WO2022190596A1

WO2022190596A1 - Information processing device, stock management system, information processing method, and program

Info

Publication number: WO2022190596A1
Application number: PCT/JP2022/000016
Authority: WO
Inventors: 真弘上野
Original assignee: 三菱電機株式会社
Priority date: 2021-03-09
Filing date: 2022-01-04
Publication date: 2022-09-15
Also published as: JPWO2022190596A1

Abstract

This information processing device (10) comprises: an acquisition unit for acquiring the target value of a service rate, and acquiring sales performance data pertaining to physical objects for each unit period; a seasonal variation coefficient computation unit for computing, on the basis of the sales performance data, a seasonal variation coefficient that is the coefficient of variation of a sales volume for each unit period; a group number determination unit for determining, on the basis of the seasonal variation coefficient, a group number for classifying respective unit periods; a clustering unit for classifying the respective unit periods into groups of the group number determined by the group number determination unit, in accordance with the sales volume for each unit period; and a reference stock quantity calculation unit for specifying, from the result of processing by the clustering unit, a unit period that is classified into the same group as a relevant unit period for which a reference stock quantity is to be computed, and computing the reference stock quantity on the basis of the sales volume of the specified unit period and the acquired target value of the service rate.

Description

Information processing device, inventory control system, information processing method, and program

The present disclosure relates to an information processing device, an inventory management system, an information processing method, and a program.

As a method for finding an appropriate inventory level, there is a known standard inventory calculation method that calculates the standard inventory quantity at which the probability of out-of-stock occurrence is below the allowable value based on statistical values such as past sales results and actual lead time values. ing. However, when calculating the standard inventory of a product whose demand fluctuates greatly depending on the sales season, if the demand characteristics differ between the period used as statistical values and the target period for calculating the standard inventory, the accuracy of the calculated standard inventory will be low. There is a problem of becoming To address this problem, Patent Document 1 discloses product inventory monitoring that sets the demand characteristics of either a busy season or an off-season for each month and calculates the standard inventory quantity using statistical values for periods with the same demand characteristics. A system is disclosed.

Japanese Patent Application Laid-Open No. 2003-160228

This product inventory monitoring system only categorized the sales period into two periods, and there was room for improvement in terms of properly determining the standard inventory amount of products with more complex demand characteristics.

The present disclosure has been made in view of the above circumstances, and is an information processing device and an inventory management system capable of calculating an appropriate standard inventory amount by taking into consideration the demand characteristics of the target period for determining the standard inventory amount for each object. , an information processing method, and a program.

In order to achieve the above object, the information processing device according to the present disclosure realizes a target value of service rate, which is the ratio of inventory to demand for objects to be sold in a predetermined unit period. An information processing device for calculating a standard inventory amount, comprising: an acquisition unit for acquiring a target value of a service rate and actual sales data indicating a sales volume of an object for each unit period; a seasonal variation coefficient calculation unit for calculating a seasonal variation coefficient, which is a coefficient of variation obtained by dividing the standard deviation of the sales volume for each unit period in a predetermined period by the average of the sales volume, based on Based on the seasonal variation coefficient calculated by the calculation unit, the number of groups determination unit that determines the number of groups to classify each unit period, and the groups determined by the number of groups determination unit for each unit period according to the sales volume for each unit period number of groups, and from the results of the processing by the clustering unit, identify the unit period classified into the same group as the unit period for which the standard inventory amount is to be calculated, and obtain the sales volume for the identified unit period a reference inventory amount calculation unit for calculating a reference inventory amount based on the target value of the service rate acquired by the unit.

According to the present disclosure, a group number determination unit that determines the number of groups for classifying each unit period based on a seasonal variation coefficient that indicates the variation in sales volume for each predetermined unit period, and a group number determination unit that determines the number of groups for each unit period A clustering unit that classifies into groups of the number of groups determined by the department, and a standard inventory amount calculation unit that calculates the standard inventory amount based on the sales volume of the unit period classified into the same group as the unit period for which the standard inventory amount is to be calculated. And prepare. Therefore, it is possible to calculate an appropriate standard inventory amount by taking into account the demand characteristics of each target object for which the standard inventory amount is to be obtained.

FIG. 1 is a block diagram showing the functional configuration of an information processing device according to the first embodiment; A diagram showing an example of a new/old model name correspondence table generated by the old/new product model name setting unit shown in FIG. A diagram showing an example of a calculation condition table generated by the calculation condition setting unit shown in FIG. A diagram showing an example of a seasonal group number initial candidate table generated by the seasonal group number initial candidate setting unit shown in FIG. A diagram showing an example of a manufacturing base identification master generated by the manufacturing base identification master setting unit illustrated in FIG. The figure which shows an example of the lead time master which the lead time setting part shown in FIG. 1 produces|generates. FIG. 2 shows an example of a sales data table stored in the sales data storage unit shown in FIG. 1; A diagram showing an example of a seasonal fluctuation table stored in the seasonal fluctuation calculation result storage unit shown in FIG. A diagram showing an example of a standard inventory table output by the standard inventory calculation result output unit shown in FIG. 1 is a block diagram showing an example of a physical configuration of an information processing apparatus according to Embodiment 1; Flowchart of standard inventory quantity calculation processing by the information processing apparatus according to the first embodiment Block diagram showing a functional configuration of an inventory management system according to Embodiment 2 Flowchart of production plan correction processing by the inventory management system according to the second embodiment

An information processing device, an inventory management system, an information processing method, and a program according to embodiments of the present disclosure will be described below with reference to the drawings.

(Embodiment 1)
The information processing device according to the present embodiment is a device that calculates the standard stock quantity of products manufactured at a manufacturing base and transported to a sales base. This information processing device calculates the standard stock quantity for each product, taking into account demand fluctuations due to sales periods. Specifically, this information processing device calculates a seasonal variation coefficient that indicates the degree of variation in monthly sales volume, and determines the number of groups into which each month is classified based on the calculated seasonal variation coefficient. This information processing device classifies each month into a determined number of groups according to monthly sales volume from January to December based on a predetermined criterion. This information processing device calculates the standard inventory quantity using the past statistical values of the months belonging to the same group as the target month for which the standard inventory quantity is to be calculated.

