JP6806885B2 - Logistics support system and logistics support method - Google Patents

Description

The present invention relates to a technique for supporting physical distribution by calculating a recommended number of orders.

As a background technique in this technical field, there is Japanese Patent Application Laid-Open No. 2014-26344 (Patent Document 1). This publication states that "the predicted value is calculated according to the future increase / decrease trend by automatically adjusting the predicted value based on the past actual value and the future predicted value" (see summary). ..

Patent Document 1: Japanese Unexamined Patent Publication No. 2014-26344

In recent years, in order to improve the efficiency of logistics operations, the number of logistics business consignments that outsource delivery operations to stores to logistics companies is increasing. The company that outsources the distribution business sends the goods to the distribution warehouse, and if necessary, orders the delivery from the distribution warehouse to the store from the distribution company. If there are multiple stores to which the product is delivered, the number of deliveries for the multiple stores is ordered together.

When a logistics company receives an order for delivery to a store from a company that outsources logistics operations, it first performs a picking operation to take out the product from the area where the product is stored, and then sorts the taken out product by store. Sorting work is performed and products are delivered to each store. The company that outsources the physical distribution business pays the physical distribution company a physical distribution cost proportional to the man-hours of the picking work and the sorting work.

Generally, the products in the distribution warehouse are stored in a state of being packed in a quantity called a packing unit (for example, a case) defined for each product. If the number of orders is a multiple of the packing unit, the distributor picks the goods in the packing unit. If the number of orders is not a multiple of the packing unit, the packing is unpacked and the products are picked one by one by the fraction less than the packing unit. Therefore, the work efficiency is the best when the number of orders for each work matches the multiple of the packing unit of the product. For this reason, it is most efficient if the number of orders placed at each store can be matched to a multiple of the packing unit of the product, and if the number of orders placed at each store does not become the packing unit of the product, the total number of orders placed at all stores is used as the product. It is possible to improve work efficiency by matching with a multiple of the packing unit of. By increasing work efficiency, it is possible to reduce the distribution costs paid by the outsourced company.

However, since each store determines the number of orders according to the circumstances of its own store, the outsourced company cannot freely change the number of orders for each store. For example, in a small store, the space for storing products is limited, and the number of products that can be accepted is limited. In the case of a product that sells in small numbers, there is a possibility that it will not be sold if it is accepted in excess of the number of orders placed at the store. In this way, since each store determines the number of orders according to the circumstances of its own store, it is necessary to confirm the conditions that can be additionally accepted for each store.

In the mechanism of Patent Document 1 in which the number of orders placed at a store is changed based on past results, the acceptance conditions on the store side are not taken into consideration, so there is a possibility that an unrealizable number of orders may be determined. In addition, since the number of orders for one store is determined, it is not possible to determine how many additional orders are to be distributed to which store in order to make the total number of orders for all stores a multiple of the packing unit. The challenge is to calculate the number of orders that contribute to the reduction of work man-hours while satisfying the acceptance conditions of the store so that the distribution costs paid by the company can be reduced.

In order to solve the above problems, the distribution support system of the present invention includes case entry information indicating the maximum value of the number of pieces, which is the number of articles stored in one case, and the work manpower unit price for each case of the articles. Work manpower unit price information indicating the work manpower unit price for each piece, order information indicating the number of orders for each delivery destination of the goods, and the number of the goods that can be accepted by each delivery destination in addition to the order number are determined. Based on the storage unit that holds the acceptance condition information indicating the conditions and the past order information indicating the number of past orders for each delivery destination of the goods, and the order information and the acceptance condition information, each delivery destination Calculate the maximum number of orders received, which is the maximum number of the goods that can be accepted, and generate one or more proposed order numbers by increasing the number of orders of at least one of the delivery destinations within the range not exceeding the maximum number of received items. The distribution cost for each proposed number of orders is calculated based on the order quantity calculation unit, the case entry information, and the work manpower unit price information, and the distribution cost is smaller than before the change in the order quantity. possess a number of orders extractor for extracting one or more ordered number plan, and a display unit for outputting one or more order quantity draft the extracted, the receiving condition information for each of the delivery destination, It is characterized by including information that specifies a specific number of items that can be accepted in addition to the number of items ordered, and information that indicates the number based on the number of items ordered in the past .

According to one aspect of the present invention, the work man-hours in the warehouse can be reduced and the distribution cost can be reduced by rounding up the number of orders to the packing unit after satisfying the acceptance conditions of each store. Issues, configurations, and effects other than those described above will be clarified by the description of the following embodiments.

It is a block diagram which shows the example of the functional structure of the recommended order quantity calculation apparatus of the Example of this invention. It is a block diagram which shows the example of the hardware configuration and the system configuration of the recommended order quantity calculation apparatus of the Example of this invention. It is a flowchart which shows the example of the recommended order quantity calculation process executed by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the example of the product master information held by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the example of the work man-hour master information held by the recommended order number calculation apparatus of the Example of this invention. It is explanatory drawing which shows the example of the past order information held by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the example of the past order information held by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the example of the past order information held by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the example of the past allocation information held by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the example of the acceptance condition information held by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the example of the ordering information held by the recommended order number calculation apparatus of the Example of this invention. It is a flowchart which shows the example of the recommended order quantity calculation / extraction process executed by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the example of the acceptance maximum number information held by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the example of the demand forecast information held by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the 1st example of the recommended order quantity draft information generated by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the 2nd example of the recommended order quantity draft information generated by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the 3rd example of the recommended order quantity draft information generated by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the example of the screen display of the maximum number of acceptance information by the recommended order quantity calculation apparatus of the Example of this invention. It is explanatory drawing which shows the example of the graph display of the distribution cost and the number of stores which changed the order number by the recommended order number calculation apparatus of the Example of this invention.

Hereinafter, examples of the present invention will be described with reference to the drawings.

FIG. 1 is a block diagram showing an example of the functional configuration of the recommended order quantity calculation device according to the embodiment of the present invention.

The recommended order quantity calculation device 100 is a device that supports logistics by calculating the recommended order quantity, which will be described later, and includes an input unit 110, a storage unit 120, an order quantity calculation unit 130, an order quantity extraction unit 140, and a display. It has a part 150.

The input unit 110 is a user interface for reading data (external data) stored outside the device.

The storage unit 120 stores the data read by the input unit 110.

