JP2017102889A - Information processing device, information processing method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an information processing device, an information processing method, and a program which improve the demand prediction accuracy of season-limited goods by discriminating whether or not to be the season-limited goods.SOLUTION: The information processing device for predicting a feature demand on the basis of past actual results acquires past demand actual result values of goods to be a demand prediction object, and accumulates the acquired actual result values for a prescribed period unit. The information processing device specifies periods in which an actual result value is zero among the accumulated actual result values of the prescribed period unit, and predicts a feature demand on the basis of actual result values except the specified periods.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an information processing apparatus, an information processing method, and a program.

  As a general method of demand prediction, there are many methods for predicting future demand using past demand data. However, when this method is used, if the demand for products that are only in demand at a certain time each year (for example, products that are only in the summer season, hereinafter referred to as seasonal products) is predicted, Not good. Specifically, the moving average method and exponential smoothing method, which are widely used as demand forecasting methods, focus on the most recent demand data, so when forecasting occurs when there is a switch from when there is no demand to when there is demand Because the forecast is made with emphasis on the most recent data with no demand, the forecast value will be low.

  On the other hand, in order to improve the accuracy of demand forecasts for products whose demand increases or decreases at the same time every year, demand forecasts taking seasonality into account are performed. For example, Patent Document 1 describes a demand prediction method considering seasonality. Here, the demand forecast in consideration of seasonality means that the increase or decrease in demand in the same period every year is indexed (called a seasonal index), and a forecast value is calculated using the index.

Japanese Patent Laying-Open No. 2015-32034

  In the method described in Patent Document 1, seasonality is considered, but the season (time) in which there is no demand data is not considered. For example, Patent Document 1 describes a method of performing a calculation for dividing an actual value by a seasonal index. According to this, since the seasonal index of a season in which there is no demand is 0, the subsequent calculation cannot be performed.

  In the method described in Patent Document 1, past demand data is referred to obtain a seasonal index. However, if the data is so small that it can be considered that there is no demand in a certain season (seasonally limited product and The seasonal index is also calculated if it can be considered. As a result, the predicted value is calculated although the predicted value for that season is preferably calculated as 0.

  In view of the above, an object of the present invention is to provide a mechanism for determining whether or not a seasonal limited product is available and improving the demand prediction accuracy of the seasonal limited product.

  An information processing apparatus according to the present invention is an information processing apparatus that predicts future demand based on past results, and obtains a past demand result value of a product that is a target of demand prediction; A totaling means for totaling demand actual values acquired by means in units of cycles; a specifying means for identifying a cycle in which the actual demand values do not satisfy a predetermined value among the cycles in which the actual demands are totaled by the totaling means; Prediction means for predicting future demand based on the actual demand value in the period excluding the cycle specified by the means.

  The information processing method of the present invention is an information processing method in an information processing device that predicts future demand based on past results, wherein the result acquisition means of the information processing device A result acquisition step of acquiring past demand result values, a totaling step in which the totaling means of the information processing apparatus totals demand actual values acquired in the actual result acquisition process in units of cycles, and a specifying means of the information processing apparatus The specific process of specifying the cycle in which the actual demand value does not satisfy a predetermined value among the cycles in which the actual demand is totaled by the totaling process, and the prediction unit of the information processing apparatus includes the cycle specified by the specific process. A prediction step of predicting future demand based on the actual demand value in the excluded period.

  Further, the program of the present invention is a program that can be executed in an information processing apparatus that predicts future demand based on past results, and the information processing apparatus is used as a past demand result value of a product that is a target of demand prediction. The actual result acquisition means satisfying a predetermined value among the actual result acquisition means, the totaling means for totaling the demand actual value acquired by the actual result acquisition means in units of cycles, and the cycle in which the actual demand is totaled by the totalization means A program for functioning as a predicting means for predicting a future demand based on a specifying means for specifying a non-existing cycle and a demand actual value in a period excluding the cycle specified by the specifying means.

  According to the present invention, it is possible to improve the demand prediction accuracy of seasonally limited products.

