Prediction device, prediction method, and ordering support device

Abstract

To predict the number of sales while taking into consideration an abnormal situation that affects the number of sales of a commodity.SOLUTION: A prediction device comprises: a first prediction unit for predicting a first predicted number of sales in a future prediction period on the basis of a past actual number of sales of a commodity; a reference value setting unit for setting a reference value on the basis of specific information indicating a specific situation related to a change in the number of sales in the prediction period; and a second prediction unit for determining, on the basis of the reference value, whether or not to set a second predicted number of sales, which is to be output as a predicted number of sales of the commodity, so as to be the same as the first predicted number of sales.SELECTED DRAWING: Figure 8

Description

[Technical field]

[0001] The present disclosure relates to a prediction device, a prediction method, and an order support device. [Background technology]

[0002] Patent Document 1 discloses an ordering support system that includes a demand forecasting model that has learned actual past demand for a product, and a demand forecasting unit that calculates a predicted demand for the product using the demand forecasting model. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6814302 Summary of the Invention [Problem to be solved by the invention]

[0004] However, because the demand forecasting model is based on learning from past long-term sales performance, if an unusual situation occurs that affects the demand for a product, the predicted demand calculated using the demand forecasting model may deviate significantly from the actual demand.

[0005] An object of the present disclosure is to provide a technology that can predict demand while taking into account cases where unusual circumstances that affect the demand for a product occur. [Means for solving the problem]

[0006] A prediction device according to one embodiment includes a first prediction unit that predicts a first predicted sales volume for a future prediction period based on actual sales volumes of a product in the past, a reference value setting unit that sets a reference value based on specific information indicating specific circumstances related to fluctuations in sales volumes during the prediction period, and a second prediction unit that determines, based on the reference value, whether a second predicted sales volume to be output as the predicted sales volume of the product should be the same as the first predicted sales volume.

[0007] A prediction method according to one embodiment predicts a first predicted sales volume for a future prediction period based on actual sales volumes of a product in the past, sets a reference value based on specific information indicating specific circumstances related to fluctuations in sales volumes during the prediction period, and determines, based on the reference value, whether a second predicted sales volume to be output as the predicted sales volume of the product should be the same as the first predicted sales volume.

[0008] An ordering support device according to one embodiment provides ordering support for the product using the prediction method described above.

[0009] Furthermore, these comprehensive or specific aspects may be realized by a system, an apparatus, a method, an integrated circuit, a computer program, or a recording medium, or may be realized by any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium. Effect of the Invention

[0010] According to the present disclosure, it is possible to predict demand while taking into account unusual circumstances that may affect the demand for a product. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing a configuration example of an order support system according to a first embodiment. [Figure 2A] FIG. 13 is a diagram showing an example of an order support screen according to the first embodiment. [Figure 2B] Graph showing an example of a case where the safety rate, sales volume, and inventory volume change as predicted in the first embodiment. [Diagram 3] FIG. 1 is a diagram showing an example of a product list screen according to the first embodiment; [Figure 4] FIG. 13 is a diagram showing an example of an ordering support screen for manual ordering according to the first embodiment; [Diagram 5] FIG. 13 is a diagram showing an example of an order support screen for automatic ordering according to the first embodiment. [Figure 6]FIG. 1 is a diagram showing an example of an order support screen for fixed orders according to the first embodiment. [Figure 7] A block diagram showing a configuration example of an order support system according to a second embodiment. [Figure 8] FIG. 13 is a block diagram showing a configuration example of a sales volume prediction unit according to the third embodiment. [Figure 9] FIG. 13 is a diagram showing an example of specific information according to the third embodiment. [Figure 10] FIG. 13 is a diagram showing an example of specific information including information on a determination condition according to the third embodiment; [Figure 11] FIG. 13 is a diagram showing a display example of an achievement forecast comparison screen according to the third embodiment; [Figure 12] FIG. 13 is a diagram showing a display example of a predicted value change screen according to the third embodiment. [Figure 13] FIG. 13 is a diagram showing an example of a review screen for specific information including information on a judgment condition according to the third embodiment; [Figure 14] FIG. 13 is a diagram for explaining a method for correcting a first predicted sales volume based on trend information according to the third embodiment. [Figure 15] FIG. 1 is a diagram showing a hardware configuration of a computer according to the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, the embodiments of the present disclosure will be described in detail with reference to the drawings as appropriate. However, more detailed description than necessary may be omitted. For example, detailed description of already well-known matters and duplicated description of substantially the same configuration may be omitted. This is to avoid the following description becoming unnecessarily redundant and to facilitate understanding by those skilled in the art. Note that the accompanying drawings and the following description are provided to enable those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter described in the claims.

[0013] (Embodiment 1) <System configuration> FIG. 1 is a block diagram showing an example of a configuration of an order support system 10 according to the first embodiment.

[0014] The order support system 10 is a device that supports a product orderer 1 in determining the order quantity of products having expiration dates so as to minimize opportunity loss and waste loss.

[0015] For example, the ordering support system 10 displays on the terminal 12 the recommended ordering quantity for each product that minimizes opportunity loss and waste loss. The purchaser 1 inputs the order quantity of each product to the ordering device 4, referring to the recommended ordering quantity for each product displayed on the terminal 12. The ordering device 4 transmits the order quantity of each product input by the purchaser 1 to the product delivery center 3. The product delivery center 3 delivers each product in the order quantity received from the ordering device 4, for example, to the store of the purchaser 1.

[0016] In this embodiment, the product is food and drink with an expiration date that is provided to customers at a store, and the orderer 1 is described as a person who orders the food and drink (e.g., a store manager). However, the product is not limited to this. For example, the product may be food and drink with an expiration date that is provided to patients hospitalized in a hospital, and the orderer 1 may be a person who consolidates orders for the food and drink. For example, the product may be medicine with an expiration date (e.g., eye drops) sold at a pharmacy, and the orderer 1 may be a person who orders the medicine.

[0017] The ordering support system 10 will be described in detail below.

[0018] The ordering support system 10 includes an actual sales quantity DB (DataBase) 41, a sales quantity prediction unit 31, a predicted sales quantity DB 42, an expiration date master 47, a safety factor calculation unit 33, an inventory quantity DB 43, an order quantity DB 44, a recommended order quantity calculation unit 32, and an input display control unit 34.

[0019] The actual sales DB 41 is a DB that holds the number of items actually sold in the past for each product (hereinafter referred to as the actual sales number). For example, the actual sales DB 41 holds, for each product, a product ID, a product name, a past date, and the actual sales number of the product on that date in association with each other.

[0020] The sales volume prediction unit 31 calculates the sales volume of a product at each future date and time (hereinafter referred to as the predicted sales volume) based on the past actual sales volume of the product stored in the actual sales volume DB 41. The sales volume prediction unit 31 calculates the predicted sales volume for each product. The sales volume prediction unit 31 registers the calculated predicted sales volume at each future date and time in the predicted sales volume DB 42.

[0021] The sales volume prediction unit 31 may calculate the predicted sales volume by the following (A1) and / or (A2).

[0022] (A1) The sales volume prediction unit 31 calculates predicted sales based on statistical analysis. For example, the sales volume prediction unit 31 statistically analyzes past actual sales of the product stored in the actual sales volume DB 41, and calculates predicted sales of the product at a future date and time based on the analysis results. For example, the sales volume prediction unit 31 statistically analyzes the actual sales volumes for multiple past weeks to identify sales trends for the week (e.g., sales trends for each day of the week), and calculates predicted sales for a future week based on the identified sales trends.

[0023] (A2) The sales forecasting unit 31 calculates the predicted sales number using AI (Artificial Intelligence). For example, the sales forecasting unit 31 calculates the predicted sales number for a target date and time based on the weather information at the target date and time in the future using a learning model (e.g., a deep learning model) that learns the relationship between the actual sales number and weather information using the past actual sales number of the product and past weather information as learning data. The weather information may include, for example, weather forecasts such as sunny, cloudy, rainy, and snowy, temperature forecasts, humidity forecasts, and atmospheric pressure forecasts. The sales forecasting unit 31 may acquire weather information for the target date and time from a predetermined weather information server 5. In addition, the sales forecasting unit 31 calculates the predicted sales number for a target date and time in the future using a learning model that learns the relationship between the actual sales number and calendar information using the past actual sales number of the product and past calendar information as learning data. The calendar information may include, for example, dates, days of the week, holidays, national events, anniversaries, and festivals. The sales volume prediction unit 31 may acquire calendar information for a target date and time from a predetermined calendar information server 6.

[0024] The sales volume prediction unit 31 may register in the predicted sales volume DB 42 both the predicted sales volume calculated by the above method (A1) and the predicted sales volume calculated by the above method (A2).

[0025] The predicted sales volume DB 42 holds the predicted sales volume of each product at each future date and time registered by the sales volume prediction unit 31. For example, the predicted sales volume DB 42 holds, for each product, a product ID, a product name, a future date and time, and a predicted sales volume of the product at that date and time in association with each other.

[0026] The expiration date master 47 holds the expiration date of each product. For example, the expiration date master 47 holds a product ID, a product name, and an expiration date for each product in association with each other.

[0027] The stock quantity DB 43 stores the actual stock quantity of each product in the store. For example, the stock quantity DB 43 stores a product ID, a product name, a date and time, and the stock quantity of the product at that date and time in association with each other.

