Estimation system and method

Abstract

To propose an estimation system and method that can reduce loss cost due to manufacturing of defective articles and loss risk due to recovery of defective articles.SOLUTION: An estimation system estimates an influence range when a doubt arises in raw material used for the manufacture of products in a plurality of manufacturing bases and / or estimates raw material when a defect is detected in a product. The estimation system collects site data of each process performed in each manufacturing base, defines a first data model obtained by modeling each process performed in each manufacturing base on the basis of the collected site data, and defines a second data model obtained by modeling a manufacturing process of the product while associating the first data model for each process sequentially with the order of the manufacturing processes of the product, and estimates, on the basis of information on raw material in which a doubt arises, a range of the product manufactured by using the raw material by using the second data model, or estimates raw material of the product in which a defect is detected.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] The present invention relates to an estimation system and method, and is suitable for application to an estimation system that, for example, traces a manufacturing process across multiple bases to estimate the extent of the impact of a raw material in which a defect has been discovered, or that estimates the raw materials of a product in which a defect has been discovered. [Background technology]

[0002] When a defective product is found, it is important to take prompt action, such as tracing the manufacturing process to determine the cause and investigating the extent of the cause's impact.However, due to the need to contact various parties and the fact that information is scattered, tracing the manufacturing process to determine the cause and investigating the extent of the cause's impact is a heavy burden.

[0003] In this regard, for example, Patent Document 1 discloses a method for tracing that reduces the burden on the user by linking performance information in the manufacturing process up to product shipment and registering storage information that indicates the linking content. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2021 / 005747 Summary of the Invention [Problem to be solved by the invention]

[0005] However, Patent Document 1 does not take into consideration tracing across multiple manufacturing bases. When a suspect raw material is discovered, there are cases where that raw material (hereinafter referred to as the suspect raw material) is procured from multiple manufacturing bases, and therefore, when identifying the range of products manufactured using the suspect raw material (the range of impact of the suspect raw material), tracing across multiple manufacturing bases may be required.

[0006] In order to trace across multiple manufacturing sites, it would be ideal to have the same granularity of information management (hereafter referred to as the information management level) at each manufacturing site, but it is expected to be difficult to achieve the same information management level at all manufacturing sites.

[0007] For this reason, it is desirable to realize a method that can quickly grasp the extent of the impact of suspected raw materials, even when the information management levels at each manufacturing site differ. If such a method could be realized, it would be possible to reduce the loss costs caused by manufacturing defective products and the risk of losses caused by recalling defective products.

[0008] The present invention has been made in consideration of the above points, and aims to propose an estimation system and method that can reduce loss costs due to the production of defective products and reduce the risk of loss due to the recall of defective products. [Means for solving the problem]

[0009] In order to solve the above problem, the present invention provides an estimation system for estimating the extent of the impact when a problem is discovered with a raw material used in the manufacture of a product at a plurality of manufacturing bases, and / or estimating the raw material when a defect in the product is discovered, the system comprising: a data collection unit that collects on-site data for each process carried out at each of the manufacturing bases; a data model definition unit that defines a first data model that models each of the processes carried out at the manufacturing bases based on the collected on-site data, and that sequentially associates the first data model for each of the processes in the order of the manufacturing process of the product, thereby defining a second data model that models the manufacturing process of the product; and a data model definition unit that uses the second data model to estimate the extent of the product manufactured using the raw material based on information on the raw material for which a problem is discovered. of Presumably, or Based on information about the product in which a defect has been discovered, using the second data model, and a data access control unit for estimating the raw materials of the product in which a defect has been discovered. The data model definition unit sequentially associates the first data model of each of the processes, for which the on-site data is managed in common among the plurality of manufacturing bases, with the order of the manufacturing processes of the product, and defines a second data model that models the manufacturing processes of the product by collectively linking the on-site data of each of the manufacturing bases to each of the processes. I did so.