The order quantity calculation unit 130 reads the product master information 121, the past order information 123, the past allocation information 124, the acceptance condition information 125 and the order information 126, and receives the maximum acceptance quantity information 127, the demand forecast information 128 and the recommended order quantity proposal information 129. Is calculated.

The order quantity extraction unit 140 reads the work man-hour master information 122 and the recommended order quantity draft information 129, and extracts the recommended order quantity draft to reduce the distribution cost.

The display unit 150 displays the maximum number of accepted information 127, the distribution cost of the proposal for reducing the distribution cost calculated from the work man-hour master information 122 and the recommended number of order proposal information 129, and the number of stores for which the number of orders has been changed. User interface for.

The recommended order quantity calculation device 100 is controlled by a recommended order quantity calculation program (not shown) that implements a recommended order quantity calculation process.

FIG. 2 is a block diagram showing an example of a hardware configuration and a system configuration of the recommended order quantity calculation device 100 according to the embodiment of the present invention.

The recommended order quantity calculation device 100 includes a CPU (Central Processing Unit) 201, a RAM (Random Access Memory) 210, a ROM (Read Only Memory) 220, an auxiliary storage device 230, a display device 240, an input device 250, and a media reader 260. And has a communication device 270.

The CPU 201 is a unit that executes various operations. The CPU 201 executes various processes by executing a predetermined recommended order quantity calculation program loaded from the auxiliary storage device 230 into the RAM 210. That is, in the following description, the processing executed by the order quantity calculation unit 130 and the order quantity extraction unit 140 is actually executed by the CPU 201 according to the recommended order quantity calculation program. Further, the processing executed by the input unit 110 and the display unit 150 is actually executed by the CPU 201 controlling the input device 250, the media reading device 260, the communication device 270, the display device 240, and the like according to the recommended order quantity calculation program. To.

The recommended order quantity calculation program is, for example, an application program that can be executed on an OS (Operation System) program.

The recommended order quantity calculation program may be installed in the auxiliary storage device 230 from the portable storage medium via, for example, the media reader 260.

The RAM 210 is a memory that stores a program executed by the CPU 201, data necessary for executing the program, and the like.

The ROM 220 is a memory for storing a program or the like necessary for starting the recommended order quantity calculation device 100.

The auxiliary storage device 230 is, for example, a device such as an HDD (Hard Disk Drive). The auxiliary storage device 230 may be an SSD (Solid State Drive) using a flash memory or the like.

The display device 240 is, for example, a device such as a CRT display, an LCD (Liquid Crystal Display), and an organic EL (Electro-Luminescence) display.

The input device 250 is, for example, a device such as a keyboard, a mouse, and a microphone.

The media reading device 260 is a device that reads information from a portable storage medium such as a CD-ROM.

The communication device 270 inputs / outputs data to / from an external device via the network 280.

The recommended order quantity calculation device 100 may acquire the product master information 121, the work man-hour master information 122, the past order information 123, and the past allocation information 124 from the Warehouse Management System (WMS) 290 via the network 280.

The recommended order quantity calculation device 100 may acquire the acceptance condition information 125 and the order information 126 from the customer system 292 via the network 280.

FIG. 3 is a flowchart showing an example of the recommended order number calculation process executed by the recommended order number calculation device 100 according to the embodiment of the present invention.

In the recommended order quantity calculation process, the following processes (1) to (3) are executed. (1) The input unit 110 performs the external data reading process 310 and stores it in the storage unit 120. (2) The order quantity calculation unit 130 and the order quantity extraction unit 140 execute the recommended order quantity calculation / extraction process 320 to extract the order quantity proposal for reducing the distribution cost. (3) In the result output process 330, the result stored in the storage unit 120 is searched and displayed on the display unit 150.

Hereinafter, each process (1) to (3) of FIG. 3 will be described with reference to FIGS. 4 to 17.

(1) External data reading process 310
In the external data reading process 310 of FIG. 3, the input unit 110 reads the product master information, work man-hour master information, past ordering information, past allocation information, acceptance condition information, and ordering information which are external data, and stores them in the storage unit 120. .. Specifically, the media reading device 260 or the communication device 270 takes in external data and stores it in the auxiliary storage device 230.

FIG. 4 is an explanatory diagram showing an example of product master information 121 held by the recommended order quantity calculation device 100 according to the embodiment of the present invention.

The product master information 121 has at least data items that define the product code 410, the product name 420, and the number of cases 430. The product code 410 and the product name 420 are information for identifying the item of each product. These correspond to the product code 510 and the like and the product name 520 and the like in FIG. 5 and later, respectively. The number of pieces in the case 430 is the maximum number of pieces of the product that can be stored in one case.

FIG. 5 is an explanatory diagram showing an example of work man-hour master information 122 held by the recommended order quantity calculation device 100 according to the embodiment of the present invention.

The work man-hour master information 122 has at least data items that define a product code 510, a product name 520, a piece work man-hour unit price 530, and a case work man-hour unit price 540. The product code 510 and the product name 520 are information for identifying the item of each product. The piece work man-hour unit price 530 is the unit price of the work man-hours for each product (that is, the work man-hours per piece). The case work man-hour unit price 540 is the unit price of the work man-hours for each packing unit (that is, the work man-hours per case).

6A to 6C are explanatory views showing an example of past order information 123 held by the recommended order number calculation device 100 according to the embodiment of the present invention.

The past order information 123 has at least data items that define a date 610, a store name 620, a product code 630, and an order quantity 640. These are the date when the product was actually ordered in the past, the information for identifying the delivery destination of the product, the information for identifying the item of the product, and the number of pieces (pieces) for which the product was ordered. 6A to 6C show past order information 123 regarding product codes A0001 to A0003, respectively.

FIG. 7 is an explanatory diagram showing an example of past allocation information 124 held by the recommended order quantity calculation device 100 according to the embodiment of the present invention.

The past allocation information 124 has at least data items that define a date 710, a store name 720, a product code 730, and a past allocation number 740. These are the date when the product was actually distributed in the past, the information that identifies the delivery destination that was the target of the distribution, the information that identifies the distributed product, and the number of distributed products (pieces). Number). Here, the allocation means that the number of ordered products is added, and the added quantity is the number of allocations.

FIG. 8 is an explanatory diagram showing an example of acceptance condition information 125 held by the recommended order quantity calculation device 100 according to the embodiment of the present invention.