First, the information processing apparatus 10 according to the present embodiment will be described in detail with reference to the drawings.

FIG. 1 shows the functional configuration of the information processing device 10. As shown in FIG. The information processing apparatus 10 includes a setting unit 100 for setting the contents of processing by the processing unit 300, a storage unit 200 for storing information on past sales of products, a processing unit 300 for executing various types of processing, and a processing unit 300. and a standard inventory table output unit 400 for outputting the result of processing by.

The setting unit 100 includes a new/old product model name setting unit 110 (hereinafter referred to as a new/old correspondence setting unit 110) that sets the correspondence between the latest product currently on sale and the old product, and and a seasonal group number initial candidate setting unit 130 (hereinafter referred to as the initial group number setting unit 130) that sets seasonal group number candidates, which are the number of groups for classifying each month. ), a manufacturing base information setting unit 140 for setting manufacturing base information including information on products sold at each sales base and the manufacturing base for each product, and setting lead time information between the product manufacturing base and the sales base. and a lead time setting unit 150 to set the lead time.

The new/old correspondence setting unit 110 sets the correspondence relationship between the latest product currently on sale and the old product. Specifically, the old/new correspondence setting unit 110 generates a new/old model name correspondence table (hereinafter referred to as a new/old correspondence table) showing the correspondence between the latest model product and the old product according to the user's input operation. As shown in FIG. 2, the old/new correspondence table has information of "latest model name" which is information for identifying the latest model product and "old model name" which is information for identifying the old model product. For example, for a product whose "latest model name" is "XXX-3" entered in the second and third rows of the old/new correspondence table in FIG. -2” and “XXX-1” are older models of the same series. The old/new correspondence table is an example of new/old correspondence data.

Returning to FIG. 1, the calculation condition setting unit 120 sets various conditions necessary for calculating the standard stock quantity of the target product. Specifically, calculation condition setting unit 120 receives input of information defining conditions necessary for calculating the standard inventory quantity, generates a calculation condition table showing the content of the conditions, and outputs the table to storage unit 200 . . As shown in FIG. 3, the calculation condition table includes a "calculation target year and month" indicating the target year and month for calculating the standard inventory quantity, a "required number of data" indicating the number of monthly sales data required for calculation, and a product It includes information on the "target service rate", which indicates the target value of the service rate, which is the rate at which orders can be fulfilled without causing product shortages, and the "arrival cycle", which indicates the frequency of product delivery from manufacturing bases. In the illustrated example, the target year and month for calculating the standard inventory quantity is August 2021, 12 pieces of sales data are required to calculate the standard inventory quantity, and the target service rate is 95%. , indicating that the deposition cycle is 0.5 months.

Returning to FIG. 1, the initial group number setting unit 130 sets seasonal group number candidates, which are the number of groups into which each month is classified. Specifically, the initial number-of-groups setting unit 130 follows a user's input operation to create a seasonal group number initial candidate table (hereinafter referred to as a candidate table) that shows a list of seasonal group number candidates used when calculating the standard stock quantity. ). As shown in FIG. 4, the candidate table includes "seasonal group number" indicating candidates for the number of seasonal groups, and "seasonal It contains the information “Coefficient of variation reference value”.

In the illustrated example, 1, 2, 3, 4, 6, and 12 are set as candidates for the number of seasonal groups. This indicates that 0, 0.2, 0.3, 0.4, 0.5, and 0.8, which are the "seasonal variation coefficient reference values", are set corresponding to the number of seasonal groups. For example, if the number of seasonal groups is 1, it indicates that all months are classified into the same group. Moreover, when the number of season groups is 2, it indicates that each month is classified into two groups, such as a busy season and a quiet season. The seasonal variation coefficient is a coefficient of variation calculated from the monthly sales ratio, which indicates the ratio of the monthly sales volume to the total sales volume in the analysis target period, and the average and standard deviation of the monthly sales ratio. The seasonal variation coefficient reference value set in the candidate table is information used when the seasonal group number calculation unit 340 shown in FIG. 1 compares with the seasonal variation coefficient in the process of determining the number of seasonal groups. Details of the processing of the seasonal group number calculation unit 340 will be described later. Note that the seasonal variation coefficient reference value is an example of group number correspondence information.

Returning to FIG. 1, the manufacturing base information setting unit 140 sets the products sold at each sales base and the manufacturing base for each product. Specifically, the manufacturing base information setting unit 140 creates a manufacturing base identification master (hereinafter referred to as a base master) having information for identifying products sold at each sales base and the manufacturing base of each product according to the user's operation. to generate As shown in FIG. 5, the base master includes a "sales base" indicating information identifying a sales base, a "model name" indicating a product model name, and a "manufacturing base" indicating information identifying a product manufacturing base. ” has information. In the example shown in the figure, products with model names "XXX-3" and "YYY-3" are sold at the sales base "AAA", and each product is manufactured at the manufacturing base "DDD". is shown. Manufacturing base information setting unit 140 outputs the generated base master to storage unit 200 .

Returning to FIG. 1, the lead time setting unit 150 sets the lead time at each sales base, which indicates the transportation period from when the product is shipped from the manufacturing base to when the product is delivered to the sales base. Specifically, the lead time setting unit 150 generates a lead time master having information on the lead time required for procuring products from manufacturing bases at each sales base according to the user's operation. As shown in FIG. 6, the lead time master includes "sales bases" indicating information identifying sales bases, "manufacturing bases" indicating information identifying product sales bases, and It has information of "lead time" indicating the transportation period of the product. In the example of the figure, the lead time for the manufacturing base "DDD" in the sales base "AAA" is "2 months", and the lead time for the manufacturing base "EEE" is "3 months". The lead time setting section 150 outputs the generated lead time master to the storage section 200 .

Returning to FIG. 1, the storage unit 200 includes a sales data storage unit 210 that stores past sales results for each product, and a seasonal variation table storage unit 220 that stores the results of processing by the seasonal variation calculation unit 320.

