calculated in the integrated grouping is input to the integrated grouping (S108). As an example of integrated grouping with reference to the device grouping and period grouping described above, one field may be created as follows.

Integrated group code '001', integrated group name 'College-On-School-Summer Vacation',

Device group name 'Region-University-School', period group name 'Vacation'

Weight (%) '-30', group description 'reduced drop during vacation'

The final present amount may be calculated from the weight and the predicted amount (S110). For example, the weight (%) is '-30', so the automated device is set to send only 100-30 = 70% of the estimated amount. Therefore, if the forecast amount was 2 million won, the final cash value would be 2 million won * 70% = 1.4 million won. On the contrary, when the weight (%) is '+30', the automatic device is set to send 100 + 30 = 130% of the forecast amount, so if the forecast amount is 2 million won, the final cash amount is 2 million won * 130% = 2.6 million won. Becomes Through this process, the final cash value can be more accurately estimated.

The result of processing at each step is saved in the log file, and if an error occurs, the error code is left in the log file so that the cause can be easily identified or error processed later. . Through the above process it is possible to more accurately predict the number of withdrawal or withdrawal amount of the automated device. This further has the advantage of being able to expand the demand forecasts by office, team, and branch offices, rather than by forecasting only automation devices.

As mentioned above, although this invention was demonstrated in detail using the preferable embodiment, the scope of the present invention is not limited to a specific embodiment, Comprising: It should be interpreted by the attached claim. In addition, those skilled in the art should understand that many modifications and variations are possible without departing from the scope of the present invention.

As described above, the present invention receives the performance of the device, performs a grouping of devices and periods, enables accurate prediction by correcting a special event that the OCM server did not predict, and appropriately distributes limited operating funds. In addition, we can reduce the number of operations required for in-service planning, and reduce the unnecessary work and increase the number of transactions through proper forecasting and optimization to maximize operational efficiency.

By using the above system, it is possible to properly estimate the amount of cash flow, thereby reducing the loss of interest income on cash unnecessarily oversupplied to the cash dispenser, safe management of cash, and the adjustment of the number of times of payment. This can increase customer satisfaction by reducing workload and enabling the right supply when needed.

Claims (8)

In the grouping method used for the prediction of the cash amount of the automated device, The first step of performing device grouping including the characteristics of the surrounding environment, the region, and the region, such as the number of peripheral automation devices, the day / night trade, the fixed / floating trade zone, etc., for the devices exhibiting the same cash consumption pattern. , A second step of performing period grouping on devices exhibiting the same cash consumption pattern for a specific date or period such as a holiday or a holiday; A third step of integrating grouping by integrating the results of the device grouping and the period grouping; A fourth step of assigning a weight to the integrated grouping based on the average cash withdrawal amount calculated in the integrated grouping; A fifth step of calculating an actual present amount using the estimated present amount and the above weight; Grouping method of automated devices, characterized in that consisting of. The method of claim 1, wherein the device grouping in the first step A device group code, a device group name, a device number, and a field of a device group description. The method of claim 1, wherein the region-specific characteristics A method for grouping automation devices, comprising information on the location characteristics of the devices divided according to where the automation devices are located, such as university, entertainment, and shopping malls. The method of claim 1, wherein the region-specific characteristics A method for grouping automation devices, comprising information on the geographical characteristics of the devices divided according to the area of the automation devices, such as Gangnam Station, Bang Bang Intersection, and Sillim Intersection. delete The method of claim 1, wherein the period grouping in the second step is A grouping method of an automated device, comprising a period group code, a period group name, an application range, a division (positive / negative), a set period, and a field of a period group description. The method of claim 1, wherein in step 4, the unified grouping is performed. A grouping method for an automated device, comprising a field of integration group code, integration group name, device group name, period group name, weight, and integration group description. The method of claim 1, wherein in each of said steps The processing result is stored in a log file, and when an error occurs, the error code is left as an error code log file.

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