The acceptance condition information 125 has at least data items that define the store name 810, the product code 820, and the acceptance condition 830. These are the information that identifies the delivery destination of the product, the information that identifies the item of the product, and the conditions under which the store of the delivery destination accepts the distribution of the product (in other words, the store orders the product first. It is a condition to determine the acceptable quantity in addition to the given quantity). As the acceptance condition 830, in addition to the condition by specifying a specific number, a condition based on the number of orders placed in the past may be set. As a result, it is possible to create an order quantity plan that suits the actual situation of each store.

Here, a specific example of the acceptance condition 830 shown in FIG. 8 will be described. For example, in the first record of the acceptance condition information 125 in FIG. 8, the store whose store name is "delivery destination A" (hereinafter, also simply referred to as "delivery destination A"; the same applies to other stores) has a product code. Can accept up to 2 pieces per day (in other words, for example, the initial number of orders) for a product whose value is "A0001" (hereinafter, also simply referred to as "product A0001"; the same applies to other products). When it is "8", the number of orders can be changed to "9" or "10").

The third record shows that the delivery destination A cannot accept the distribution of the goods A0003.

The sixth record shows that the delivery destination B can accept the distribution of the product A0003 up to the maximum number of distributions of the product A0003 that the delivery destination B has received in the past.

The ninth record shows that if the delivery destination C has accepted the distribution of the product A0003 in the past, the delivery destination C can accept the distribution of the product A0003.

For the 10th and 11th records, if the delivery destination D has accepted the distribution of the product A0001 in the past and the number of distributions for 4 consecutive days is within 5 pieces, the delivery destination D is the product A0001. Indicates that the allocation is acceptable.

The 16th and 17th records can be accepted if the delivery destination E accepts the product A0003 up to 2 pieces per day, but if the delivery destination E accepts the distribution of the product A0003 at the time of the previous order, at the time of this order. Indicates unacceptable.

FIG. 9 is an explanatory diagram showing an example of order information 126 held by the recommended order number calculation device 100 according to the embodiment of the present invention.

The order information 126 has at least data items that define a date 910, a store name 920, a product code 930, and an order quantity 940. These are the date of the order, the information that identifies the store to which the ordered product is delivered, the information that identifies the item of the ordered product, and the quantity of the ordered product, respectively. In this embodiment, for example, an order for a product whose delivery destination is a store identified by "delivery destination A" may be described as "order from delivery destination A" for convenience. May be placed by a person other than the delivery destination A (for example, a person who controls and manages a plurality of stores including the delivery destination A).

When the reading of the external data shown in FIGS. 4 to 9 is completed, the recommended order quantity calculation device 100 starts (2) the recommended order quantity calculation / extraction process 320.

(2) Recommended order quantity calculation / extraction process 320
In the recommended order quantity calculation / extraction process 320 of FIG. 3, the order quantity calculation unit 130 calculates the maximum number of orders received and the order quantity proposal, and the order quantity extraction unit 140 calculates the distribution cost to reduce the distribution cost. Extract.

FIG. 10 is a flowchart showing an example of the recommended order number calculation / extraction process 320 executed by the recommended order number calculation device 100 according to the embodiment of the present invention.

In the recommended order quantity calculation / extraction process 320, the following processes (2-1) to (2-3) are executed. (2-1) The order quantity calculation unit 130 searches the acceptance condition information 125 stored in the storage unit 120, and performs a process 1010 to calculate the maximum acceptance number of each store. (2-2) The order quantity calculation unit 130 generates a proposal of the order quantity that can round up the order quantity of the store to a multiple of the packing unit of the product within the range of the maximum number of acceptances calculated in (2-1). Process 1020 is performed. (2-3) The order quantity extraction unit 140 calculates the distribution cost for each proposal of the generated order quantity, and performs the process 1030 to extract the order quantity proposal for which the distribution cost is reduced. After that, the process 1040 for determining whether or not the end condition is satisfied is performed, and (2-2) and (2-3) are repeatedly executed until the end condition is satisfied. For example, it may be determined that the termination condition is satisfied when the processing is executed for all the proposed number of orders. Hereinafter, each process of FIG. 10 will be described.

(2-1) Maximum number of acceptance calculation process 1010
In the maximum number of receipts calculation process 1010 of FIG. 10, the order quantity calculation unit 130 satisfies the acceptance condition 830 of the acceptance condition information 125 stored in the storage unit 120, so that each delivery destination can accept the goods of each item. Calculate the maximum number of acceptances, which is the maximum number.

The order quantity calculation unit 130 first searches the past order information 123 stored in the storage unit 120, and calculates the demand forecast information by the following procedure. That is, the order quantity calculation unit 130 extracts the store name 920, the product code 930, and the order quantity 940 line by line from the order information 126. Then, the order quantity calculation unit 130 has the same store name 620 and product code 630 as the store name 920 and product code 930 of the extracted row, and has an order quantity 640 of 940 or more orders of the extracted row. All rows are searched from the data for a predetermined number of days (6 days in this embodiment) included in the past order information 123. Then, the order quantity calculation unit 130 calculates a ratio from the store name 920, the product code 930 and the order quantity 940 extracted from the order information 126, and the number of rows searched from the past order information 123, and calculates this in the future. Obtained as the demand probability, which is the probability that the store will place an order.

Here, referring to the examples of FIGS. 6A to 6C and FIG. 9, the number of orders 940 for the product A0001 from the delivery destination A on December 7 is 8. On the other hand, of the 6 days from December 1st to 6th, there were 1 day when the number of orders from the delivery destination A to product A0001 was 8, 1 day when it was 9, and 4 days when it was 10. It was. That is, 6 days out of 6 days (that is, 100%) when the number of orders was 8 or more, 5 days out of 6 days (that is, about 83%) when the number of orders was 9 or more, and 4 out of 6 days when the number of orders was 10 or more. It is the day (that is, about 67%), and these values are the demand probabilities for the product A0001 of the delivery destination A calculated as of December 7.

This demand probability is an index showing the magnitude of the demand for the product A0001 at the delivery destination A. For example, if the initial order quantity for the product A0001 from the delivery destination A on December 7 is "8" and the order quantity is changed to "10" according to the recommended order quantity proposal, the additional 2 is added. Individuals can be surplus as of December 7. However, if the demand probability of the number of orders "10" is high, there is a high probability that the order for the 10-piece product A0001 will be placed in the near future. Therefore, the order for 10 pieces on December 7 is ahead of schedule for the future order. You can think of it as something you did. In order to avoid the risk of unsold products for a long period of time, it is desirable to change the number of orders within a range where the demand probability is high to some extent.

