JP2021174452A - Order support device and order support method - Google Patents

Abstract

To provide an order support device and an order support method for diagnosing a transition of a future stock state and visualizing the diagnosis result in order to maintain an appropriate stock state of items, and thereby supporting order work.SOLUTION: An order support device A has: a demand prediction processing part 111 for predicting a demand from PSI (production/selling/stockpile) data; a stock diagnosis processing part 112 for determining shortage and excess stock of items from the PSI data; and a visualization processing part 115 for calculating future stock data on the basis of the PSI data and the demand prediction result by the demand prediction processing part 111, and graphically displaying a future stock status on a temporal axis of a graph defined by the temporal axis and stock quantities together with future production/sales data. The visualization processing part 115 displays a diagnosis result of the stock state on a future date at an interval of dates on the temporal axis of the graph as a background of the graph in a visually distinguishable form.SELECTED DRAWING: Figure 1

Description

The present invention relates to an ordering support technology, and in particular, in order to maintain an appropriate inventory state of an item, it diagnoses a transition of the future inventory state, visualizes the result, and automatically proposes an ordering date and an ordering quantity. Regarding inventory management technology that supports ordering operations.

In recent years, a wide variety of products and parts have increased due to the diversification of customer needs, leading to an increase in inventory items to be managed and an increase in ordering operations.
As a technique for visualizing the inventory status to support the ordering business, there is a technique for confirming inventory transition and shortage information (see, for example, Patent Document 1 below).

According to the distribution management method and the information processing device described in Patent Document 1, the first step of setting the product and the distribution base of the product on the information display screen of the information processing device, and a plurality of types presented on the information display screen. The second step of selecting one inventory management method from the inventory management methods, the display area of the time transition model of the inventory amount in the selected inventory management method, and the products and distribution required for the simulation of the inventory management method. The third step of displaying at least one of the display area and the input area of the numerical information about the base on the information display screen, and the execution result of the simulation using the numerical information by the selected inventory management method in the information processing apparatus are inventoried. By providing a logistics management technology that includes a fourth step of visualizing a transition as a graph and outputting it to an information display screen, and a fifth step of determining one inventory management method based on the execution result of a simulation. It is easy to check the inventory transition of the number of days in stock (= total period inventory / total period shipment) and the shortage rate (= total number of period shortages / total number of period orders), and the inventory management method is reflected in the actual inventory planning system. It can be set. Therefore, it is possible to finely set the optimum inventory management method for each base / product. In addition, the simulation result confirmation when setting the inventory management method in detail for each base / product can be easily performed on the screen, and the inventory management method setting work can be efficiently performed.
Further, Patent Document 2 discloses a technique for grasping a transitional state of PSI (Product on, Sales and Inventory) using a convolutional neural network.

Japanese Unexamined Patent Publication No. 2004-078487 Japanese Unexamined Patent Publication No. 2019-21870

The problems in the prior art will be described below.
1) Problem 1: Insufficient visualization information In the prior art, there is a prior art that visualizes how shortages occur according to inventory changes. However, only the state of shortage that can be judged from the inventory transition can be visualized. In order to perform better inventory management, it is necessary to have a diagnosis result that considers the mutual conditions of production, sales, and inventory, whether the item is likely to be out of stock, whether the item is in excess, etc. It is preferable that the information on the diagnosis result of the inventory status of the product is also visualized.

2) Issue 2: Burden of the number of times inventory status diagnosis is performed Inventory management personnel are required to manage inventory to maintain an appropriate inventory status that does not cause shortages or excesses, and production of each item for a large number of items -It is necessary to diagnose the inventory status based on the sales / inventory data (hereinafter referred to as PSI data). Furthermore, in order to maintain an appropriate inventory status so that the inventory status does not become short or excessive in consideration of the upside and downside of the demand forecast, the inventory status is diagnosed multiple times even for one item. It is necessary, and it is a heavy load on the person in charge of inventory management, and it is required to reduce the workload.

3) Issue 3: Recording the basis for judging the diagnosis result In ordering work, it is usually preferable that not only the person in charge of inventory management but also multiple people such as the order coordinating department and procurement check the correctness of the order. PSI data and safety inventory information that change according to the demand forecast result are the main judgment materials, and the appropriateness of inventory at any time point is diagnosed from changes in the time series of demand and production / inventory like an inventory manager. It's difficult to do. Therefore, it is difficult to know the basis information of the ordering decision of the person in charge of inventory management.