[0028] The order quantity DB 44 holds the number of items already ordered for each product in the store. For example, the order quantity DB 44 holds a product ID, a product name, an order date and time, a stocking date and time, and the order quantity of the product ordered at the order date and time (i.e., the stocking quantity of the product at the stocking date and time) in association with each other.

[0029] The safety rate calculation unit 33 calculates the safety rate of the product based on the expiration date of the product stored in the expiration date master 47, the predicted sales volume of the product stored in the predicted sales volume DB 42, and the inventory volume of the product stored in the inventory volume DB 43. Next, the safety rate will be described assuming that the expiration date of the product is 48 hours, for example.

[0030] <Safety factor> If there is no inventory at the start of work on day X and the number of items received on day X is less than the actual number of items sold on day X, an opportunity loss will occur. Therefore, to prevent this opportunity loss, the inventory at the start of work on day X (i.e., inventory that has been carried over from the previous day or earlier and has not exceeded its expiration date) is used. This means that no opportunity loss will occur unless the actual number of items sold on day X exceeds the sum of the number of items in stock at the start of work on day X and the number of items received on day X. On the other hand, if the inventory at the start of work on day X is sold within its expiration date (i.e., within 48 hours of the items being received), then it will not be a waste loss.

[0031] Therefore, if the following conditions can be maintained: "all inventory at the start of business is sold within its expiration date" and "the number of products sold on any given day will never exceed the total number of products in stock at the start of business and the number of products received on that day," then both opportunity loss and waste loss can be reduced.

[0032] Therefore, the safety factor is defined as an index for the inventory quantity at the start of work. The safety factor is calculated by the following formula (1A), (1B), or (1C). Safety factor on day X = (actual inventory at the start of business on day X) / (actual sales on day X) (1A) Safety factor on day X = (actual inventory at the start of business on day X) / (projected sales on day X) (1B) Safety factor for day X = (projected inventory at the start of business on day X) / (projected sales on day X) (1C) The safety factor calculation unit 33 calculates the safety factor of each date of each product by the above formula (1A), formula (1B), or formula (1C). The safety factor calculation unit 33 may use the above formula (1A) when calculating the safety factor of a past date, the above formula (1B) when calculating the safety factor of the current date, and the above formula (1C) when calculating the safety factor of a future date.

[0033] This safety factor indicates the extent to which the demand for the predicted sales amount can be met with the inventory at the start of business on day X. Since inventory that has expired on day X is discarded, the inventory quantity at the start of business on day X used to define the safety factor on day X indicates the inventory quantity that will not expire on day X. Therefore, if the condition "all inventory at the start of business will be sold on day X" is met (in terms of the predicted value), the condition "all inventory at the start of business will be sold within the expiration date" can be met (in terms of the predicted value). In other words, the condition "all inventory at the start of business will be sold within the expiration date" can be met with certainty. In other words, if the safety factor is less than 100%, no waste loss will occur in the predicted value. Note that if the expiration date is expected to arrive after day X, there is a possibility that the inventory can be sold after day (X+1) and before the expiration date even if the safety factor exceeds 100%. However, since there is little profit in securing surplus inventory at the risk of incurring waste loss, it is basically desirable to manage inventory so that the safety factor is less than 100%. Also, in order to meet (in terms of forecast values) the condition that "on any given day, the number of sales for that day will not exceed the total of the number of items in stock at the start of the day and the number of items received for that day," it is best to avoid a safety margin of 0%. If the safety margin were 0%, the entire number of items sold for that day would need to be met solely by the number of items received for that day, but it is extremely difficult to maintain this state. The reasons for this are explained in detail below.

[0034] Generally, it takes a certain period of time (hereafter referred to as α days) from ordering to receiving the goods, so the quantity received on that day will be the quantity ordered α days prior. In other words, if you try to meet the demand on day X with a safety margin of 0%, you will have to perfectly predict the number of sales on day X as of α days prior. If you can operate with a safety margin of 0%, the stock will never expire, so if you can perfectly predict the number of sales, it is desirable to continue with a safety margin of 0%. However, since it is extremely difficult to make such perfect predictions, a realistic approach is to place orders so as to obtain a safety margin that is likely to suppress both opportunity loss and waste loss.

[0035] The recommended order quantity calculation unit 32 calculates the recommended order quantity for today's order based on the predicted sales quantity for the target period from today (order date) to the arrival date of the ordered product, obtained from the predicted sales quantity DB 42, and the safety factor for each date in the target period, obtained from the safety factor calculation unit 33. The calculation method for the recommended order quantity will be described later in detail (see Figs. 2A and 2B).

[0036] The input display control unit 34 causes an order support screen including the safety factor and the recommended order quantity to be displayed on the terminal 12. The input display control unit 34 may be connected to the terminal 12 via a predetermined communication network (e.g., a wired LAN (Local Area Network) or a wireless LAN, etc.).

[0037] The terminal 12 displays an order support screen including a safety factor and a recommended order quantity based on an instruction from the input display control unit 34. Examples of the terminal 12 include a PC, a smartphone, a tablet terminal, and the like. The purchaser 1 determines the order quantity of the product with reference to the recommended order quantity displayed on the terminal 12 and inputs the determined order quantity to the ordering device 4. This allows the purchaser 1 to order the product in an order quantity that minimizes opportunity loss and waste loss. Since the purchaser 1 can determine the order quantity if he / she can at least confirm the recommended order quantity, the terminal 12 may omit displaying information other than the recommended order quantity (e.g., safety factor, etc.). The terminal 12 may be connected to the ordering device 4 via a communication network, and the ordering device 4 may be configured to automatically order the recommended order quantity of the product based on an instruction from the terminal 12. In this case, the purchaser 1 does not need to perform the task of determining the order quantity, so the terminal 12 may omit displaying the recommended order quantity.

[0038] The terminal 12 may appropriately reflect the actual sales number of the product, which changes over time, in the actual sales number DB 41, and may appropriately reflect the inventory number of the product, which changes over time, in the inventory number DB 43. The terminal 12 may also reflect the order number of the product for which an order has been completed in the order number DB 44. Alternatively, the actual sales number and inventory number may be automatically reflected in the actual sales number DB and inventory number DB 43 in response to an order from a customer.

[0039] At least one of the actual sales number DB 41, the stock number DB 43, and the order number DB 44 may be arranged in a so-called cloud, or may be arranged locally (for example, in a store). Alternatively, a DB that is a copy or synchronized DB of at least one of the actual sales number DB 41, the stock number DB 43, and the order number DB 44 arranged in the cloud may be arranged locally. In this case, the terminal 12 may access the locally arranged DB instead of accessing the DB arranged in the cloud.

[0040] In addition, a robot that automatically picks up products from inventory in response to customer orders may be installed in a store that sells products. This reduces mistakes made by store staff because the staff only need to provide the products picked up by the robot to the customer.

[0041] <First example of ordering support screen> 2A is a diagram showing an example of the order support screen 100 according to the embodiment 1. The order support screen 100 may be generated by the input display control unit 34 and displayed on the terminal 12.

[0042] Here, it is assumed that the work start time is 8:00, the product expiration date is 48 hours, and the ordered product will be received at 15:00 two days after the order date. However, these work start times, expiration dates, and receiving times are merely examples. In addition, the order time and receiving time are not limited to once a day, and may be two or more times a day.

[0043] The order support screen 100 includes a product list area 101 , a graph area 102 , and an information area 103 .

[0044] A list of products is displayed in the product list area 101. The orderer 1 selects one product from the product list area 101.

[0045] In the graph area 102, a line graph showing the change in sales volume over time for a product selected from the product list area 101 (hereinafter referred to as sales volume graph 104) and a line graph showing the change in stock volume over time (hereinafter referred to as stock quantity graph 105) are displayed. Note that the line graph is only an example, and other types of graphs may be used. The sales volume graph 104 and the stock quantity graph 105 may be calculated by the recommended order quantity calculation unit 32 and drawn in the graph area 102.

[0046] The safety factor and the inventory quantity for each date are displayed in the information area 103. The input display control unit 34 displays the safety factor for each date calculated by the safety factor calculation unit 33 in the information area 103. The input display control unit 34 displays the inventory quantity for each date in the information area 103 based on the order quantity for each date acquired from the order quantity DB 44.

[0047] Next, the safety factor and inventory quantity will be described in detail with reference to FIG. 2A.

[0048] On February 9th (yesterday), the safety margin was 12% and the number of plates in stock was 46. The safety margin on February 9th was calculated by (actual number of plates in stock at the start of work on February 9th) / (actual sales on February 9th). The number of plates in stock on February 9th corresponds to the number of plates ordered on February 7th.

[0049] As of 8:00 on February 10th (today), the safety margin is 56% and the number of plates in stock is 40. The safety margin for February 10th is calculated by (actual number of plates in stock at the start of work on February 10th) / (forecasted sales for February 10th). The actual number of plates in stock at the start of work on February 10th matches the actual number of plates in stock at the end of work on February 9th. Additionally, the number of plates in stock on February 10th corresponds to the number of plates ordered on February 8th.

[0050] At 8:00 on February 11th (one day later), the safety margin is 38% and the number of plates in stock is 40. The safety margin for February 11th is calculated by (projected inventory at the start of work on February 11th) / (projected sales on February 11th). The projected inventory at the start of work on February 11th is calculated by (actual inventory at the start of work on February 10th) + (inventory on February 10th) - (projected sales on February 10th). Note that the projected inventory at the start of work on February 11th is the same as the projected inventory at the end of work on February 10th. Additionally, the number of plates in stock on February 11th corresponds to the number of orders placed on February 9th (yesterday).