Next, the order quantity calculation unit 130 calculates the maximum number of acceptances. Specifically, the order quantity calculation unit 130 refers to the acceptance condition 830 of the acceptance condition information, and specifies the acceptance condition that can be calculated based only on the current information, for example, "up to x pieces per day". In that case, the maximum number of acceptances is calculated based on the acceptance conditions. If the acceptance conditions that require past information, for example, "possible if there is an acceptance record" or "possible up to the maximum number of past records" are specified, the specified acceptance conditions, past order information 123, order information The maximum number of acceptances is calculated based on 126, past allocation information 124, and demand forecast information 128.

Here, a specific example of calculating the maximum number of acceptances in this embodiment will be described. When calculating the maximum number of receipts for a certain store and product, first, the order quantity calculation unit 130 sets the store name 810 and the product code 820 (these values) for identifying the target store and product from the acceptance condition information 125. The acceptance condition 830 corresponding to the store name and the product code) is extracted. Next, the order quantity calculation unit 130 identifies the store name and the order quantity 940 corresponding to the product code from the order information 126 when the extracted acceptance condition 830 is "possible up to x pieces per day". The value obtained by adding x to the number of orders placed 940 is the maximum number of orders received.

When the extracted acceptance condition 830 is "possible up to the maximum number of past results", the order quantity calculation unit 130 extracts and extracts all the past allocation numbers 740 corresponding to the store name and the product code from the past allocation information 124. The maximum number of orders received is the sum of the maximum number of past allocations of 740 and the number of orders of 940.

When the extracted acceptance condition 830 is "the total number of distributions in x days is within y pieces", the order quantity calculation unit 130 performs the past x days corresponding to the store name and the product code from the past distribution information 124. The past allocation number 740 is extracted and the total amount thereof is calculated. When the calculated total quantity is less than y pieces, the order quantity calculation unit 130 sets the value obtained by adding the difference between y and the total quantity to the order quantity 940 corresponding to the store name and the product code as the maximum number of receipts. ..

When the extracted acceptance condition 830 is "impossible when accepted last time", the order quantity calculation unit 130 searches the past order information 123 for the store name and the date corresponding to the product code, and the last date in them. Identify the date of. Further, the order quantity calculation unit 130 searches the past distribution information 124 for the date corresponding to the store name and the product code, and there is a record having a date 610 that matches the last date specified from the past order information 123. In the case, it is not accepted, if it does not exist, it is accepted, and the maximum number of orders 640 corresponding to the store name and the product code searched from the past order information 123 is the maximum number of the store name and the product code accepted. Let it be a number.

When the extracted acceptance condition 830 is "possible if it has been allocated in the past", the order quantity calculation unit 130 extracts the information corresponding to the store name and the product code from the past allocation information 124, and before the date. If the record does not exist, it will not be accepted. If there is a record before the date, it is accepted, and the maximum number of orders 640 corresponding to the store name and the product code searched from the past order information 123 is the maximum number of orders received from the store name and the product code. Let it be a number.

When there are a plurality of acceptance conditions 830 corresponding to the store name and the product code, the order quantity calculation unit 130 sets the minimum number among the maximum acceptance numbers calculated for each acceptance condition as the final maximum acceptance number. ..

The order quantity calculation unit 130 uses the demand forecast information 128 to obtain the store name and the product code in order to evaluate the possibility that the additional order quantity calculated from the acceptance conditions will be within the range of the order quantity ahead of schedule from the next time onward. , And keep the demand probability that the number of orders changed by adding the additional amount matches. For example, when an order quantity proposal for changing the order quantity of the product code A0003 of the delivery destination D to "4" by adding "1" to the initial value "3" is generated, "delivery destination D" will be described later from FIG. , "A0003" and "4 pieces or more", the demand probability "67%" is specified, and is held as the demand probability corresponding to the order quantity proposal.

FIG. 11 is an explanatory diagram showing an example of the maximum number of received information 127 held by the recommended order number calculation device 100 according to the embodiment of the present invention.

The maximum number of receipts information 127 has at least data items that define the store name 1110, the product code 1120, and the maximum number of receipts 1130. These are the information for identifying the delivery destination of the product, the information for identifying the item of the product, and the maximum number of receipts calculated by the process of FIG. For example, as shown in FIG. 9, when the delivery destination A has ordered eight products A0001 on December 7, and as shown in FIG. 8, the delivery destination A can accept two products A0001. The maximum number of 1130 items that can be received for the item A0001 of the delivery destination A on December 7 is 10. This means that the actual number of goods A0001 received by the delivery destination A on December 7 can be either 8, 9, or 10, in other words, the goods A0001 of the delivery destination A on December 7th. It is shown that the order quantity of can be changed from the original order quantity of 8 pieces to 9 pieces or 10 pieces.

FIG. 12 is an explanatory diagram showing an example of demand forecast information 128 held by the recommended order quantity calculation device 100 according to the embodiment of the present invention.

The demand forecast information 128 has at least data items that define the store name 1210, the product code 1220, the number of orders 1230, and the probability 1240. This probability 1240 is the probability (that is, the demand probability) that the delivery destination identified by the store name 1210 orders more than the number of products identified by the product code 1220 as shown in the number of orders 1230, and is calculated by the processing of FIG. Will be done.

(2-2) Recommended order quantity calculation process 1020
In the recommended order quantity calculation process 1020 of FIG. 10, the order quantity calculation unit 130 searches the maximum receipt number information 127 stored in the storage unit 120, and orders at least one store within a range not exceeding the maximum acceptance number 1130. Generate one or more draft orders by increasing the number. For example, in the order quantity calculation unit 130, the total number of orders for each product code is the total number of orders for all store names, or the number of orders for at least one store name among the number of orders for each product code is the number of cases. Calculate the number of orders so that it is a multiple of 430.

Further, in this process, the order quantity calculation unit 130 rounds up the total order quantity to 430 units in the case, and when the order quantity in each store can be rounded up to a multiple of the case quantity 430, the total order is placed. In order to evaluate the distribution cost reduction effect of both when the number and the number of orders for each store are multiples of the number of cases 430, the total number of orders is rounded up to a multiple of the number of cases 430, and further store orders are placed. The number of orders is calculated by rounding up the number to a multiple of 430 in the case.