The figure which showed the function with which information processing apparatus 101 is provided The figure which showed an example of the data registered into item master DB The figure which showed an example of the data registered into cycle master DB The figure which showed an example of the data registered in basic setting master DB The figure which showed an example of the data registered into cycle master DB classified by item The figure which showed an example of the data registered into demand performance DB The figure which shows an example of the data which set the seasonal limited goods flag The figure which showed an example of the data registered into cycle master DB classified by item The figure which showed an example of the data registered in demand result DB according to cycle Diagram showing an example of data registered in the seasonal index DB by cycle The figure which shows an example of the data table in which the calculation result of the demand forecast value was registered The figure which shows an example of the data table in which the value which returned the seasonality was registered with respect to the predicted value of FIG. The figure which showed an example of the data registered in demand forecast DB according to cycle The flowchart which shows the flow of the process in this invention Flowchart showing details of the process in step S101 of FIG. The flowchart which showed the detail of the process of step S103 of FIG. The figure which shows an example of the hardware constitutions of the information processing apparatus 101 The flowchart which showed the detail of the process of FIG.14 S101 in 2nd Example.

  FIG. 1 is a diagram illustrating an example of functions provided in the information processing apparatus 101.

  As illustrated in FIG. 1, the information processing apparatus 101 performs demand prediction for seasonally limited products using data registered in various DBs. Various DBs are described as being included in the information processing apparatus 101 in the present embodiment, but may be included in an external storage device.

  In the basic setting master DB 151, the current date, the cycle unit for performing the prediction, the number of cycles of the past results to be referred to for performing the processing of the present invention (the number of actual reference cycles), the number of future cycles for performing the prediction (the number of cycles in the prediction period) ) For setting items related to the entire system.

  For example, in the example illustrated in FIG. 4, the current date is January 1, 2015, the cycle unit is “month”, and the actual reference cycle number is 24 (that is, the prediction is performed with reference to the actual results for 24 months). ), 12 is stored as the number of prediction period cycles (that is, 12 months are predicted).

  The item master DB 152 includes an item code for identifying the item, an item name that is the name of the item, a seasonally limited product flag that indicates whether the item is a seasonally limited item, and a seasonally limited that indicates whether the item is also a seasonally limited item. The product manual setting flag stores a value set for each item.

  For example, in the example illustrated in FIG. 2, the item code is information related to “A”, the item name is “Aiueo”, the seasonally limited product flag is 0 (that is, it is not a seasonally limited product), seasonally limited The product manual setting flag stores 0 (that is, it is not a seasonally limited product).

  The seasonally limited product flag is a flag that is set by the processing of step S101, and the seasonally limited product manual setting flag is a flag that is set by the user.

  The cycle master DB 153 stores the correspondence between cycles and actual dates (periods).

  For example, the example shown in FIG. 3 is data when the cycle unit is “month”, and cycle number “1” corresponds to “January” and cycle number “2” corresponds to “February”. It is attached. That is, the period indicated by the cycle number “1” means “January 1 to January 31”. When the cycle unit of the basic setting master is set to “week”, the cycle number is stored in association with the month and day at 7-day intervals. That is, the period indicated by the cycle number “1” means “January 1 to January 7”.

  In the item-specific cycle master DB 154, an item code and a cycle number are registered in association with each other, and a flag (prediction zero flag) indicating whether or not a predicted value in a period specified by the cycle number of the item is set to zero is associated. Registered.

  For example, in the example illustrated in FIG. 5, the predicted value in the period of the cycle number “1” of the item code “A” does not become the predicted value zero (the flag is 0).

  In the demand record DB 155, an item code and a date are registered in association with each other, and a record of the actual demand of the item specified by the item code is associated.

  For example, in the example illustrated in FIG. 6, the actual value on January 4, 2013 of the item identified by the item code A is “10”. The date when there is no record indicates that the quantity is zero.

  In the cycle-specific demand result DB 156, an item code, a cycle number, and a period specified by the cycle number are registered in association with each other, and the actual value of demand in the cycle of the item identified by the item code is registered. .

  For example, in the example illustrated in FIG. 9, the actual value in January 2013 of the item specified by the item code A is 200.

  In the cycle-specific demand prediction DB 157, an item code, a cycle number, and a period specified by the cycle number are registered in association with each other, and a predicted value of the demand in the cycle of the item identified by the item code is registered. .