4) Issue 4: Deterioration of reliability of safety stock that is not reviewed When placing irregular orders, there is a method of placing an order at a timing below the fixed value of safety stock set for each product in the conventional technology, but due to fluctuations in demand Safety stock is fluctuating and should be reviewed regularly.
However, when dealing with a large number of items, regular review is difficult and may remain unreviewed. Therefore, there is a problem that the fixed safety stock does not always give a good order date.

5) Issue 5: Adjustment load of order quantity In the case of indefinite ordering, the order quantity is determined based on a fixed value such as the number of months in stock in the conventional technology, but the order quantity required fluctuates due to fluctuations in demand. It should be reviewed regularly. In addition, it is necessary to determine the order quantity that does not cause shortages or excesses in consideration of the upside and downside of the demand forecast, and it is necessary to adjust the order quantity and diagnose the inventory status associated therewith. It is a big burden for.

An object of the present invention is to diagnose future changes in inventory status and visualize the results in order to solve the above problems and maintain an appropriate inventory status. Then, the purpose is to provide a technology for supporting the ordering business, which automatically proposes the ordering date and the ordering amount.

According to one aspect of the present invention, a demand forecast processing unit that forecasts demand from PSI (production / sales / inventory) data, an inventory diagnosis processing unit that determines whether an item is out of stock or excess inventory from the PSI data, and the like. Further, future inventory data is calculated based on the PSI data and the demand forecast result by the demand forecast processing unit, and the future inventory is on the time axis of the graph defined by the time axis and the inventory amount. It has a visualization processing unit that displays the status as a graph together with future production / sales data, and the visualization processing unit has a diagnosis result (out of stock or shortage) of the inventory status of the future date at a certain date interval on the time axis of the graph. An ordering support device is provided that displays ideal, excess, etc.) in a visually identifiable form as the background of the graph. The date interval is an example, and may be a predetermined interval such as a week interval.

By displaying the inventory diagnosis result in the background of the PSI data or in the vicinity, it is easy to grasp the appropriate state of the inventory. Diagnosis results are displayed in colors, patterns, symbols, etc. according to the type (out of stock, ideal, excess, etc.).

Of the future PSI data from the PSI data and the demand forecast result, future inventory data is created, the inventory status on a future date is diagnosed, and in the result of the diagnosis, the date A on which the risk of shortage occurs is In some cases, it is preferable that the ordering date is a date earlier than the number of lead time days from the date A.
As a result, the ordering date can be automatically determined.

The future inventory data of the future PSI data obtained from the PSI data (input 1) and the demand forecast result (future sales data: input 2) is obtained (input 3), and the demand forecast result and the above are obtained. It is advisable to calculate the order quantity by the following processes 1) and 2) based on the future inventory data.
1) The amount of inventory received on the production date (the day when the ordered inventory is received) is taken as the production amount, and while increasing the production amount, the inventory amount is increased on the production date and the days after that, and the inventory amount before and after the production date is increased. Performs inventory diagnosis processing during the monitoring period affected by changes.
2) The order quantity is the production quantity that is in the (ideal) state before the inventory diagnosis processing result of 1) becomes excessive.
As a result, the order quantity can be automatically determined.

Further, from the future inventory status and the future production / sales data displayed on the display unit, the inventory amount is calculated by subtracting the subsequent sales amount from the order amount on the order date to calculate the inventory amount. It is preferable to display on.
Further, from the future inventory status and the future production / sales data displayed on the display unit, the order quantity on the order date is calculated by subtracting the subsequent sales quantity from the order quantity on the order date. It is preferable to display the data on the display unit.
When the order date of an item is manually changed, the diagnosis may be performed in conjunction with the manual change, and the diagnosis result may be displayed to support the manual adjustment.
Further, when the order quantity of the item is manually changed, the diagnosis may be performed in conjunction with the manual change, and the diagnosis result may be displayed to support the manual adjustment.
When the order quantity is changed, the appropriate degree of inventory may be diagnosed by calculating the quantity that becomes the appropriate inventory state during the monitoring period affected by the change in the order quantity as the order quantity.
As a result, the load on the person in charge of inventory management can be reduced.