In the examples of FIGS. 9 and 11, among the store names for which the product code A0001 is ordered, the store names for which the maximum number of orders received is 1130 and the number of orders is 940 or more are the delivery destination A and the delivery destination D. Since the number of orders for the delivery destination A is 8 pieces and the maximum number of receipts is 10 pieces, when changing the number of orders for one store name to the maximum number of receipts, the order quantity calculation unit 130 uses the delivery destination A of the product code A0001. The number of orders will be changed to 9 pieces and 10 pieces in sequence. As shown in the product master information 121 of FIG. 4, since the number of cases 430 of the product code A0001 is 10 pieces, the order quantity calculation unit 130 has the original order quantity of 8 pieces and the changed order quantity of 9 pieces of the product code A0001. Among the pieces and 10 pieces, the number of orders for 10 pieces in which the total number of orders for the product code A0001 or the number of orders for the delivery destination A is a multiple of the packing unit (1 in this example) is generated as a recommended order number proposal.

Similarly, for the delivery destination D, the order quantity calculation unit 130 sequentially changes the original order quantity 3 pieces of the delivery destination D of the product code A0001 to 4 pieces and 5 pieces. Among these, there is no proposal that the total number of orders of the product code A0001 or the number of orders of the delivery destination D is a multiple of the packing unit, so the recommended number of orders is not generated.

When changing the number of orders for two store names, the order quantity calculation unit 130 sets the number of orders for delivery destination A to 9 pieces, the number of orders for delivery destination D to 4 pieces, and the order number proposal for delivery destination A. Among the proposals for the number of orders, where the number is 9 pieces and the number of orders for the delivery destination D is 5 pieces, the total number of orders for product code A0001 is a multiple of the number of cases in product code A0001, and A proposal in which the number of orders for each of the delivery destination A and the delivery destination D is a multiple of the number of cases in the product code A0001 is generated as the recommended number of orders.

In this embodiment, the order quantity calculation unit 130 generates a recommended order quantity proposal in which the order quantity of the delivery destination A and the delivery destination D is changed to 9 pieces and 5 pieces, respectively (proposal 1 in FIG. 13 to be described later). .. In this proposal, neither the number of orders for the delivery destination A nor the number of orders for the delivery destination D is a multiple of the number of cases in the product code A0001, but the total number of orders for all the delivery destinations is 30 pieces, that is, the number of cases in the product code A0001. It is a multiple of (10 pieces). Further, the order quantity calculation unit 130 generates a recommended order quantity proposal in which the order quantity of the delivery destination A and the delivery destination D is changed to 10 pieces and 4 pieces, respectively (proposal 3 in FIG. 13 to be described later). In this proposal, the number of orders placed by the delivery destination A and the total number of orders placed by all the delivery destinations are multiples of the number of cases in the product code A0001. In this way, the order quantity calculation unit 130 generates as a recommended order quantity proposal a combination of one or more pieces when the order quantity of the delivery destination whose order quantity can be changed is changed for each item of the product.

Further, the order quantity calculation unit 130 stores the proposal that does not change the order quantity as the proposal 0 in the recommended order quantity proposal information 129.

13, FIG. 14 and FIG. 15 are explanatory views showing an example of recommended order quantity draft information 129 for the product codes A0001, A0002 and A0003 generated by the recommended order quantity calculation device 100 of the embodiment of the present invention, respectively.

The recommended order quantity proposal information 129 has at least data items that define proposal No. 1310, store name 1320, product code 1330, order quantity 1340, and distribution cost 1350. These are the information that identifies the proposed recommended order quantity generated, the information that identifies the delivery destination of the product, the information that identifies the item of the product, the number of orders for the product to the delivery destination, and the number of orders. The logistics cost (ie cost) of the case. The distribution cost 1350 is calculated in the recommended order quantity extraction process 1030 described below.

(2-3) Recommended order quantity extraction process 1030
The order quantity extraction unit 140 determines the recommended order quantity from the order information 126 for each recommended order quantity proposal included in the recommended order quantity proposal information 129 created by (2-2), and calculates the recommended order quantity. Based on the product master information and the work man-hour master information, the case work man-hour and the piece work man-hour are obtained from, and the distribution cost 1350 is calculated.

First, the procedure for determining the recommended number of orders will be described. The order quantity extraction unit 140 selects one value of the proposal No. 1310 for determining the recommended order quantity from the recommended order quantity proposal information 129, and extracts all the recommended order quantity proposals having the selected value as the proposal No. 1310. The information extracted here is referred to as the proposal No. recommended order quantity proposal information. For example, when the value "Proposal 8" of Proposal No. 1310 is selected in the recommended order quantity proposal information 129 for the product code A0003 shown in FIG. 15, the 14th to 16th records are extracted as the Proposal No. recommended order quantity proposal information. To.

Next, the order quantity extraction unit 140 extracts one piece of information from the proposal number recommended order quantity proposal information, searches the order information 126 using the value of the product code 1330 of the proposal number recommended order quantity proposal information as a key, and searches for the order information 126. All order information having a value matching the value of the product code 1330 as the product code 930 is extracted. The extracted ordering information is called the product code ordering information. For example, when the 14th to 16th records of the recommended order quantity proposal information 129 regarding the product code A0003 shown in FIG. 15 as described above are extracted as the proposal No. recommended order quantity proposal information, the order information 126 shown in FIG. Therefore, the 11th to 15th records in which the value of the product code 930 is "A0003" are extracted as the product code ordering information.

Next, the order quantity extraction unit 140 extracts order information one by one from the product code order information, and determines the recommended order quantity for the store name of the order information. Specifically, the order quantity extraction unit 140 searches for the proposal No. recommended order quantity proposal information using the value of the store name 920 of the order information as a key, and sets a value that matches the value of the store name 920 as the store name. If there is recommended order quantity proposal information as 1320, the value of the order quantity 1340 of the searched recommended order quantity proposal information is set as the recommended order quantity. On the other hand, if there is no recommended order quantity proposal information having a value matching the value of the store name 920 as the store name 1320, the order quantity extraction unit 140 uses the value of the order quantity 940 of the order information as the recommended order quantity. And. The order quantity extraction unit 140 performs the above procedure for all the order information included in the product code order information. After that, the order quantity extraction unit 140 calculates the total recommended order quantity by totaling the recommended order quantity determined for all the order information included in the product code order information.

