JP6690882B2 - Cash Management Information Providing Device, Cash Management Information Providing Method, and Cash Management Information Providing Program - Google Patents

Description

  The present invention relates to a cash management information providing apparatus, a cash management information providing method, and a cash management information providing program for automatically generating future cash management information from financial institution transaction information, for example.

  Conventionally, there has been proposed a business management support system or the like in which an accounting department or the like of a company or the like can simulate its own future profit and loss statement and the like (for example, Patent Document 1).

JP, 2016-134053, A

  However, such a simulation device is a device in which future income statement data is generated only when a person in charge with accounting knowledge, etc., inputs necessary data for predetermined items, etc. There was a problem that it was inconvenient for managers of large-scale stores.

  Therefore, the present invention provides a cash flow information providing device and a cash flow information provision device that can automatically provide cash flow information such as future profit and loss statement information to a user only by the user inputting transaction information of a financial institution or the like. The purpose is to provide a method and financing information provision program.

In the present invention, the above object is to divide a financial institution transaction information storage unit that stores financial institution transaction information and item information of the financial institution transaction information into withdrawal items and deposit items that belong to an income statement. A deposit / withdrawal item information generation processing unit that stores the information, and a deposit / withdrawal item prediction information generation process for generating deposit / withdrawal item prediction information for predicting future withdrawal or deposit based on the information of the deposit / withdrawal item and the withdrawal item. A cash flow forecasting information generation processing unit that generates cash flow forecasting information based on the cash deposit / withdrawal item forecasting information, and a display unit that displays the cash flow forecasting information.
At least a portion of the entering payment items prediction information, the dispensing item information Homata removes the maximum and minimum values for each month from at least one of the deposited item information, the average value information of the remaining values Then, the seasonal index of the month corresponding to the average value information for each month is divided to obtain the trend value information after removal of the seasonal variation for each month, and the future monthly trend is obtained from the trend value information after removal of the seasonal variation for each month. This is achieved by a cash flow information providing device characterized by predicting a value and multiplying the future monthly trend value by a seasonal index to generate a seasonally adjusted future monthly trend value .

According to the above configuration, item information (for example, deposit / withdrawal, summary, etc.) from financial institution transaction information (for example, passbook data, etc.), and withdrawal items belonging to the income statement (for example, personnel expenses, fixed expenses, Variable expenses, repayments, etc.) and deposit items (for example, sales, etc.) are automatically memorized, and future deposit or withdrawal items are predicted based on the information of deposit items and withdrawal items. Prediction information (for example, future labor cost data) can also be automatically generated.
Then, cash flow forecast information (for example, income statement information, etc.) is generated based on the deposit / withdrawal item forecast information, and the cash forecast information is displayed on the display unit. The situation can be obtained easily and without hassle.
Further, according to the above configuration, at least a part of the deposit / withdrawal item prediction information is the trend information obtained by removing the monthly peculiar variation information (eg, seasonal index) from the deposit / withdrawal item information and / or the deposit / withdrawal item information ( For example, it is trend information with variation information (for example, seasonally adjusted future trend value) obtained by adding the variation information of the corresponding month to the prediction information generated based on the trend value after removal of seasonal variation.
For this reason, when the deposit / withdrawal item prediction information is trend information with fluctuation information, the prediction is information that takes into account both the tendency of changes in the entire data and peculiar fluctuation information such as the month, and therefore It becomes high deposit / withdrawal item prediction information.

Preferably, the deposit / withdrawal item prediction information of at least a part of the cash flow information providing apparatus is generated based on moving average processed tendency information obtained by moving average processing the trend value information after the seasonal variation removal. And

According to the above configuration, at least a part of the deposit / withdrawal item prediction information is the moving average processed trend information (for example, the first 3 months) obtained by performing the moving average processing of the trend information (for example, the trend value after seasonal fluctuation removal). It is generated based on a moving average value).
By using the trend information as moving average processed trend information obtained by moving average processing in this way, it becomes easier to understand the tendency of changes in the entire data and the prediction accuracy is improved.

  Preferably, at least a part of the deposit / withdrawal item prediction information is generated based on overlapping moving average processed tendency information obtained by further moving average processing the moving average processed tendency information.

According to the above configuration, the moving average processed tendency information (for example, the second 3-month moving average value) is further subjected to moving average processing to obtain the overlapping moving average processed tendency information (for example, the second future tendency value). It is generated based on.
Therefore, it becomes easier to understand the tendency of change in the entire data, and the prediction accuracy is further improved.

  Preferably, based on the cash flow prediction information, a warning is output regarding an increase in cash demand that may occur in the future.

  According to the above configuration, a warning regarding an increase in the demand for funds that may occur in the future is output based on the cash flow forecast information, so that an increase in demand for funds, such as a shortage of deposit amount in the future or an increase in working capital, is predicted, for example. Then, a warning is output and the user is notified.

