JP2018109887A - Information processor, information processing method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a calculation method of a quantity of a safety stock that is capable of corresponding flexibly when a lead time is not constant.SOLUTION: A method calculates a static value of a prediction error based on a per-calculated delivery schedule amount and a delivery result amount which is an amount actually delivered, then calculates a quantity of a safety stock based on the calculated static value of the prediction error. The method calculates a replenishment quantity of an order placement based on the calculated quantity of the safety stock.SELECTED DRAWING: Figure 1

Description

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

という計算式により算出する方法が存在する（非特許文献１）。 Traditionally, as a method of calculating safety stock,

There is a method of calculating by the calculation formula (Non-Patent Document 1).

ここで調達期間の単位は需要予測の予測単位期間とする。すなわち需要予測を日次で行う場合は日、週次に行う場合は週、月次に行う場合は月が単位となる。この方法は、予測誤差の特性が常に一定であるという仮定に基づいており、予測誤差の特性に変化がある場合には、適正な安全在庫量を計算することができない。 Here, the safety factor is determined by an allowable missing item rate, and the larger the safety factor, the smaller the missing item probability. The procurement period is the period from when an order is placed until the product arrives. In addition, when appropriate demand prediction is possible, the variation in demand prediction error is smaller than the variation in demand itself, so it is known that the same shortage rate can be realized with a smaller amount of safety stock. (Patent Document 1). In this case, the safety stock quantity is calculated as follows.

Here, the unit of the procurement period is the forecast unit period of the demand forecast. That is, the unit is a day when the demand forecast is performed daily, a week when it is performed weekly, and a month when it is performed monthly. This method is based on the assumption that the characteristics of the prediction error are always constant, and if there is a change in the characteristics of the prediction error, an appropriate safety stock quantity cannot be calculated.

  Here, the part excluding the safety factor in the above formula for calculating the safety stock quantity (√ procurement period x degree of variation in forecast error) means the degree of variation in forecast period forecast error. The degree of variation in the prediction error for the period 1 calculates the degree of variation in the prediction error for the procurement period. In other words, when the procurement period is 5 days, the fluctuation degree of the prediction error for 5 days is calculated by multiplying the fluctuation degree of the prediction error for 5 days by √5 to obtain the fluctuation degree of the prediction error for 1 day. doing.

  It is known that the dispersion in the N cycle period is N times the dispersion in one cycle. Since the standard deviation, which is the degree of variation, is √ variance, the standard deviation of the N cycle period is √N times the standard deviation of one cycle, and this is the reason why the procurement period is the root.

  Therefore, since it is not accurate to calculate the variation degree of the prediction error of the procurement period from the prediction error of the period 1, there is also a method for directly obtaining the degree of variation of the cumulative prediction error of the procurement period in advance.

“Appropriate Inventory Approach and How to Find” by Takao Suguro

Japanese Patent No. 3260333

  If the lead time is always constant, there is no problem with a method for directly obtaining the degree of variation in the cumulative prediction error of the lead time in advance. For example, if the unit of the lead time is a month, the lead time seems to be relatively stable. However, when the lead time is weekly or daily, the lead time is often not stable. Particularly in the case of daily units, it is conceivable that the procurement period may be extended due to holidays or the like being within the procurement period.

  In the method of directly obtaining the degree of variation in the cumulative prediction error in the procurement period described above, it is necessary to maintain the period of the degree of prediction error variation to be obtained when the procurement period is not constant, which is troublesome. Up to now, the procurement period was 3, but when it reaches 5 from a certain timing, the user needs to change the accumulation period.

  In addition, when storing the demand prediction amount, it is necessary to store the demand prediction amount for a certain period for each timing of performing the demand prediction. For example, since the current procurement period is 3, even if the demand forecast amount for 3 cycles is stored, if the procurement period is changed to 6, the demand forecast amount for 6 cycles does not remain and cannot be calculated. For this reason, it is necessary to reserve a large number of cycles in which the demand forecast amount is left in advance, and the data amount tends to be enormous.

  In addition, in the method of directly obtaining the degree of variation in the cumulative prediction error of the procurement period in advance, since the past prediction amount is used, it is necessary to store the prediction amount of each cycle at the prediction execution timing. However, if the cumulative period is not constant, it is not necessary to store how much period, so it is necessary to store a large amount, and it is necessary to store a large amount of data when the predicted amount for each prediction execution timing is reached. There are problems that it is necessary to secure resources and that it takes a long time to search due to a large amount of data.

  Therefore, an object of the present invention is to provide a safety inventory amount calculation method that can flexibly cope with a case where the procurement period is not constant.

