JP2021089649A - Required amount planning system and required amount planning method - Google Patents

Abstract

To provide a required amount calculation technique for order production.SOLUTION: A required amount planning system comprises: an information acquisition unit for acquiring inquiry information about a product for which constituents ranging from an object to be purchased to a finished product form a hierarchy; a product constituent estimation unit for estimating the use probability of the object to be purchased based on the inquiry information; and a required amount calculation unit for calculating the required amount of the object to be purchased based on the use probability of the object to be purchased. The product constituent estimation unit estimates the use probability of the object to be purchased by performing repetitive processing for estimating the use probability of a constituent that is one rank lower than a finished product based on the specification of the finished product that fits the inquiry information and then estimating the use probability of a constituent that is one rank lower than the estimated constituent.SELECTED DRAWING: Figure 1

Description

The present invention relates to requirements planning.

In build-to-order manufacturing such as elevators, reducing the supply lead time from ordering to shipping is an issue in order to respond to orders with short delivery times. In order to reduce the supply lead time, it is effective to prepare and manufacture parts before receiving an order.

There is Patent Document 1 as a background technique relating to the preparation production. According to Patent Document 1, "The advance arrangement determination unit 13 of the item requirement prediction device 1 has the negotiation information acquired by the input unit 11, the prediction pattern information stored in the prediction pattern DB 122, and the component configuration information and purchase stored in the component configuration DB 124. The advance order parts are determined based on the purchase master information stored in the master DB 123 and the common inventory information stored in the common inventory DB 126. "

Japanese Unexamined Patent Publication No. 2014-130466

Generally, a product is composed of a plurality of layers such as a model, a device, a unit, and a part. In build-to-order manufacturing, the product configuration is customized according to the customer's order, so even if the model is the same, the equipment, units, and parts are different for each case, and the order is not confirmed.

In the technology of Patent Document 1, the models that can be taken by each project before receiving an order are estimated, but the devices, units, and parts of each model are the same regardless of the customer's order. For this reason, it is not possible to calculate the required quantity with high accuracy in the build-to-order manufacturing in which the product composition is different for each project.

The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a system and a method for calculating the required amount of each part for a project before receiving an order.

In order to solve the above problems, one of the typical requirements planning system and requirements planning method of the present invention is an inquiry for a product in which the components from the purchase target to the finished product are layered. An information acquisition unit that acquires information, a product configuration estimation unit that estimates the usage probability of the purchase target based on the inquiry information, and a required amount of the purchase target from the usage probability of the purchase target. The product configuration estimation unit is provided with a required quantity calculation unit for calculating the above, and the product configuration estimation unit estimates the usage probability of the component one level lower than the finished product from the specifications of the finished product conforming to the inquiry information, and estimates the component. It is characterized in that the usage probability of the purchased object is estimated by repeating the process of estimating the usage probability of the component one level lower than the above.

According to the present invention, it is possible to calculate the required amount in make-to-order production. Issues, configurations, and effects other than those described above will be clarified by the following description of the embodiments.

It is a figure which shows an example of the functional block of the requirement planning system. It is a figure which shows an example of the data structure of inquiry information. It is a figure which shows an example of the data structure of the past matter information. It is a figure which shows an example of the data structure of the past product composition information. It is a figure which shows an example of the data structure of product constraint information. It is a figure which shows an example of the data structure of inventory information. It is a figure which shows an example of the data structure of inventory risk information. It is a figure which shows an example of the data structure of the product composition estimation information. It is a figure which shows an example of the data structure of the required quantity information for each part. It is a flowchart which shows an example of the processing by the requirement planning system. It is a figure which shows an example of the information about the model adopted in the past project. It is a figure which shows an example of the information about the apparatus adopted in the past project. It is a figure which shows an example of the information which shows whether or not the apparatus can be combined with each model. It is a figure which shows an example of the calculation method of the use probability of an apparatus. It is a figure which shows an example of the use probability and the number of uses. It is a figure which shows an example of an output screen.

Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In this specification, the matter means an order from a customer, and the registration information thereof is composed of a customer name, a delivery date, and the like. In this specification, the registration information includes the customer name, the delivery date, the price of the product, the delivery destination of the product, the floor indicating the height of the building, the desired speed, the interior specifications of the product, and the number of units delivered. However, the present invention is not limited to this.

Further, the product configuration shall be composed of four layers of a model, a device, a unit, and a component. Specifically, products are classified into a plurality of models, and each model is composed of a plurality of devices. Further, the device is composed of a plurality of units, and the unit is composed of a plurality of parts. That is, the model of the product corresponds to the finished product within the claims, and the device, the unit and the component correspond to the component. And the parts are the objects to be purchased within the scope of claims. In the present specification, there are four layers of a model, a device, a unit, and a component, but the present invention is not limited to this. Further, the name of the device shall be described as "device DA-1". "DA" represents a category of a device such as a hoist, and "1" represents a type within the category. Name the unit and parts in the same way as for the device.

Hereinafter, an embodiment of the requirement planning system according to the present invention will be described with reference to the drawings. FIG. 1 is a diagram showing an example of a functional block of the requirement planning system 100 of the present embodiment. The requirement planning system 100 of the present embodiment includes a requirement planning device 101, each of which is communicably connected via a network 102, a user terminal 103 used by a user, and a database 104 in which data is stored. ..

The user terminal 103 is an information processing device such as a PC (Personal Computer). The user issues a process execution instruction to the requirement planning device 101 through the user terminal 103. Further, the user terminal 103 has a function of displaying the information output by the requirement planning device 101 to the user.

The database 104 is, for example, a system such as ERP (Enterprise Resource Planning), or a database or storage device that stores data equivalent thereto.

The network 102 communicatively connects the user terminal 103, the database 104, and the requirement planning device 101. The network 102 is, for example, any one of a communication network using a general public line such as LAN (Local Area Network), WAN (Wide Area Network), VPN (Virtual Private Network), and the Internet in part or in whole.

The requirement planning device 101 is an information processing device such as a PC or a server computer, and calculates the requirement by estimating the probability of possible product configurations for each project and calculating the usage probability for each component. The requirement planning device 101 includes a storage unit 110, a calculation unit 120, an input unit 130, and an output unit 140.

The storage unit 110 requires inquiries information 111, past case information 112, past product configuration information 113, product constraint information 114, inventory information 115, inventory risk information 116, product configuration estimation information 117, and parts. The quantity information 118 and are stored.

FIG. 2 is an example of the data structure of the inquiry information 111. Inquiry information 111 stores registration information of the matter in the inquiring stage before receiving an order, and includes matter No. 1111, customer name 1112, delivery date 1113, order accuracy 1114, amount of money 1115, and delivery destination 1116. It consists of a floor 1117, a speed 1118, an interior specification 1119, and the number of units delivered 1110.

Item No. 1111 indicates number information for identifying the item. The customer name 1112 indicates the customer name of the project. The delivery date 1113 indicates the deadline for delivery of the product to the customer. The order accuracy 1114 indicates the probability that the project will receive an order in the future. For example, a project whose project number is "1" in FIG. 2 means that an order is received with a probability of 80%. Amount 1115 indicates the estimated price of the product. Delivery destination 1116 indicates the delivery destination of the product. The floor 1117 indicates the height of the delivery destination building. For example, in the case of the project No. 1 in FIG. 2, the project is delivered to a 60-story building. The speed 1118 indicates the speed of the product desired by the customer. For example, in the case of the case where the case number of FIG. 2 is "1", the customer wants a product having a speed of 120 m / min. Interior specification 1119 indicates the type of interior of the product. In this specification, general-purpose "standard" and "order" customized according to customer's request are taken up as types of interior specifications, but the present invention is not limited to these. The number of delivered products 1110 indicates the number of products purchased by the customer.