The sales data storage unit 210 stores a sales data table showing monthly sales results for each product at each sales base. As shown in FIG. 7, the sales data table includes a "sales office" indicating information identifying a sales office, a "model name" indicating the model name of the product, a "year" indicating the year, and a "year" indicating the month. It has items of "month" and "sales volume" indicating the sales volume. Note that the sales data table is an example of actual sales data.

Returning to FIG. 1, the seasonal variation table storage unit 220 stores the seasonal variation table that is the result of processing by the seasonal variation calculation unit 320. The seasonal variation calculation unit 320 performs a process of classifying each month into a predetermined number of groups, and generates a standard inventory table showing the result of the process. As shown in FIG. 8, the standard inventory table includes a "sales base" indicating information identifying a sales base, a "model name" indicating the model name of a product, and a "seasonal group" indicating the number of groups for classifying each month. and "A, B, . . . " indicating information identifying each group. For example, in the example where the number of seasonal groups is 2, April and October to March are categorized into Group A for a product whose "sales base" is "AAA" and whose "model name" is "XXX-1". , indicates that May to September were classified into Group B. Details of the processing of the seasonal variation calculation unit 320 will be described later.

The processing unit 300 includes a sales data conversion unit 310 that integrates the sales data of the old model product with the sales data of the latest model product, a seasonal variation calculation unit 320 that classifies each month into a predetermined number of groups, and a product model by model. A sales start time identification unit 330 (hereinafter referred to as a time identification unit 330) that identifies the sales start time of each month, a seasonal group number calculation unit 340 that determines the number of seasonal groups, which is the number of groups for classifying each month, and a reference inventory quantity calculation unit 350 for calculating the reference inventory quantity.

The sales data conversion unit 310 performs processing to integrate the sales data of the old model product with the sales data of the latest model product. The sales data conversion unit 310 collates the information entered in the "model name" field in the sales data table shown in FIG. 7 with the information entered in the "old model name" field in the old/new correspondence table shown in FIG. If the "model name" in the sales data table and the "old model name" in the new/old correspondence table match, the sales data converter 310 determines that the product with the "model name" in the sales data table is the old model product. The sales data conversion unit 310 acquires the "latest model name" information entered in the same row as the old model name from the old/new correspondence table, and converts the "model name" information of the sales data to the acquired "latest model name." ” information. Next, in the sales data after conversion, if there is data with the same model name and date, the sales data conversion unit 310 adds up the sales volume of these data, and converts the sales data of the old model to the newest model. Generate a post-conversion sales data table that integrates the sales data of the mold product.

Returning to FIG. 1, the seasonal variation calculation unit 320 performs a process of classifying each month into a predetermined number of groups. The seasonal variation calculation unit 320 performs classification processing according to a total of 6 patterns of 1, 2, 3, 4, 6, and 12 seasonal groups preset in the candidate table shown in FIG. Specifically, the seasonal variation calculation unit 320 calculates, for each product, from the converted sales data data generated by the sales data conversion unit 310, the monthly sales ratio indicating the ratio of the monthly sales volume to the total sales volume during the analysis target period. Calculate Next, seasonal variation calculation unit 320 classifies months with similar monthly sales ratios into the same group by the k-means method. Seasonal variation calculation section 320 generates a seasonal variation table shown in FIG. Note that the seasonal variation calculation unit 320 is an example of a clustering unit, the total number of units sold is an example of a total sales volume, and the monthly sales ratio is an example of a unit period sales ratio.

Returning to FIG. 1, the sales start time identification unit 330 performs processing to identify the month and year when sales of each product started. A sales start time specifying unit 330 acquires the first year and month when the number of units sold is counted from the converted sales data table generated by the sales data conversion unit 310, and specifies the acquired year and month as the sales start year and month.

The seasonal group number calculation unit 340 performs processing to determine the number of seasonal groups, which is the number of groups into which each month is classified. The seasonal group number calculation unit 340 determines the number of seasonal groups that satisfies the conditions from among the candidates for the number of seasonal groups in the candidate table shown in FIG. Specifically, first, the seasonal group number calculation unit 340 calculates a seasonal variation coefficient, which is a coefficient of variation calculated by dividing the standard deviation of the monthly sales ratio by the average of the monthly sales ratios. The seasonal group number calculation unit 340 compares the calculated seasonal variation coefficient with the seasonal variation reference value set for each seasonal group number in the seasonal group number initial setting candidate table shown in FIG. The number of seasonal groups corresponding to the seasonal variation reference value that is equal to or greater than the value of and has the smallest difference from the calculated coefficient of variation is specified. Next, the seasonal group number calculation unit 340 determines that the number of monthly sales data items belonging to the same group as the target year and month for which the reference inventory amount is to be calculated satisfies the "required number of data items" set in the calculation condition table shown in FIG. The number of season groups is determined depending on whether or not Specifically, the seasonal group number calculation unit 340 refers to the information specifying groups such as A and B set for each month from the standard inventory table shown in FIG. Identify. Next, the seasonal group number calculation unit 340 refers to the post-conversion sales data table generated by the sales data conversion unit 310, and determines whether or not the number of past sales data in the specified month is equal to or greater than the "necessary number of data". judge. The seasonal group number calculation unit 340 outputs the specified number of seasonal groups to the standard stock amount calculation unit 350 when determining that the number of past sales data for the specified month is greater than or equal to the “required number of data”. Note that the seasonal group number calculation unit 340 is an example of a seasonal variation coefficient calculation unit, a group number determination unit, and an acquisition unit.

Returning to FIG. 1, the standard inventory quantity calculation unit 350 calculates the standard inventory quantity. The reference inventory quantity calculation unit 350 calculates the reference inventory quantity for each product at each sales base based on the calculation condition table shown in FIG. 3 set by the calculation condition setting unit 120 . The standard stock quantity is a standard value of the minimum stock quantity that each sales base should have in order to comply with the set ideal delivery date. Note that the standard inventory quantity calculation unit 350 is an example of a standard inventory quantity calculation unit.