  The information processing apparatus according to the present invention includes a scheduled delivery amount storage unit that stores a planned delivery amount that is calculated in advance, a delivery actual amount storage unit that stores an actual delivery amount that is the amount actually delivered, and the scheduled delivery amount. A prediction error statistic value calculating means for calculating a statistical value of a prediction error based on data stored in the storage means and the output actual amount storage means; and a prediction error statistic calculated by the prediction error statistic value calculating means. A safety stock quantity calculating means for calculating a safety stock quantity based on the value, and an order replenishment quantity calculating means for calculating an order replenishment quantity based on the safety stock quantity calculated by the safety stock quantity calculating means. Features.

  Further, the information processing method of the present invention includes a scheduled delivery amount storage unit that stores a planned delivery amount calculated in advance, and a delivery result amount storage unit that stores a delivery actual amount that is an actually delivered amount. An information processing method in the information processing apparatus, wherein the prediction error statistical value calculation means of the information processing apparatus is based on data stored in the scheduled delivery amount storage means and the actual delivery amount storage means. A prediction error statistic value calculating step for calculating a statistic value and a safety stock amount calculating means of the information processing device calculate a safety stock amount based on the prediction error statistic value calculated by the prediction error statistic value calculating step. An order replenishment amount calculating step, wherein the order replenishment amount calculating means of the information processing apparatus calculates an order replenishment amount based on the safety stock amount calculated by the safety stock amount calculating step; Characterized in that it comprises.

  In addition, the program of the present invention is an information processing comprising: a scheduled delivery amount storage unit that stores a planned delivery amount calculated in advance; and a delivery actual amount storage unit that stores an actual delivery amount that is the amount actually delivered. A prediction error statistic that is a program that can be executed in an apparatus, and that calculates the statistical value of a prediction error based on data stored in the scheduled delivery amount storage unit and the actual delivery amount storage unit A safety stock quantity calculating means for calculating a safety stock quantity based on a statistical value of a prediction error calculated by a value calculating means, a prediction error statistical value calculating means, and a safety stock quantity calculated by the safety stock quantity calculating means. A program for functioning as an order replenishment amount calculating means for calculating an order replenishment amount based on the above.

  According to the present invention, it is possible to provide a safety inventory amount calculation method that can flexibly cope with a case where the procurement period is not constant.

It is a functional block diagram of the inventory management apparatus (information processing apparatus) of this invention. It is an example of a delivery performance DB. It is an example of a delivery plan DB. It is an example of an order parameter (order calendar) DB. It is an example of order parameter (lead time) DB. It is an example which showed the period which should be considered in ordering logic. It is an example of order replenishment amount history DB (future). This is a flow for calculating an order replenishment amount. It is an example of an order replenishment amount history DB (past). It is an example of prediction error calculation object data. It is an example of the calculation process of the order point inventory quantity. It is a time series image figure which calculates an order replenishment amount and a stock scheduled amount. It is an example of prediction history DB. The figure which shows the hardware constitutions of information processing equipment

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a functional configuration diagram of an inventory management apparatus (information processing apparatus) according to the present invention. As shown in FIG. 1, the information processing apparatus according to the present embodiment includes a database 100, a prediction error statistical value calculation unit 110, a safety stock amount calculation unit 120, and an order replenishment amount calculation unit 130.

  In the following description of the drawings, today is assumed to be November 28, 2016.

  The delivery performance DB is a storage of delivery performance values for each product by location and delivery date. An example of the delivery record DB is shown in FIG. The data in the first row of FIG. 2 indicates that the actual record value of the product code AAAAA and the base code K001 at the sunrise of November 2, 2016 was 80.

  The shipping schedule DB is an accumulation of scheduled shipping volume by shipping base by product and by delivery date. An example of the delivery schedule DB is shown in FIG. The data in the first row in FIG. 3 indicates that the scheduled delivery amount of the product code AAAAAA on November 28, 2016 is 100.

  The ordering parameter DB stores ordering calendar information for each ordering date for each warehousing site for each product. This is the scheduled calendar data for when each product is ordered and when it is received. An example of the order parameter DB is shown in FIG. The first line in FIG. 4 indicates that if the product code AAAAA is ordered on November 28, 2016, it will be received on December 2, 2016. The lead time is the number of days for the order date and the receipt date.