FIG. 3 is an example of the data structure of the past project information 112. The past project information 112 stores registration information of projects that have been ordered in the past, and includes project No. 1121, customer name 1122, delivery date 1123, amount of money 1124, delivery destination 1125, floor 1126, and speed 1127. It is composed of the interior specification 1128 and the number of units delivered 1129. The content of each item is the same as that of the inquiry information 111.

FIG. 4 is an example of the data structure of the past product configuration information 113. The past product configuration information 113 stores information on the product configuration of the past project registered in the past project information 112, and is used with the project No. 1131, the model 1132, the device 1133, the unit 1134, and the component 1135. It is composed of the number 1136.

Item No. 1131 is number information for identifying the item. Model 1132 indicates a model adopted in a past project. Device 1133 indicates a device that has been adopted in a past project. Unit 1134 indicates a unit that has been adopted in a past project. Part 1135 indicates a part adopted in a past project. The number of used 1136 indicates the number of used parts 1135. For example, in FIG. 4, the "unit UA-1" is composed of four "parts PA-1", one "part PA-2", and one "part PA-3". ing.

FIG. 5 is an example of the data structure of the product constraint information 114. The product constraint information 114 describes information regarding whether or not the device, the unit, and the parts can be combined. In the present specification, the product constraint information 114 is composed of device constraint information 114a, unit constraint information 114b, and component constraint information 114c, but is not limited thereto, for example, between components. It may be information such as whether or not the combination of the above is possible.

The model 114a1 and the device 114a2 that can be combined with each model are registered in the device constraint information 114a. For example, in the example of FIG. 5, the devices that can be combined with the "high-speed / standard type" model in the device DA category are "device DA-1", "device DA-2", and "device DA-3". Is. Similarly, in the constraint information 114b of the unit, the device 114b1 and the unit 114b2 that can be combined with each device are registered. Further, in the component constraint information 114c, the unit 114c1 and the component 114c2 that can be combined with each unit are registered.

FIG. 6 is an example of the data structure of the inventory information 115. The inventory information 115 stores inventory information of each component, and is composed of a component 1151, a safety inventory 1152, and an inventory 1153.

Part 1151 indicates a part name. The safety stock 1152 indicates the number of stocks to be held in order to respond to fluctuations in demand and the like. Inventory 1153 indicates the number of parts in stock held at the time of processing by the requirement planning device 101.

FIG. 7 is an example of inventory risk information 116. The inventory risk information 116 stores information on the risk of being stored as excess inventory without being used in the future, and is composed of a part 1161 and an inventory risk 1162.

Part 1161 indicates a part name. Inventory risk 1162 indicates the risk of parts becoming excess inventory. The larger the inventory risk, the less likely it is to become excess inventory, and the smaller the inventory risk, the more likely it is to become excess inventory. To do. Specifically, if the inventory risk value is less than 1, the inventory is held above the safety inventory, and it is unlikely that the inventory will be insufficient, but there is a possibility that the inventory will be surplus. If the value of inventory risk is 1, it is unlikely that the inventory will be less than the safety inventory and will be surplus inventory, but there is a possibility that the inventory will be insufficient.

FIG. 8 is an example of product configuration estimation information 117. The product configuration estimation information 117 stores the usage probabilities of the product configurations for each layer, and is derived from the model usage probability 117a, the device usage probability 117b, the unit usage probability 117c, and the component usage probability 117d. It is configured.

In the model usage probability 117a, the project No. 117a1, the model 117a2, and the usage probability 117a3 are registered. For example, in the example of FIG. 8, the probability that the project whose project number is "1" uses the "high-speed / standard" model is 90%, and the probability of using the "medium-speed / standard" model is 5%. The probability of using a "low speed / standard type" model is 0%. Similarly, in the device usage probability 117b, the case No. 117b2, the device 117b2, and the usage probability 117b3 are registered. In the unit usage probability 117c, the case No. 117c1, the unit 117c2, and the usage probability 117c3 are registered. In the component usage probability 117d, the project No. 117d1, the component 117d2, the usage probability 117d3, and the number of components used 117d4 are registered.