The standard inventory table output unit 400 outputs a table of standard inventory quantities calculated by the standard inventory quantity calculation unit 350 (hereinafter referred to as a standard inventory table). As shown in Fig. 9, the standard inventory table contains the "cycle inventory", which is the number of inventory for each product at each sales base, with a margin to prevent out-of-stock in consideration of the lead time, and the variation in the shipment amount. Generates a standard inventory table that includes the "safety stock quantity", which is the inventory quantity with a margin to prevent out of stock, and the "standard inventory quantity", which is the sum of the cycle inventory quantity and the safety stock quantity. , to the storage unit 200 .

The information processing apparatus 10 having the functional configuration described above physically includes a CPU (Central Processing Unit) 11 that executes processing according to a program, and a RAM (Random Memory) that is a volatile memory, as shown in FIG. Access Memory) 12, ROM (Read Only Memory) 13 which is a non-volatile memory, a storage section 14 for storing data, an input section 15 for accepting input of information, and a display section 16 for visualizing and displaying information. , which are connected via an internal bus 99 .

The CPU 11 executes various processes by reading the programs stored in the storage unit 14 to the RAM 12 and executing them. The CPU 11 functions as a setting unit 100 and a processing unit 300 as main functions provided by the program, and executes each process.

The RAM 12 is used as a work area for the CPU 11. The ROM 13 stores control programs executed by the CPU 11 for basic operations of the information processing apparatus 10, BIOS (Basic Input Output System), and the like.

The storage unit 14 includes a hard disk drive, a flash memory device, etc., stores programs executed by the CPU, and stores various data used when executing the programs. CPU 11 functions as storage unit 200 .

The input unit 15 is a user interface equipped with a keyboard, mouse, and the like. The display unit 16 is a display device such as a liquid crystal display or an organic EL (Electro Luminescence) display that visualizes and displays information.

Next, the operation of the information processing device 10 will be described with reference to FIG. The information processing device 10 calculates the standard stock quantity of each product sold at the sales base designated by the user in the month and year designated by the user. A sales data table shown in FIG. 7 is stored in advance in the sales data storage unit 210 of the information processing device 10 . The sales data table is information indicating the product sales volume by product and by month at each sales base.

2 and the standard stock quantity shown in FIG. A calculation condition table containing the details of necessary conditions, a candidate table containing candidates for the number of seasonal groups, which is the number of groups for classifying each month shown in FIG. 4, and products sold at each sales base shown in FIG. A base master containing information on the manufacturing base of each product and a lead time master containing information indicating the lead time between the manufacturing base of the product and the sales base shown in FIG. 6 are set in advance.

Returning to FIG. 11, when the user operates the input unit 15 of the information processing device 10 to request the start of the standard inventory quantity calculation process, the information processing device 10 starts the processing.

The information processing device 10 receives input of information specifying the target sales base for which the standard inventory quantity is to be calculated and the target period, which is the period of the sales data to be referenced (step S101). When the user operates the input unit 15 to input the identification information that identifies the sales base and the target period that is the period of the sales data to be referenced, the information processing device 10 outputs the acquired information to the sales data conversion unit 310. do.

Next, the sales data conversion unit 310 processes the received sales data of the sales base, and performs a process of integrating the sales data of the old model product and the sales data of the latest model product (step S102). Specifically, first, the sales data conversion unit 310 extracts the sales base and the products sold at the sales base from the sales data table shown in FIG. 7 based on the acquired identification information of the sales base. Next, the information entered in the "model name" field of the sales data table is collated with the information entered in the "old model name" field of the new/old correspondence table shown in FIG. Specifically, when the "model name" in the sales data table and the "old model name" in the new/old correspondence table match, the sales data conversion unit 310 determines that the product with the "model name" in the sales data is the old model product. to decide. Next, the sales data conversion unit 310 obtains the "latest model name" information entered in the same line as the old model name from the old/new correspondence table, and acquires the "model name" information from the sales data table. Convert to "latest model name" information. Next, in the sales data table after conversion, if there is sales data with the same model name and date, the sales data conversion unit 310 adds up the sales volume of these sales data, and calculates the sales of the old model product. Generate a post-transformation sales data table that integrates the data with the sales data of the latest model products. On the other hand, if the "model name" in the sales data does not match the "old model name" in the new/old model name correspondence table, the sales data conversion unit 310 determines that the product with this model name is the latest model product, and integrates it. No processing is performed. The sales data conversion unit 310 outputs the generated sales data after conversion to the storage unit 200 .

Returning to FIG. 11, next, the seasonal variation calculation unit 320 classifies each month into a predetermined number of groups (step S103). Specifically, first, the seasonal fluctuation calculation unit 320 calculates, for each product, the monthly sales data representing the ratio of the monthly sales volume to the total sales volume during the analysis target period from the converted sales data generated by the sales data conversion unit 310. Calculate the percentage.

Next, the seasonal variation calculation unit 320 classifies each month into groups with similar demand trends based on the calculated monthly sales ratio, and generates a standard inventory table showing the classification results. Specifically, seasonal variation calculation unit 320 classifies months with similar monthly sales ratios into the same group using the number of seasonal groups set in the candidate table shown in FIG. 4 by the k-means method. In the illustrated example, the candidate table is set with 6 patterns of 1, 2, 3, 4, 6, and 12 seasonal groups. The seasonal variation calculator 320 processes all the set patterns and divides each month into 1, 2, 3, 4, 6 and 12 groups. As shown in FIG. 8, when the seasonal group is 2, October to April is Group A, and May to September are divided into two clusters, which are group B. In addition, when the number of seasonal groups is 3, it is divided into three groups, with January to April as group A, May to August as group B, and September to December as group C. indicates Seasonal variation calculation unit 320 outputs the generated standard inventory table to storage unit 200 .

Returning to FIG. 11, next, the sales start time identification unit 330 identifies the month and year when the product was sold (step S104). Specifically, the sales start date identification unit 330 acquires the first year and month when the number of units sold is counted from the converted sales data generated in step S102, and identifies the acquired year and month as the sales start year and month.