  In addition, the order parameter DB may not be data indicating when the order is placed and when the goods are received, but may be lead time information for each product base as shown in FIG. The first line in FIG. 5 indicates that the product code AAAAA and the warehousing base code K001 are ordered every week, the order day is Monday, and the lead time is 4 days, that is, if the order is placed on Monday, it is normally scheduled to be received on Friday. . However, when calculating the warehousing date based on the order day of the week or the lead time, the non-working day may be included / excluded, and the non-working day information needs to be held for each warehousing site code day.

  FIG. 6 shows an order consideration period based on the order calendar shown in FIG. The horizontal direction in FIG. 6 is time-series and daily. The data on the first line in FIG. 4 indicates the data on the first line in FIG. 6. Since the order is placed on November 28, “Order” is placed on the 28th, and the goods are received on December 2, so 12 “Receiving” is shown in the column of the month 2nd.

  Here, when placing an order on November 28, the period to be considered not to be out of stock is 12 December, the day before the next ordering and warehousing date (December 5) from December 2 of the warehousing date. It turns out that it is until the 4th of the month. This is because the order replenishment quantity ordered on November 28 must be considered not to run out from the second day, which is the day when the order replenishment quantity is received, to the day before the next receipt date. Because there is. For this reason, it is necessary to consider the planned total number of shipments during the period from November 28, the order date to December 4, the day before the next order entry date.

  The order replenishment amount history DB is an accumulation of order replenishment amounts by ordering date by warehousing base code by product code. The order replenishment amount history DB is shown in FIG. The first line in FIG. 7 indicates that the order replenishment amount is 210 on November 28, 2016 for the product code AAAAA and the warehousing base code K001, and 210 on December 2, 2016 for the warehousing date.

  Further, the target end date calculated from the ordering calendar in FIG. 4 when calculating the order replenishment amount is December 4, 2016, the target period from the ordering date is six days, and the planned shipping amount in this period is 540. It shows that there is.

  It should be noted that the order replenishment amount history DB is not deleted even if the order date is in the past, but is accumulated.

  FIG. 8 shows a flow for calculating the order replenishment amount.

  In S101, order replenishment history information data is acquired. It is assumed that a past acquisition period is defined in the master for each warehousing base code for each product code, and data including the order date in the acquisition period is acquired from the order replenishment history DB.

  FIG. 9 shows order replenishment amount history data. Although the DB table is the same as FIG. 7, FIG. 7 illustrates data with an order date after today as an example, but FIG. 9 illustrates data with an order date before today (past data) as an example. . In S101, the data for the order date is acquired in the past, so that data as shown in FIG. 9 is acquired.

  In S102, the total amount of the delivery results for the period from the order date of the data obtained in S101 to the target end date (see FIG. 9) is obtained from the delivery performance DB (FIG. 2). For example, in the case of the data in the first row in FIG. 9, the total amount of goods issued in the period from November 2 to November 7 is acquired, and in the case of the data in the second row, from November 5 to November 11 Get the total amount of goods issued during the period.

  In S103, an error between the scheduled shipping amount acquired in S101 and the total shipping amount acquired in S102 is calculated, and a statistical amount of prediction error in a certain period is calculated. That is, a statistical amount of an error between the amount of delivery scheduled in the past (the above-mentioned scheduled delivery amount for the period) and the amount actually delivered (the above-mentioned actual delivery amount) is calculated. Here, the statistic may be the average of prediction error, standard deviation, or √square error average.

Average prediction error = {Σ (prediction error)} / N
Standard deviation of prediction error = √ [{Σ (prediction error-average of prediction error) ** 2} / N]
Root mean square error = √ [{Σ (prediction error ** 2)} / N]
FIG. 10 shows prediction error calculation target data in which acquired data are displayed side by side.

  The start date is the order date acquired in S101, the end date is the target end date of S101, the target period is the number of days in the start date and end date, the total actual delivery amount is the total actual delivery amount acquired in S102, and the planned period outgoing amount is This is the scheduled shipping amount acquired in S101.

The prediction error is calculated by (period scheduled shipping amount−total shipping actual amount) ÷ target period. When calculating the statistical value of the prediction error, the target period of each data may be different. In this case, the period needs to be the same. In the example of FIG. 10, it is calculated as a prediction error per day.
In addition, when it is desired to change the safety stock amount depending on the size of the planned delivery quantity, it is conceivable to calculate a prediction error rate (= prediction error / period delivery schedule quantity) instead of the forecast error. However, in FIG. 10, the prediction error is calculated instead of the prediction error rate.

  In the data displayed in FIG. 10, the mean square error is calculated as follows.

√ {(26 × 26 + 10 × 10 + −20 × −20
+ ... +-18.6 × -18.6) ÷ 9}
= 21.3
In S104, order calendar information from today is acquired. If there is no order calendar data, it is conceivable to obtain the order day and lead time and create an order calendar (when ordering and when receiving goods) on the memory. FIG. 4 shows the acquired data.