Furthermore, the inventory status is lacking by setting the order quantity so that it is above the lower limit of the appropriate degree when the demand forecast shows an upside, and by setting the order quantity so that it is below the upper limit of the appropriateness when the result is a downside. It facilitates the work of maintaining proper inventory so that there are no excess items or excesses.

According to another aspect of the present invention, there is a demand forecast processing step for forecasting demand from PSI data (production / sales / inventory), and an inventory diagnosis processing step for determining shortage and excess inventory of items from the PSI data. , And further, the future inventory data is calculated based on the PSI data and the demand forecast result by the demand forecast processing step, and the future inventory data is calculated on the time axis of the graph defined by the time axis and the inventory amount. It has a visualization processing step for displaying the inventory status in a graph together with future PSI data, and the visualization processing step displays the diagnosis result of the inventory status of the future date at a certain date interval on the time axis of the graph. An ordering support method is provided, which comprises displaying the background in a visually identifiable form.

According to the present invention, it is possible to diagnose the future transition of the inventory state of an item and visualize the diagnosis result. As a result, the order date and the order quantity can be automatically proposed.

It is a functional block diagram which shows one configuration example of the ordering support device by one Embodiment of this invention. It is a flowchart which shows an example of the flow of ordering support processing by this Embodiment. It is a display showing an example of visualization processing, the horizontal axis is a time axis such as a date, and the vertical axis is a quantity (inventory amount, etc.). It is a flowchart which shows an example of the process flow of the automatic determination of an ordering date in an ordering date calculation processing unit. It is a flowchart which shows an example of the process flow of the automatic determination of an order quantity in an order quantity calculation processing unit. It is a flowchart which shows the flow of the iterative process 1 of FIG. It is a flowchart which shows the flow of the iterative process 2 of FIG. It is a figure which shows the 2nd display example of visualization.

In the present specification, PSI data refers to production / sales / inventory data.
Hereinafter, the ordering support device according to the embodiment of the present invention will be described in detail with reference to the drawings. The contents of Patent Document 2 will also be described with reference to them as appropriate.
FIG. 1 is a functional block diagram showing a configuration example of an ordering support device according to the present embodiment. The ordering support device A shown in FIG. 1 has a database DB 100 such as a memory for storing various data, a processing unit 110 such as a CPU for performing various processes, and a display unit 116 for displaying the results.

The processing unit 110 includes a demand forecast processing unit 111, an inventory diagnosis processing unit 112, an order date calculation processing unit 113, an order quantity calculation processing unit 114, and a visualization processing unit 115.
The processing unit 110 acquires the data stored in the database DB 100 and performs processing, and stores the data obtained as the processing result in the database DB 100. Then, based on the processing result data stored in the database DB 100, the display unit 116 displays the PSI data inventory diagnosis result and the like for ordering support.

First, the past to present PSI data 101 (for example, the data shown in Table 1 of Patent Document 2) is input, and the demand forecast processing unit 111 performs arbitrary processing to predict the future sales of the PSI data 102. Generate inventory data.
The demand forecast processing refers to the following.
Technology for predicting when and how many items will be sold Input information example Sales data for past items (periodic information)
Calendar (information on days of the week, holidays, and events)
PSI data output of similar products of products (items) subject to weather forecast Sales forecast number of items

First, Table 1 shows an example of a format in which the PSI data of the item to be diagnosed is held in the memory. Table 1 shows the item code, date, production quantity, sales quantity, and inventory quantity, and is the data corresponding to Table 1 described in Patent Document 2.
Table 1 shows, for example, the daily PSI actual values for one year (April 1, 2017 to March 31, 2018) for 100 items managed by the inventory manager. However, Table 1 is an example, and although the display items are the same as the data shown in FIG. 3 described later, the actual values and the like are different, but this difference is not essential.