Next, the seasonal group number calculation unit 340 determines a candidate value for the number of seasonal groups, which is the number of groups for classifying each month (step S105). Specifically, first, the seasonal group number calculation unit 340 uses the average and standard deviation of the monthly sales ratio, which indicates the ratio of the monthly sales volume to the total sales volume in the analysis target period, calculated in step S103, as shown below. Equation 1 is used to calculate the seasonal variation coefficient that indicates the variation in the monthly sales ratio of each product.

Coefficient of seasonal variation = (standard deviation value of monthly sales ratio) / (average value of monthly sales ratio) ... formula 1

The seasonal group number calculation unit 340 compares the calculated seasonal variation coefficient with the seasonal variation reference value set in the seasonal group number initial setting candidate table shown in FIG. , the seasonal variation reference value that minimizes the difference from the seasonal variation coefficient. The seasonal group number calculator 340 determines the number of seasonal groups corresponding to the specified seasonal variation reference value as a candidate value. For example, if the calculated seasonal variation coefficient is 0.26, the seasonal group number calculation unit 340 selects seasonal group number initial setting candidate table shown in FIG. The seasonal variation reference value that minimizes the difference between the coefficient and the seasonal variation reference value is specified to be 0.3. is determined as the initial value of

Returning to FIG. 11, next, the seasonal group number calculation unit 340 confirms whether or not the number of sales data for the month belonging to the same group as the target month for which the reference inventory quantity is calculated satisfies the setting condition (step S106). . Specifically, the seasonal group number calculation unit 340 calculates the number of sales data belonging to the same group as the target month from the post-conversion sales data generated by the sales data conversion unit 310, and the calculated number of sales data is the calculation condition. It is checked whether or not the setting condition is satisfied depending on whether or not the required number of data set in advance by the setting unit 120 is exceeded. First, the seasonal group number calculation unit 340 calculates the number of months in the same category as the target month from the reference inventory table shown in FIG. 8 generated in step S103. Specifically, when the number of seasonal groups determined in step S105 is 3, the seasonal group number calculation unit 340 determines that the months in the same category as August, which is the target month, are four months from May to August, including the current month. It is calculated that Next, the seasonal group number calculation unit 340 acquires 12, which is the set required number of data, from the calculation condition table shown in FIG. Next, the seasonal group number calculation unit 340 reads the post-conversion sales information generated in step S102, and checks whether or not the number of past sales data for May to August is 12 or more, which is the required number of data. .

When the seasonal group number calculation unit 340 determines that the number of sales data is less than the required number of data and does not satisfy the setting condition (step S106; No), the seasonal group number determined as the candidate value from the candidate table shown in FIG. It is checked whether or not a smaller number of season groups exists (step S107). When the seasonal group number calculation unit 340 determines that there is a seasonal group number smaller than the number of seasonal groups determined as the candidate value (step S107; Yes), the process returns to step S105, and the number of seasonal groups next smaller than the number of seasonal groups determined as the candidate value is determined. Determine the number of seasonal groups as a new candidate value, and confirm again whether the number of monthly sales data satisfies the set conditions. On the other hand, when the seasonal group number calculation unit 340 determines that there is no seasonal group number smaller than the seasonal group number determined as the candidate value (step S107; No), it outputs an error message to the effect that the standard inventory number cannot be calculated ( Step S108), the process ends.

Returning to step S106, when the seasonal group number calculation unit 340 determines that the number of sales data is equal to or greater than the required number of data and satisfies the set condition (step S106; Yes), the standard stock quantity calculation unit 350 determines that the set condition is satisfied. to notify. The reference inventory quantity calculation unit 350 extracts the necessary number of latest sales data from the sales data, and calculates the average and standard deviation of the sales volume of the extracted sales data (step S109).

Next, the reference inventory quantity calculation unit 350 uses the average and standard deviation of the number of units sold calculated in step S109 to calculate the cycle inventory quantity, which is the quantity of inventory with a margin to prevent out-of-stock in consideration of the lead time. , and the safety stock quantity, which is the stock quantity with a margin for preventing out-of-stock, is calculated in consideration of variations in the shipment amount (step S110). The reference inventory quantity calculation unit 350 calculates the cycle inventory quantity for the target year and month using Equation 2 below.

　Cycle inventory = (Average sales volume × Arrival cycle) / 2 ··· Formula 2

Here, the arrival cycle is preset in the calculation condition table shown in FIG. 3, and the standard inventory quantity calculation unit 350 calculates the cycle inventory quantity using the arrival cycle set in the calculation condition table. do.

The standard stock quantity calculation unit 350 calculates the safety stock quantity for the target year and month using Equation 3 below.

　Safety stock quantity = safety coefficient x standard deviation value of sales volume x √ (lead time) ... formula 3

Here, the safety coefficient is a coefficient generated by the service rate preset in the calculation condition table shown in FIG. is the value of the inverse function of The lead time is preset in the lead time master shown in FIG. 6, and the reference inventory quantity calculation unit 350 acquires the lead time from the combination of the sales base and the manufacturing base of the product to be calculated. Note that the reference inventory quantity calculation unit 350 is an example of an acquisition unit.

For example, the average number of units sold per month for products with model name XXX-3 at the sales base AAA is 100 units, the standard deviation value is 20 units, the arrival cycle is 0.5 months, the target service rate is 95%, and the lead time is 3 months. In this case, the number of cycles in stock and the number of safety stocks of model name XXX-3 at the sales base AAA are as follows from

equations

2 and 3.

Cycle inventory = (100 units x 0.5 months) / 2 = 25 units

　Safety inventory = 1.65 x 20 units x √ (3 months) ≒ 57 units

Returning to FIG. 11, next, the reference inventory quantity calculation unit 350 calculates the sum of the cycle inventory quantity and the safety inventory quantity calculated in step S110 as the reference inventory quantity (step S111). The reference inventory quantity calculation unit 350 outputs the calculated cycle inventory quantity, safety inventory quantity, and reference inventory quantity for each product to the reference inventory table output unit 400 . The standard inventory table output unit 400 generates the standard inventory table shown in FIG. 9 based on the acquired cycle inventory quantity, safety inventory quantity, and standard inventory quantity for each product, stores it in the storage unit 200, and ends the process. The standard inventory table output unit 400 also outputs the standard inventory table to the inventory management system 500, which will be described later.