  In S105, the target end date and the order date to the target end date are calculated for each future order date. The order date and receipt date in FIG. 11 are the order calendar data acquired in S104, the target end date is the target end date calculated in S105, and the target period is the number of days in the order date and the target end date.

  In S106, future shipping schedule data is acquired. FIG. 3 shows the data of future shipping schedule.

  In S107, for each order date in S105, the total scheduled delivery amount acquired in S106 during the period from the order date to the target end date is calculated. It is the result of having calculated the scheduled shipping amount in FIG.

Further, the safety stock quantity from the order date to the target end date is calculated from the statistical value of the prediction error calculated in S103. For example, assuming that the safety stock quantity = safety coefficient × √ (target period) × root error square of prediction error, the safety stock quantity on the first line in FIG. However, the safety factor is 1.65 1.65 × √6 × 21.3 = 86
For example, when the order replenishment amount is calculated by the order point method, the order point stock amount is the sum of the planned delivery amount + the safety stock amount.

  FIG. 12 shows the process of calculating the order replenishment amount when the inventory amount on November 27, which is the previous day, is 320.

  The order remaining amount on the first line in FIG. 12 is the confirmed order remaining, that is, the warehousing schedule.

  The second line is the planned delivery amount.

  The third line is the actual inventory, which has a value only on November 27 of the previous day.

  The fourth line indicates whether there is an order date and whether it is an order date.

  The fifth line is the order point inventory, which indicates the order point inventory calculated in FIG. Note that only the order date column has a value.

The sixth line is the order replenishment amount (based on the order date), and the seventh line is the order replenishment amount (based on the receipt date). The calculated order replenishment amount is displayed in the order line on the sixth row, and the received date column is displayed on the seventh line.
The last 8th line is the planned inventory quantity. The previous day, November 27, is the same as the actual inventory quantity, and the following day is calculated by the following formula.

Scheduled stock quantity = Scheduled stock quantity on the day before that day
+ Order remaining on that day
-Scheduled amount of sunrise
+ Order replenishment amount on that day (receipt date)
The order replenishment amount is calculated by the following formula on the order date. The current stock quantity is the total value of the stock quantity on the day before that day and the quantity already ordered. If the current stock quantity is less than the order point stock quantity, the quantity that is insufficient is considered as the order replenishment quantity.

  Although it is conceivable that the order replenishment amount obtained here is rounded up and corrected so as to be a multiple of the order replenishment lot, in FIG. 12, the order replenishment lot is not rounded up.

Order replenishment amount = Min (0, order point inventory-current inventory)
Current stock quantity = Stock quantity on the day before the current day + Total order remaining amount for the period from the order date to the target end date + Total order replenishment amount (receipt date) for the period from the order date to the target end date FIG. The order replenishment amount and the planned inventory amount are calculated day by day from the date, and are calculated in order of November 29 and November 30 in the next day.

  In S108, the order replenishment amount calculated in S107, the target end date, the target period, and the scheduled delivery date are stored as the order replenishment amount history.

  If the order date of the data stored here is in the past, it becomes an acquisition target in S101.

  As described above, according to the present invention, the safety stock amount and the order point stock amount are calculated in consideration of when the next order receipt date is calculated. The correct order replenishment amount can be calculated in case of irregular.

  In addition, since the statistical value of the prediction error is calculated from the scheduled delivery quantity in the period used when placing an order in the past, the statistical value of the prediction error, that is, the safety stock quantity can be calculated more accurately.

  In addition, in the method of directly obtaining the degree of variation in the cumulative forecast error in the procurement period in advance, as shown in FIG. Prediction history) must be maintained. That is, it is necessary to store data of the number of times of prediction execution × data retention prediction days.

  On the other hand, in the present invention, as shown in FIG. 9, the shipping schedule is stored as the total of the target period for each order date, and therefore it is not necessary to store it at every timing when the shipping schedule data is created. Therefore, there is an advantage that the data capacity can be reduced. If the data capacity is small in this way, resources can be used effectively, and the data search time becomes faster and the calculation time becomes faster.

  FIG. 14 is a block diagram illustrating an example of a hardware configuration of the information processing apparatus according to the embodiment of this invention.

  As shown in FIG. 14, the information processing apparatus includes a CPU (Central Processing Unit) 201, a ROM (Read Only Memory) 202, a RAM (Random Access Memory) 203, an input controller 205, a video controller 206, a system bus 204, and the like. A memory controller 207 and a communication I / F controller 208 are connected.