FIG. 9 is an example of the required amount information 118 for each part. The required amount information 118 for each part stores the required amount of each part in the future, and is composed of the part 1181, the month 1182, and the required amount 1183.

Part 1181 indicates a part name. Month 1182 indicates the month in which the requirement for parts occurs. The required quantity 1183 indicates the required quantity of parts in each month. For example, in the case of FIG. 9, 1200 parts "PA1" are required in September. In this specification, the timing at which the requirement for parts occurs is tabulated on a monthly basis, but it may be an aggregation unit period such as a daily unit or a weekly unit.

Returning to the details of the requirement planning device 101 of FIG. The calculation unit 120 includes an information acquisition unit 121, a product configuration estimation unit 122, an inventory risk calculation unit 123, and a required quantity calculation unit 124.

The information acquisition unit 121 includes information necessary for the product configuration estimation unit 122, the inventory risk calculation unit 123, and the required quantity calculation unit 124 from the database 104 (inquiry information 111, past case information 112, past product configuration information 113, product restrictions). Information 114, inventory information 115, etc.) are acquired and stored in the storage unit 110.

The product configuration estimation unit 122 calculates the usage probability of the model, the device, the unit, and the parts from the inquiry information 111, the past case information 112, the past product configuration information 113, and the product constraint information 114, and produces the product. It is stored in the configuration estimation information 117.

The inventory risk calculation unit 123 calculates the inventory risk of each part from the inventory information 115 and stores it in the inventory risk information 116. The required quantity calculation unit 124 calculates the required quantity of each component from the inventory risk information 116 and the product configuration estimation information 117, and stores the required quantity in the required quantity information 118 for each component.

The input unit 130 is connected to the user terminal 103 and the database 104 via the network 102. The information acquisition unit 121 receives the inquiry information 111, the past case information 112, the past product configuration information 113, the product constraint information 114, and the inventory information 115 from the database 104 via the input unit 130, and receives the storage unit 110. Store in.

The output unit 140 transmits the product configuration estimation information 117 stored in the storage unit 110 and the required amount information 118 for each part to the user terminal 103 connected by the network 102, and the user is required to perform the product configuration and each part. It has a function to display the calculation result of the quantity.

Next, the flow of processing executed by the requirement planning system 100 in this embodiment will be described with reference to the flowchart of FIG. The following series of processes is based on the premise that a predetermined number of past matter information 112 and past product configuration information 113 are recorded in the database 104, and responds to, for example, a start command from the user to the user terminal 103. Is started.

First, in step S1, the information acquisition unit 121 obtains the inquiry information 111, the past case information 112, the past product configuration information 113, the product constraint information 114, and the inventory information 115 from the database 104 via the network 102. , And store it in the storage unit 110. Next, the processes of steps S2 to S6 are repeated for the number of cases stored in the inquiry information 111.

First, in step S2, the product configuration estimation unit 122 extracts the product configuration information of the past project similar to the project to be processed from the inquiry information 111, the past project information 112, and the past product configuration information 113.

Specifically, first, the product composition estimation unit 122 compares the amount of the inquiry information 111 and the past project information 112, the floor, the speed, the interior specifications, and the number of delivered units, and the past similar to the project to be processed. Extract the matter. For example, in the case of the case where the case number is "1" in the inquiry information 111, the amount is "100M yen", the floor is "60th floor", the speed is "120m / min", the interior specification is "standard", and the number of units delivered is Extract past projects that are "5 units". As a result, past projects having project numbers such as "101", "104", and "105" are extracted from the past project information 112.

Then, the product composition estimation unit 122 extracts the product composition information of the extracted past project from the past product composition information 113. In the above case, records such as "101", "104", and "105" are extracted from the past product configuration information 113.