As described above, the information processing device 10 calculates monthly sales ratios and classifies them by month. The information processing device 10 calculates the reference inventory quantity for the target month based on the monthly sales data classified into the same group. As a result, it is possible to obtain an appropriate reference inventory quantity in consideration of monthly demand characteristics, and it is possible to obtain a highly accurate reference inventory quantity.

The information processing device 10 also calculates seasonal variation coefficients, which are variation coefficients calculated from the average and standard deviation of monthly sales ratios, and sets seasonal groups, which are the number of groups into which each month is classified. Accordingly, by setting different numbers of seasonal groups according to demand fluctuations for each product, it is possible to obtain an appropriate reference stock quantity for various types of products.

In addition, the information processing device 10 integrates the sales data of the old model product with the sales data of the latest model product, and calculates the standard stock quantity based on the integrated sales data. By utilizing the sales data of old-type products even for products with insufficient sales data, it is possible to obtain an appropriate standard stock quantity.

Therefore, for example, using the product inventory monitoring system described in Patent Document 1, the process of comparing the obtained standard inventory quantity with the production/shipment plan, the process of updating the production/shipment plan, the process of predicting demand for each season, It becomes possible to more appropriately perform processing for managing whether or not an appropriate inventory level is maintained.

As long as it does not deviate from the gist of the present disclosure, it is possible to select the configurations given in the above embodiments or change them to other configurations as appropriate.

In the above embodiment, six patterns of 1, 2, 3, 4, 6, and 12 seasonal groups are preset in the candidate table shown in FIG. number may be set.

In addition, in the processing of step S103, the seasonal variation calculation unit 320 calculates the seasonal variation coefficient using the monthly sales ratio that indicates the ratio of the monthly sales volume to the total sales volume in the analysis target period. do not have. For example, the seasonal variation coefficient may be calculated using monthly sales volume, sales amount, and the like.

In addition, in the process of step S103, the seasonal variation calculation unit 320 classifies each month by the k-means method, but it is not limited to this. For example, other clustering methods such as the minimum mean variance method and the fuzzy c-means method may be used.

In addition, although a product was exemplified as an object, the product may be either a finished product or parts. In addition, the object is not limited to those counted by indivisible pieces, but any kind of object such as substances, materials, raw materials, energy, agricultural products, fishery products, etc. can be used. The sales data storage unit 210 may store sales data including information that enables each object to be identified according to the type of object.

In addition, although the information processing device 10 expresses inventory, production volume, and shipment quantity in terms of "number", they may also be expressed in terms of "quantity". In that case, the weight (kilogram), volume (cubic meter, liter), length (meter), etc. of the object may be used as the amount of inventory, production, and shipment. The sales data storage unit 210 may store sales data indicating the sales volume of the target object, and the standard inventory quantity calculation unit 350 may calculate the standard inventory quantity based on the sales volume. In this specification, the term "inventory amount" means both the number of inventories and the amount of inventory.

In addition, although the information processing apparatus 10 obtains the standard inventory quantity by month as the unit period for obtaining the reference inventory quantity, any unit period such as daily, weekly, or quarterly is used as the period for obtaining the inventory quantity. may The sales data storage unit 210 stores sales data for each arbitrary unit period such as daily or quarterly basis, and the standard inventory quantity calculation unit 350 may calculate the standard inventory quantity for each unit period same as the sales data.

In the present embodiment, the processing is executed by the information processing device 10 using one device, but the system configuration is arbitrary. For example, the functions of the information processing apparatus 10 may be implemented by a system including a user terminal that displays an interface screen for accepting user input and a server device that implements the functions of the setting unit 100 and the processing unit 300 . Furthermore, the system configuration may include a plurality of user terminals.

The information stored in the storage unit 200 may be collectively managed by a cloud server existing on the network, and the processing unit 300 may access the cloud server as necessary to read and write information. In this case, the information processing device 10 does not have to include the storage unit 200 .

In addition, the information processing device 10 can be realized using a normal computer system without using a dedicated device. For example, a program for realizing each function in the information processing device 10 is stored in a computer-readable recording medium such as a CD-ROM (Compact Disc Read Only Memory) or a DVD-ROM (Digital Versatile Disc Read Only Memory). By distributing the program as a program and installing the program on the computer, a computer capable of realizing each of the functions described above may be constructed.

Also, if each function is shared between an OS (Operating System) and an application, or if the OS and an application work together, only the application may be stored in the recording medium.

(Embodiment 2)
The standard inventory quantity calculated by the information processing apparatus of the first embodiment is used in an inventory management system that maintains the product inventory quantity at an appropriate level. The inventory management system stores an inventory quantity for each product, and a production/shipment plan including planned production quantity and planned shipment quantity for each product. The inventory management system calculates a predicted inventory quantity for each product based on the current inventory quantity and the planned production quantity and planned shipment quantity determined by the production/shipping plan. The inventory management system compares the calculated predicted inventory quantity with the standard inventory quantity calculated by the information processing device for each product. If there is a product whose estimated inventory quantity is less than the standard inventory quantity, the inventory management system will adjust the planned production volume in the production and shipping plan for the product to the planned production volume for which the predicted inventory quantity is greater than or equal to the standard inventory quantity. update to. The planned production quantity is an example of a planned production quantity, the planned shipment quantity is an example of a planned shipment quantity, and the predicted value of the inventory quantity is an example of a predicted inventory quantity.

As shown in FIG. 12, the inventory management system 50 of Embodiment 2 includes a processing unit 600 that executes various processes, a storage unit 700 that stores information, and a communication unit 800 that communicates with external devices.

The processing unit 600 includes a standard inventory table acquisition unit 610 that acquires a standard inventory table from the information processing device 10, an inventory information acquisition unit 620 that acquires inventory information from an external database, and an external database (hereinafter referred to as DB). It has a production plan acquisition unit 630 that acquires the plan, a shipping plan acquisition unit 640 that acquires the shipping plan from an external database, and a production plan correction unit 650 that corrects the production plan.