  For example, in the example illustrated in FIG. 13, the predicted value for January 2015 of the item specified by the item code A is 281.

  The cycle-specific seasonal index DB 158 stores an item code, a cycle number, and a seasonal index value of the item in the cycle.

  For example, the example shown in FIG. 10 indicates that the seasonal index in the period specified by the cycle number 1 of the item identified by the item code A is “1.00”.

  Next, the hardware configuration of the information processing apparatus 101 will be described with reference to FIG.

  FIG. 17 is a block diagram showing an example of a hardware configuration of the information processing apparatus 101 according to the embodiment of the present invention.

  As shown in FIG. 17, in the information processing apparatus 101, a CPU (Central Processing Unit) 201, a ROM (Read Only Memory) 202, a RAM (Random Access Memory) 203, a storage device 204, and an input controller 205 are connected via a system bus 200. , An audio input controller 206, a video controller 207, a memory controller 208, and a communication I / F controller 209 are connected.

  The CPU 201 comprehensively controls each device and controller connected to the system bus 200.

  The ROM 202 or the storage device 204 is a control program executed by the CPU 201 such as a basic input / output system (BIOS) or an operating system (OS), a computer-readable program for realizing this information processing method, and various necessary programs. Holds data (including data table).

  The RAM 203 functions as a main memory, work area, and the like for the CPU 201. The CPU 201 implements various operations by loading a program or the like necessary for executing the processing from the ROM 202 or the storage device 204 to the RAM 203 and executing the loaded program.

  The input controller 205 controls input from an input device such as a keyboard / touch panel 210. The input device is not limited to this, and may be a touch panel capable of detecting a position touched by a plurality of fingers, such as a mouse or a multi-touch screen.

  Various instructions can be performed when the user presses (touches with a finger or the like) in accordance with an icon, a cursor, or a button displayed on the touch panel.

  This input device is used to input a destination for a communication destination that can be used by various communication devices.

  The voice input controller 206 controls input from the microphone 211. Voice input from the microphone 211 can be recognized.

  The video controller 207 controls display on an external output device such as the display 212. The display includes a display of a notebook computer integrated with the main body. The external output device is not limited to a display, and may be a projector, for example. Further, for devices that can be received by the touch operation described above, it is also possible to receive input from the keyboard / touch panel 210.

  Note that the video controller 207 can control a video memory (VRAM) for display control, and a part of the RAM 203 can be used as a video memory area, or a dedicated video memory can be provided separately. Is possible.

  In the present invention, the first video memory area used for display when the user normally uses the information processing apparatus and the display overlaid on the display content of the first video memory area when a predetermined screen is displayed. A second video memory area used in The number of video memory areas is not limited to two, and a plurality of video memory areas can be provided as long as the resources of the information processing apparatus permit.

  The memory controller 208 controls access to the external memory 213. The external memory is connected via an adapter to an external storage device (hard disk), flexible disk (FD), or PCMCIA card slot that stores boot programs, various applications, font data, user files, editing files, and various data. A compact flash (registered trademark) memory or the like can be used.

  The communication I / F controller 209 is connected to and communicates with an external device via the network 214, and executes communication control processing in the network. For example, communication using TCP / IP, telephone lines such as ISDN, and communication using 3G lines of mobile phones are possible.

  The storage device 204 is a medium for permanently storing information, and the form is not limited to a storage device such as a hard disk. For example, a medium such as SSD (Solid State Drive) may be used.

  It can also be used as a temporary memory area during various processes performed by the communication terminal in the present embodiment.

  Next, the flow of processing contents in the present invention will be described using the flowchart shown in FIG.

  The process shown in the flowchart of FIG. 14 is a process in which the CPU 201 of the information processing apparatus 101 reads and executes a predetermined control program.

  In step S <b> 101, the CPU 201 of the information processing apparatus 101 executes a seasonal limited product flag assignment process.

  Details of the processing in step S101 will be described later using the flowchart of FIG.

  In step S <b> 102, the CPU 201 of the information processing apparatus 101 determines whether the seasonally limited product flag or the seasonally limited product manual setting flag is set for the product for which the demand is to be predicted as a result of the processing of step S <b> 101.

  If the flag is set (step S102: YES), the process proceeds to step S103.

  If the flag is not set (step S102: NO), the process proceeds to step S104.