For example, as described above, the 14th to 15th records in FIG. 15 were extracted as the proposal No. recommended order quantity proposal information, and the 11th to 15th records in FIG. 9 were extracted as the product code order information. In this case, the value of the store name 920 of the 12th to 14th records in FIG. 9 (that is, the delivery destination B to the delivery destination D) matches the value of the store name 1320 of the 14th to 16th records of FIG. 15, respectively. To do. In this case, the recommended number of orders corresponding to the delivery destination B, the delivery destination C, and the delivery destination D is the value of the number of orders 1340 instead of the values "3", "7", and "3" of the number of orders 940, respectively. It becomes "4", "12" and "4". The recommended number of orders for the delivery destination A and the delivery destination E is the values "9" and "12" of the number of orders 940, respectively.

Next, the order quantity extraction unit 140 searches the product master information 121 using the value of the product code 930 of the product code ordering information (“A0003” in the above example) as a key, and matches the value of the product code 930. The product master information (the third record shown in FIG. 4 in the above example) having the value to be used as the product code 410 is extracted, and the value of the extracted product master information in the case number 430 (“12” in the above example). Is specified as the number of cases in the product code.

Next, the order quantity extraction unit 140 puts the product code in the case for each of the total recommended order quantity of the proposal No. obtained by the above procedure and the recommended order quantity of each order information of the product code order information. Calculate the remainder and quotient divided by the number. The remainder value is defined as the piece work man-hour, and the quotient value is defined as the case work man-hour. In the above example, the recommended number of orders for the delivery destination A to the delivery destination E of the product code A0001 is "9", "4", "12", "4", and "12", respectively, and the total recommended number of orders is "12". 41 ". In this case, assuming that the number of cases is "12", the piece work man-hours and the case work man-hours related to the total recommended order number are "5" and "3", respectively. The piece work man-hours and the case work man-hours related to the delivery destination A are "9" and "0", respectively. The piece work man-hours and the case work man-hours related to the delivery destination B are "4" and "0", respectively. The piece work man-hours and the case work man-hours related to the delivery destination C are "0" and "1", respectively. Although the description is omitted for other stores, the piece work man-hours and the case work man-hours are calculated in the same manner.

Then, the order quantity extraction unit 140 calculates the cost for each work by multiplying each by the piece work man-hour unit price 530 and the case work man-hour unit price 540 of the product code searched from the work man-hour master information 122. To do. Finally, the order quantity extraction unit 140 adds the cost calculated from the piece work man-hours and the cost calculated from the case work man-hours to obtain the distribution cost for the ordering of the product code.

For example, in the above example, when the piece work man-hours and the case work man-hours of the delivery destination A are "9" and "0", respectively, the order quantity extraction unit 140 has a piece work man-hour unit price of 530 corresponding to the product code A0003. From the value "30" and the value "50" of the case work man-hour unit price 540, the cost related to the piece work is calculated as "270" and the cost related to the case work is calculated as "0", and the total "270" of both is the distribution related to the delivery destination A. Calculate as an expense. The order quantity extraction unit 140 performs the same calculation for other delivery destinations. Further, the order quantity extraction unit 140 performs the same calculation for the piece work man-hours “5” and the case work man-hours “3” obtained from the total recommended order quantity “30”. Then, the order quantity extraction unit 140 sets the total of the distribution cost calculated for all stores and the distribution cost calculated from the total recommended number of orders (“910” in the above example) as the distribution cost 1350, and recommends the order quantity proposal information 129. Register with.

The order quantity extraction unit 140 carries out the procedure from the selection of the recommended order quantity proposal No. to the distribution cost calculation described above for all the recommended order quantity proposal Nos included in the recommended order quantity proposal information.

Next, the order quantity extraction unit 140 compares the distribution cost 1350 of each proposal No. of the recommended order quantity proposal information with the distribution cost 1350 of proposal 0, and calculates the distribution cost that is equal to or more than the distribution cost of proposal 0. Delete the recommended order quantity proposal information of No. As a result, out of the generated recommended number of orders, the one whose distribution cost is smaller than the distribution cost calculated based on the initial number of orders is extracted. For recommended order quantity proposal information that is less than the distribution cost of proposal 0, the proposal number is reassigned in ascending order from 1.

In this embodiment, as a result of implementing (2-2) and (2-3), as a recommended number of orders to be reduced from the distribution cost when the number of orders of Plan 0 is not changed, FIGS. 13, 14, and FIGS. As shown in 15, the recommended order number proposals of 3 proposals for the product code A0001, 12 proposals for A0002, and 8 proposals for A0003 are extracted.

For example, in the example of FIG. 15, five proposals in which the number of orders for two store names is changed and one proposal in which the number of orders for three store names are changed are extracted. In Proposal 8, where the number of orders for the three store names has been changed, the number of orders for delivery destination B, delivery destination C, and delivery destination D is set to 4 pieces, 12 pieces, and 4 pieces, respectively, and as a result, the distribution cost 1350 is 910. This indicates that the distribution cost of 1350 could be reduced by 200 compared to Plan 0.

(3) Result output processing 330
In the result output process 330 of FIG. 3, the display unit 150 displays the maximum number of received information 127 stored in the storage unit 120 and the recommended order quantity draft information 129. Examples of the results to be displayed are shown in FIGS. 16 and 17.

FIG. 16 is an explanatory diagram showing an example of screen display of the maximum number of received information 127 by the recommended order number calculation device 100 according to the embodiment of the present invention.

The screen display of the maximum number of acceptance information has at least data items for displaying the store name 1610, the product code 1620, the maximum number of acceptances 1630, and the acceptance condition 1640. Of these, the store name 1610, the product code 1620, and the acceptance condition 1640 correspond to the store name 810, the product code 820, and the acceptance condition 830 of the acceptance condition information 125 shown in FIG. 8, respectively. The maximum number of acceptances 1630 corresponds to the maximum number of acceptances 1130 of the maximum number of acceptance information 127 shown in FIG.

This allows the user to confirm the maximum number of acceptances for the acceptance conditions.

FIG. 17 is an explanatory diagram showing an example of graph display of the distribution cost and the number of stores where the number of orders is changed by the recommended number of orders calculation device 100 according to the embodiment of the present invention.

The graph of the distribution cost and the number of stores for which the number of orders has been changed is displayed based on the recommended order number proposal information 129 stored in the storage unit 120. In the example of FIG. 17, a graph is displayed in which each recommended order quantity proposal is plotted in a two-dimensional space in which the distribution cost is set on the horizontal axis and the number of stores that change the number of orders required for reducing the distribution cost is set on the vertical axis. The number of stores that change the number of orders is the number of stores that need to change the number of orders in order to reduce distribution costs in each recommended number of orders. In the example of FIG. 17, with respect to the product code A0003, circular figures corresponding to the plans 1 to 8 shown in FIG. 15 are plotted. Further, the display unit 150 displays the store name in which the number of orders for each proposal corresponding to the circle in the graph is changed by a balloon or the like.