The standard inventory table acquisition unit 610 acquires the standard inventory table shown in FIG. Note that the standard inventory table acquisition unit 610 is an example of standard inventory amount acquisition means.

The inventory information acquisition unit 620 periodically acquires inventory information indicating the current inventory quantity for each sales location and product from an external DB, for example, via a network, and stores it in the storage unit 700 .

The production plan acquisition unit 630 collects production plans for each product from one or more external DBs, obtains the planned production quantity for each product, and stores it in the storage unit 700 .

The shipping plan acquisition unit 640 collects the shipping plans for each product for each sales base from one or more external DBs, obtains the planned number of shipments for each product, and stores it in the storage unit 700 .

The production plan correction unit 650 predicts the inventory for each combination of sales base and product, and if the predicted inventory quantity is less than the standard inventory quantity, the planned production quantity is set to a value at which the predicted inventory quantity is equal to or greater than the standard inventory quantity. to be corrected. The production plan correction section 650 outputs the corrected production plan to an external DB via the communication section 800 .

The processing unit 600 is an example of production plan correction means.

The storage unit 700 includes a standard inventory table storage unit 710 that stores a standard inventory table, an inventory information storage unit 720 that stores inventory information, a production plan storage unit 730 that stores production plans, and a shipping plan that stores shipping plans. It comprises a storage unit 740 and a corrected production plan storage unit 750 that stores the corrected production plan.

The standard inventory table storage unit 710 stores the standard inventory table acquired by the standard inventory table acquisition unit 610. Note that the standard inventory table storage unit 710 is an example of standard inventory amount storage means.

The inventory information storage unit 720 stores the inventory information acquired by the inventory information acquisition unit 620 and indicating the current inventory quantity of each product at each sales base.

The production plan storage unit 730 stores the production plan acquired by the production plan acquisition unit 630.

The shipping plan storage unit 740 stores the shipping plan acquired by the shipping plan acquisition unit 640.

The corrected production plan storage unit 750 stores the shipping plan corrected by the production plan correction unit 650.

The communication unit 800 communicates between the processing unit 600, the external information processing device 10, and the external DB.

The inventory management system 50 has the same configuration as in FIG. In this case, the CPU 11 functions as the processing section 600 . Storage unit 14 functions as storage unit 700 . The communication section 17 functions as a communication section 800 .

Next, the operation of the inventory management system 50 having the above configuration will be described with reference to FIG.

As a premise, the latest standard inventory table is stored in the standard inventory table storage unit 710 by the standard inventory table acquisition unit 610, the latest inventory information is stored in the inventory information storage unit 720 by the inventory information acquisition unit 620, and the production plan acquisition unit It is assumed that the latest production plan is stored in the production plan storage unit 730 by 630 and the latest shipping plan is stored in the shipment plan storage unit 740 by the shipping plan acquisition unit 640 .

The processing unit 600 of the inventory management system 50 identifies one sales base and one product (step S201).

Next, the processing unit 600 predicts future changes in the current stock quantity for each sales base and product, according to the production plan and shipping plan, at least up to the lead time (step S202). Specifically, according to the shipping plan, the new stock quantity is obtained by subtracting the shipping quantity from the expected stock quantity at the timing when the product is shipped. Similarly, according to the production plan, the new stock quantity is calculated by adding the new stock quantity to the estimated stock quantity at the timing when the product is stocked. Predict the inventory quantity at least beyond the lead time. For example, if the sales base is AAA and the product is XXX-3, the lead time is two months from the lead time master in FIG. Therefore, we anticipate the number of inventories for at least two months ahead. It should be noted that it is also possible to estimate the period up to that point by adding the number of days required for production to the lead time.

The expected inventory quantity is compared with the standard inventory quantity registered in the standard inventory table, and it is determined whether or not there is a timing when the expected inventory becomes less than the standard inventory quantity after shipment (step S203).

If it is determined that there is no timing when the expected inventory is less than the standard inventory quantity (step S203: No), the process proceeds to step S205.

If it is determined that there is a timing when the expected inventory is less than the standard inventory quantity (step S203: Yes), the production plan is revised so that the expected inventory is greater than or equal to the standard inventory quantity (step S204). For example, if the standard stock quantity is 5 and the expected stock quantity is 2 at the first timing, the production plan is revised so that the stock receipt is +3 (=5-2) or more by the first timing.

Next, it is determined whether or not the production plan correction process has been completed for all sales bases and all products (step S205).

If not completed (step S205: No), update the sales base and/or product (step S206), and return to step S202.

If completed (step S205: Yes), the corrected production plan is stored in the corrected production plan storage unit 750 and transmitted via the communication unit 800 to an external DB storing the production plan (step S207), the current process is terminated.

According to the inventory management system 50, inventory management is performed based on the standard inventory quantity obtained by the information processing device 10, so inventory can be appropriately managed.

In the second embodiment, the inventory management system 50 expresses the amount of products by "number", but it may be expressed by "amount" as in the first embodiment. When referring to inventory, production, and shipment, we mean both numbers and quantities.

In Embodiment 2, an example of configuring the inventory management system 50 with one device was shown, but the system configuration is arbitrary. For example, the functions of the inventory management system 50 may be implemented by a system that includes a user terminal that displays an interface screen that accepts user input and a server that implements the functions of the processing unit 600 . Furthermore, the system configuration may include a plurality of user terminals.

Information stored in the storage unit 700 may be centrally managed by a cloud server existing on a network, and the processing unit 600 may access the cloud server as necessary to read and write information. In this case, the inventory management system 50 does not have to include the storage section 700 .
Further, the information processing device 10 and the inventory management system 50 may be configured by one device.

Also, the inventory management system 50 can be realized using a normal computer system without using a dedicated device.