Next, processing is performed by an arbitrary inventory diagnosis processing unit 112 with the past to present PSI data 101 and the future PSI data 102 as inputs, and the inventory diagnosis result data 103 (for example, as shown in the diagnosis result type in Table 2 of Patent Document 2). Data) is generated.
The inventory diagnosis process refers to the following.
Technology for diagnosing the soundness of inventory based on PSI data Input information example Production volume Sales volume: Sales volume, sales movement sum, monthly sales Inventory volume: Inventory quantity, Order remaining inventory quantity Holiday information Output information example Diagnosis result (Example below)
Out of stock Risk of out of stock Ideal Normal excess inventory Super excess inventory

Table 2 is a table showing an example of the format held on the teacher data memory used for learning the CNN model, for example.
The teacher data according to the present embodiment is composed of PSI data of an item that has been subject to inventory diagnosis in the past and a diagnosis result of the item. In the example of Table 2, for example, the daily PSI actual value and the diagnosis result type for one year (April 1, 2016 to March 31, 2017) for 1,000 items that have been inventoried in the past are shown. It is stored. Since the relationship between the transition status of PSI data for one year and the diagnosis result is learned by the CNN model, the same value is stored in the daily diagnosis result type of each item. However, Table 2 is an example, and although the display items are the same as the data shown in FIG. 3 described later, the actual values and the like are different.

The diagnosis result type is a numerical value corresponding to "0" for no problem on the memory, "1" for excess inventory, and "2" for understock. The corresponding numerical value may be a numerical value representing an ordinal scale such as understock "0", no problem "1", and excess inventory "2". The ordinal scale is useful in the processing of Patent Document 2. This makes it easy to judge the degree of deviation of the judgment if the label is attached by the ordinal scale when comparing the teacher label (excess inventory, etc.) given by the expert with the label judged by the system (normal). Because. It is also possible to subdivide the types of diagnosis results into, for example, "no problem", "excess", "rapid increase", "rapid decrease", and "retention", and assign numerical values corresponding to each. Further, although the PSI data in Tables 1 and 2 are quantities (number of pieces and number of packages), any numerical value such as an amount of money that records daily results can be used in the same manner as the quantity.

(Visualization process of inventory diagnosis result)
The diagnosis result visualization processing unit 115 performs the following processing.
Information (color, pattern, mark) representing the diagnosis result is displayed for each period.
By displaying the diagnosis results for the past period to the future period together with the inventory amount, sales amount, and production amount, the orderer can easily confirm the information that is the basis of the order.
By diagnosing and visualizing the results of changes in conditions such as inventory and sales, it will be easier to understand adjustments to production dates and production and fluctuations in demand forecast results.

FIG. 2 is a flowchart showing an example of the flow of ordering support processing according to the present embodiment. FIG. 3 is a display showing an example of visualization processing, in which the horizontal axis is a time axis such as a date and the vertical axis is a quantity (inventory amount or the like).

The process is started (started), and in step S1, the demand forecast processing unit 111 executes the demand forecast process, uses the demand forecast information from the past to present PSI data, and generates future PSI data.
In step S2, the inventory diagnosis processing unit 112 executes the inventory diagnosis process and generates inventory diagnosis result data from the past to present PSI data and the future PSI data.
The inventory diagnosis processing unit 112 diagnoses the current inventory from the production amount, the sales amount, and the inventory amount up to the present.
In addition, the future inventory is diagnosed from the future production volume, sales volume, and inventory volume based on the demand forecast result.

In step S3, the visualization processing unit 115 displays the production, sales, and inventory transitions of the past to present PSI data and the future PSI data on the PSI data inventory diagnosis result display unit 116 as a bar graph, a line graph, or the like. To visualize.
In step S4, the inventory diagnosis result data is displayed on the PSI data inventory diagnosis result display unit 116 as the background color of the region visualized in step S3.
This completes the process (end).
As shown in FIG. 3, the visualization display example by the PSI data inventory diagnosis result display unit 116 shows the inventory amount R1 and the sales (shipment) amount L1. The broken line L2 at the left end of FIG. 3 represents the current date (t1), and the subsequent dates represent future dates.

Then, in the future, parts and the like are stored so as to increase the inventory at the timing when the inventory amount becomes low (for example, t2). This line L3 indicates the warehousing timing (= production date) and the warehousing amount (= production amount).
Further, as the background of the display, the background color R2 indicating the period of excess inventory and the background color R3 indicating the period of appropriate inventory are displayed in an identifiable manner.
In addition, by diagnosing the upside / downside state of the demand forecast, it is possible to calculate the production amount (= order amount) that does not cause a shortage or excess even if the demand is upside down or downside.
Inventory is diagnosed from the production volume, sales volume, and inventory volume up to now. In addition, inventory is diagnosed from future production volume, sales volume, and inventory volume based on the demand forecast results.