  In step S <b> 103, the CPU 201 of the information processing apparatus 101 performs a seasonal limited product demand prediction process.

  Details of the processing in step S103 will be described later with reference to the flowchart of FIG.

  In step S104, the CPU 201 of the information processing apparatus 101 performs demand prediction using a general demand prediction method.

  The general demand forecasting method here is a method for forecasting demand without distinguishing whether or not it is a seasonally limited product. That is, it is a method that does not perform a determination process for determining whether the product is a seasonally limited product, which is a characteristic process of the present invention, or a prediction process that takes into account a period without a demand record for the seasonally limited product. As a specific method, for example, there is a method of predicting by a moving average method using a demand actual value for a predetermined period, and any method may be used.

  Next, details of the processing in step S101 in FIG. 14 will be described with reference to FIG.

  In step S <b> 201, the CPU 201 of the information processing apparatus 101 determines whether a seasonally limited product manual setting flag is set for a processing target product (a product for which a demand prediction is performed).

  For example, in the example of FIG. 2, for the item (product) with the item code A, the seasonally limited product manual flag is 0, so it is determined that the flag is not set.

  In step S202, the CPU 201 of the information processing apparatus 101 aggregates past demand results in cycle units (predetermined period units) set in the basic setting master DB. In the example shown in FIG. 4, since the cycle unit is “month”, the calculation is performed on a monthly basis.

  For example, in the demand record DB shown in FIG. 6, the total of the demand record in January for the item code A is 300. This value is registered in the cycle-specific demand result DB shown in FIG.

  The reason why the past actual values are aggregated in units of cycles is to perform demand prediction in units of the cycles.

  In step S203, the CPU 201 of the information processing apparatus 101 determines whether there is a cycle in which all the demand record data of the same cycle in the record reference cycle number of the basic setting master DB is zero.

  If it exists (step S203: YES), the process proceeds to step S204.

  If it does not exist (step S203: NO), the process proceeds to step S206.

  In the present embodiment, the actual reference cycle number is 24 and the cycle unit is a month, so the actual demand for the past two years is referred to. Of the past two years (since the current date is January 1, 2015, from January 2013 to December 2014), the actual value is zero, and all the cycles corresponding to the cycle are zero. It is determined whether it is. If there are all zero cycles, it is determined as YES. In the cycle-specific demand result DB shown in FIG. 9, since cycle numbers 3, 4, 5, 6, and 7 have a record value of 0 for both 2013 and 2014, step S203 is determined to be YES for item code A. The

  In step S204, the CPU 201 of the information processing apparatus 101 sets a seasonally limited product flag for the product targeted in step S203.

  An example of data with the seasonally limited product flag set is shown in FIG. As shown in FIG. 7, 1 is set in the seasonally limited product flag of the record whose item code is A in the item master DB.

  In step S205, the CPU 201 of the information processing apparatus 101 sets a prediction zero flag in the cycle in which the demand results are all determined to be 0 in step S203 in the item-specific cycle master DB.

  That is, a record having an item code of A, a cycle number of 3 to 7, and a predicted zero flag of 1 is created.

  In step S <b> 206, the CPU 201 of the information processing apparatus 101 determines whether there is a cycle that is greatly deviated below the other cycles among the cycles excluding 0 in the demand actual data by cycle. To do.

  If it exists (step S206: YES), the process proceeds to step S207.

  If it does not exist (step S206: NO), the process of this flowchart is terminated.

  For example, in the example of FIG. 9, since all the actual values (August 2013 and August 2014) of the cycle of cycle number 8 are greatly different from those of other cycles, YES is determined in step S206. Determination is made, and the process proceeds to step S207.

  As a method for determining whether or not there is a divergence, a method of determining whether or not a value determined by using the actual value in another cycle (period) is satisfied is conceivable. For example, of the cycles excluding the cycle where the actual value is 0, it is calculated in cycle units (predetermined period units), and it is assumed that the result of aggregation is in accordance with the normal distribution, the standard deviation is obtained, and the average value−standard deviation × A method of determining that a value below α is “deviation” can be considered. In addition to this method, other methods may be used.