[0010] The present invention also provides an estimation method executed in an estimation system for estimating the extent of impact when doubts are discovered about raw materials used in the manufacture of products at a plurality of manufacturing sites, and / or estimating raw materials when a defect in the product is discovered, comprising: a first step of collecting on-site data for each process carried out at each of the manufacturing sites; a second step of defining a first data model that models each of the processes carried out at the manufacturing sites based on the collected on-site data, and associating the first data model for each process with the order of the manufacturing process of the product, thereby defining a second data model that models the manufacturing process of the product; and a second step of estimating the extent of impact of the product manufactured using the raw materials based on information about the raw materials for which doubts are discovered, using the second data model. of Presumably, or Based on information about the product in which a defect has been discovered, using the second data model, and a third step of estimating the raw materials of the product in which the defect was discovered. In the second step, the estimation system sequentially associates the first data model of each process, for which the on-site data is managed in common among the plurality of manufacturing bases, with the order of the manufacturing process of the product, and defines a second data model that models the manufacturing process of the product by collectively linking the on-site data of each of the manufacturing bases to each of the processes. I did so.

[0011] According to the estimation system and method of the present invention, it is possible to quickly estimate the extent of the impact of a suspected raw material or the raw material of a product in which a defect has occurred. [Effects of the Invention]

[0012] According to the present invention, it is possible to realize an estimation system and method that can reduce loss costs due to the production of defective products and reduce the risk of loss due to the recall of defective products. [Brief explanation of the drawings]

[0013] [Figure 1] 1A is a conceptual diagram for explaining a business data model, and FIG. 1B is a conceptual diagram for explaining an integrated data model. [Figure 2] 10A and 10B are conceptual diagrams illustrating a data model common to all factories and an individual factory data model. [Figure 3] 1 is a block diagram showing a configuration of an estimation system according to an embodiment of the present invention. [Figure 4] 1 is a diagram illustrating an example of the configuration of master data. [Figure 5] 1 is a diagram showing an example of the configuration of transaction data. [Figure 6A] 1 is a diagram showing an example of the configuration of integrated 4M information. [Figure 6B] This is a conceptual diagram showing how the integrated 4M information of each process is linked in the data model common to all factories. [Figure 7A] 1 is a diagram showing an example of the configuration of 4M information. [Figure 7B] This is a conceptual diagram conceptually showing how 4M information for each process is linked in an individual factory data model. [Figure 8] FIG. 10 is a conceptual diagram illustrating a trace forward process. [Figure 9] FIG. 10 is a conceptual diagram illustrating traceback processing. [Figure 10A] FIG. 10 is a diagram showing an example of a first condition setting and result display screen for a data model common to all factories. [Figure 10B] FIG. 10 is a diagram showing an example of a first condition setting and result display screen for an individual factory data model. [Figure 11] FIG. 10 is a diagram showing an example of the screen configuration of a second condition setting / result display screen. [Figure 12] 10 is a flowchart showing the processing procedure of a trace forward process. [Figure 13] 10 is a flowchart showing a processing procedure of a traceback process. DETAILED DESCRIPTION OF THE INVENTION

[0014] An embodiment of the present invention will be described in detail below with reference to the drawings.

[0015] (1) Data Model in the Present Embodiment As shown in Figure 1(A), all production activities can be defined by information about the content of the work (hereinafter referred to as work information), information about the parts (materials) input into the work and the finished products (materials) produced as a result of the work, information about the workers (Man) and machines that performed the work, and information about the work procedures (Method) of the work.

[0016] In the following, the data model in Figure 1(A) will be referred to as business data model 1, and the nodes corresponding to business, parts, finished products, workers, machines, and work procedures will be referred to as business node 1B, part node 1A, finished product node 1C, worker node 1D, machine node 1E, and work procedure node 1F, respectively.

[0017] In the following, information related to business operations will be referred to as business information, and information related to parts, finished products, workers, machines, and work procedures will be collectively referred to as 4M information, taking the initial letter "M" from the English names of parts, finished products, workers, machines, and work procedures.

[0018] Therefore, each business can be represented as a business data model 1 as shown in Figure 1(A), in which business information is mapped to business node 1B, and 4M information data (hereinafter referred to as site data) obtained at the actual site is mapped to parts node 1A, finished product node 1C, worker node 1D, machine node 1E, and work procedure node 1F.