Various embodiments and modifications of the present disclosure are possible without departing from the broad spirit and scope of the present disclosure. Moreover, the embodiments described above are for explaining the present disclosure, and do not limit the scope of the present disclosure. In other words, the scope of the present disclosure is indicated by the claims rather than the embodiments. Various modifications made within the scope of the claims and within the scope of equivalent disclosure are considered to be within the scope of the present disclosure.

This application is based on Japanese Patent Application No. 2021-037578 filed on March 9, 2021. The entire specification, claims, and drawings of Japanese Patent Application No. 2021-037578 are incorporated herein by reference.

10 information processing device, 11 CPU, 12 RAM, 13 ROM, 14 storage unit, 15 input unit, 16 display unit, 17 communication unit, 50 inventory management system, 60 processing unit, 70 storage unit, 99 internal bus, 100 setting unit , 110 Old and new product type name setting part, 120 Calculation condition setting part, 130 Seasonal group number initial candidate setting part, 140 Manufacturing base information setting part, 150 Lead time setting part, 200 Storage part, 210 Sales data storage part, 220 Seasonal fluctuation Table storage unit 300 Processing unit 310 Sales data conversion unit 320 Seasonal variation calculation unit 330 Sales start time identification unit 340 Seasonal group number calculation unit 350 Standard inventory quantity calculation unit 400 Standard inventory table output unit 500 Inventory Management system 600 Processing unit 610 Standard inventory table acquisition unit 620 Inventory information acquisition unit 630 Production plan acquisition unit 640 Shipping plan acquisition unit 650 Production plan correction unit 700 Storage unit 710 Standard inventory table storage unit 720 Inventory information storage unit 730 Production plan storage unit 740 Shipping plan storage unit 750 Corrected production plan storage unit 800 Communication unit.

Claims

An information processing device that calculates a reference inventory amount that realizes a target value of a service rate, which is a ratio of inventory to demand for an object to be sold in a predetermined unit period,
an acquisition unit that acquires the target value of the service rate and actual sales data including the sales volume of the target object for each unit period;
A seasonal variation coefficient obtained by dividing the standard deviation of the sales volume for each unit period in a predetermined period by the average of the sales volume, based on the actual sales data acquired by the acquisition unit. a seasonal variation coefficient calculation unit that calculates
a group number determination unit that determines the number of groups for classifying each unit period based on the seasonal variation coefficient calculated by the seasonal variation coefficient calculation unit;
a clustering unit that classifies each unit period into the number of groups determined by the group number determination unit according to the sales volume for each unit period;
From the result of processing by the clustering unit, identify the unit period classified into the same group as the unit period for which the standard inventory amount is to be calculated, and determine the sales volume for the identified unit period and the service rate obtained by the obtaining unit. a reference inventory amount calculation unit that calculates the reference inventory amount based on the target value;
Information processing device.
The acquisition unit acquires the plurality of numbers of groups and group number correspondence information corresponding to each of the numbers of groups,
The seasonal variation coefficient calculation unit calculates a unit period sales ratio indicating a ratio of the sales amount for each unit period to the total sales amount of the object in a predetermined period, and calculates the unit period sales ratio Based on, calculate the seasonal variation coefficient,
The group number determination unit determines group number correspondence information that satisfies a predetermined condition from among the group number correspondence information acquired by the acquisition unit, and determines the number of groups corresponding to the determined group number correspondence information as determining the number of groups for classifying each unit period;
The information processing device according to claim 1 .
The clustering unit classifies each unit period based on the unit period sales ratio calculated by the seasonal variation coefficient calculation unit.
The information processing apparatus according to claim 2.
The actual sales data includes the sales volume of old-model products that are old-model objects corresponding to the objects,
The acquisition unit acquires new/old correspondence data that associates the target object with the old model product,
a sales data conversion unit that integrates the sales volume of the old-model product included in the sales performance data with the sales volume of the corresponding object based on the new/old correspondence data acquired by the acquisition unit;
The information processing apparatus according to any one of claims 1 to 3.
Standard inventory amount storage means for storing the standard inventory amount generated by the information processing apparatus according to any one of claims 1 to 4;
stock quantity acquisition means for acquiring the current stock quantity for each of the objects;
Based on the scheduled production volume and scheduled shipment volume for each of said objects determined by the production/shipping plan and the current inventory quantity acquired by said inventory quantity acquisition means, the predicted inventory quantity after shipment is calculated for said object a production plan correction means for correcting the planned production quantity determined in the production/shipment plan when the calculated predicted inventory quantity is less than the standard inventory quantity;
inventory management system.
An information processing method executed by an information processing device for calculating a reference inventory amount that realizes a target value of a service rate, which is a ratio of inventory to demand for an object to be sold in a predetermined unit period. There is
a step of obtaining a target value of the service rate and actual sales data indicating a sales volume of the object for each unit period;
a step of calculating a seasonal variation coefficient, which is a variation coefficient obtained by dividing the standard deviation of the sales volume for each unit period in a predetermined period by the average of the sales volume, based on the actual sales data;
determining the number of groups for classifying each unit period based on the seasonal variation coefficient;
classifying each unit period into the number of groups according to the sales volume for each unit period;
A unit period classified into the same group as the unit period for which the standard inventory amount is to be calculated is specified, and the standard inventory amount is calculated based on the sales volume of the specified unit period and the target value of the service rate. and
Information processing method including.
A computer, which is an information processing device that calculates a standard inventory amount that realizes a target value of a service rate, which is a ratio of inventory to demand for an object to be sold in a predetermined unit period,
a process of acquiring the target value of the service rate and actual sales data indicating the sales volume of the object for each unit period;
A process of calculating a seasonal variation coefficient, which is a variation coefficient obtained by dividing the standard deviation of the sales volume for each unit period in a predetermined period by the average of the sales volume, based on the actual sales data;
a process of determining the number of groups for classifying each unit period based on the seasonal variation coefficient;
A process of classifying each unit period into groups of the number of groups according to the sales volume for each unit period;
A unit period classified into the same group as the unit period for which the standard inventory amount is to be calculated is specified, and the standard inventory amount is calculated based on the sales volume of the specified unit period and the target value of the service rate. and
program to run.