  In step S207, the CPU 201 of the information processing apparatus 101 sets a seasonally limited flag for the product that has been determined in step S206. In this embodiment (item code A), since the flag has already been set in step S204, there is no change in the setting of the flag in step S207.

  By this processing, even if there is a product that does not have a cycle with an actual value of 0, if there is an actual value that deviates greatly compared to other cycles, it can be treated as a seasonally limited product. . In other words, in the case of “A small year sold only in August last year even though it was not sold at all in August in the past year”, the forecast value is generated in August by assuming that demand in August last year is zero. Can be prevented.

  In step S208, the CPU 201 of the information processing apparatus 101 sets a prediction zero flag in the cycle in which it is determined in step S206 that there is a difference in the actual demand in the item-specific cycle master DB. That is, in the present embodiment, a predicted zero flag is set in a record having an item code of A and a cycle number of 8.

  FIG. 8 shows the item-specific cycle master DB after the processing of steps S205 and S208 is executed. As shown in FIG. 8, the predicted zero flag is set to 1 for cycle numbers 3 to 8.

  A cycle in which the prediction zero flag is set is treated as a season (cycle) in which there is no demand.

  With the above processing, it is possible to classify whether or not the product to be processed is a seasonally limited product using the past actual demand value. In addition, in the case of a seasonally limited product, it is possible to specify a season (cycle, period) where there is no demand and a season (cycle, period) where there is demand.

  Next, details of the processing in step S103 in FIG. 14 will be described with reference to FIG.

  In step S301, the CPU 201 of the information processing apparatus 101 identifies a cycle in which the prediction zero flag is set in the item-specific cycle master DB, and excludes the cycle from calculation targets when performing prediction. And the actual value of the cycle which was not excluded is acquired.

  As a result, out of the cycle-by-cycle demand results for the performance reference cycle, the results of the cycle numbers in which the prediction zero flag is registered in the item-specific cycle master DB are not used for the prediction calculation.

  Specifically, when processing a product whose item code is A, the current date is January 1, 2015 and the actual reference cycle number is 24 in the basic setting master DB. Acquire the actual value of item code A and year / month from January 2013 to December 2014. Next, referring to the record whose item code of the item-specific cycle master DB is A, since the predicted zero flag is set at the cycle number 3-8, the data with the cycle number 3-8 (2013 from the acquired cycle-specific demand results) March to August, 2014 March to August). And the performance value of the cycle (January-February 2013, September 2013-February 2014, September-December 2014) which was not excluded is acquired.

  In step S302, the CPU 201 of the information processing apparatus 101 calculates a seasonal index for each cycle from the actual demand value acquired in step S301.

  There are many methods for calculating the seasonal index. For example, there is a method in which the average of the actual demand in each cycle is divided by the average of all the cycles. The results obtained by this method are shown in the cycle-specific seasonal index DB of FIG. Moreover, you may obtain | require the method of calculating a seasonal index by methods other than the above.

  In step S303, the CPU 201 of the information processing apparatus 101 divides the cycle-specific demand record acquired in step S301 by the seasonal index of each cycle obtained in step S302 in order to remove seasonality from the demand record value.

  For example, in September 2013, the actual demand value is 200 and the seasonal index is 0.8. Therefore, when seasonality is removed from the actual demand value, 200 ÷ 0.8 = 250.

  In step S304, the CPU 201 of the information processing apparatus 101 calculates a demand prediction using the actual demand from which the seasonal index has been removed in step S303.

  There are various methods such as a moving average method and an exponential smoothing method for predicting future demand from the actual demand, and any method may be used.

  FIG. 11 shows an example of the result of calculating the predicted value using the first-order exponential smoothing method with the smoothing parameter set to 0.5.

  In step S305, the CPU 201 of the information processing apparatus 101 multiplies the demand prediction result calculated in step S304 by the seasonal index of each cycle in order to return seasonality to the demand prediction result.

  FIG. 12 shows an example of the result of returning the seasonality to the demand prediction result (before returning the seasonal index) of FIG.

  In step S306, the CPU 201 of the information processing apparatus 101 sets the predicted value of the cycle excluded from the calculation target in step S301 as 0 and adds it to the demand prediction result DB.