[0019] Furthermore, in a manufacturing site such as a factory, if we note that the output components of a preceding process are the input components of a succeeding process, the entire manufacturing process from receiving raw materials to manufacturing and shipping the product can also be expressed as a series of data models (hereinafter referred to as an integrated data model) 2 as shown in Figure 1(B), which associates business data models 1 of individual processes in the order in which they are performed.

[0020] Therefore, by mapping the on-site data of each process to an integrated data model 2 that represents a series of manufacturing processes from the receipt of raw materials to the shipment of products, the manufacturing process from raw materials to the shipment of products can be defined as a single integrated data model 2.

[0021] Furthermore, when the same product is made using the same raw materials at multiple factories, by associating the 4M information for each process at each factory with the individual process in the integrated data model 2 that represents that manufacturing process, it is possible to represent the entire manufacturing process from raw materials to product shipment at multiple factories using a single integrated data model 2. Note that "the same raw materials" here simply means the same type of raw materials, and "the same product" means the same product item. The same applies hereinafter.

[0022] By representing a series of manufacturing processes in each factory that manufactures the same product using the same raw materials in a single integrated data model 2, it is possible to easily manage the performance of each factory.

[0023] Furthermore, for example, if a defect is discovered in a certain lot of a certain raw material, by starting from that lot of that raw material (suspect raw material) (hereinafter referred to as the suspect lot) and tracing the integrated data model 2 along the flow of the manufacturing process (hereinafter referred to as trace forward processing), it is possible to detect all at once the location and status of products that have been manufactured or are in the manufacturing process at each factory using that suspect lot of the suspect raw material, and thereby the scope of the impact of the suspect lot of the suspect raw material can be quickly identified.

[0024] Furthermore, even when a product defect is discovered, the raw materials and their lots that may have caused the defect can be identified by tracing the integrated data model 2 backward from the manufacturing process, starting from the product (hereinafter referred to as traceback processing).In addition, if an investigation of that raw material lot reveals that there is a defect in that raw material lot, the integrated data model 2 can be traced back along the manufacturing process, starting from that raw material lot (traceforward processing), making it possible to quickly estimate the scope of the impact of the raw material defect, such as which products manufactured in which factories the defect may have occurred.

[0025] However, when the same product is manufactured at multiple factories using the same raw materials, the information management level at each factory may differ. For example, in some factories A1, A2, etc., the process from manufacturing to shipping the product may be managed using on-site data for four processes, "Process 1" to "Process 4," as shown in Figure 2(A), while in other factories B, C, etc. that manufacture the same product using the same raw materials, the process from manufacturing to shipping the product may be managed using on-site data for six processes, "Process 1" to "Process 6," as shown in Figure 2(B) (in the case of Factory B in Figure 2(B)), or may be managed using on-site data for five processes, "Process 1" to "Process 5" (in the case of Factory C in Figure 2(B)).

[0026] Figure 2(A) shows an example in which the process from manufacturing a product to shipping it is managed using on-site data for two processes: "Process 1," which receives raw materials and other parts, and "Process 2," which uses those parts to manufacture a finished product (product); and the process from manufacturing to shipping the finished product is managed using on-site data for two processes: "Process 3," which stacks a specified number of finished products onto pallets, and "Process 4," which ships the finished products in pallet units.

[0027] The top row of Figure 2(B) shows an example in which the process from manufacturing a product to shipping it is managed using on-site data for three processes: "Process 1" for receiving raw materials and other parts, "Process 2" for manufacturing finished products using those parts, and "Process 3" for individually packaging the manufactured finished products; and the process from manufacturing to shipping the finished products is managed using on-site data for three processes: "Process 4" for putting a specified number of finished products into cases, "Process 5" for loading a specified number of those cases onto pallets, and "Process 6" for shipping the finished products in pallet units.

[0028] Furthermore, the bottom section of Figure 2(B) shows an example in which the process from manufacturing a product to shipping it is managed using on-site data for three processes: "Process 1," which receives raw materials and other parts; "Process 2," which uses those parts to manufacture a finished product; and "Process 3," which individually packages (packages) the manufactured finished products; and the process from manufacturing to shipping the finished products is managed using on-site data for two processes: "Process 4," which stacks a specified number of finished products (whether individually packaged or not) onto pallets; and "Process 5," which ships the finished products in pallet units.