[0051] At 8:00 on February 12th (two days later), the safety margin is 13% and the number of plates in stock is 60. The safety margin for February 12th is calculated by (projected inventory at the start of work on February 12th) / (projected sales on February 12th). The projected inventory at the start of work on February 12th is calculated by (projected inventory at the start of work on February 11th) + (inventory on February 11th) - (projected sales on February 11th). Note that the projected inventory at the start of work on February 12th is the same as the projected inventory at the end of work on February 11th. Additionally, the number of plates in stock on February 12th corresponds to the number of orders scheduled to be placed on February 10th (today).

[0052] Here, the recommended order quantity calculation unit 32 calculates the recommended order quantity for February 10 (today) (i.e., equivalent to the number of items to be stocked on February 12) based on the safety rate of 13% for February 12 and the predicted sales volume for February 12. For example, the recommended order quantity calculation unit 32 calculates the recommended order quantity for stocking on February 12 so that it is equal to or greater than the predicted sales volume for February 12 minus the number that can be covered by the safety rate of 13%. The input display control unit 34 displays the calculated recommended order quantity on the terminal 12. The purchaser 1 may input the number of orders to the ordering device 4 with reference to the recommended order quantity displayed on the terminal 12. This allows the purchaser 1 to easily place an order that minimizes the sum of opportunity loss and waste loss.

[0053] As described above, in order to prevent an opportunity loss on February 12th, the recommended order quantity must be equal to or greater than the predicted sales quantity on February 12th minus the quantity that can be covered with a safety factor of 13% (hereinafter referred to as the shortage quantity for that day). However, if an order is placed exactly for this shortage quantity for that day, there is a risk of an opportunity loss occurring on February 12th if the actual sales quantity on February 12th exceeds the predicted sales quantity. However, the recommended order quantity calculation unit 32 calculates the recommended order quantity to be stored on February 12th so as to satisfy a predetermined safety factor on the following day, February 13th, and a certain margin is added to the recommended order quantity exactly for this shortage quantity for that day. Therefore, according to this embodiment, even if the actual sales quantity on February 12th slightly exceeds the predicted sales quantity, an opportunity loss does not occur due to this margin. On the other hand, even if the actual sales quantity is slightly lower than the predicted sales quantity and the inventory quantity at the start of business on February 13th is higher than expected, if the inventory quantity is equal to or less than the actual sales quantity on February 13th, it will be consumed within the expiration date (48 hours) on February 13th, so no waste loss will occur.

[0054] Furthermore, when the purchaser 1 places more importance on reducing waste loss than on opportunity loss, the recommended order quantity may be a number lower than the predicted sales quantity minus the number that can be covered by the safety margin.

[0055] In addition, by checking the sales quantity graph 104 and the inventory quantity graph 105 shown in Figure 2A, the purchaser 1 can visually confirm the relationship between the change in the inventory quantity at the start of business corresponding to the safety factor on each date and the inventory quantity after storage.

[0056] FIG. 2B is a graph showing an example of a case in which the safety rate, sales volume, and inventory volume change as predicted according to the first embodiment.

[0057] As shown in FIG. 2B, the recommended order quantity calculation unit 32 may calculate the recommended order quantity so that the daily safety rate is a predetermined value (for example, 30%).

[0058] For example, if the expiration date of a product is 48 hours, the product is stored on March 7th, and the remaining stock at the end of work on March 7th must be consumed on the following day, March 8th. The stock at the end of work on March 7th becomes the stock at the start of work on the following day, March 8th, which corresponds to a safety factor of 30% on March 8th, and is sold preferentially on March 8th. Therefore, no waste loss occurs. Also, a product that is stored at 3:00 p.m. on March 8th is partially consumed on March 8th, and becomes partial stock at the end of work on March 8th without generating opportunity loss. The stock at the end of work on March 8th becomes the stock at the start of work on the following day, March 9th, which corresponds to a safety factor of 30% on March 9th, and is sold preferentially on March 9th. This is repeated every day, so as shown in Figure 2B, if an order is placed to satisfy an appropriate safety factor (e.g., 30%) and the sales and inventory numbers change as predicted, no waste loss or opportunity loss occurs.

[0059] In the above, an example was explained in which the expiration date was 48 hours, but the same applies when the expiration date is longer than 48 hours (for example, 72 hours, 96 hours, etc.). In other words, by setting an appropriate safety margin so that the stock at the start of each day is consumed within the subsequent expiration date, and placing orders to satisfy that safety margin, if sales and stock levels change as predicted, neither waste nor opportunity loss will occur.

[0060] <Second example of ordering support screen> 3 is a diagram showing an example of the product list screen 120 according to the embodiment 1. The product list screen 120 may be generated by the input display control unit 34 and displayed on the terminal 12.

[0061] The product list screen 120 has a calendar area 121 and a graph area 122 .

[0062] A monthly calendar is displayed in the calendar area 121. The purchaser 1 selects a date for which a graph is to be displayed in the graph area 122 from the calendar displayed in the calendar area 121.

[0063] Graph area 122 displays a bar graph 123 relating to the number of orders for each product on the date selected in calendar area 121. The size of the frame of bar graph 123 indicates the number of orders on the selected date. In other words, the frame of bar graph 123 becomes larger for products with a larger number of orders.

[0064] The details of the bar graph 123 indicate the percentage of inventory, the percentage of opportunity loss, and the percentage of waste loss among the number of orders. In the bar graph 123, each percentage may be shown in a different color or pattern. For example, the percentage of inventory may be shown in green, the percentage of opportunity loss in red, and the percentage of waste loss in purple. If it is predicted that none of the number of orders will result in opportunity loss or waste loss (i.e., all will be sold), the percentage of inventory in the bar graph 123 is 100%. If it is predicted that a1% of the number of orders will result in opportunity loss, the percentage of opportunity loss in the bar graph 123 is a1%. If it is predicted that a2% of the number of orders will result in waste loss, the percentage of waste loss in the bar graph 123 is a2%. Note that the bar graph is just an example, and other types of graphs (e.g., pie charts) may also be used.

[0065] 4 is a diagram showing an example of an ordering support screen 140 for manual ordering according to the embodiment 1. The ordering support screen 140 may be generated by the input display control unit 34 and displayed on the terminal 12.

[0066] The ordering support screen 140 for manual ordering includes a graph area 141, a manual ordering tab 142, an automatic ordering tab 143, a fixed ordering tab 144, a recommended order quantity area 151, an order quantity input area 152, a stock quantity at the end of business hours area 153, a waste loss area 154, an opportunity loss area 155, and a total loss area 156. The ordering support screen 140 for manual ordering shown in FIG. 4 is displayed when the manual ordering tab 142 is selected.

[0067] In the graph area 141, a line graph showing the change in the predicted sales quantity over time for the target product on each date (hereinafter referred to as a predicted sales quantity graph 145) and a bar graph showing the change in the predicted stock quantity over time (hereinafter referred to as a predicted stock quantity graph 146) are displayed. In addition, in the graph area 141, a line graph showing the change in the waste loss quantity over time for the target product on each date (hereinafter referred to as a waste loss graph 147) and a bar graph showing the change in the opportunity loss quantity over time (hereinafter referred to as an opportunity loss graph 148) are displayed. Note that the bar graph and the line graph are only examples, and other types of graphs may be used.

[0068] In the recommended order quantity area 151, the recommended order quantity for the target date and time (April 4 in FIG. 4) calculated by the recommended order quantity calculation unit 32 is displayed.

[0069] In the order quantity input area 152, the purchaser 1 inputs the order quantity.

[0070] The stock quantity at closing time area 153 displays the predicted stock quantity at the closing time of the day before the target date and time (that is, the opening time of the target date and time).

[0071] The waste loss area 154 displays the number and amount of waste loss predicted when the order quantity input in the order quantity input area 152 is placed.

[0072] The opportunity loss area 155 displays the number and amount of the opportunity loss predicted when the order quantity input in the order quantity input area 152 is placed.

[0073] The total loss area 156 displays the total of the predicted waste loss amount and the opportunity loss amount.

[0074] When the purchaser 1 inputs an order quantity in the order quantity input area 152 while referring to the recommended order quantity displayed in the recommended order quantity area 151, the predicted stock quantity graph 146, waste loss graph 147, and opportunity loss graph 148 in the graph area 141 change according to the input order quantity. This allows the purchaser 1 to visually check the changes in waste loss and opportunity loss and determine the order quantity that will reduce waste loss and opportunity loss.

[0075] The manual ordering shown in FIG. 4 is suitable for ordering when a group reservation has already been made, or for ordering products where the accuracy of predicted sales figures is not very high.

[0076] Fig. 5 is a diagram showing an example of an ordering support screen 160 for automatic ordering according to the embodiment 1. The ordering support screen 160 for automatic ordering shown in Fig. 5 is displayed when the automatic ordering tab 143 is selected. In addition, the ordering support screen 160 may be generated by the input display control unit 34 and displayed on the terminal 12.

[0077] The order support screen 160 for automatic ordering includes a graph area 141, a manual ordering tab 142, an automatic ordering tab 143, a fixed ordering tab 144, a recommended order quantity area 151, an order quantity area 161, inventory quantity at the end of business hours area 153, a waste loss area 154, an opportunity loss area 155, and a total loss area 156.