  FIG. 13 shows a result of adding the predicted value 0 of the cycle excluded from the calculation target to the demand predicted value calculated in step S305. As shown in FIG. 13, the predicted value for the period of cycle numbers 3 to 8 is registered as 0.

<Second embodiment>
Next, a second embodiment will be described.
In the first example, when there is a cycle in which the actual results are all zero in the past demand data (or when there is a cycle of actual results that deviates from other cycles), it is determined that the product is a seasonal limited product. For example, as shown in FIG. 9, the product with the item code A has a track record of 0 in both March 2013 and March 2014, so the product is determined to be a seasonally limited product.

On the other hand, the second embodiment determines that the product is a seasonally limited product when the number of cycles equal to or greater than the predetermined number is continuously zero. For example, if “predetermined number” is defined as 3 cycles or more, and the actual value is 0 for 3 months (3 cycles) in May, June, and July 2014, the product is a seasonally limited product. Judge that there is.
Hereinafter, the processing content will be described with reference to FIG.

  In step S <b> 1801, the CPU 201 of the information processing apparatus 101 determines whether a seasonally limited product manual setting flag is set for a product to be processed (a product to be subjected to demand prediction).

  For example, in the example of FIG. 2, for the item (product) with the item code A, the seasonally limited product manual flag is 0, so it is determined that the flag is not set.

  In step S1802, the CPU 201 of the information processing apparatus 101 totals past demand results in cycle units (predetermined period units) set in the basic setting master DB. In the example shown in FIG. 4, since the cycle unit is “month”, the calculation is performed on a monthly basis.

  For example, in the demand record DB shown in FIG. 6, the total demand record of item code A in January 2013 is 300. This value is registered in the cycle-specific demand result DB shown in FIG.

  The reason why the past actual values are aggregated in units of cycles is to perform demand prediction in units of the cycles.

  In step S <b> 1803, the CPU 201 of the information processing apparatus 101 determines whether or not a cycle with a performance value of 0 continues for a predetermined cycle or more. The predetermined cycle is defined as three cycles or more in this embodiment, but is set as appropriate by the user.

  For example, since the product value of the item code A has a record value of 0 continuously for 5 months (5 cycles) from March to July 2013, YES is determined in step S1803.

  When the cycle with the actual value of 0 continues for a predetermined cycle or more (step S1803: YES), the process proceeds to step S1804.

  If it does not exist (step S1803: NO), the process of this flowchart is terminated.

  In step S1804, the CPU 201 of the information processing apparatus 101 sets a seasonally limited product flag for the product to be processed.

  An example of data with the seasonally limited product flag set is shown in FIG. As shown in FIG. 7, 1 is set in the seasonally limited product flag of the record whose item code is A in the item master DB.

  In step S1805, the CPU 201 of the information processing apparatus 101 sets a prediction zero flag in the cycle in which the demand record is determined to be 0 in step S1803 in the item-specific cycle master DB.

  That is, a record having an item code of A, a cycle number of 3 to 7, and a predicted zero flag of 1 is created.

  Although the second embodiment has been described above, the first embodiment and the second embodiment may be combined. Specifically, when the demand record data of the same cycle in the record reference cycle number of the basic setting master DB is all 0 cycles and the cycle with the record value of 0 continues for a predetermined cycle or more, it is limited to the season. It is determined that the product is a product, and a prediction zero flag is set for a cycle with a performance value of 0.

  Also in the second embodiment, a cycle in which the actual value greatly deviates downward compared to other cycles may be treated as the actual value 0.

  In the first and second embodiments, the predicted value in the cycle unit is registered, but it may be further converted into the predicted value in the day unit. As a specific method, for example, there is a method in which a daily ratio is obtained from the actual demand value of last year, and a demand forecast result is distributed by that ratio.

  With the above processing, it is possible to predict future demand without considering the period when the past demand record is zero. As a result, it is possible to improve the accuracy of demand prediction of a product (seasonal limited product) having a period in which the past demand record is zero.

  Moreover, the program in this invention is a program which makes a computer perform the process of FIGS. 14-16, and FIG. The program in the present invention may be a program for each process in FIGS. 14 to 16 and 18.

  As described above, a recording medium that records a program that implements the functions of the above-described embodiments is supplied to a system or apparatus, and a computer (or CPU or MPU) of the system or apparatus stores the program stored in the recording medium. It goes without saying that the object of the present invention can also be achieved by reading and executing.