[0029] In such a case, since the contents of "Process 1," "Process 2," etc. are different between factories A1, A2, etc. and factories B and C, it is not possible to create an integrated data model 2 like that shown in Figure 2(A) that is common to all factories. If it is not possible to create an integrated data model 2 that is common to all factories, it is not possible to enjoy the above-mentioned benefits that can be obtained by creating such an integrated data model 2.

[0030] Therefore, in this embodiment, when the information management level of some factories differs from that of other factories, an integrated data model 2 (hereinafter referred to as a data model common to all factories) is created that collects only the processes for which on-site data is managed in common across all factories (hereinafter referred to as processes common to all factories). Note that here, factories other than factories A1, A2, ..., factory B, and factory C also have the same processes as "Process 1" to "Process 4" of factories A1, A2, ....

[0031] For example, in the examples of Figures 2(A) and 2(B), "Process 1," "Process 2," "Process 3," and "Process 4" of factories A1, A2, etc. are processes with the same content as "Process 1," "Process 2," "Process 5," and "Process 6," respectively, of factory B, and are also processes with the same content as "Process 1," "Process 2," "Process 4," and "Process 5," respectively, of factory C. Therefore, a common data model for all factories, as shown in Figure 2(A), is created, which consists only of "Process 1," "Process 2," "Process 3," and "Process 4" of factory A, which are common to all of factories A1, A2, etc., factory B, and factory C.

[0032] In addition, we manage on-site data for processes other than those common to all factories. 、 For factories B, C, etc., which have finer information granularity, an integrated data model 2 (hereafter referred to as an individual factory data model) dedicated to that factory B is created, as shown in the upper part of Figure 2(B) consisting of each process ("Process 1" to "Process 6") of that factory B. Similarly, for factory C, an individual factory data model dedicated to that factory C is created, as shown in the lower part of Figure 2(B) consisting of each process ("Process 1" to "Process 5") of that factory C.

[0033] When a defect is discovered in a certain lot of a certain raw material and the extent of the impact is identified, for factories whose information management level is the same as that of the all-factory common data model (i.e., factories that manage only on-site data for the same processes as those that make up the all-factory common data model), the all-factory common data model is used to estimate the extent of the impact of that raw material in each factory whose information management level is the same as that of the all-factory common data model.

[0034] Furthermore, for factories whose information management level differs from that of the all-factory common data model (i.e., factories that also manage on-site data for processes different from those that make up the all-factory common data model), the individual factory data model for that factory is used to estimate the extent of the impact of raw materials at that factory.

[0035] Then, by combining the estimated range of impact of the raw material for each factory whose information management level is the same as the all-factory common data model with the estimated range of impact of the raw material for each factory whose information management level is different from the all-factory common data model, it is possible to estimate the range of impact of the defective raw material lot across multiple factories.

[0036] By doing so, the extent of the impact of a defect in a raw material can be estimated more easily and quickly than in the case where individual factory data models are created for all factories.

[0037] The following describes an estimation system according to this embodiment, which defines and manages the manufacturing processes of products at multiple factories that manufacture the same product using the same raw materials through the above-mentioned all-factory common data model and individual factory data model, and when a defect in the raw materials or product is discovered, estimates the extent of the impact using the all-factory common data model and the individual factory data model and presents this to the user.

[0038] (2) Configuration of the estimation system according to this embodiment 3, the overall system for estimating the present embodiment is designated by 10. This system for estimating the present embodiment is configured with manufacturing systems and equipment 11 installed in multiple factories that manufacture the same product using the same raw materials, a central management device 12 installed in a headquarters or other location that oversees the factories, a data extraction device 13, a client 14, and an estimation device 15.

[0039] Note that the communication between the integrated management device 12 and the data extraction device 13, the communication between the data extraction device 13 and the estimation device 15, and the communication between the client 14 and the estimation device 15 can be performed using a general public line network, whether wired or wireless, such as a fifth generation mobile communication system (so-called 5G (5th Generation)) that enables "multiple simultaneous connections" and "ultra-low latency." Furthermore, by utilizing the features of new mobile phone systems beyond 5G, it is possible to expect improved effects of the present embodiment, which will be described below.