In the present invention, the deposit / withdrawal item information generation processing unit of the device stores the item information of the financial institution transaction information by dividing it into withdrawal items and deposit items belonging to the profit and loss calculation table, The deposit / withdrawal item prediction information generation processing unit of the device generates deposit / withdrawal item prediction information in which future withdrawal or deposit is predicted based on the information of the deposit item and the withdrawal item, and cash management forecast information of the device. The generation processing unit generates cash flow forecasting information based on the deposit / withdrawal item prediction information, displays the cash flow forecasting information on a display unit of the device, and at least a part of the deposit / withdrawal item forecast information is dispensing item information Homata removes the maximum and minimum values for each month from at least one of the deposited item information, the average value information of the remaining values, the month corresponding to the mean value information for each month Seasonal variation by dividing the seasonal index of After the seasonal trend information is obtained, the future monthly trend value is predicted from the monthly trend value information after removal of the seasonal fluctuation, and the seasonal index corresponding to the future monthly trend value is multiplied to obtain the seasonally adjusted future month. This is achieved by a cash flow information providing method characterized by generating a tendency value .

In the present invention, the above-mentioned object is a function of storing item information of financial institution transaction information in a cash flow information providing device by dividing it into withdrawal items and deposit items belonging to an income statement, the deposit item and the item. Based on the information of the withdrawal item, a function to generate deposit and withdrawal item prediction information for which future withdrawal or deposit is predicted, a function to generate cash flow forecasting information based on the deposit and withdrawal item prediction information, on the display unit function to display the cash flow prediction information, at least a portion of the entering payment items prediction information, the dispensing item information Homata removal maximum and minimum values for each month from at least one of the deposited item information Then, the function to obtain the average value information of the remaining values, the function to obtain the trend value information after removing the seasonal variation for each month by dividing the seasonal index of the month corresponding to the average value information for each month, the seasonal variation for each month Trend value information after removal Predict et future monthly trend values, by multiplying the seasonal factors corresponding to the future of the monthly trend value, characterized in that it is configured to execute the function, to generate a seasonally adjusted future Monthly tendency value Achieved by a funding information program.

  INDUSTRIAL APPLICABILITY As described above, the present invention is a cash management information providing device capable of automatically providing cash management information such as future income statement information to a user only by the user inputting transaction information of a financial institution or the like. There is an advantage that a cash flow information providing method and a cash flow information providing program can be provided.

It is the schematic which shows the external appearance of the "financing information provision apparatus" concerning the 1st Embodiment of this invention. FIG. 2 is a schematic diagram showing a main configuration of a “financing information providing device main body” of the “financing information providing device” of FIG. 1. It is a schematic block diagram which shows the main structures of a "1st various information storage part." It is a schematic block diagram which shows the main structures of a "2nd various information storage part." It is a schematic block diagram which shows the main structures of a "3rd various information storage part." It is a schematic block diagram which shows the main structures of a "4th various information storage part." It is a schematic block diagram which shows the main structures of a "5th various information storage part." "It is a schematic block diagram showing a main configuration of a sixth various information storage unit. It is a schematic explanatory drawing which shows some "passbook data" memorize | stored in the "passbook data storage part" of FIG. It is a schematic flow chart which shows a "future sales data" generation process. It is another schematic flowchart which shows the "future sales data" production | generation process. It is explanatory drawing regarding sales information. It is a schematic explanatory drawing which shows the "seasonal index data" memorize | stored in the "seasonal index data memory | storage part" of FIG. It is a schematic explanatory drawing which shows the characteristic part of 2nd Embodiment. It is a graph which compared monthly change of a "trim average value" and a "2nd 3 month moving average value."

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Since the embodiments described below are preferred specific examples of the present invention, various technically preferable limitations are attached, but the scope of the present invention particularly limits the present invention in the following description. Unless otherwise stated, the present invention is not limited to these modes.

(First embodiment)
FIG. 1 is a schematic diagram showing an appearance of a “financing information providing device 1” according to a first embodiment of the present invention.
As shown in FIG. 1, the device 1 includes a cash management information providing device main body 10, a “display 21” that is a display unit for displaying various information, a “keyboard 22” and a “mouse 23” for inputting various information. There is.
Further, the cash management information providing apparatus main body 10 is provided with various data input units 13 that receive an information recording medium such as a CD and acquire the data.

Therefore, by inserting the CD or the like in which the program relating to “financing information provision” is stored in the various data input unit 13 of the present apparatus 1 shown in FIG. 1 and reading the data, the “financing information providing apparatus 1” is obtained. Function.
In addition, the program or the like relating to “financing information provision” may be acquired by the present apparatus 1 by wireless communication other than the CD or the like, or may be stored in the financing information providing apparatus main body 10 in advance.

  Each device 10 or the like of the cash management information providing device 1 of FIG. 1 is a computer, has a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), a hard disk, and the like, and has a bus. Connected through.

FIG. 2 is a schematic diagram showing the main configuration of the “financing information providing device main body 10” of the “financing information providing device 1” of FIG.
As shown in FIG. 1, the main body 10 has a control unit 11, and the control unit 11 is configured to control a communication device 12 and various data input units 13 for communicating with an external device such as a display 21. ing.
Further, the display 21, the keyboard 22 and the mouse 23 are communicatively connected to the communication device 12.
Further, the control unit 11 includes a first various data storage unit 30, a second various data storage unit 40, a third various data storage unit 50, a fourth various data storage unit 60, and a fifth various data storage unit 60 shown in FIG. The various data storage units 70 and the sixth various data storage units 80 are also controlled.