  The CPU 201 comprehensively controls each device and controller connected to the system bus 204.

  The ROM 202 or the external memory 211 includes a BIOS (Basic Input / Output System) and an OS (Operating System) that are control programs executed by the CPU 201, and a computer-readable program and various necessary programs for realizing the information processing method. 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 external memory 211 to the RAM 203 and executing the loaded program.

  The input controller 205 controls input from an input device such as a keyboard 209 or a pointing device such as a mouse (not shown). When the input device is a touch panel, the user can perform various instructions by pressing (touching with a finger or the like) in accordance with an icon, a cursor, or a button displayed on the touch panel.

  The touch panel may be a touch panel capable of detecting a position touched with a plurality of fingers, such as a multi-touch screen.

  The video controller 206 controls display on an external output device such as the display 210. 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. An input device is also provided for the device that can accept the touch operation described above.

  Note that the video controller 206 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.

  The memory controller 207 controls access to the external memory 211. 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 connects and communicates with an external device via a network, and executes communication control processing on the network. For example, communication using TCP / IP, telephone lines such as ISDN, and communication using 3G lines of mobile phones are possible.

  Note that the CPU 201 enables display on the display 210 by executing outline font rasterization processing on a display information area in the RAM 203, for example. Further, the CPU 201 enables a user instruction with a mouse cursor (not shown) on the display 210.

  Although the embodiment of the information processing apparatus has been described above, the present invention can take an embodiment as a system, apparatus, method, program, recording medium, or the like. Specifically, the present invention may be applied to a system composed of a plurality of devices, or may be applied to an apparatus composed of a single device.

  The program in the present invention is a program that can be executed by the computer in the processing method of the flowchart shown in FIG. 8, and the storage medium of the present invention stores a program in which the computer can execute the processing method in FIG. The program in the present invention may be a program for each processing method of each apparatus in FIG.

  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.

100 Database 110 Prediction Error Calculation Unit 120 Safety Stock Quantity Calculation Unit 130 Order Replenishment Amount Calculation Unit

Claims (4)

A planned delivery amount storage means for storing a planned delivery amount calculated in advance;
An actual output amount storage means for storing an actual output amount that is an actual output amount;
A prediction error statistic value calculating means for calculating a statistical value of a prediction error based on the data stored in the scheduled delivery quantity storage means and the actual delivery quantity storage means;
Safety stock quantity calculating means for calculating a safety stock quantity based on the statistical value of the prediction error calculated by the prediction error statistical value calculating means;
Order replenishment amount calculating means for calculating an order replenishment amount based on the safety stock amount calculated by the safety stock amount calculating means;
An information processing apparatus comprising: The order replenishment amount calculating means calculates the order replenishment amount based on a delivery amount in a period specified by a lead time from ordering to warehousing,
The prediction error statistic value calculation means is a statistic of a difference between a scheduled shipping amount in a period that is a basis of the order replenishment amount calculated in the past by the order replenishment amount calculation means and a total actual delivery amount in the period. 2. The information processing apparatus according to claim 1, wherein a value is calculated. An information processing method in an information processing apparatus comprising: a planned delivery amount storage unit that stores a planned delivery amount calculated in advance; and a delivery result amount storage unit that stores an actual delivery amount that is the amount actually delivered. ,
A prediction error statistic value calculating step in which the prediction error statistic value calculating means of the information processing device calculates a statistic value of a prediction error based on the data stored in the expected shipping amount storage means and the actual shipping amount storage means. When,
A safety stock quantity calculating unit of the information processing apparatus, based on the prediction error statistic value calculated by the prediction error statistic value calculating process, to calculate a safety stock quantity;
An order replenishment amount calculating step in which the order replenishment amount calculating means of the information processing apparatus calculates an order replenishment amount based on the safety stock amount calculated by the safety stock amount calculating step;
An information processing method comprising: This is a program that can be executed in an information processing apparatus that includes a scheduled delivery amount storage unit that stores a planned delivery amount calculated in advance and a delivery actual amount storage unit that stores an actual delivery amount that is the amount actually delivered. And
The information processing apparatus;
A prediction error statistic value calculating means for calculating a statistical value of a prediction error based on the data stored in the scheduled delivery quantity storage means and the actual delivery quantity storage means;
Safety stock quantity calculating means for calculating a safety stock quantity based on the statistical value of the prediction error calculated by the prediction error statistical value calculating means;
A program for functioning as an order replenishment amount calculation unit for calculating an order replenishment amount based on the safety stock amount calculated by the safety stock amount calculation unit.

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