Here, for the sake of simplicity, we have illustrated the extraction of cases where each item matches the inquired case, but it is not limited to past cases where the items match exactly, for example, the degree of similarity is obtained for each item and the similarity for each item. It is possible to comprehensively evaluate the degree and extract past projects similar to the inquired project. Any method may be used for similar evaluation.

Next, in step S3, the product configuration estimation unit 122 estimates the usage probability of each model in the matter to be processed. Based on the product configuration information of the past projects extracted in step S2, the product configuration estimation unit 122 totals the number of past projects for each model as shown in FIG. 11 and calculates the usage probability. FIG. 11 is a calculation example for a case in which the case number of the inquiry information 111 is “1”. After that, the product configuration estimation unit 122 stores the calculation result of the usage probability in the product configuration estimation information 117.

In FIG. 11, 100 past cases similar to the specifications of the inquired case are extracted, 90 cases are high-speed / standard type, 5 cases are medium-speed / standard type, and 5 cases are high-speed / order type. there were. Therefore, the usage probability of the high-speed / standard type is 90%, the usage probability of the medium-speed / standard type is 5%, and the usage probability of the high-speed / order type is 5% in the inquiry project.

Next, in step S4, the product configuration estimation unit 122 calculates the usage probability of the device in the matter to be processed. The following processing is performed for all models such as "high-speed / standard type" and "medium-speed / standard type", and the usage probability of the device combined with each model is calculated.・ An example of calculating the usage probability of the device combined with the "standard type" is shown.

First, as in step S3, the product configuration estimation unit 122 totals the number of projects for each device as shown in FIG. 12 based on the product configuration information of the past projects extracted in step S2, and calculates the usage probability. FIG. 12 is an example of calculating the usage probability of the device whose case number is “1” and the category is “DA” in the inquiry information 111. As shown in FIG. 12, the devices of the category "DA" are "device DA-1", "device DA-2", "device DA-3", "device DA-4", and "device". It is assumed that there are six types, "DA-5" and "device DA-6".

In FIG. 12, of the 100 past cases extracted as being similar to the specifications of the inquired case, 35 cases are “device DA-1”, 5 cases are “device DA-2”, and 5 cases are. There were "device DA-3", 20 cases were "device DA-4", 20 cases were "device DA-5", and 15 cases were "device DA-6". Therefore, the usage probability of "device DA-1" is 35%, the usage probability of "device DA-2" is 5%, the usage probability of "device DA-3" is 5%, and "device DA-4" in the inquiry project. The usage probability of "Device DA-5" is 20%, the usage probability of "Device DA-5" is 20%, and the usage probability of "Device DA-6" is 15%.

Here, in step S3, the devices are totaled for all the extracted past projects. For example, if the devices are aggregated for the cases using the model "high-speed / standard type", the items can be aggregated by reflecting the relationship between the model and the device, but the items are subdivided as the hierarchy is traced. Therefore, the population parameter of the total decreases. For this reason, when aggregating for a certain hierarchy, all the extracted past projects are used as parameters without depending on the upper hierarchy.

Subsequently, the product configuration estimation unit 122 acquires a device that can be combined with each model from the product constraint information 114. In the example of FIG. 5, among the devices of the category "A", the devices that can be combined with the "high-speed / standard type" are "device DA-1", "device DA-2", and "device DA-". 3 ”, and the“ device DA-4 ”,“ device DA-5 ”, and“ device DA-6 ”in FIG. 12 cannot be combined. As a result, the usage probability of the device is calculated as shown in FIG. 13, considering only the possibility of combination with the "high-speed / standard type". That is, "device DA-1" is 33%, "device DA-2" is 33%, "device DA-3" is 33%, "device DA-4" is 0%, and "device DA-5". Is 0%, and "device DA-6" is 0%.

In this way, by applying the restrictions on the combination with the components of other layers and excluding the combinations that conflict with the restrictions, even if the relevant layer is aggregated for all the extracted past projects, the above It can reflect the relationship with the hierarchy.