[0078] In the graph area 141, a graph similar to that shown in FIG.

[0079] In the recommended order quantity area 151, the recommended order quantity for the target date and time (April 4 in FIG. 4) calculated by the recommended order quantity calculation unit 32 is displayed.

[0080] In the order quantity area 161, the order quantity corresponding to the recommended order quantity displayed in the recommended order quantity area 151 is automatically displayed.

[0081] The stock quantity at closing time area 153 displays the predicted stock quantity at the closing time of the day before the target date and time (that is, the opening time of the target date and time).

[0082] The waste loss area 154 displays the number and amount of waste loss predicted when the order quantity displayed in the order quantity area 161 is placed.

[0083] The opportunity loss area 155 displays the number and amount of the expected opportunity loss when the order quantity displayed in the order quantity area 161 is placed.

[0084] The total loss area 156 displays the total of the predicted waste loss amount and the opportunity loss amount.

[0085] The recommended order quantity calculation unit 32 displays in the graph area 141 a predicted stock quantity graph 146, a waste loss graph 147, and an opportunity loss graph 148 corresponding to the order quantity displayed in the order quantity area 161. This allows the purchaser 1 to visually confirm the changes in waste loss and opportunity loss when an order is automatically placed based on the recommended order quantity.

[0086] The automatic ordering shown in FIG. 5 is suitable for ordering products with a relatively short expiration date and / or products that are sold in large quantities per day.

[0087] Fig. 6 is a diagram showing an example of an ordering support screen 180 for fixed ordering according to the embodiment 1. The ordering support screen 180 for fixed ordering shown in Fig. 6 is displayed when the fixed ordering tab 144 is selected. In addition, the ordering support screen 180 may be generated by the input display control unit 34 and displayed on the terminal 12.

[0088] The order support screen 180 for fixed orders includes a graph area 141, a manual order tab 142, an automatic order tab 143, a fixed order tab 144, a recommended order quantity area 151, a lower limit input area 181, a fixed order input area 182, inventory quantity at the end of business hours area 153, waste loss area 154, opportunity loss area 155, and total loss area 156.

[0089] In the graph area 141, a graph similar to that shown in FIG.

[0090] In the recommended order quantity area 151, the recommended order quantity for the target date and time (April 4 in FIG. 4) calculated by the recommended order quantity calculation unit 32 is displayed.

[0091] In the lower limit input area 181, the purchaser 1 inputs the lower limit of the stock quantity.

[0092] In the fixed order input area 182, the purchaser 1 inputs the order quantity to be automatically ordered when the stock quantity falls below the lower limit value input in the lower limit input area 181.

[0093] The stock quantity at closing time area 153 displays the predicted stock quantity at the closing time of the day before the target date and time (that is, the opening time of the target date and time).

[0094] The waste loss area 154 displays the number and amount of waste loss predicted when an order is placed for the number of orders entered in the fixed order input area 182.

[0095] The opportunity loss area 155 displays the number and amount of the predicted opportunity loss when the order quantity entered in the fixed order entry area 182 is placed.

[0096] The total loss area 156 displays the total of the predicted waste loss amount and the opportunity loss amount.

[0097] When the purchaser 1 inputs an order quantity in the fixed order input area 182 while referring to the recommended order quantity displayed in the recommended order quantity area 151, the predicted stock quantity graph 146, waste loss graph 147, and opportunity loss graph 148 in the graph area 141 change according to the input order quantity. This allows the purchaser 1 to decide on a fixed order quantity while visually checking the changes in waste loss and opportunity loss.

[0098] The fixed order shown in FIG. 6 is suitable for ordering products with a relatively long expiration date and / or products that are not sold in large numbers per day.

[0099] <Using short-term and long-term sales figures> Next, a method for calculating the forecasted sales volume using the short-term and long-term actual sales volumes will be described.

[0100] The sales result DB 41 may hold short-term sales results and long-term sales results. The short-term sales results may be sales results in units of, for example, one week or one month. The long-term sales performance may be, for example, the sales performance over a one-year period or over a two-year period.

[0101] The sales volume prediction unit 31 calculates a short-term predicted sales volume based on the short-term actual sales volume, and registers it in the predicted sales volume DB 42. The sales volume prediction unit 31 also calculates a long-term predicted sales volume based on the long-term actual sales volume, and registers it in the predicted sales volume DB 42. The short-term predicted sales volume may be read as the first predicted sales volume, and the long-term predicted sales volume may be read as the second predicted sales volume.

[0102] The sales volume prediction unit 31 may calculate short-term predicted sales volumes based on statistical analysis as described above in (A1), and calculate long-term predicted sales volumes using AI as described above in (A2).

[0103] The safety factor calculation unit 33 calculates a short-term safety factor by using the short-term predicted sales volume registered in the predicted sales volume DB 42. The safety factor calculation unit 33 also calculates a long-term safety factor by using the long-term predicted sales volume registered in the predicted sales volume DB 42. The short-term safety factor may be read as a first safety factor, and the long-term safety factor may be read as a second safety factor.

[0104] The recommended order quantity calculation unit 32 calculates the short-term recommended order quantity using the short-term safety factor, and calculates the long-term recommended order quantity using the long-term safety factor. Note that the short-term recommended order quantity may be read as the first recommended order quantity, and the long-term recommended order quantity may be read as the second recommended order quantity. The recommended order quantity calculation unit 32 may calculate the recommended order quantity using the following method (B1) or (B2).

[0105] (B1) The recommended order quantity calculation unit 32 compares the short-term recommended order quantity with the long-term recommended order quantity, and determines the recommended order quantity based on the comparison result. For example, when the difference between the short-term recommended order quantity and the long-term recommended order quantity is less than a predetermined threshold, the recommended order quantity calculation unit 32 selects either the short-term recommended order quantity or the long-term recommended order quantity as the recommended order quantity. For example, when the difference between the short-term recommended order quantity and the long-term recommended order quantity is equal to or greater than a predetermined threshold, the recommended order quantity calculation unit 32 calculates the recommended order quantity by varying the weighting of the short-term recommended order quantity and the long-term recommended order quantity, as shown in (B2) below.

[0106] (B2) The recommended order quantity calculation unit 32 determines the recommended order quantity by the following formula. Recommended order quantity = (β1 × short-term recommended order quantity) + (β2 × long-term recommended order quantity) The short-term coefficient β1 and the long-term coefficient β2 may be determined, for example, by the following (B2-1) or (B2-2).

[0107] (B2-1) For example, in normal mode, β1 = 0.5, β2 = 0.5 (i.e., the short-term coefficient and the long-term coefficient are equal), if the most recent accuracy is quite high, β1 = 0.8, β2 = 0.2 (i.e., the short-term coefficient is larger than the long-term coefficient), and if the weather this year is significantly different from last year's weather, β1 = 1.0, β2 = 0 (i.e., the long-term coefficient is 0). It may be determined that the smaller the difference between the short-term recommended order quantity and the long-term recommended order quantity, the higher the most recent accuracy.

[0108] (B2-2) Determine the short-term coefficient β1 and the long-term coefficient β2 based on the correlation between the sales volume at a certain time in the past (e.g., April 2021) and the sales volume at the same time this year (e.g., April 2022). For example, if the correlation coefficient r is equal to or greater than a certain threshold (e.g., 0.5), set β1 = 0.5 and β2 = 0.5 as the normal mode (i.e., make the short-term coefficient equal to the long-term coefficient), and if the most recent accuracy is quite high, set β1 = 0.8 and β2 = 0.2 (i.e., make the short-term coefficient larger than the long-term coefficient), and if the correlation coefficient r is less than a certain threshold (e.g., 0.5), set β1 = 0.9 and β2 = 0.1 (i.e., make the short-term coefficient larger than the long-term coefficient) so as not to be dragged down by past data.

[0109] (Embodiment 2) In the second embodiment, the same components as those described in the first embodiment are given the same reference numerals, and the description thereof may be omitted.

[0110] FIG. 7 is a block diagram showing an example of a configuration of an order support system 10 according to the second embodiment.

[0111] The order support system 10 according to the second embodiment includes a store information DB 48 in addition to the components included in the order support system 10 shown in FIG.

[0112] The store information DB 48 is a DB that holds store information, which is information about each store that sells products. Examples of information included in the store information include the location conditions of the store, the surrounding environment of the store, the distance from the product delivery center 3 to the store, weather information for the store location, events held around the store, etc.

[0113] The sales volume prediction unit 31 calculates the predicted sales volume for each store, taking into consideration the store information in the store information DB 48. For example, if the store information includes information that an event that will attract many people will be held near a certain store on a prediction target date and time, the sales volume prediction unit 31 calculates the predicted sales volume for the target date and time to be higher than the predicted sales volume under normal circumstances.

[0114] <If there is insufficient data in the sales performance DB> The actual sales volume of a new product immediately after its release is not stored in the actual sales volume DB 41. In such a case, the sales volume prediction unit 31 may generate a tentative actual sales volume of the new product using the actual sales volumes of existing products similar to the new product, and predict the sales volume of the new product using the tentative actual sales volume. This allows the sales volume prediction unit 31 to predict the sales volume of the new product as well.