  In this case, the program itself read from the recording medium realizes the novel function of the present invention, and the recording medium recording the program constitutes the present invention.

  As a recording medium for supplying the program, for example, a flexible disk, hard disk, optical disk, magneto-optical disk, CD-ROM, CD-R, DVD-ROM, magnetic tape, nonvolatile memory card, ROM, EEPROM, silicon A disk or the like can be used.

  Further, by executing the program read by the computer, not only the functions of the above-described embodiments are realized, but also an OS (operating system) operating on the computer based on an instruction of the program is actually It goes without saying that a case where the function of the above-described embodiment is realized by performing part or all of the processing and the processing is included.

  Furthermore, after the program read from the recording medium is written to the memory provided in the function expansion board inserted into the computer or the function expansion unit connected to the computer, the function expansion board is based on the instructions of the program code. It goes without saying that the case where the CPU or the like provided in the function expansion unit performs part or all of the actual processing and the functions of the above-described embodiments are realized by the processing.

  The present invention may be applied to a system constituted by a plurality of devices or an apparatus constituted by a single device. Needless to say, the present invention can be applied to a case where the present invention is achieved by supplying a program to a system or apparatus. In this case, by reading a recording medium storing a program for achieving the present invention into the system or apparatus, the system or apparatus can enjoy the effects of the present invention.

  Furthermore, by downloading and reading a program for achieving the present invention from a server, database, etc. on a network using a communication program, the system or apparatus can enjoy the effects of the present invention. In addition, all the structures which combined each embodiment mentioned above and its modification are also included in this invention.

101 Information processing apparatus

Claims (7)

An information processing apparatus that predicts future demand based on past results,
A performance acquisition means for acquiring a past demand performance value of a product subject to demand prediction;
Aggregating means for aggregating the demand actual value acquired by the results acquiring means in units of cycles;
Among the cycles in which the actual demand is totaled by the totaling means, a specifying means for identifying a cycle in which the actual demand value does not satisfy a predetermined value;
Predicting means for predicting future demand based on the actual demand value in the period excluding the cycle specified by the specifying means;
An information processing apparatus comprising:   The prediction means is a cycle specified by the specifying means, and the demand actual value does not satisfy a predetermined value for all the cycles corresponding to the cycle in the period in which the actual demand value is acquired by the actual result acquiring means. 2. The information processing apparatus according to claim 1, wherein a future demand is predicted based on a demand actual value in a period excluding the identified cycle.   The predicting means predicts future demand based on actual demand values in a period excluding the specified cycle when the cycle specified by the specifying means continues for a predetermined number of cycles or more. The information processing apparatus according to claim 1 or 2. The predicting means further includes:
The information processing apparatus according to claim 1, wherein a demand prediction value of a future cycle corresponding to the cycle specified by the specifying unit is set to zero.   5. The information processing apparatus according to claim 1, wherein the specifying unit specifies a cycle in which the actual demand value is zero in a period in which the actual demand is totaled by the totaling unit. . An information processing method in an information processing apparatus that predicts future demand based on past results,
The result acquisition unit of the information processing apparatus acquires a past demand result value of a product that is a target of demand prediction;
A tally process in which the tally unit of the information processing apparatus tally the demand actual value acquired by the result obtaining process in units of cycles;
Of the cycles in which the specifying means of the information processing apparatus totals the demand results in the counting step, a specifying step for specifying a cycle in which the demand record value does not satisfy a predetermined value;
A prediction step in which the prediction means of the information processing device predicts future demand based on the actual demand value in a period excluding the cycle specified by the specific step;
An information processing method comprising: A program that can be executed in an information processing apparatus that predicts future demand based on past results,
The information processing apparatus;
A performance acquisition means for acquiring a past demand performance value of a product subject to demand prediction;
Aggregating means for aggregating the demand actual value acquired by the results acquiring means in units of cycles;
Among the cycles in which the actual demand is totaled by the totaling means, a specifying means for identifying a cycle in which the actual demand value does not satisfy a predetermined value;
A program for functioning as a predicting means for predicting future demand based on a demand actual value in a period excluding the cycle specified by the specifying means.

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