Finally, the product composition estimation unit 122 combines the usage probability calculated based on the product composition information of the past project and the usage probability calculated based on the product constraint information to calculate the final usage probability. Specifically, the product configuration estimation unit 122 multiplies the two usage probabilities as shown in FIG. FIG. 14 is an example of multiplying the calculation results of FIGS. 12 and 13. However, since the simple multiplication of the two usage probabilities cannot reflect the usage probability of each model calculated in step S3, the sum of the products of the usage probabilities for each device is the usage probability of the model as shown in FIG. Correct to match. In the example of FIG. 14, since the usage probability of the "high-speed / standard type" is "90%", the product of the two usage probabilities is divided by "90%". Finally, the product configuration estimation unit 122 stores the estimation result of the usage probability in the product configuration estimation information 117. The combination method of usage probabilities may use other methods such as Bayesian estimation, and is not limited to the method described in the present specification.

Next, in step S5, the product configuration estimation unit 122 calculates the usage probability of the unit in the matter to be processed. The processing content of step S5 is the same as the processing content of step S4. First, the product composition estimation unit 122 calculates the usage probability based on the product composition information of the past project extracted in step S2. Next, the product configuration estimation unit 122 calculates the usage probability based on the product constraint information. After that, the product configuration estimation unit 122 calculates the final usage probability by combining the two usage probabilities and stores it in the product configuration estimation information 117.

Next, in step S6, the product configuration estimation unit 122 calculates the usage probability of the parts in the matter to be processed. The processing content of step S6 is the same as the processing content of step S4. First, the product composition estimation unit 122 calculates the usage probability based on the product composition information of the past project extracted in step S2. Next, the product configuration estimation unit 122 calculates the usage probability based on the product constraint information. After that, the product configuration estimation unit 122 calculates the final usage probability by combining the two usage probabilities and stores it in the product configuration estimation information 117. As for the number of parts used, the number of parts used in the past project extracted in step S2 is totaled and the mode is adopted, but the number of parts used may be determined by a method other than this. ..

Next, the product configuration estimation unit 122 repeats the processing of step S7 and step S8 for the number of parts to be the target of the requirement planning.

First, in step S7, the inventory risk calculation unit 123 calculates the inventory risk of the parts to be processed from the inventory information 115. Specifically, the inventory risk calculation unit 123 calculates the inventory risk by dividing the safety stock by the current number of inventories. For example, in the case of "part PA-1" in FIG. 6, since the safety stock is "200" and the current stock is "250", the inventory risk is 200 ÷ 250 = 0.8. Similarly, in the case of "part PA-2" in FIG. 6, since the safety stock is "150" and the current stock is "100", the inventory risk is 150 ÷ 100 = 1.5. However, when the inventory risk becomes larger than 1 in this way, the inventory risk is set to 1. Finally, the inventory risk calculation unit 123 stores the inventory risk calculation result in the inventory risk information 116.

Next, in step S8, the required quantity calculation unit 124 calculates the required quantity of the parts to be processed monthly from the inventory risk information 116 and the product configuration estimation information 117. First, as shown in FIG. 15, the required quantity calculation unit 124 extracts the delivery date of all the projects, the usage probability of all the projects for the parts to be processed, and the number of uses from the inquiry information 111 and the product configuration estimation information 117. FIG. 15 is an example of extracting the usage probabilities of all the projects for “Part PA-1”. Then, the required amount calculation unit 124 adds up the sum of the products of the number of uses and the probability of use of each project for each month of the delivery date. For "September" in FIG. 15, it is calculated as 40% × 2 + 80% × 1 + 90% × 4 ... = 1500.

Next, the required quantity calculation unit 124 extracts the inventory risk of the parts to be processed from the inventory risk information 116 and multiplies the calculation result. In the case of “Part PA-1” in FIG. 15, the inventory risk is “0.8”, so the required quantity in September is calculated as 1500 × 0.8 = 1200. Finally, the required amount calculation unit 124 stores the calculation result in the required amount information 118 for each part.