[0040] The manufacturing system / equipment 11 is composed of a barcode reader that acquires the work logs of workers, a computer or server device that collects the work logs, machines that process parts or assemble finished products, and sensors that collect inspection information from RFID (Radio Frequency Identifier) ​​attached to parts or finished products (products).

[0041] The integrated management device 12 is composed of a general-purpose computer device and holds pre-registered information such as the items, names and raw materials of products manufactured in each factory as common management master information 16. The integrated management device 12 also collects various information output from the manufacturing systems and equipment 11 of each factory, and holds this collected information together with pre-given information such as the manufacturing plans for products in each factory as common system information 17.

[0042] The data extraction device 13 is configured, for example, by a general-purpose server device. The data extraction device 13 reads out the common management master information 16 held by the central management device 12 periodically (for example, every few hours, every day, or every few days) and transmits the read common management master information 16 to the estimation device 15. This common management master information 16 is stored in a master data storage unit configured by a large-capacity nonvolatile storage device such as a hard disk device or SSD (Solid State Drive) mounted on the estimation device 15. 18 The data is stored as master data 19 as shown in FIG.

[0043] The data extraction device 13 also periodically (for example, at the same period as the period for reading the common management master information 16) extracts business information and 4M information for each process carried out in each factory from the common system information 17 held and managed by the integrated management device 12, and transmits the extracted business information and 4M information for each process in each factory to the estimation device 15. 15 The transaction data is stored as transaction data 21 as shown in FIG. 5 in a transaction data storage unit 20, which is configured from a large-capacity storage device such as a hard disk drive or SSD mounted on the computer.

[0044] The client 14 is a computer device used by a user. By performing a predetermined operation, the user can cause the client 14 to display a first condition setting and result display screen 30, which will be described later with reference to Figures 10A and 10B, or a second condition setting and result display screen 50, which will be described later with reference to Figure 11. The user can then use these first and second condition setting and result display screens 30, 50 to estimate the extent of the influence of a raw material, if a defect in the raw material is discovered, by a trace-forward process, or to set conditions (hereinafter referred to as search conditions) for estimating the range of the raw material of a product, if a defect in the product is discovered, by a trace-back process.

[0045] The estimation device 15 is composed of a general-purpose computer device equipped with physical devices such as a processor such as a CPU (Central Processing Unit), non-volatile memory such as semiconductor memory, and a large-capacity non-volatile storage device such as a hard disk drive or SSD.

[0046] 3, the estimation device 15 is logically configured to include a data model definition unit 22, a data access control unit 23, and a display unit 24. The data model definition unit 22, the data access control unit 23, and the display unit 24 are realized by the processor executing corresponding programs stored in the memory or storage device.

[0047] The data model definition unit 22 is a functional unit that has the function of defining the all-factory common data model described above with reference to FIG. 2(B) and the individual factory data model of the required factory, using the master data 19 (FIG. 4) stored in the master data storage unit 18 and the transaction data 21 (FIG. 5) stored in the transaction data storage unit 20.

[0048] In practice, the data model definition unit 22 first extracts all processes for which on-site data is managed in common across all factories, based on the master data 19 stored in the master data storage unit 18 and the transaction data 21 stored in the transaction data storage unit 20, and then defines the business data model 1 described above for each of the extracted processes in Figure 1(A). In this case, the method disclosed in Japanese Patent Application Laid-Open No. 2019-153051 can be applied as a method for defining the business data model 1.

[0049] At this time, the 4M information mapped to the business data model 1 does not necessarily need to include all of the information on parts, finished products, workers, machines, and work procedures; only the information that can be obtained can be mapped to the corresponding part node 1A, finished product node 1C, worker node 1D, machine node 1E, and work procedure node 1F, respectively.