[0115] Furthermore, the actual sales figures for the new store are not stored in the actual sales figure DB 41. In such a case, the sales figure prediction unit 31 may generate tentative actual sales figures for the new store using actual sales figures for existing stores of a similar size to the new store or existing stores with similar location conditions to the new store, and use the tentative actual sales figures to predict the sales figures for each product in the new store. This allows the sales figure prediction unit 31 to predict the sales figures for each product in the new store.

[0116] (Other variations) In the above-described embodiments, the safety factor is defined by the following formulas (1A) to (1C). Safety factor on day X = (actual inventory at the start of business on day X) / (actual sales on day X) (1A) Safety factor on day X = (actual inventory at the start of business on day X) / (projected sales on day X) (1B) Safety factor for day X = (projected inventory at the start of business on day X) / (projected sales on day X) (1C) However, these formulas (1A) to (1C) are simplified formulas. More specifically, the safety rate is a value that indicates the ratio of the amount of inventory that is secured in advance before the product is received to the sales volume that should be covered by the product corresponding to the current order. Therefore, the detailed definition of the safety rate is given by the following formula (2). Safety factor = (projected inventory quantity before the current order is received) / (projected sales quantity from after the current order is received until before the next order is received) (2) This formula (2) can be simplified based on several assumptions to give the above formulas (1A) to (1C). For example, in formula (2), if we assume that the product ordered on day (X-α) is scheduled to arrive on day X, and the product ordered after day (X-α) arrives on day (X+1), the numerator of formula (2) is the "inventory quantity at the start of work on day X," and the denominator is the predicted sales quantity from day X to day (X+1) before arrival = predicted sales quantity on day X. As a result, the safety factor on day X is as shown in formula (1C).

[0117] (Embodiment 3) In the third embodiment, a modified example of the sales number prediction unit 31 shown in the first or second embodiment will be described. The sales number prediction unit 31 may be a function or device included in the ordering support system 10. Alternatively, the sales number prediction unit 31 may be a prediction device or prediction system independent of the ordering support system 10, and may be called by the ordering support system 10 for use.

[0118] <Sales forecast section configuration> FIG. 8 is a block diagram showing an example of the configuration of the sales volume prediction unit 31 according to the third embodiment.

[0119] The sales volume prediction unit 31 includes a prediction model 200 , a first prediction unit 201 , a unique information management unit 202 , a reference value setting unit 203 , a reliability determination unit 204 , a second prediction unit 205 , and a unique information DB 206 .

[0120] The prediction model 200 is a model for predicting the sales volume (demand) of a product in a certain future period (hereinafter referred to as a prediction period). The prediction model 200 may be a model trained based on the actual sales volume for each day of the week and / or date and time in the past stored in the actual sales volume DB 41. The prediction model 200 is, for example, a moving average model, an exponential smoothing model, an ARIMA model, a multiple regression model, or a deep learning model. Note that, although an example using a model realized by machine learning is described here, the prediction of the sales volume may be realized by a method that does not use a machine learning model. For example, the sales volume may be predicted by inputting the actual sales volume into a predetermined function. As a simple example, the average value of the actual sales volume may be used as a predicted value of the sales volume, but a technique using a more complicated function is also known.

[0121] The first prediction unit 201 predicts the sales volume of the product in a prediction period using the prediction model 200. Hereinafter, the sales volume predicted by the first prediction unit 201 will be referred to as the first predicted sales volume. For example, the first prediction unit 201 inputs the prediction period to the prediction model 200 and obtains the first predicted sales volume from the prediction model 200.

[0122] The specific information DB 206 manages the specific information 210 (see FIG. 9) as a DB.

[0123] 9 is a diagram showing an example of the unique information 210 according to embodiment 3. Next, the unique information 210 will be described with reference to FIG.

[0124] The specific information 210 is information indicating a specific situation regarding a fluctuation in sales volume that is difficult to predict using the prediction model 200. The specific information 210 has a period 211, a classification 212, a cause 213, and a response method 214 as items.

[0125] The period 211 indicates a period during which a unique situation regarding fluctuations in sales volume may occur. Note that the period 211 may be one day, a number of consecutive days, or a time period.

[0126] The category 212 indicates a category of a specific situation. Examples of the category 212 include "an event expected to attract more visitors," "an event expected to attract less visitors," "a holiday or public holiday that differs from the calendar," and "a change in customer demographics."

[0127] The cause 213 indicates the cause of the occurrence of the unique situation indicated by the category 212 in the period 211. Examples of the cause 213 of the category 212 "event expected to attract more customers" include "discount event at a store" and "event held at a nearby facility". Examples of the cause 213 of the category 212 "event expected to attract less customers" include "construction at the nearest station", "construction on surrounding roads", and "construction on a building in which a store is located". Examples of the cause 213 of the category 212 "holiday or public holiday different from the calendar" include "newly established public holiday", "abolition of an existing public holiday", and "public holiday specific to the region". Examples of the cause 213 of the category 212 "change in customer demographics" include "summer vacation at a nearby school", "long vacation in a certain foreign country", and "holiday at a nearby large company".

[0128] The response method 214 indicates a method of responding to the first predicted sales volume according to the cause 213. The response method 214 may be set differently according to the reliability of the first predicted sales volume, which will be described later. The response method 214 may define the processing of the second prediction unit 205, which will be described later. The response method 214 will be described in detail later.

[0129] Returning to the explanation of Figure 8.

[0130] The specific information management unit 202 manages the specific information 210. The specific information management unit 202 may generate the specific information 210 and register it in the specific information DB 206. The specific information management unit 202 may change or delete the specific information 210 registered in the specific information DB 206. For example, the specific information management unit 202 may display candidates for the classification 212, the cause 213, the response method 214, etc. that can be set in the above-mentioned specific information 210 through the input display control unit 34, and the user may easily generate or change the specific information 210 by selecting the displayed candidate.

[0131] The reference value setting unit 203 sets a reference value for the prediction period based on the actual sales volume DB 41 and the specific information DB 206. For example, the reference value setting unit 203 acquires the specific information 210 corresponding to the prediction period from the specific information DB 206, and sets the reference value based on the cause 213 of the specific information 210. For example, when the cause 213 of the specific information 210 is a "discount event", the reference value setting unit 203 sets the average value of the respective actual sales volumes when a similar "discount event" was conducted in the past as the reference value. For example, when the cause 213 of the specific information 210 is a "newly set holiday", the reference value setting unit 203 sets the average value of the respective actual sales volumes during past holidays as the reference value. For example, when the cause 213 of the specific information 210 is the "summer vacation of a neighboring school", the reference value setting unit 203 sets the actual sales volume during the past summer vacation period as the reference value. In this case, the reference value may be the transition for each date during the past summer vacation period. Thereby, the reference value setting unit 203 can calculate a predicted sales volume (that is, a reference value) that is not likely to deviate significantly during the prediction period by a method different from the prediction model 200. That is, the reference value does not necessarily have to be a single value, and may be a set of multiple values.

[0132] The reliability determination unit 204 determines the reliability of the first predicted sales volume based on a comparison between the reference value set by the reference value setting unit 203 and the first predicted sales volume predicted by the first prediction unit 201.

[0133] For example, when the difference value D between the reference value and the first predicted sales volume is less than a predetermined threshold Th1 (D < Th1), the reliability determination unit 204 sets the reliability of the first predicted sales volume as the highest "1". When the difference value D is equal to or greater than the threshold Th1 and less than the threshold Th2 (Th1 ≤ D < Th2), the reliability determination unit 204 sets the reliability of the first predicted sales volume as the second highest "2". When the difference value D is equal to or greater than the threshold Th2 (Th2 ≤ D), the reliability is set as the lowest "3". That is, the reliability determination unit 204 may determine that the smaller the difference value D, the higher the reliability of the first predicted sales volume.

[0134] Alternatively, the reliability determination unit 204 may set the reliability of the first predicted sales volume to the highest level of "1" when the correlation value R between the reference value and the first predicted sales volume in the prediction period is equal to or greater than a predetermined threshold value Th3 (R≧Th3), set the reliability of the first predicted sales volume to the second highest level of "2" when the correlation value R is less than threshold value Th3 and equal to or greater than threshold value Th4 (Th3>R≧Th4), and set the reliability to the lowest level of "3" when the correlation value R is less than threshold value Th4 (Th4>R). In other words, the reliability determination unit 204 may determine that the reliability of the first predicted sales volume is higher when the correlation value R is larger.

[0135] The ranking of the reliability is not limited to three levels as in this embodiment, but may be two levels or four or more levels.

[0136] The second prediction unit 205 determines the second predicted sales number based on the unique information 210 used to determine the reference value, the reliability determined by the reliability determination unit 204, and the first predicted sales number predicted by the first prediction unit 201. Here, the second predicted sales number is a value output as the predicted sales number of the product, and as described below, it may be the same as the first predicted sales number or may be a different value.