Next, in step S9, the output unit 140 generates an output screen 1400 showing the product configuration estimation result of each project and the required quantity calculation result based on the product configuration estimation information 117 and the required quantity information 118 for each part. Then, it is displayed on the user terminal 103 via the network 102.

FIG. 16 shows an example of the output screen 1400. The output screen 1400 is composed of a matter number column 1401, a product hierarchy column 1402, a name column 1403, a usage probability column 1404, a part name column 1405 column, a month column 1406, and a required quantity column 1407. ..

Information about the matter No. specified by the user is displayed in the matter No. column 1401. In the product hierarchy column 1402, among the models, devices, units, and parts, the hierarchy in which the user wants to confirm the usage probability is displayed. In the name column 1403, the names of the models, devices, units, and parts of the hierarchy corresponding to the product hierarchy column 1402 are displayed. In the usage probability column 1404, the usage probability for the name column 1403 is displayed. In the part name column 1405, a part for which the user wants to confirm the required amount is displayed. The month ahead is displayed in the month column 1406. In the requirement column 1407, the requirement for the month displayed in the month column 1406 is displayed.

As described above, the requirements planning system and the requirements planning method disclosed in this embodiment acquire inquiries for products in which the components from the purchase target to the finished product are layered. By repeating the process of estimating the usage probability of the component one level lower than the finished product from the specifications of the finished product conforming to the inquiry information and estimating the usage probability of the component one level lower than the estimated component. Estimate the usage probability of the purchased object. Then, the required amount of the purchase target is calculated from the usage probability of the purchase target.

In this way, by assuming customization of the configuration and predicting the configuration sequentially by following the hierarchy, even products whose configuration is not fixed before receiving an order, such as made-to-order products, are subject to purchase at the inquiry stage. The required amount of goods can be calculated.

Specifically, the past project information indicating the past record of the finished product is acquired, the record similar to the inquiry information is extracted from the past project information, and the component is used in the finished product that matches the inquiry information from the extracted record. By estimating the probability, it is possible to calculate the required quantity of the purchase target.

In addition, when estimating one lower component that constitutes the predetermined component, the probability of using the lower component is estimated based on the ratio of the lower component in the extracted actual results. Therefore, the usage probability of the component can be predicted even in the lower hierarchy.

In addition, for the finished product and the above-mentioned components, by using the constraint information indicating the restrictions regarding the combination with the one lower component, and excluding the combinations that conflict with the constraints, the usage probability of the lower components is estimated. It is possible to make accurate predictions by reflecting the relationship with the lower-level components.

Further, in this embodiment, the expected value obtained by multiplying the usage probability of the purchased object and the corresponding number of used objects is calculated as the required amount of the purchased object. In addition, the required amount of the purchase target obtained for a plurality of inquiry information can be added up for each purchase target. Therefore, it is possible to comprehensively manage the required amount of the purchase target in the plurality of inquiries. In other words, for individual projects, the necessity of purchase targets varies depending on the success or failure of orders, but if the required quantity of purchase targets for multiple projects is managed by the expected value, the variation in necessity for each project can be absorbed. Therefore, it is possible to avoid excess and deficiency of inventory. At this time, the expected value of the required quantity of the purchase target of the inquiry information may be obtained by using the order accuracy of the inquiry information.

Further, in this embodiment, a hierarchy including a finished product, a device as a component of the finished product, a unit as a component of the device, and a part as a component of the unit is illustrated, and the part is a purchase target. However, the hierarchical structure and the object to be purchased are not limited to the examples of the examples, and may be any structure. Further, the object to be purchased is not limited to parts, but may be a material or the like. Further, the items to be purchased may be in different layers. For example, for a certain unit, parts as a component are purchased and assembled, but for a specific unit, the part itself may be a purchase target.