  3 to 8 are schematic block diagrams showing the main configurations of the "first various information storage unit 30" to the "sixth various information storage unit 80", respectively. The contents of these will be described later.

Hereinafter, an operation example of the “financing information providing device 1” according to the present embodiment will be described.
In the present embodiment, the manager of the A company inputs the financial institution transaction information of the A company, for example, the passbook data into the “financing information providing device 1” of FIG. An explanation will be given below by taking an example of grasping the cash flow situation (fund shortage, etc.) of the next month.

First, the manager of the A company stores the passbook data of the A company in, for example, a CD and inputs it through the "various data storage section 13" of the "financing information providing apparatus main body 10" in FIG.
Then, the passbook data is stored in, for example, the "passbook data storage unit 31" which is the financial institution transaction information storage unit of FIG. 3 of the "financing information providing device main body 10".

FIG. 9 is a schematic explanatory view showing a part of the “passbook data 31a” stored in the “passbook data storage unit 31” of FIG.
As shown in FIG. 9, the passbook data 31a includes item information of the financial institution transaction information of the passbook of the company A, for example, date of deposit / withdrawal, whether deposit or withdrawal, summary thereof, amount, and the like. include.

The present apparatus 1 classifies and stores, from the passbook data 31a, items such as "past labor cost", "past fixed cost", "past variable cost", "past repayment", which are items to be dispensed to which a past income statement and the like belong. At the same time, past payment items such as “past sales” are also stored separately.
Furthermore, based on these “past labor costs”, “past fixed costs”, “past variable costs”, “past repayments”, “past sales”, etc., in the future, for example, “future personnel costs”, “future fixed costs”, “future fluctuations” in the next month. Predict expenses, future repayments, future sales, etc.
Hereinafter, each item will be described.

(About "Future labor cost data" generation process)
First, the “personnel cost judgment acquisition unit (program) 32” of FIG. 3, which is a deposit / withdrawal item information generation processing unit, operates, and there is a predetermined number or more of transfer data from the company A to an individual. In the case of 9, the withdrawal data such as “salary” on May 25, 2017 is stored in the “past labor cost data storage unit 33” of FIG. 3 as the “labor cost”.

  For example, in the passbook data 31a shown in FIG. 9, data such as “salary” in May, 40,000 yen, “salary” in June, 40,000 yen, “salary” in July, 40,000 yen, etc., can be used as “past personnel”. It is stored in the expense data storage unit 33 ”.

  Next, for example, the “future labor cost data generation processing unit (program) 34” of FIG. 3 which is a deposit / withdrawal item prediction information generation processing unit operates, and the data of the “past labor cost data storage unit 33” (for example, May). "40,000 yen", "40,000 yen" in June, "40,000 yen" in July, etc. are obtained, and the average value is calculated, which is, for example, future deposit / withdrawal item prediction information for August. For example, “future labor cost data” is generated and stored in the “future labor cost data storage unit 35” of FIG.

  For example, (40,000 yen (May) + 40,000 yen (June) + 40,000 yen (July)) ÷ 3 = 40,000 yen (August forecast) for August The data is stored in the “future personnel cost data storage unit 35” as “data”.

(About "Future fixed cost data" generation process)
Next, the “future fixed cost data” generation process will be described below.
First, for example, a “fixed cost determination acquisition unit (program) 36”, which is a deposit / withdrawal item information generation processing unit of the “financing information providing apparatus main body 10” in FIG. 3, operates, and “passbook data storage” in FIGS. 3 and 9 is performed. A specific summary (for example, "electricity n months" etc.) of the passbook data 31a of the "section 31" is determined as "fixed cost" by machine learning or the like and stored in the "past fixed cost data storage section 37" of FIG. To do.

  For example, among the passbook data 31a in FIG. 9, “Electric May” for May is 30,000 yen, “Electric June” is 36,000 yen, and “Electric July” is for July. The data such as 30,000 yen is stored in the "past fixed cost data storage unit 37".

  Next, for example, the “future fixed cost data generation processing unit (program) 38” of FIG. 3 which is a deposit / withdrawal item prediction information generation processing unit operates, and the data of the “past fixed cost data storage unit 37” of FIG. 3 (for example, May "30,000 yen", June "36,000 yen", July "30,000 yen", etc. are obtained, and the average value is calculated, and in the future, for example, August " "Future fixed cost data" is generated and stored in the "future fixed cost data storage unit 41" of FIG.

  For example, (30,000 yen (May) + 36,000 yen (June) + 30,000 yen (July)) / 3 = 32,000 yen (August forecast value) Information, for example, is stored in the "future fixed cost data storage unit 41" as "future fixed cost data".

(About "Future variable cost data" generation process)
Next, the “future variable cost data” generation process will be described below.
First, for example, the “variable cost determination acquisition unit (program) 42”, which is the deposit / withdrawal item information generation processing unit of the “financing information providing apparatus main body 10” of FIG. 4, operates and the passbook data storage unit of FIGS. 3 and 9 is operated. Of the passbook data 31a of 31, payments other than fixed costs (however, excluding repayment for loans and loans) are set as "variable costs" and stored in the "past variable cost data storage unit 43" of FIG.