[0050] The data model definition unit 22 then defines a data model common to all factories by sequentially associating the business data models 1 of the defined individual processes in the order of the processes. Such a data model common to all factories can be defined by identifying a factory in which all processes for which on-site data is managed are common to all factories, and sequentially associating the business data models 1 of each process of that factory in the order of processes on a lot-by-lot basis so that the finished products of the preceding process and their lot numbers sequentially match the input parts of the succeeding process and their lot numbers, while focusing on the fact that the finished products of the preceding process are input parts of the succeeding process.

[0051] At this time, the data model definition unit 22 links (maps) the 4M information for each process in the data model common to all factories (corresponding transaction data 21 extracted from the transaction data storage unit 20) to that process as integrated 4M information 25A, which is compiled as one as shown in Figure 6A.

[0052] For ease of understanding, Figure 6A simply shows only the information relating to the part node 1A in Figure 1(A) from the integrated 4M information 25A associated with the process of inputting raw materials into the manufacturing equipment ("Process 2" in Figure 2(A) and "Process 2" in Figure 2(B)). Therefore, in reality, for this process, in addition to the information relating to the part node 1A, each piece of information relating to the finished product node 1C, worker node 1D, machine node 1E and work procedure node 1F in Figure 1(A) for that process in each factory will also be collected together as part of the integrated 4M information 25A and linked to that process.

[0053] By linking the integrated 4M information 25A for each process in the data model common to all factories in this way, the integrated 4M information 25A for each process is linked as shown in Fig. 6B. This makes it easy to search for information such as the current location of a product made using a certain lot of raw materials (for example, the pallet number of the pallet on which it is loaded), the manufacturing date and time of the product, and the production volume.

[0054] In addition, for a factory that manages the on-site data of a process other than the process for which the on-site data is managed in common among all factories, the data model definition unit 22 defines the on-site data of each process in the factory as shown in FIG. A The business data model 1 is defined by linking the 4M information 25B as shown above.

[0055] Like Fig. 6A, Fig. 7A, for ease of understanding, simply shows only the information relating to part node 1A in Fig. 1(A) among the 4M information 25B linked to the process of inputting raw materials into the manufacturing equipment ("Process 2" in Fig. 2(A) or "Process 2" in Fig. 2(B)). Therefore, in reality, for this process, in addition to the information relating to part node 1A, each piece of information relating to finished product node 1C, worker node 1D, machine node 1E, and work procedure node 1F in Fig. 1(A) for that process in each factory will also be linked to that process as part of the 4M information 25B.

[0056] By linking the 4M information 25B for each process in the individual factory data model in this way, it is possible to link information from the "parts" information for the process where raw materials are input into the manufacturing equipment to the "finished products" information for the process that has been completed up to that point, as shown in Fig. 7B. This makes it easy to search for more detailed information about the products, such as the serial number of each product made using a certain lot of raw materials, the case number of the case in which the product is stored, and the pallet number of the pallet on which the product is loaded.

[0057] The data model definition unit 22 then defines an individual factory data model for the factory by sequentially associating these defined business data models 1 in the order of processes so that the finished products and their lots of the previous process match the input parts and their lots of the subsequent process, while taking into account that the output parts of the previous process are the input parts of the subsequent process.

[0058] The data model definition unit 22 then stores the thus defined data model common to all factories and the individual factory data model in a data model accumulation unit 26, which is made up of a large-capacity nonvolatile storage device such as a hard disk drive or SSD.

[0059] The display unit 24 is a functional unit having a function of transmitting screen data for a first condition setting / result display screen 30 (FIGS. 10A and 10B) and screen data for a second condition setting / result display screen 50 (FIG. 11) to the client 14 in response to a request from the client 14. Based on this screen data, the first or second condition setting / result display screen 30, 50 is displayed on the client 14.

[0060] The display unit 24 also has a function of acquiring search conditions specified by the user on these first and second condition setting / result display screens 30, 50 and transferring them to the data access control unit 23, and transmitting the extent of the impact of defective raw materials estimated by the data access control unit 23 based on these search conditions, as will be described later, and the estimated results of the raw material lot of the product in which the defect was discovered, to the client 14. As a result, the estimated results are displayed on the first and second condition setting / result display screens 30, 50 displayed on the client 14.