In this embodiment, the case where the monthly requirement is obtained as an example of inventory management is shown, but the use of the present invention is not limited to this, and the estimated requirement of the purchase target can be arbitrarily used. Can be done. For example, the required quantity of the purchase target may be subtracted from the actual inventory to determine the purchase quantity so that the safety stock remains. Further, although the description is omitted in this embodiment, the required amount of the purchased object may be managed by using information such as compatibility of the purchased object.

As described above, the present invention is not limited to the above-mentioned examples, and includes various modifications. For example, the above-described embodiment has been described in detail in order to explain the present invention in an easy-to-understand manner, and is not necessarily limited to the one including all the described configurations. Moreover, not only the deletion of such a configuration but also the replacement or addition of the configuration is possible.

100: Requirements planning system, 101: Requirements planning device, 102: Network, 103: User terminal, 104: Database, 110: Storage unit, 120: Calculation unit, 130: Input unit, 140: Output unit, 111: Inquiry Information, 112: Past project information, 113: Past product composition information, 114: Product constraint information, 115: Inventory information, 116: Inventory risk information, 117: Product composition estimation information, 118: Requirements information for each part, 121: Information Acquisition unit, 122: Product composition estimation unit, 123: Inventory risk calculation unit, 124: Requirements calculation unit, 1400: Output screen

Claims (9)

An information acquisition department that acquires inquiries about products in which the components from the purchase target to the finished product, which are the targets of purchase, form a hierarchy.
A product composition estimation unit that estimates the usage probability of the purchased object based on the inquiry information,
It is provided with a required quantity calculation unit that calculates the required quantity of the purchased object from the usage probability of the purchased object.
The product composition estimation unit estimates the usage probability of the component one level lower than the finished product from the specifications of the finished product conforming to the inquiry information, and determines the usage probability of the component one level lower than the estimated component. A requirements planning system characterized in that the usage probability of the purchased object is estimated by repeating the estimation process. The information acquisition means further acquires past project information indicating the results of past finished products.
The product configuration estimation unit extracts a record similar to the inquiry information from the past project information, and estimates the usage probability of the component in the finished product conforming to the inquiry information from the extracted record. Item 1. The requirement planning system according to item 1. When the product composition estimation unit estimates one lower component constituting the component for a predetermined component, the lower component is based on the ratio of the lower component to the total extracted results. The requirement planning system according to claim 2, wherein the usage probability of the component is estimated. The information acquisition unit further acquires constraint information indicating restrictions on the combination of the finished product and the component with the one lower component.
The requirement planning system according to claim 3, wherein the product configuration estimation unit estimates the usage probability of the lower component by excluding combinations that conflict with the constraints. Any of claims 1 to 4, wherein the required amount calculation unit calculates an expected value obtained by multiplying the usage probability of the purchased object by the corresponding number of used items as the required amount of the purchased object. The requirements planning system described in one. The layer includes the finished product, a device as a component of the finished product, a unit as a component of the device, and a component as a component of the unit, and the component is the purchase target. The requirement planning system according to any one of claims 1 to 4, which is characterized. The required amount calculation unit is described in any one of claims 1 to 4, wherein the required amount of the purchased object obtained for a plurality of inquiry information is added up for each of the purchased objects. Requirements planning system. The requirement planning system according to claim 7, wherein the requirement calculation unit calculates a requirement for each predetermined aggregation unit period for each purchase target. An information acquisition step for acquiring inquiries for products in which the components from the purchase target to the finished product, which are the targets of purchase, form a hierarchy, and
A product configuration estimation step for estimating the usage probability of the purchased object based on the inquiry information, and
Including a required amount calculation step of calculating the required amount of the purchased object from the usage probability of the purchased object.
In the product configuration estimation step, the usage probability of the component one level lower than the finished product is estimated from the specifications of the finished product conforming to the inquiry information, and the usage probability of the component one level lower than the estimated component is calculated. A requirement planning method characterized in that the usage probability of the purchased object is estimated by repeating the estimation process.

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