[0137] For example, when the first row of the specific information 210 shown in FIG. 9 is used to set the reference value, the second prediction unit 205 performs the following process based on the response method 214 included in the specific information 210. If the reliability is "1", the second prediction unit 205 sets the first predicted sales number as the second predicted sales number without any change. When the reliability is "2", the second prediction unit 205 corrects the first predicted sales number so as to approach the reference value set by the reference value setting unit 203, and sets the corrected number as the second predicted sales number. For example, when the first predicted sales number is less than the reference value, the second prediction unit 205 adds half of the difference α between the first predicted sales number and the reference value to the first predicted sales number, and sets the second predicted sales number as the second predicted sales number. For example, when the first predicted sales number is greater than the reference value, the second prediction unit 205 subtracts half of the difference α between the first predicted sales number and the reference value from the first predicted sales number, and sets the second predicted sales number as the second predicted sales number. This makes it possible to calculate the second predicted sales number according to a specific situation while making use of the prediction by the prediction model 200 (first predicted sales number). When the reliability is "3", the second prediction unit 205 sets the reference value set by the reference value setting unit 203 as the second predicted sales number. When the reliability is "3", there is a possibility that the first predicted sales number is fundamentally wrong, and therefore there is a high risk that the prediction will be far off during the prediction period, whether the first predicted sales number is used as is as the second predicted sales number or the first predicted sales number is revised and used as the second predicted sales number.

[0138] For example, when the fourth row of the specific information 210 shown in FIG. 9 is used to set the reference value, the second prediction unit 205 performs the following process based on the response method 214 included in the specific information 210. If the reliability is "1", the second prediction unit 205 sets the first predicted sales number as the second predicted sales number without any change. If the reliability is "2", the second prediction unit 205 modifies the first predicted sales number for a child-oriented product so that it approaches the reference value set by the reference value setting unit 203, and sets the result as the second predicted sales number. This is because the cause 213 is "school summer vacation", and it is estimated that the reference value calculated based on the actual sales number during past summer vacations is more reliable for the predicted sales number of children's products. This makes it possible to calculate the second predicted sales number suited to the specific situation while making use of the prediction by the prediction model 200 (first predicted sales number). When the reliability is "3", the second prediction unit requests the user to input the predicted sales number, and the sales number input by the user is set as the second predicted sales number. When the reliability is "3", there is a possibility that the first predicted sales number is fundamentally wrong, and therefore, whether the first predicted sales number is used as is as the second predicted sales number or the first predicted sales number is revised and used as the second predicted sales number, it is estimated that there is a high risk of it being significantly off during the prediction period.

[0139] The second prediction unit 205 registers the determined second predicted sales volume in the predicted sales volume DB 42 as the "predicted sales volume" described in the first or second embodiment. This makes it possible to prevent the predicted sales volume from being significantly off in a period in which a unique situation occurs related to fluctuations in sales volume that is difficult to predict using the prediction model 200. In other words, according to this embodiment, the sales volume can be stably predicted even if various changes in circumstances occur.

[0140] In the above description, the reference value is calculated by the reference value setting unit 203 using the unique information 210, but the reference value itself may be recorded as the unique information 210. The reference value does not have to be a value comparable to the first predicted sales number. For example, the period during which a specific event is held may be recorded as the reference value, and if today's date is included in the reference value, the first predicted sales number may be revised. In this case, information on the judgment condition indicating how the reference value is to be used in the judgment may be recorded as the unique information. Specific examples will be described below with reference to the drawings.

[0141] FIG. 10 is a diagram showing an example of special information including information on a determination condition according to the third embodiment.

[0142] As shown in FIG. 10, the unique information 210 including the information on the judgment condition has the following items: number, comparison target, reference value, judgment condition, response method, and remarks.

[0143] For example, the row numbered "1" in the table shown in Fig. 10 indicates that if today's date and time (subject to comparison) is within the range (judgment condition) of May 1, 2023 to May 4, 2023 (reference value), the first predicted sales number (predicted value) will be doubled (response method) to become the second predicted sales number. It also indicates that the reason for this is that "an event will be held at the facility, and an increase in customers is expected" (note).

[0144] For example, the row numbered "2" in the table shown in Figure 10 indicates that if today's date and time (comparison target) matches August 1 (reference value) (judgment condition), the first predicted sales number will be replaced with the actual sales number for the holiday (response method) to become the second predicted sales number. It also indicates that the reason for this is "because this day is a newly established holiday" (note).

[0145] For example, the row numbered "4" in the table shown in FIG. 10 indicates that when today's date and time (comparison target) is within the range (judgment condition) of January 21, 2023 to January 29, 2023 (base value), for menus whose menu type (comparison target) is within the range (judgment condition) of Menu Group A (base value), the first predicted sales number is replaced with the actual sales number on the holiday (response method) to become the second predicted sales number. Also, it indicates that the reason for this is that "only a specific customer group is on holiday during this period, and an increase in orders for menus that correspond to this customer group is predicted" (note). An example of a specific customer group is foreign children from a country with a nearby school.

[0146] <Displaying and modifying information> The input display control unit 34 may display the reference value set by the reference value setting unit 203 on the terminal 12. The user may modify the displayed reference value by operating the terminal 12. The input display control unit 34 may reflect the reference value modified by the user in the reference value setting unit 203. This allows the user to manually modify the reference value when the user considers that the reference value is not appropriate.

[0147] The input display control unit 34 may display the reliability determined by the reliability determination unit 204 on the terminal 12. The user may modify the displayed reliability by operating the terminal 12. The input display control unit 34 may reflect the reliability modified by the user in the reliability determination unit 204. This allows the user to manually modify the reliability when the user considers that the reliability is not appropriate.

[0148] The input display control unit 34 may display the handling method 214 selected by the second prediction unit 205 according to the reliability on the terminal 12. The user may change the displayed handling method 214 to another handling method 214 by operating the terminal 12. The input display control unit 34 may reflect the handling method 214 changed by the user in the second prediction unit 205. In this case, the second prediction unit 205 applies the handling method 214 changed by the user to the first predicted sales number and determines the second predicted sales number. This allows the user to change the handling method 214 to another appropriate handling method 214 when the user considers that the handling method 214 is not appropriate.

[0149] FIG. 11 is a diagram showing a display example of the performance forecast comparison screen according to the third embodiment.

[0150] The input display control unit 34 may display a performance prediction comparison screen 300 including a graph 301 comparing the first predicted sales volume and the actual sales volume for a predetermined period in the past (e.g., one week) as shown in Fig. 11. The input display control unit 34 may also include a graph 302 showing changes in precipitation and solar radiation during the same predetermined period, and a calendar 303 showing dates when events were held during the same predetermined period, on the performance prediction comparison screen 300. By looking at these graphs, the user can determine whether the prediction by the first prediction unit 201 is appropriate.

[0151] Furthermore, the input display control unit 34 may include options 304 of response methods that can be taken when the user judges that the prediction is invalid, on the performance prediction comparison screen 300. This allows the user to select from the options 304 of response methods and easily change the response method 214 to another appropriate response method 214 when the user judges that the prediction by the first prediction unit 201 is invalid.

[0152] Furthermore, the input display control unit 34 may output an alert when the error rate between the first predicted sales volume and the actual sales volume for a predetermined period is greater than a predetermined threshold. This allows the user to be promptly notified that the prediction by the first prediction unit 201 is highly likely to be invalid.

[0153] Furthermore, the input display control unit 34 may display the following information based on the statistical value of the error between the first predicted sales volume and the actual sales volume for a predetermined period. If the variance of the error is smaller than a certain threshold and there are many samples in which the first predicted sales volume is smaller than the actual sales volume, the input display control unit 34 displays information such as "The predicted values ​​tend to be smaller than the actual sales volume." If the variance of the error is smaller than a certain threshold and there are many samples in which the first predicted sales volume is larger than the actual sales volume, the input display control unit 34 displays information such as "The predicted values ​​tend to be higher than the actual values." If the variance of the errors is greater than a certain threshold, the input display control unit 34 displays information such as "There is a large discrepancy between the actual value and the predicted value on X month X day" for the sample with the largest error.

[0154] FIG. 12 is a diagram showing a display example of a predicted value change screen according to the third embodiment.

[0155] The input display control unit 34 may display a predicted value change screen 400 including a graph 401 showing the past predicted sales volume and actual sales volume, and a graph 402 of the second predicted sales volume for a predetermined future period, as shown in FIG. 12. The input display control unit 34 may provide a UI that accepts a change to the graph 402 of the second predicted sales volume. If the user thinks that the displayed graph 402 of the second predicted sales volume is not appropriate, the user can change the graph 402 of the second predicted sales volume while referring to the graph 401 showing the past predicted sales volume and actual sales volume. This allows the user to easily change the second predicted sales volume. The input display control unit 34 may also generate a new response method 214 based on the change to the second predicted sales volume. For example, the input display control unit 34 may generate a new response method 214, such as multiplying the first predicted sales volume by α under the conditions of the changed day (e.g., day of the week, weather, etc.), and add it to the unique information 210.

[0156] <Review of specific information> The unique information management unit 202 may review the contents of the unique information 210 used when determining the second predicted sales volume based on the first predicted sales volume and second predicted sales volume predicted for a certain target period and the actual sales volume for that target period.

[0157] For example, the unique information management unit 202 may compare the error E1 between the first predicted sales volume and the actual sales volume in the target period with the error E2 between the second predicted sales volume and the actual sales volume in the target period, and if the error E2>the error E1, execute at least one of the following (C1) and (C2). If the error E2>the error E1, it is because it is estimated that the original first predicted sales volume has a higher prediction accuracy than the second predicted sales volume determined using the unique information 210, and the content of the unique information 210 is not very appropriate. (C1) The unique information management unit 202 deletes the unique information 210 used to determine the second forecast sales volume from the unique information DB 206. (C2) The unique information management unit 202 stores the unique information 210 used to determine the second forecast sales amount. The user is notified to review the content of the specific information 210. The user who has received this notification may, for example, review the content of the method of handling 214 for the specific information 210 or delete the specific information 210.