By referring to the display example shown in FIG. 3, the diagnosis is made in consideration of the mutual state of production, sales, and inventory, and the diagnosis result of the inventory state such as whether the item is likely to be out of stock or is in an excess state. You can see the information of at a glance.
In this way, by using the display of FIG. 3, the future inventory is diagnosed in chronological order, and the result is displayed together with the inventory graph so that the result can be identified, and whether or not the inventory status is sound is visually observed. Can be recognized / grasped.

In addition, when the production date or production volume is changed, it is possible to re-diagnose the future inventory accordingly and grasp the change in inventory status according to the adjustment of the production date or production volume. Then, the order date and the order quantity can be automatically proposed.
Further, when the order date or the order quantity is manually changed, the diagnosis may be performed in conjunction with the manual change of the order date or the order quantity, and the diagnosis result may be displayed to support the manual adjustment. ..
It is possible to manage inventory to maintain an appropriate inventory status that does not cause shortages or excesses, and maintain an appropriate inventory status so that inventory status does not become shortages or excesses, taking into consideration the upside and downside of demand forecasts. It is possible to reduce the load on the person in charge of inventory management due to the diagnosis of the inventory status a plurality of times even for one item.
In addition, like a person in charge of inventory management, it is possible to know the basis information of ordering decision for diagnosing the appropriateness of inventory at an arbitrary time from changes in demand and production / inventory in time series. It is also easy to review the production plan and ordering plan.
Furthermore, it becomes easy to adjust the order quantity and diagnose the inventory status associated therewith in order to determine the order quantity that does not become shortage or excess in consideration of the upside and downside of the demand forecast.

(Automatic order determination process)
FIG. 4 is a flowchart showing an example of a flow of processing for automatically determining the order date in the order date calculation processing unit 113.
As shown in FIG. 4, the process is started (Start), and in step S11, the process target date is set to A. In step S12, the inventory diagnosis process is performed on the date A.
Next, in step S13, the inventory diagnosis result is updated. In step S14, it is determined whether or not there is a risk of shortage on the date A. If a display is made that means that the sales volume increases and the inventory volume tends to decrease, it can be judged that there is a shortage risk.

If there is no shortage risk on date A in step S14 (No), the ordering date of the item is not determined at that time (End).
If it is determined in step S14 that there is a risk of shortage on date A (Yes), in step S15, the date of (date A-lead time days) is obtained, and this is set as the order date B, and the order date is the order date. Order the item corresponding to B (End).
In either case of Yes or No in step S14, the process is repeated while changing the process target date A at an appropriate timing.

(Automatic order determination process)
FIG. 5 is a flowchart showing an example of the flow of the order quantity automatic determination process in the order quantity calculation processing unit 114. Here, it is premised that it has a function of diagnosing the state of inventory such as shortage to excess from PSI data and a function of forecasting demand from PSI data as in Patent Document 2. Then, (1) past to present PSI data and (2) demand forecast result are required as inputs. Then, the future PSI data is used from the inputs (1) and (2). Here, a plurality of information including upside and downside may be used for the prediction result for creating future inventory data.
The order quantity is roughly obtained by the following processing.
1) The amount of inventory received on the production date (the day when the ordered inventory is received) is taken as the production amount, and while increasing the production amount, the inventory amount is increased on the production date and the days after that, and the inventory amount before and after the production date is increased. Performs inventory diagnosis processing during the monitoring period affected by changes.
The order quantity is the production volume that is in the (ideal) state before the inventory diagnosis processing results of 2) and 1) are in the excess state.
However, it goes without saying that the order quantity is not limited to the value itself obtained by the above processes 1) and 2), and a certain margin may be provided.
When increasing the inventory amount, the amount in the ideal state is specified in% so that the ideal state can be adjusted.

As shown in FIG. 5, the process is started (Start), and in step S21, the warehousing date due to the ordering of the item is set to C.
In step S22, the inventory amount as of date C is set to I ₀ . In step S23, the appropriate degree of the target inventory amount after the order is set as the appropriate degree P. In step S24, let D be the date for diagnosing the appropriateness of inventory after ordering. There may be a plurality of dates D. Then, as the iterative process, two steps of the iterative process 1 (step S25) and the iterative process 2 (step S26) are executed.
Next, the iterative process 1 of step S25 will be described in detail.