In addition, the display unit 150 demands using the store name, product code, and order quantity of each recommended order quantity information that matches the proposal No. of the recommended order quantity proposal information as keys in order to show the future order possibility for each proposal. The forecast information is searched, the demand probability that matches the key is multiplied to calculate the ordering possibility, and the calculated ordering possibility is expressed as the size of the circle. For example, in the case of the above plan 8, from the order information 126 (FIG. 9) and the recommended order quantity proposal information 129 (FIG. 15), the number of orders after the change of the delivery destination B to the delivery destination D whose order quantity has been changed is each. The demand probabilities 1240 corresponding to "4", "12" and "4" are "100%", "16%" and "67%" from the 9th, 10th and 19th records in FIG. Become. In this case, "11%", which is the total product of those demand probabilities, is calculated as the orderability. The larger the yen, the higher the possibility of future orders, and the smaller the yen, the lower the possibility.

In general, it is desirable that the distribution cost is small, the number of stores that change the number of orders is small, and the possibility of ordering is high (because the possibility of unsold products is low). However, in reality, not all of them are satisfied. As shown in FIG. 17, by displaying a graph so that the magnitude of each value can be intuitively understood, the user can change the number of stores and the changed amount for the reduced distribution cost. It is possible to confirm the possibility of ordering in the future and to consider the recommended number of orders that the store can easily accept.

In the above embodiment, the present invention is applied to the ordering of the goods to be delivered to the store, but the present invention is not limited to such goods and can be applied to any kind of goods.

As described above, according to the embodiment of the present invention, there is provided a recommended order quantity calculation system that reduces the work man-hours by rounding up the total order quantity of all stores to the packing unit within the range satisfying the acceptance condition of each store. .. Furthermore, by using the past results, it is possible to determine the success or failure of the acceptance conditions for the past results and the predicted amount of future orders.

Specifically, the recommended order quantity calculation device, which is an embodiment of the present invention, is an order that can be accepted at the maximum at each store based on past results and the like with respect to past allocation information, order information, and acceptance conditions of each store. Identify the number. Then, the recommended order quantity calculation device selects a plurality of order quantity proposals to reduce the distribution cost by creating a plurality of store order quantity proposals within the range of the order quantity that can be accepted by each specified store and calculating the distribution cost. However, it has a function to display the relationship between the distribution cost to be reduced and the number of stores to change the number of orders to the calculated plan. As a result, the number of orders can be rounded up to the packing unit after satisfying the acceptance conditions of each store, so that the man-hours for work in the warehouse can be reduced and the distribution cost can be reduced.

The present invention is not limited to the above-mentioned examples, and includes various modifications. For example, the above-mentioned examples have been described in detail for a better understanding of the present invention, and are not necessarily limited to those having all the configurations of the description.

Further, each of the above configurations, functions, processing units, processing means and the like may be realized by hardware by designing a part or all of them by, for example, an integrated circuit. Further, each of the above configurations, functions, and the like may be realized by software by the processor interpreting and executing a program that realizes each function. Information such as programs, tables, and files that realize each function can be stored in non-volatile semiconductor memories, hard disk drives, storage devices such as SSDs (Solid State Drives), or non-readable computers such as IC cards, SD cards, and DVDs. It can be stored in a temporary data storage medium.

In addition, the control lines and information lines are shown as necessary for explanation, and not all control lines and information lines are shown in the product. In practice, it can be considered that almost all configurations are interconnected.

Claims (14)