  For example, in the passbook data 31a shown in FIG. 9, for May, a total of 60,000 yen of 10,000 yen for "Ka) carrot" and 50,000 yen for "Ka) cabbage" is used, and "6" for June. Data such as "Ca) cabbage" 40,000 yen and "Ka) carrot 20,000 yen for July are stored in the" past variable cost data storage unit 43 "in FIG.

  Next, for example, the “future variable cost data generation processing section (program) 44” of FIG. 4 which is a deposit / withdrawal item prediction information generation processing section operates, and the data of the “past variable cost data storage section 43” of FIG. 4 (for example, May "60,000 yen", June "40,000 yen", July "20,000 yen", etc. are obtained, and the average value is calculated, and in the future, for example, August " "Future variable cost data" is generated and stored in the "future variable cost data storage unit 45" of FIG.

  For example, (60,000 Yen (May) + 40,000 (June) + 20,000 Yen (July)) / 3 = 40,000 Yen (August forecast value) is August deposit / withdrawal item forecast information. For example, it is stored in the “future variable cost data storage unit 45” as “future variable cost data”.

(About the process of generating "future repayment data")
Next, the “future repayment data” generation process will be described below.
First, for example, a “payment / withdrawal item information generation processing unit” of the “financing information providing device main body 10” of FIG. 4, for example, a “repayment determination acquisition unit (program) 46” operates, and the passbook data storage unit 31 of FIGS. In the deposit column of the passbook data 31a, the summary is stored as “repayment” data in the “past payment data storage unit 47” of FIG.

  For example, of the passbook data 31a in FIG. 9, data such as 0 yen for May, 6,000 yen for June, 6,000 yen for July, etc. is stored in the "past repayment data storage unit 47" of FIG. Remember.

  Next, for example, the “future repayment data generation processing unit (program) 48” of FIG. 4 which is a deposit / withdrawal item prediction information generation processing unit operates, and the data of the “past repayment data storage unit 47” of FIG. 4 (for example, 5 "0 yen" for the month, "6,000 yen" for June, "6,000 yen" for July, etc.) were obtained, and the repayment amount was judged to be the same as the previous month, and "future repayment data" for August. Is generated and stored in the "future repayment data storage unit 51" of FIG.

  For example, in the above example, since the previous month (July) was 6,000 yen, it was determined that it was 6,000 yen in August, and 6,000 yen was set as the “future repayment data” for August. It is stored in the “future repayment data storage unit 51” of.

(About the process of generating "future sales data")
Next, the process of generating “future sales data” will be described below.
10 and 11 are schematic flowcharts showing the “future sales data” generation process.
The process of generating the “future sales data” will be described below with reference to the flowcharts of FIGS. 10 and 11.
First, in step (hereinafter referred to as “ST”) 1, for example, a “sales determination acquisition unit (program) 52” which is a deposit / withdrawal item information generation processing unit of FIG. In the deposit column of the passbook data 31a in the passbook data storage unit 31 ", all deposits other than" certificate lending "are set as" sales "and stored in the" past sales data storage unit 53 "in FIG.

For example, in the passbook data 31a of FIG. 9, "Ka) apple" of 70,000 yen, "Ka) mandarin" of 90,000 yen, "Ka) banana" of 100,000 yen, etc. Extract the data.
Also, for June, data such as 80,000 yen for "Ka) apple" and 110,000 yen for "Ka) mandarin orange" are extracted.
Then, for July, data such as 80,000 yen for "Ka) Mikan and 90,000 yen for" Ka) Banana "is extracted and stored in the" past sales data storage unit 53 "in FIG.

  Then, it proceeds to ST2. In ST2, the “trim sales average value calculation processing unit (program) 54” in FIG. 5 operates to remove the maximum and minimum values from the monthly sales data in the “past sales data storage unit 53” in FIG. The average of the remaining sales values is calculated and stored in the "trim sales average value storage unit 55" in FIG.

FIG. 12 is an explanatory diagram regarding sales information and the like.
The "trim average value" stored in the "trim sales average value storage unit 55" in FIG. 5 is calculated as shown in the "trim average value" in FIG.
For example, as shown in FIG. 12, the trim average value in July 2016 is 178,000 yen, the trim average value in August is 224,000 yen, and the trim average value in September is 173,000 yen.

  As described above, in the present embodiment, the reliability of the average value can be improved by not including the abnormal value in the calculation of the average of the sales value of each month.

  Then, it proceeds to ST3. In ST3, the “seasonal index data generation processing unit (program) 56” in FIG. 5 operates, and is a fluctuation index for each month, for example, a seasonal index, based on sales data such as trimmed averages for each month of past years. (Sales change index for each month) is calculated and stored in the "seasonal index data storage unit 57" in FIG.

FIG. 13 is a schematic explanatory diagram showing the “seasonal index data 57a” stored in the “seasonal index data storage unit 57” of FIG.
As shown in FIG. 13, for example, the seasonal index in January is “0.85”, the seasonal index in February is “0.83”, and the seasonal index in March is “0.89”. And the like, and the “variation index” that indicates the trend of sales fluctuations for the month is stored.