(Repeat processing 1)
FIG. 6 is a flowchart showing a detailed processing flow of the iterative process 1.
In the iterative process 1 in step S25, the inventory amount to be increased in step S25-1 is dI1. In step S25-2, the inventory amount increases by dI1 with the inventory amount on the date C and the dates after that. The amount of increase dI1 can be set arbitrarily. For example, the number of stocks / 20. In step S25-3, the diagnosis is performed on date D. In step S25-4, it is determined whether the inventory amount reaches the appropriate degree P on the date D. When the inventory amount reaches the appropriate degree P on the date D (Yes), the iterative process 1 is terminated (End). If the stock quantity does not reach the appropriate degree P on the date D (No), the process returns to step S25-1.

(Repeat processing 2)
FIG. 7 is a flowchart showing a detailed processing flow of the iterative process 2.
In the iterative process 2 of step S26, the inventory amount to be increased in step S26-1 is dI2. In step S26-2, the inventory amount is increased by dI2 on the date C and the dates after that.
In step S26-3, the diagnosis is performed on date D. There may be a plurality of dates D. In step S26-4, it is determined whether or not the inventory amount reaches the appropriate degree P on the date D.

When the appropriate degree P is reached on the date D (Yes in step S26-4), the process returns to step S26-1. Then, the process is repeated until the diagnosis result on the date D does not reach the appropriate degree P (No in step S26-4).
If the appropriate degree P is not reached on the date D (No in step S26-4), the inventory amount is reduced by dI2 in order to cancel the change of the last increase on the date after the date C including the date C (step S26-4). S26-5), (stock amount on date C in the (ideal) state before the excess stock)-(stock amount I ₀ as of date C) is set as the order quantity (step S26-6), and processing is performed. End (End).
Here, the appropriateness P determined in step S26-4 can be specified by a ratio such as% of the certainty by using the certainty of the diagnosis result. As a result, it is possible to end the repetitive processing when the target appropriate degree P deviates from the target appropriate degree P, and to maintain an appropriate inventory state in which the order quantity is kept low. It is also possible to calculate the inventory amount in the case of moving inventory to another warehouse or disposing of inventory by decreasing the inventory amount instead of increasing it.
Here, the date C is the production date when the inventory is received, the date before the lead time days from the date C is the ordering date, and the production amount (= ordering amount) is placed on the ordering date.
By the above processing, the order quantity can be automatically determined.

(Example)
FIG. 8 is a diagram showing a second display example of visualization. FIG. 8 includes a display example of 2 rows and 4 columns from (a) to (h). However, in FIG. 8, the display is omitted except for FIGS. 8 (b) and 8 (f).
The basic display in FIG. 8 is the same as that in FIG.
With reference to the display in FIG. 8B, it can be seen that the production is in excess of the background (p1), and thereafter it is in excess (p2).

With reference to the display in FIG. 8 (f), it can be seen that since the production was not carried out in an excess state from the background (p3), the state changed from the normal state (p4) to the ideal state (p5) thereafter.
For example, if a display template other than the current display is used as a typical display template in the past among the above eight displays, it is possible to support the ordering so as to maintain the state of proper inventory by referring to the past experience.

In addition, for example, if the future display is made when various order dates and order quantities are set other than the current display among the above eight displays, the future state can be referred to and the proper stock state can be maintained. It can also help determine the current order date and order quantity.
In this way, by using the ordering support technology according to the present embodiment, it is possible to grasp the current situation, refer to the past experience, refer to the ordering operation of a skilled person, refer to the ordering operation of a skilled person, and time such as the season. You can refer to or refer to past ordering operations that take into account specific factors.

In addition, since it is possible to display a display for predicting the future inventory status and the like, it is possible to place an effective order.
Note that, for example, as shown in FIG. 8, a plurality of displays may be performed, and by selecting one of these displays, the ordering operation in the case of the selected display may be executed. In addition, a plurality of patterns of upside and downside demand may be displayed side by side, and these may be referred to as a reference for obtaining appropriate demand.

Processing and control can be performed by software processing by CPU (Central Processing Unit) or GPU (Graphics Processing Unit), by ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Hardware).