Case entry information indicating the maximum value of the number of pieces, which is the number of articles stored in one case, work man-hour unit price for each case of the article, work man-hour unit price for each piece, and the article. Order information indicating the number of orders for each delivery destination, acceptance condition information indicating conditions for determining the number of the goods that can be accepted by each delivery destination in addition to the number of orders , and the past for each delivery destination of the goods. A storage unit that holds past order information indicating the number of orders placed in
Based on the ordering information and the acceptance condition information, the maximum number of receipts, which is the maximum number of the goods that can be accepted by each delivery destination, is calculated, and the number of orders of at least one delivery destination is the maximum number of receipts. The order quantity calculation unit that generates one or more order quantity proposals increased within the range not exceeding
Based on the case quantity information and the work man-hour unit price information, the distribution cost for each of the order quantity proposals is calculated, and one or more order quantity proposals in which the distribution cost is smaller than before the change in the order quantity. Order quantity extraction unit to extract
Have a, a display unit for outputting one or more order quantity proposal that the extracted,
The acceptance condition information is information that specifies a specific number of items that can be accepted in addition to the number of items ordered for each delivery destination, and information that indicates the number based on the number of items ordered in the past. A logistics support system characterized by including any of the above . The logistics support system according to claim 1.
Based on the case quantity information, the order quantity calculation unit stores at least one of the order quantity of at least one delivery destination and the total number of orders of all the delivery destinations in the one case. A logistics support system characterized in that one or more of the above-mentioned order quantity proposals are generated so as to be a multiple of the maximum value of the number of pieces. The logistics support system according to claim 1.
The order quantity extraction unit
Based on the case quantity information, the piece work man-hours and the case work man-hours corresponding to the order number included in the order number proposal are specified for each delivery destination.
Specify the piece work man-hours and the case work man-hours corresponding to the total number of orders included in the order number proposal for all the delivery destinations.
To calculate the distribution cost of the order quantity proposal by summing the specified number of the piece work man-hours and the case work man-hours multiplied by the work man-hour unit price for each piece and the work man-hour unit price for each case, respectively. Characteristic logistics support system. The logistics support system according to claim 1.
The information indicating the number based on the number of orders placed in the past is information indicating that the maximum number of items with a track record of being accepted in the past can be accepted, and can be accepted if the item was not accepted last time. A logistics support system characterized by containing any of the information indicating the number of goods . The logistics support system according to claim 1.
The order number calculating section, based on the past order information, for each of the delivery destination, to predict the magnitude of the demand of the article when the number of orders is changed,
The display unit is a physical distribution support system characterized by outputting the magnitude of the predicted demand corresponding to a change in the number of orders in the extracted draft number of orders. The logistics support system according to claim 5.
The display unit is characterized by outputting the distribution cost, the number of delivery destinations for which the number of orders is changed in the proposed number of orders, and the magnitude of the predicted demand for each of the proposed number of orders. Logistics support system. Case entry information indicating the maximum value of the number of pieces, which is the number of articles stored in one case, work man-hour unit price for each case of the article, work man-hour unit price for each piece, and the article. A storage unit that holds order information indicating the number of orders for each delivery destination, and acceptance condition information indicating conditions for determining the number of the goods that can be accepted by each delivery destination in addition to the number of orders.
Based on the ordering information and the acceptance condition information, the maximum number of receipts, which is the maximum number of the goods that can be accepted by each delivery destination, is calculated, and the number of orders of at least one delivery destination is the maximum number of receipts. The order quantity calculation unit that generates one or more order quantity proposals increased within the range not exceeding
Based on the case quantity information and the work man-hour unit price information, the distribution cost for each of the order quantity proposals is calculated, and one or more order quantity proposals in which the distribution cost is smaller than before the change in the order quantity. Order quantity extraction unit to extract
It has a display unit that outputs one or more extracted order number proposals, and has
The storage unit further holds past order information indicating the number of past orders for each delivery destination of the article.
Based on the past order information, the order quantity calculation unit predicts the magnitude of demand for the goods when the order quantity is changed for each delivery destination.
The display unit has a size corresponding to the predicted size of demand in a two-dimensional space in which the first axis is the distribution cost and the second axis is the number of delivery destinations whose order quantity is changed. By outputting a graph plotting the figure of, the distribution cost, the number of delivery destinations whose order quantity is changed in the order quantity proposal, and the predicted demand magnitude are calculated for each order quantity proposal. A logistics support system characterized by outputting . A logistics support method executed by a computer having a processor, a storage device connected to the processor, and a display device connected to the processor.
The storage device has case entry information indicating the maximum value of the number of pieces, which is the number of articles stored in one case, and the work man-hour unit price indicating the work man-hour unit price for each case and the work man-hour unit price for each piece. Information, ordering information indicating the number of orders for each delivery destination of the goods, acceptance condition information indicating conditions for determining the number of the goods that can be accepted in addition to the number of orders by each delivery destination, and acceptance condition information of the goods. Holds past order information showing the number of past orders for each delivery destination ,
The logistics support method is
The first procedure in which the processor calculates the maximum number of receipts, which is the maximum number of the goods that each delivery destination can accept, based on the ordering information and the acceptance condition information.
A second step in which the processor generates one or more proposed order numbers by increasing the number of orders of at least one delivery destination within a range not exceeding the maximum number of orders received.
The processor calculates the distribution cost for each of the order quantity proposals based on the case quantity information and the work man-hour unit price information, and one or more of the distribution costs are smaller than before the change in the order quantity. The third step to extract the order quantity plan of
The display device includes a fourth step of outputting the extracted one or more proposed order numbers .
The acceptance condition information is information that specifies a specific number of acceptable goods in addition to the number of ordered goods for each delivery destination, and information indicating a number based on the number of past orders. A logistics support method characterized by including any of the above . The logistics support method according to claim 8.
In the second step, the processor stores at least one of the number of orders of at least one delivery destination and the total number of orders of all the delivery destinations in the one case based on the case quantity information. A logistics support method characterized in that one or more of the above-mentioned order number proposals are generated so as to be a multiple of the maximum number of pieces of the goods to be made. The logistics support method according to claim 8.
In the third procedure, the processor
Based on the case quantity information, the piece work man-hours and the case work man-hours corresponding to the order number included in the order number proposal are specified for each delivery destination.
Specify the piece work man-hours and the case work man-hours corresponding to the total number of orders included in the order number proposal for all the delivery destinations.
To calculate the distribution cost of the order quantity proposal by summing the specified number of the piece work man-hours and the case work man-hours multiplied by the work man-hour unit price for each piece and the work man-hour unit price for each case, respectively. Characteristic logistics support method. The logistics support method according to claim 8.
The information indicating the number based on the number of orders placed in the past is information indicating that the maximum number of items with a track record of being accepted in the past can be accepted, and can be accepted if the item was not accepted last time. A logistics support method characterized by containing any of information indicating the number of goods . The logistics support method according to claim 8.
In the first procedure, the processor predicts the magnitude of demand for the goods when the number of orders is changed for each delivery destination based on the past order information.
In the fourth procedure, the display device outputs the predicted magnitude of demand corresponding to the change in the number of orders in the extracted draft order number. The logistics support method according to claim 12.
In the fourth procedure, the display device outputs the distribution cost, the number of delivery destinations for which the number of orders is changed in the proposed number of orders, and the magnitude of the predicted demand for each of the proposed number of orders. Logistics support method characterized by doing. A logistics support method executed by a computer having a processor, a storage device connected to the processor, and a display device connected to the processor.
The storage device has case entry information indicating the maximum value of the number of pieces, which is the number of articles stored in one case, and the work man-hour unit price indicating the work man-hour unit price for each case and the work man-hour unit price for each piece. Information, order information indicating the number of orders for each delivery destination of the goods, and acceptance condition information indicating conditions for determining the number of the goods that can be accepted in addition to the number of orders are retained. ,
The logistics support method is
The first procedure in which the processor calculates the maximum number of receipts, which is the maximum number of the goods that each delivery destination can accept, based on the order information and the acceptance condition information.
A second step in which the processor generates one or more proposed order quantitys in which at least one order quantity of the delivery destination is increased within a range not exceeding the maximum number of orders received.
The processor calculates the distribution cost for each of the order quantity proposals based on the case quantity information and the work man-hour unit price information, and one or more of the distribution costs are smaller than before the change in the order quantity. The third step to extract the order quantity plan of
The display device includes a fourth step of outputting the extracted one or more proposed order numbers.
The storage device further holds past order information indicating the number of past orders for each delivery destination of the article.
In the first procedure, the processor predicts the magnitude of demand for the goods when the number of orders is changed for each delivery destination based on the past order information.
In the fourth procedure, the display device has the predicted magnitude of demand in a two-dimensional space in which the first axis is the distribution cost and the second axis is the number of delivery destinations whose order quantity is changed. By outputting a graph in which a figure of a corresponding size is plotted , the distribution cost, the number of delivery destinations in which the number of orders is changed in the proposed number of orders, and the predicted number of orders are predicted for each proposed number of orders. A logistics support method characterized by outputting the magnitude of demand .

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