[0158] FIG. 13 is a diagram showing an example of a review screen of specific information including information on a determination condition according to the third embodiment.

[0159] For example, if the error E2 between the second forecasted sales volume and the actual sales volume for the target period calculated by applying row number "1" of the table shown in Figure 10 is greater than the error E1 between the first forecasted sales volume and the actual sales volume for the target period (error E2 > error E1), the unique information management unit 202 may display a unique information review screen 500 as shown in Figure 13 via the input display control unit 34.

[0160] 13, a specific information review screen 500 may display applied specific information 501, a button 502 for instructing to use the specific information 501, a button 503 for instructing not to use the specific information 501 this time, and a button 504 for instructing not to use the specific information 501 this time and to delete the specific information 501 from the specific information DB 206. By selecting one of the buttons 502, 503, and 504, the user can easily review the specific information 501 that does not seem to have a very good accuracy.

[0161] <Review of standard values> The reference value setting unit 203 may correct (review) the current reference value based on the error E3 between the second predicted sales volume calculated for the most recent past predetermined period (e.g., the most recent one week) and the actual sales volume in the same predetermined period. For example, the reference value setting unit 203 corrects the current reference value when the error E3 is equal to or greater than a predetermined threshold. This is because the accuracy of the original reference value may not be very good. For example, the reference value setting unit 203 corrects the average value of the actual sales volumes (i.e., the original reference value) of the respective cases where a similar "discount event" was held in the past so as to approach the actual sales volume in the most recent past predetermined period (e.g., the most recent one week), and sets the result as the current reference value. This makes it possible to prevent a reference value with low accuracy from being continuously used. In addition, it is possible to prevent the setting of a reference value that is far removed from the recent actual sales volume, thereby improving the reliability of the reference value.

[0162] The method of correcting the reference value is not limited to the method of approaching the sales performance number in the most recent past predetermined period. For example, the reference value setting unit 203 may correct (review) the current reference value based on the sales performance number in a more distant past predetermined period (e.g., one month) rather than the most recent period, or the sales performance number in the same season in the past.

[0163] Furthermore, the reference value setting unit 203 may be realized by machine learning. In this case, the reference value setting unit 203 learns information indicating the situation in which the error E3 occurred and a correction value that cancels out the error E3. When the reference value setting unit 203 predicts that a situation similar to the situation in which the error E3 occurred will occur during the prediction period, the reference value setting unit 203 sets the reference value using the learned correction value that cancels out the error E3. By repeating this, the reference value setting unit 203 becomes able to calculate a reference value with less error as the learning progresses.

[0164] <Modification> The second prediction unit 205 may correct the first predicted sales volume obtained from the first prediction unit 201 as necessary based on the trend (e.g., upward or downward trend) of the sales performance in the most recent period (e.g., several weeks) to obtain the second predicted sales volume. This will be described in detail below.

[0165] Fig. 14 is a diagram for explaining a method for correcting the first predicted sales volume based on trend information according to embodiment 3. In the graph shown in Fig. 14, the horizontal axis indicates the date, and the vertical axis indicates the sales volume.

[0166] In the specific information 210, the following contents may be set for a certain period. Category 212: Reflecting trends in judgment results · Cause 213: An upward or downward trend in sales performance · Solution 214 Confidence level 1: Use the first forecast sales number as is. Reliability level 2: The first predicted sales volume is replaced with a corrected value calculated based on the trend coefficient and the first predicted sales volume.

[0167] The reference value setting unit 203 calculates the slope of an approximation line (regression line) of the sales volume for a predetermined period (for example, the past month) as a reference value (that is, a trend coefficient) representing the sales trend.

[0168] The reliability determination unit 204 determines the reliability according to the magnitude of the reference value. For example, when the absolute value of the reference value is less than a predetermined threshold, the reliability determination unit 204 determines the reliability to be "1," and when the absolute value of the reference value is equal to or greater than the predetermined threshold, the reliability determination unit 204 determines the reliability to be "2."

[0169] If the reliability determined by the reliability determination unit 204 is "2", the second prediction unit 205 replaces the first predicted sales volume with a corrected value calculated based on the trend coefficient and the first predicted sales volume, and sets this as the second predicted sales volume.

[0170] Next, an example of the above-mentioned processing will be described with reference to Fig. 14. For example, the order support system 10 performs the following processing. (S101) The reference value setting unit 203 calculates an average value A of the sales performance figures for the most recent past month (March 1st to March 28th). (S102) The reference value setting unit 203 calculates the slope B of an approximation line (regression line) of the sales figures for the most recent past month (March 1st to March 28th) as trend information obtained from the sales figures for that period. (S103) The reliability determination unit 204 compares the amount of change C for one week based on the trend information (= gradient B × 7 days) with the average sales volume A, and selects a response method. For example, if "the amount of change C for one week < (10% of the average A)", the reliability determination unit 204 determines the reliability to be "1." If "the amount of change C for one week ≧ (10% of the average A)", the reliability determination unit 204 determines the reliability to be "2." (S104) The second prediction unit 205 refers to the above-mentioned unique information 210, and when the reliability is "1", the second prediction unit 205 sets the first predicted sales volume as the second predicted sales volume for the prediction period (March 29th to April 5th) as is. When the reliability is "2", the second prediction unit 205 calculates the second predicted sales volume for the prediction period (March 29th to April 5th) using the following formula. Second forecast sales volume = First forecast sales volume + change in one week C It should be noted that (first predicted sales volume+change amount C for one week) is an example of the above-mentioned correction value.

[0171] As a result, the forecast sales volume from March 29th to April 5th in the graph shown in the upper part (a) of FIG. 14 is corrected to the forecast sales volume from March 29th to April 5th in the graph shown in the lower part (b) of FIG.

[0172] Using only the prediction model 200, there is a risk that the sales forecast will be far off when a large change in trend occurs, but by calculating or correcting the forecast sales in this way while taking into account recent changes in trend (e.g., an upward or downward trend), it is possible to reduce this risk. Note that various regression analysis methods other than the method of finding a regression line are known for analyzing a set of data to determine a function indicating a trend. Therefore, in the third embodiment, instead of a regression line, a function indicating a trend may be estimated using these regression analysis methods, and the parameters of the function may be used as trend coefficients.

[0173] (Hardware configuration) The above-mentioned functional blocks of the order support system 10 can be realized by a computer program.

[0174] FIG. 15 is a diagram showing a hardware configuration of a computer that implements the functional blocks of the order support system 10 according to the present disclosure by a computer program.

[0175] The computer 1000 includes a processor 1001, a memory 1002, a storage 1003, an input device 1004, an output device 1005, a communication device 1006, a graphics processing unit (GPU) 1007, a reading device 1008, and a bus 1009. These devices are connected to the bus 1009 and can transmit and receive data bidirectionally via the bus 1009.

[0176] The processor 1001 is a device that executes a computer program stored in the memory 1002 and realizes the above-mentioned functional blocks. Examples of the processor 1001 include a Central Processing Unit (CPU), a Micro Processing Unit (MPU), a controller, a Large Scale Integration (LSI), an Application Specific Integrated Circuit (ASIC), a Programmable Logic Device (PLD), and a Field-Programmable Gate Array (FPGA).

[0177] The memory 1002 is a device that stores computer programs and data operated by the computer 1000. The memory 1002 may include a read-only memory (ROM) and a random access memory (RAM).

[0178] The storage 1003 is configured as a non-volatile storage medium, and is a device that stores computer programs and data handled by the computer 1000. Examples of the storage 1003 include a hard disk drive (HDD) and a solid state drive (SSD).

[0179] The input device 1004 is a device that accepts data to be input to the processor 1001 . Examples of the input device 1004 include a keyboard, a mouse, a touchpad, a microphone, and the like.

[0180] The output device 1005 is a device that outputs data generated by the processor 1001. Examples of the output device 1005 include a display and a speaker.

[0181] The communication device 1006 is a device that transmits and receives data to and from other devices (for example, the weather information server 5 or the terminal 12) via a communication network. The communication device 1006 may support either wired communication or wireless communication. An example of wired communication is Ethernet (registered trademark). Examples of wireless communication are Wi-Fi (registered trademark), Bluetooth, LTE, 4G, 5G, etc.

[0182] The GPU 1007 is a device that performs high-speed image rendering processing. The GPU 1007 may be used for artificial intelligence (AI) processing (e.g., deep learning).

[0183] The reading device 1008 is a device that reads data from a recording medium such as a Digital Versatile Disk Read Only Memory (DVD-ROM) or a Universal Serial Bus (USB) memory.

[0184] (Summary of the third embodiment) The third embodiment discloses the following technique.

[0185] <Technology 1> The prediction device (e.g., sales volume prediction unit 31) of this embodiment includes a first prediction unit (201) that predicts a first predicted sales volume for a future prediction period based on the past actual sales volume of the product, a reference value setting unit (203) that sets a reference value based on specific information (210) that indicates specific circumstances related to fluctuations in sales volume during the prediction period, and a second prediction unit (205) that determines, based on the reference value, whether or not a second predicted sales volume to be output as the predicted sales volume of the product should be set to the same value as the first predicted sales volume. This allows for an appropriate forecast of sales numbers to be obtained according to the specific circumstances related to fluctuations in sales numbers.