Further, in the above-described embodiment, the configurations and the like shown in the drawings are not limited to these, and can be appropriately changed within the range in which the effects of the present invention are exhibited. In addition, it can be appropriately modified and implemented as long as it does not deviate from the scope of the object of the present invention.

In addition, each component of the present invention can be arbitrarily selected, and an invention having the selected configuration is also included in the present invention.
Further, the program for realizing the function described in the present embodiment is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read into the computer system and executed to process each part. May be done. The term "computer system" as used herein includes hardware such as an OS and peripheral devices.

Further, the "computer system" includes a homepage providing environment (or a display environment) if a WWW system is used.
Further, the "computer-readable recording medium" refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, or a CD-ROM, or a storage device such as a hard disk built in a computer system. Further, a "computer-readable recording medium" is a communication line for transmitting a program via a network such as the Internet or a communication line such as a telephone line, and dynamically holds the program for a short period of time. In that case, it also includes the one that holds the program for a certain period of time, such as the volatile memory inside the computer system that is the server or client. Further, the program may be for realizing a part of the above-mentioned functions, and may be further realized by combining the above-mentioned functions with a program already recorded in the computer system. At least some of the functions may be realized by hardware such as integrated circuits.

The present invention can be used for an ordering support device.

A Ordering support device 100 database DB
101 Past-present PSI data 102 Future PSI data 103 Inventory diagnosis result data 110 Processing unit 111 Demand forecast processing unit 112 Inventory diagnosis processing unit 113 Order date calculation processing unit 114 Order quantity calculation processing unit 115 Visualization processing unit 116 Display unit

Claims (7)

It has a demand forecast processing unit that forecasts demand from PSI (production / sales / inventory) data, and an inventory diagnosis processing unit that determines whether items are out of stock or excess inventory from the PSI data.
Future inventory data is calculated based on the PSI data and the demand forecast result by the demand forecast processing unit, and the future inventory status is displayed on the time axis of the graph defined by the time axis and the inventory amount in the future production / It has a visualization processing unit that displays a graph together with sales data.
The visualization processing unit
An ordering support device that displays the diagnosis result of the inventory status of future dates at a certain date interval on the time axis of the graph in a visually identifiable form as the background of the graph. Of the future PSI data from the PSI data and the demand forecast result, future inventory data is created, the inventory status on a future date is diagnosed, and in the result of the diagnosis, the date A on which the risk of shortage occurs is If there is, the ordering support device according to claim 1, wherein a date earlier than the lead time days from the date A is set as the ordering date. The future inventory data of the future PSI data obtained from the PSI data and the demand forecast result is obtained.
The ordering support device according to claim 1 or 2, wherein the order quantity is calculated by the following processes 1) and 2) based on the demand forecast result and the inventory data of the future date.
1) The inventory quantity received on the production date is used as the production volume, and the inventory volume is increased on the production date and the days after that while increasing the production volume, and the inventory diagnosis during the monitoring period affected by the change in the inventory volume before and after the production date. Perform processing.
2) The order quantity is the production quantity before the inventory diagnosis processing result of 1) becomes excessive. The ordering support according to claim 2, wherein when the ordering date of the item is manually changed, the diagnosis is performed in conjunction with the manual change and the manual adjustment is supported by displaying the diagnosis result. Device. The ordering support according to claim 3, wherein when the order quantity of an item is manually changed, the diagnosis is performed in conjunction with the manual change and the manual adjustment is supported by displaying the diagnosis result. Device. Furthermore, by conducting an inventory diagnosis in the upside or downside of the demand forecast,
The ordering support device according to any one of claims 1 to 5, characterized in that the order quantity is calculated so as not to be out of stock or excessive even when the demand is up or down. It has a demand forecast processing step for forecasting demand from PSI data (production / sales / inventory), and an inventory diagnosis processing step for determining whether an item is out of stock or excess inventory from the PSI data.
Future inventory data is calculated based on the PSI data and the demand forecast result by the demand forecast processing step, and the future inventory status is displayed as future PSI data on the time axis of the graph defined by the time axis and the inventory amount. Has a visualization process step to display a graph with
The visualization processing step is
An ordering support method characterized in that the diagnosis result of the inventory status of a future date is displayed in a visually identifiable form as the background of the graph at a certain date interval on the time axis of the graph.

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