  Then, it proceeds to ST4. In ST4, the "monthly trend value generation processing unit (program) 58" in FIG. 5 operates and the "trim sales average value storage unit 55" and "seasonal index data storage unit 57" in FIG. The "seasonal index" of the corresponding month is divided from the "trim average value" data to obtain trend information, for example, "trend value after removal of seasonal variation", and the "monthly trend value storage unit 61" in FIG. Remember.

In this way, by dividing the seasonal index by the trimmed average value for each month, the trend value after removing the seasonal fluctuation can be grasped.
For example, in the example of FIG. 12, in July 2016, the trim average value is “178,000 yen” and the seasonal index is “1.08”, so the “monthly trend value” of sales is “165”. And in August. “249”, September is “164”, etc., and these “monthly tendency values” are stored in the “monthly tendency value storage unit 61” in FIG.

Then, it proceeds to ST5. In ST5, the “future trend value prediction data generation processing unit (program) 62” in FIG. 6 operates, and the “future trend value” is calculated based on the “monthly trend value” in the “monthly trend value storage unit 61” in FIG. It is generated and stored in the “future trend value storage unit 63” in FIG.
That is, in this step, for example, from the “monthly trend value after removal of seasonal fluctuation” shown in FIG. 12, the future “monthly trend value”, for example, “August 2017” that is not yet stored in FIG. "Trend value" will be predicted.

Specifically, in the example of FIG. 12, since the “monthly trend value” in July 2016 is “165” and the “monthly trend value” in August is “249”, it is from July to August. The change in the tendency value of is 1.5 times. Therefore, the “future trend value prediction data generation processing unit (program) 62” in FIG. 6 operates and multiplies the monthly trend value “146” in July 2017 by “1.5” to obtain the value in August 2017. "219" which is the "future trend value" is obtained and stored in the "future trend value storage unit 63" of FIG.
As a result, the “future trend value” in August 2017 is predicted and stored, for example, as “219”.
The "future trend value" is an example of "prediction information generated based on the trend information".

Then, it proceeds to ST6. In ST6, the “season-adjusted future trend value generation processing unit (program) 64” in FIG. 6 operates and the “future trend value (for example, August, 219)” in the “future trend value storage unit 63” in FIG. Is multiplied by the "seasonal index (for example, August, 0.9)" of the corresponding "seasonal index storage unit" to generate a "season-adjusted future trend value", and a "season-adjusted future trend value storage unit" is generated. 65 ”.
That is, the “future trend value” obtained in ST5 only indicates the trend value for the entire year, and does not take into consideration the fluctuation information peculiar to each month. Therefore, in this process, by multiplying the seasonal index of the month, the monthly tendency value considering the seasonal index is obtained, and the reliability of the numerical value is improved.

  Specifically, "219" which is the "future trend value" in the above example is multiplied by the seasonal index "0.9" in August to obtain trend information with fluctuation information, for example, "season-adjusted future trend". The value “197” is obtained and stored in the “season-adjusted future trend value storage unit 65” in FIG.

As described above, for example, forecast values regarding withdrawal such as “future labor cost”, “future fixed cost”, “future variable cost” and “future repayment” in September 2017 and “seasonal adjustment” that is a forecast value regarding sales. “Future trend value” is obtained and stored by the device 1.
Therefore, the “income statement” data for September 2017 is predicted in ST7 as follows.
In ST7, for example, the “profit and loss statement generation processing unit (program) 66” which is the cash flow prediction information generation processing unit of FIG. 6 operates, the “future personnel cost data storage unit 35” of FIG. 3 and the “future personnel cost data storage unit 35” of FIG. Refer to "Future fixed cost data storage unit 41", "Future variable cost data storage unit 45" of Fig. 4, "Future repayment data storage unit 51" of Fig. 5 and "Seasonally adjusted future trend value storage unit 65" of Fig. 6. To do.

  Then, the processing unit 66 uses the “future personnel cost data” (August, 40,000 yen), “future fixed cost data” (August, 32,000 yen), “future variable cost” in the storage unit 35 and the like. Based on "data" (August, 40,000 yen), "future repayment data" (August, 6,000 yen), "seasonally adjusted future trend value" (August, 197,000 yen), etc. For example, “future income statement” data, which is the “cash management forecast information” of August 2017, is generated and stored in the “future income statement storage unit 67” of FIG.

Thus, in the future, for example, “income statement” data for August 2017 will be generated and stored.
This "income statement" data is forecast data in which the forecast of the "sales" data is a yearly trend value and the seasonal index of the month to be forecast. Since it is data that includes both unique seasonal indices such as months, it is highly accurate forecast data.
In addition, this "income statement" data is automatically classified into each item such as "personnel costs", "fixed costs", "variable costs", "repayments", and "sales" simply by inputting passbook data. Since the “profit and loss statement” data, which is a forecast for the future, is generated as well as being created, the user can easily understand the “financing status”.

Further, in the present embodiment, the “income statement” data for August 2017 is generated in this way, and this “income statement” data can be displayed on the display 21 of FIG. 1 as necessary. it can. Therefore, the user can easily grasp the future cash flow situation and the like by visually recognizing the display 21.
Specifically, for example, the user can grasp in advance the shortage of deposits in August 2017.

  In addition, in the present embodiment, as described above, future “income statement” data can be predicted, and thus various alerts that are warnings regarding possible future increase in fund demand can be output. ing.