[0186] <Technology 2> The prediction device described in Technology 1 further includes a reliability determination unit (204) that determines the reliability of the first predicted sales volume based on a comparison between a reference value and the first predicted sales volume, and a second prediction unit (205) that determines the second predicted sales volume based on the reliability and the first predicted sales volume. This makes it possible to obtain an appropriate predicted sales number according to the reliability of the first predicted sales number.

[0187] <Technology 3> In the prediction device according to Technique 1 or 2, the specific information (210) includes information indicating a change in the number of customers or customer demographics during the prediction period. This makes it possible to obtain appropriate predicted sales figures that take into account fluctuations in customer numbers or customer demographics.

[0188] <Technology 4> In the prediction device described in any one of Techniques 1 to 3, the specific information (210) includes at least one of events that are expected to attract more customers during the prediction period, events that are expected to attract less customers, and information indicating changes in customer demographics. This makes it possible to obtain appropriate predicted sales figures that take into account events being held or changes in customer demographics.

[0189] <Technology 5> In the prediction device according to any one of Techniques 2 to 4, the reliability determination unit (204) determines the reliability based on a difference value or a correlation value between a reference value and the first predicted sales volume. This makes it possible to obtain an appropriate predicted sales number according to the reliability of the first predicted sales number.

[0190] <Technology 6> In the prediction device described in any one of claims 2 to 5, a second prediction unit (205) sets the first predicted sales number as the second predicted sales number when the reliability is equal to or greater than a predetermined threshold, and modifies the first predicted sales number to set it as the second predicted sales number when the reliability is less than the threshold. As a result, when the reliability of the first predicted sales number is relatively low, the second predicted sales number is used, so that the predicted sales number can be prevented from being significantly off the mark.

[0191] <Technology 7> In the prediction device according to Technique 6, the second prediction unit (205) determines a method for correcting the first predicted sales volume depending on the reliability. This makes it possible to obtain an appropriate predicted sales number according to the reliability of the first predicted sales number.

[0192] <Technology 8> In the prediction device described in Technique 7, the second prediction unit (205) determines a method of correcting the first predicted sales volume based on the specific information (210) used to determine the reference value. This makes it possible to obtain an appropriate predicted sales number according to the reliability of the first predicted sales number.

[0193] <Technology 9> The prediction device described in any one of Techniques 2 to 8 further includes an input display control unit (34) that displays at least one of the first predicted sales volume, the reference value, the reliability, and the second predicted sales volume, and accepts input or modification of at least one of the reference value and the reliability. This allows the user to visually view at least one of the first forecast sales number, the reference value, the reliability, and the second forecast sales number, as well as input or modify at least one of the reference value and the reliability.

[0194] <Technology 10> The prediction device described in any one of Techniques 1 to 9 further includes a unique information management unit (202) that modifies or deletes the unique information used to determine the second predicted sales volume when the error between the second predicted sales volume and the actual sales volume during the prediction period is equal to or greater than a predetermined threshold. This allows specific information that may be subject to relatively large errors to be modified or removed as necessary.

[0195] <Technology 11> In the prediction device described in Technique 10, the predetermined threshold is a value based on an error between the first predicted sales volume and the actual sales volume during the prediction period. This allows specific information that may be subject to relatively large errors to be modified or removed as necessary.

[0196] <Technology 12> In the prediction device according to any one of Techniques 1 to 11, the reference value setting unit (203) modifies the reference value when an error between the second predicted sales volume and the actual sales volume in a predetermined period in the past is equal to or greater than a predetermined threshold value. This allows the reference value to be corrected when the error is relatively large, making it possible to obtain an appropriate predicted sales volume.

[0197] <Technology 13> The prediction method of this embodiment predicts a first predicted sales number for a future prediction period based on the product's past actual sales numbers, sets a reference value based on specific information that indicates specific circumstances related to fluctuations in sales numbers during the prediction period, and determines whether or not to set the second predicted sales number output as the product's predicted sales number based on the reference value to the same value as the first predicted sales number. This allows for an appropriate forecast of sales numbers to be obtained according to the specific circumstances related to fluctuations in sales numbers.

[0198] <Technology 14> The ordering support device (eg, ordering support system 1) according to the present embodiment uses the prediction method described in Technique 13 to support ordering of products. This allows product ordering support to be provided based on appropriate forecasted sales figures in accordance with specific circumstances related to fluctuations in sales figures.

[0199] Although the embodiments have been described above with reference to the accompanying drawings, the present disclosure is not limited to such examples. It is clear that a person skilled in the art can conceive of various modifications, corrections, substitutions, additions, deletions, and equivalents within the scope of the claims, and it is understood that these also belong to the technical scope of the present disclosure. In addition, the components in the above-mentioned embodiments may be arbitrarily combined within the scope of the invention. [Industrial Applicability]

[0200] The technology disclosed herein is useful for forecasting demand for products. [Explanation of symbols]

[0201] 1. Client 3 Product distribution center 4 Ordering device 5. Weather Information Server 6 Calendar Information Server 10 Order Support System 12 Terminal 31 Sales Forecast Department 32 Recommended Order Quantity Calculation Unit 33 Safety factor calculation section 34 Input display control unit 41 Sales record DB 42 Forecast sales DB 43 Stock quantity DB 44 Order quantity DB 47 Expiration date master 48 Store Information DB 100 Order Support Screen 101 Product List Area 102 Graph Area 103 Information area 104 Sales Graph 105 Stock Quantity Graph 120 Product list screen 121 Calendar Area 122 Graph Area 123 Bar Graph 140 Order Support Screen 141 Graph Area 142 Manual Order Tab 143 Auto Order Tab 144 Fixed Order Tab 145 Forecast sales graph 146 Projected Stock Quantity Graph 147 Waste Loss Graph 148 Opportunity Loss Graph 151 Recommended Order Quantity Area 152 Order quantity input area 153 Closing Stock Area 154 Waste Loss Area 155 Opportunity Loss Area 156 Total Loss Area 160 Order Support Screen 161 Order quantity area 180 Order Support Screen 181 Lower limit input area 182 Fixed Order Entry Area 200 Predictive Models 201 First Prediction Section 202 Specific Information Management Department 203 Reference value setting unit 204 Reliability determination unit 205 Second Prediction Section 206 Specific information DB 210 Specific Information 211 period 212 Classification 213 Cause 214 How to respond 300 Actual forecast comparison screen Graphs 301 and 302 303 Calendar 400 Forecast value change screen Graphs 401 and 402 500 Specific information review screen 501 Specific Information 502, 503, 504 buttons 1000 Computers 1001 Processor 1002 Memory 1003 Storage 1004 Input Device 1005 Output device 1006 Communication equipment 1007 GPU 1008 Reading device 1009 Bus

Claims

1. a first prediction unit that predicts a first predicted sales volume for a future prediction period based on a past actual sales volume of the product; a reference value setting unit that sets a reference value based on specific information that indicates a specific situation related to the fluctuation of the sales volume during the forecast period; a second prediction unit that determines whether or not a second predicted sales number output as a predicted sales number of the product is to be the same as the first predicted sales number based on the reference value. Prediction device.

2. A reliability determination unit determines a reliability of the first predicted sales volume based on a comparison between the reference value and the first predicted sales volume, The second prediction unit determines a second predicted sales volume based on the reliability and the first predicted sales volume. The prediction device according to claim 1 .

3. The specific information includes information indicating a change in the number of customers or customer demographics during the forecast period. The prediction device according to claim 2 .

4. The specific information includes at least one of information indicating an event that is expected to attract more customers, an event that is expected to attract less customers, and information indicating a change in customer demographics during the prediction period. The prediction device according to claim 3 .

5. The reliability determination unit determines the reliability based on a difference value or a correlation value between the reference value and the first predicted sales volume. The prediction device according to claim 2 .

6. The second prediction unit is If the reliability is equal to or greater than a predetermined threshold, the first predicted sales number is set as the second predicted sales number; If the reliability is less than the threshold, the first forecasted sales volume is revised to become the second forecasted sales volume. The prediction device according to claim 3 .

7. The second prediction unit determines a method for correcting the first predicted sales volume according to the reliability. The prediction device according to claim 6 .

8. The second prediction unit determines a method for correcting the first predicted sales volume based on the specific information used to determine the reference value. The prediction device according to claim 7 .

9. an input display control unit that displays at least one of the first predicted sales number, the reference value, the reliability, and the second predicted sales number, and receives an input or correction of at least one of the reference value and the reliability; The prediction device according to claim 3 .

10. and a unique information management unit that modifies or deletes the unique information used in determining the second predicted sales volume when an error between the second predicted sales volume and an actual sales volume during the prediction period is equal to or greater than a predetermined threshold. The prediction device according to claim 3 .

11. The predetermined threshold is a value based on an error between the first predicted sales volume and an actual sales volume during the prediction period. The prediction device according to claim 10.

12. the reference value setting unit modifies the reference value when an error between the second predicted sales volume and an actual sales volume in a predetermined past period is equal to or greater than a predetermined threshold value. The prediction device according to claim 3 .

13. predicting a first predicted sales volume for a future prediction period based on the past actual sales volume of the product; setting a reference value based on specific information indicating specific circumstances related to fluctuations in sales volume during said forecast period; determining whether or not a second predicted sales number, which is output as a predicted sales number of the product, should be equal to the first predicted sales number based on the reference value; Forecasting methods.

14. 14. A method for predicting a product by using the prediction method according to claim 13. Order support device.

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