  The alerts according to the present embodiment include the “first financing examination alert”, the “second financing examination alert”, and the “tax payment preparation alert”. Therefore, each of them will be described below.

(Regarding the first funding procurement alert output process)
The "first funding procurement alert generation processing unit (program) 71" in FIG. 7 operates and refers to the "future income statement storage unit 67" in FIG. 6 to predict the passbook within the set multiple months. It is determined whether or not the amount of money is less than the set amount of money, and when it is determined that the amount of money is less than the set amount of money, the “first funding consideration alert” is output to the display 21 or the like.
Therefore, when the predicted amount of money in the passbook is less than the set amount of money, an alert is output, and the user can be made aware of the predicted amount.

(About the second funding procurement alert output process)
The "second fund raising consideration alert generation processing unit (program) 72" of FIG. 7 operates and refers to past withdrawal data and the like to determine whether or not the withdrawal amount exceeds the same month last year for a plurality of consecutive months. However, when it exceeds, it is determined that the increased working capital needs are predicted, and the “second financing alert” is output to the display 21 or the like.
Therefore, when the increased working capital needs are predicted, an alert is output, so that the user can be aware of the prediction.

(About the tax payment preparation alert output process)
The “tax payment preparation alert generation processing unit (program) 73” in FIG. 7 operates and refers to the past withdrawal data and deposit data, etc., and judges whether or not the state in which the withdrawal amount is less than the withdrawal amount continues for a plurality of months. , "Tax payment preparation alert" is output to the display 21 or the like.
Therefore, when the user needs to prepare for tax payment, an alert is output in advance.

For example, when the company's sales increase, it generally requires increased working capital, and without borrowing, the cash will decrease. Therefore, there is a possibility that cash will be insufficient at the timing of subsequent tax payment.
Therefore, in order to avoid such a situation, an alert indicating that a cash management plan considering tax payment is necessary is output.

(Second embodiment)
The present embodiment is different in that it further processes the “future trend value” relating to sales in the above-mentioned first embodiment, but since many other configurations are common to the first embodiment, The following description will be centered on the different points, and the same components as those of the first embodiment will be denoted by the same reference numerals and the description thereof will be omitted.

In the above-described first embodiment, the “monthly trend value” is obtained in ST4 of FIG. 10, and the “future trend value” is generated based on this “monthly trend value” in ST5. However, in the present embodiment, A major difference is that the “3 month moving average” process is further performed twice (3 × 3 moving average) from the “monthly trend value”, and then the “second future trend value” is obtained.
The details will be described below.

First, ST1 to ST4 in FIG. 10 for generating future sales data are common to the first embodiment.
Further, the method of calculating data on personnel costs, fixed costs, variable costs, repayments, etc. regarding withdrawals is also common to the first embodiment.
The process after the process of ST4 is finished will be described below.

In ST4 of FIG. 10, after storing the “monthly trend value” after removing the seasonal fluctuation in the “monthly trend value storage unit 61” of FIG. 6, the “first three-month moving average processing unit (program) 74 of FIG. 7 is stored. ”Operates and adds the“ monthly trend value ”of each month in the“ monthly trend value storage unit 61 ”and the value of the month before and after that, and divides by 3 to calculate the“ first three months move ”of the month. The "average value" is obtained and stored in the "first three-month moving average value storage unit 75" of FIG.
This "first three-month moving average value" is an example of moving average processed tendency information.

Further, FIG. 14 is a schematic explanatory view showing a characteristic part of the second embodiment.
As shown in FIG. 14, by processing the monthly trend value by “first three-month moving average”, as shown in FIG. 14, July 2016 is “191”, August is “193”, September. Becomes “186” and the like, and these become the “first moving average value”.

Next, the "second three-month moving average processing unit (program) 76" of FIG. 7 operates, and the "first three-month moving average value" of each month of the "first three-month moving average value storage unit 75". , And the values of one month before and after that are added and divided by three to obtain the “second three-month moving average value” of the month and stored in the “second three-month moving average value storage unit 77”.
Then, it becomes the "second moving average value" in FIG. That is, in the present embodiment, a numerical value that makes it easy to grasp the overall trend value is obtained by performing the “3 month moving average” twice (3 × 3 moving average).
This "second three-month moving average value" is an example of the overlapping moving average processed tendency information.

FIG. 15 is a graph comparing monthly changes of the “trim average value” and the “second three-month moving average value”.
As shown in FIG. 15, by performing the “3 × 3 moving average” processing as in the present embodiment, the data becomes easier to grasp the tendency.

  Next, the "second future trend value prediction data generation processing unit (program) 81" of FIG. 8 operates, and the "second 3 month moving average value storage unit 77" of FIG. The "second future tendency value" is generated based on the "three-month moving average value" and stored in the "second future tendency value storage unit 82" in FIG.

For example, the “second three-month moving average value” from July to August 2016 changes from “183” to “190” according to FIG. 14, and is about 1.04 times.
Therefore, the “second future trend value prediction data generation processing unit (program) 81” in FIG. 8 similarly multiplies “159” in July 2017 by 1.04 to calculate the expected sales value in August. “165” is obtained as this, and this value is stored in the “second future tendency value storage unit 82” as the “second future tendency value”.

  Next, the “second seasonally adjusted future trend value generation processing unit (program) 83” in FIG. 8 operates and corresponds to the “second future trend value” in the “second future trend value storage unit 82”. The "seasonally adjusted second future trend value" is multiplied by the "seasonal index" of the "seasonal index storage unit 57" of FIG. 5 to generate the "seasonally adjusted second future trend value storage unit" of FIG. 84 ”.

For example, the above "165" which is the "second future trend value" is multiplied by the seasonal index "0.9" for August to obtain the "season adjusted future trend value" (149), which is 149 thousand yen. Is stored in the “seasonally adjusted second future trend value storage unit 84” of FIG. 8 as the “seasonally adjusted second future trend value”.
In this step, the sales forecast value is accurately estimated from both the trend value and the seasonal adjustment factor.

Next, the step of predicting the “income statement” is executed.
Specifically, the “second future income statement generation processing unit (program) 85” in FIG. 8 operates, the “future personnel cost data storage unit 35” in FIG. 3 and the “future fixed cost data storage in FIG. 4”. Refer to "section 41", "future variable cost data storage section 45" in FIG. 4, "future repayment data storage section 51" in FIG. 5 and "season adjusted second future trend value storage section 84".
And "Future labor cost data" (August, 40,000 yen), "Future fixed cost data" (August, 32,000 yen), "Future variable cost data" (August, 40,000 yen), Based on "Future repayment data" (August, 6,000 yen) and "Seasonally adjusted second future trend value" (August, 149,000 yen), etc., "Second future in August 2017" The “income statement” data is generated and stored in the “second future income statement storage unit 86” in FIG.

  As described above, in the present embodiment, unlike the first embodiment, since the process of “3 × 3 moving average” is added to calculate the expected sales value, it is possible to compare with the first embodiment. It is possible to make a judgment based on the leveled trend value (second future trend value) by removing more irregular factors.

The present invention is not limited to the above-described first and second embodiments, and includes the following configurations.
For example, when there are few basic data such as passbook data and the seasonal index cannot be obtained by calculation, for example, when there is only data for one year, the above "3 months moving average (data for 1 month before and after the month concerned" ), A method of averaging data for 3 months before the month, a method of averaging data for 3 months after the month, etc.) ”.

Further, the “trend value” may be calculated by the “3 × 3 moving average” in the second embodiment.
Further, even when there is missing data in the passbook data or the like, the data may be supplemented with the above-mentioned “3 month moving average” or “3 × 3 moving average” to obtain the “monthly trend value” or the like.

  In the above-described embodiment, the case where it is realized as an apparatus has been described as an example, but the present invention is not limited to this, and as a program that can be executed by a computer, a magnetic disk (floppy (registered (Trademark) disk, hard disk, etc.), optical disk (CD-ROM, DVD, etc.), magneto-optical disk (MO), semiconductor memory, etc. may be stored and distributed.

  The storage medium may be any storage medium that can store the program and can be read by a computer. The storage format of the storage medium is not particularly limited.

  Further, the operating system (OS) operating on the computer based on the instructions of the program installed in the computer from the storage medium, the MW (middleware) such as the database management software, the network software, or the like realizes this embodiment. A part of each processing of may be executed.

  Further, the storage medium in the present invention is not limited to a medium independent of a computer, and includes a storage medium in which a program transmitted via a LAN, the Internet or the like is downloaded and stored or temporarily stored.

  Further, the computer in the present invention may execute each processing in the present embodiment based on the program stored in the storage medium, and may be a device such as a single personal computer or the like, or a plurality of devices may be network-connected. The system may be a customized system or the like.

  Further, the computer in the present invention is not limited to a personal computer, and also includes an arithmetic processing unit, a microcomputer, etc. included in information processing equipment, and is a generic term for equipment and devices capable of realizing the functions of the present invention by a program. ing.

  The embodiments of the present invention have been described above. However, the present invention is not limited to the above embodiment, and various modifications can be made without departing from the scope of the claims. The configuration of the above-described embodiment may be partially omitted or may be arbitrarily combined so as to be different from the above.

1 ... Cash flow information providing device, 10 ... Cash flow information providing device main body, 11 ... Control unit, 12 ... Communication device, 13 ... Various data input unit, 21 ... Display, 22 ... ..Keyboard, 23 ... Mouse, 30 ... First various data storage unit, 31 ... Passbook data storage unit, 32 ... Personnel cost judgment acquisition unit (program), 33 ... Past personnel Expense data storage unit, 34 ... Future labor cost data generation processing unit (program), 35 ... Future labor cost data storage unit, 36 ... Fixed cost determination acquisition unit (program), 37 ... Past fixed Expense data storage unit, 38 ... Future fixed cost data generation processing unit (program), 40 ... Second various data storage unit, 41 ... Future fixed cost data storage unit, 42 ... Variable cost judgment Acquisition unit (program), 43 ... Past variable cost data storage unit, 44 ... Future variable cost data generation processing unit (program), 45 ... Future variable cost data storage unit, 46 ... Repayment determination acquisition unit (program), 47 ... Past Repayment data storage unit, 48 ... Future payment data generation processing unit (program), 50 ... Third various data storage unit, 51 ... Future payment data storage unit, 52 ... Sales determination acquisition unit ( Program), 53 ... Past sales data storage unit, 54 ... Trim sales average value calculation processing unit (program), 55 ... Trim sales average value storage unit, 56 ... Seasonal index data generation processing unit ( Program), 57 ... Seasonal index data storage unit, 58 ... Monthly trend value generation processing unit (program), 60 ... Fourth various data storage unit, 61 ... Monthly trend value storage unit, 62 ... Future trends Prediction data generation processing unit (program), 63 ... Future trend value storage unit, 64 ... Seasonal adjusted future trend value generation processing unit (program), 65 ... Seasonal adjusted future trend value storage unit, 66 ... future profit and loss statement generation processing unit (program), 67 ... future profit and loss statement storage unit, 70 ... fifth various data storage unit, 71 ... first financing examination alert generation process Part (program), 72 ... Second funding procurement alert generation processing unit (program), 73 ... Tax payment preparation alert generation processing unit (program), 74 ... First three-month moving average processing unit (Program), 75 ... First 3-month moving average value storage unit, 76 ... Second 3-month moving average processing unit (program), 77 ... Second 3-month moving average value storage unit , 80 ・ ・ ・ Sixth data description Storage section, 81 ... Second future trend value prediction data generation processing section (program), 82 ... Second future trend value storage section, 83 ... Second seasonally adjusted future trend value generation processing Part (program), 84 ... Seasonally adjusted second future trend value storage unit, 85 ... Second future profit and loss statement generation processing unit (program), 86 ... Second future profit and loss statement Memory.

Claims (6)

A financial institution transaction information storage unit that stores financial institution transaction information;
A deposit / withdrawal item information generation processing unit that stores the item information of the financial institution transaction information by dividing it into withdrawal items and deposit items that belong to an income statement.
A deposit / withdrawal item prediction information generation processing unit that generates deposit / withdrawal item prediction information in which future withdrawal or deposit is predicted based on the information of the deposit / withdrawal item.
A cash flow forecast information generation processing unit that generates cash flow forecast information based on the deposit / withdrawal item forecast information;
A display unit for displaying the cash flow forecasting information,
At least a portion of the entering payment items prediction information, the dispensing item information Homata removes the maximum and minimum values for each month from at least one of the deposited item information, the average value information of the remaining values Seeking
Obtain the trend value information after removing the seasonal variation for each month by dividing the seasonal index of the month corresponding to the average value information for each month,
Predict future monthly trend values from the trend value information after removal of the seasonal variation for each month,
A cash flow information providing device , wherein a seasonally adjusted future monthly trend value is generated by multiplying a seasonal index corresponding to the future monthly trend value . The cash flow according to claim 1, wherein at least a part of the deposit / withdrawal item prediction information is generated based on moving average processed trend information obtained by moving average processing the trend value information after the seasonal variation removal. Information providing device.   Preferably, at least a part of the deposit / withdrawal item prediction information is generated based on overlapping moving average processed tendency information obtained by further moving average processing the moving average processed tendency information. The described cash flow information providing device.   Preferably, based on the cash flow forecast information, a warning regarding an increase in cash demand that may occur in the future is output, and the cash flow information providing device according to any one of claims 1 to 3. The deposit / withdrawal item information generation processing unit of the device stores the item information of the financial institution transaction information by dividing it into withdrawal items and deposit items belonging to the income statement,
The deposit / withdrawal item prediction information generation processing unit of the device generates deposit / withdrawal item prediction information in which future withdrawal or deposit is predicted based on the information of the deposit item and the deposit item.
The cash flow prediction information generation processing unit of the device generates cash flow prediction information based on the deposit / withdrawal item prediction information,
Display the cash management forecast information on the display unit of the device,
Average of at least in part, said dispensing item information and / or removal of the maximum and minimum values for each month from at least one of the deposited item information, the remaining value of the entering payment item prediction information Ask for value information,
Obtain the trend value information after removing the seasonal variation for each month by dividing the seasonal index of the month corresponding to the average value information for each month,
Predict future monthly trend values from the trend value information after removal of the seasonal variation for each month,
A method for providing cash flow information , wherein a seasonally adjusted future monthly trend value is generated by multiplying a seasonal index corresponding to the future monthly trend value . A function for storing the item information of the financial institution transaction information in the cash flow information providing device by dividing it into withdrawal items and deposit items belonging to the income statement,
A function of generating deposit / withdrawal item prediction information in which future withdrawal or withdrawal is predicted based on the information of the deposit / withdrawal item.
A function of generating cash flow forecast information based on the deposit / withdrawal item forecast information,
A function for displaying the cash flow forecast information on the display unit,
Average of at least in part, said dispensing item information and / or removal of the maximum and minimum values for each month from at least one of the deposited item information, the remaining value of the entering payment item prediction information A function that asks for value information,
A function of dividing the seasonal index of the month corresponding to the average value information for each month to obtain trend value information after removal of seasonal variation for each month,
Predict future monthly trend values from the trend value information after removal of the seasonal variation for each month,
A cash flow information providing program, which is configured to execute a function of generating a seasonally adjusted future monthly trend value by multiplying a seasonal index corresponding to the future monthly trend value .

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