[0061] The "search conditions" for estimating the scope of impact when a defect in a raw material is discovered are the raw material code or raw material name or other identification information of the raw material (suspect raw material) and the lot number of the lot in which the defect in the suspect raw material was discovered (hereinafter referred to as the suspect lot).

[0062] Furthermore, when a defect in a product is discovered, the "search conditions" for estimating the lot of raw materials used to manufacture the product are information that can identify the product, such as the product item, manufacturing date, manufacturing time, manufacturing plant identification code, and / or serial number printed on the product's individual packaging.

[0063] The data access control unit 23 is a functional unit that, when given search conditions from the display unit 24, estimates the scope of impact of the suspect raw material from the perspective of all factories where a defect has been discovered, and the raw material lot of the product where the defect has been discovered, using the search conditions and the data model common to all factories and the individual factory data model stored in the data model storage unit 26, and transmits the estimated results to the client 14 via the display unit 24.

[0064] In practice, when the data access control unit 23 estimates the scope of the impact of a raw material in which a defect has been discovered (suspect raw material), it starts from the suspect raw material with the identification information and lot number specified as the search conditions, and searches the all-factory common data model (upper part of Figure 8) and the individual factory data model (lower part of Figure 8) along the product manufacturing process, as shown in Figure 8, to estimate the location and status of each product that has been manufactured or is in the process of being manufactured using the suspect raw material (trace-forward processing).

[0065] Furthermore, when a defect in a product is discovered and the data access control unit 23 needs to estimate the lot of the raw materials, it first estimates the raw materials and their lot by starting from the product based on information that can identify the product, such as the product's serial number, which is specified as a search condition, and tracing the integrated data model 2 (data model common to all factories or individual factory data model) corresponding to the factory that manufactured the product in the opposite direction to the product's manufacturing process, as shown in Figure 9 (traceback processing).

[0066] (3) Configuration of the condition setting and result display screen 10A shows the screen configuration of a first condition setting / result display screen 30 that can be displayed on the client 14 by a predetermined operation. This first condition setting / result display screen 30 is a screen on which the user sets search conditions when, when a defect in a raw material is discovered, the estimation device 15 estimates the extent of its impact by trace forward processing, and on which the estimation results are displayed. Note that FIG. 10A is an example of the first condition setting / result display screen 30 displayed for the all-factory common data model described above with reference to FIG. 6A.

[0067] The first condition setting / result display screen 30 comprises a search condition setting area 31, a process designation area 32, an estimated result display area 33, and a back button .

[0068] The search condition setting area 31 is an area for setting such search conditions, and includes a raw material code setting text box 40, a raw material name setting text box 41, a lot number setting text box 42, a period start date setting text box 43, and a period end date setting text box 44.

[0069] In the search condition setting area 31, the raw material code of the raw material in which a defect has been discovered (suspect raw material) is entered in the raw material code setting text box 40, or the name of the suspect raw material is entered in the raw material name setting text box 41, and the lot number of the lot in which the defect has occurred in the suspect raw material (suspect lot) is entered in the lot number setting text box 42, thereby allowing the identification information of the suspect raw material and the lot number of the suspect lot to be set as search conditions.

[0070] In addition, in the search condition setting area 31, the start date of the desired period can be entered in the period start date setting text box 43, and the end date of that period can be entered in the period end date setting text box 44, thereby also setting the range of manufacturing dates of products manufactured using the suspect lot of suspect raw materials as search conditions.

[0071] Furthermore, in the process specification area 32, raw material marks 45 displayed in correspondence with the search condition setting area 31 and process buttons 46 corresponding to each process in the all-factory common data model are displayed aligned vertically. In this case, the process buttons 46 are displayed aligned in the order of the corresponding processes performed in the process of manufacturing the product.

[0072] On the first condition setting / result display screen 30, by clicking on the process button 46 corresponding to the desired process among the process buttons 46 displayed in the process designation area 32, the influence range up to that process can be specified as the influence range of the suspect raw material to be estimated.

[0073] Hereinafter, the process designated in this way will be called a designated process. Note that the business content of each process in the all-factory common data model and each process in the individual factory data model do not necessarily match. Individual A "designated process" in the factory data model is a process whose business content is the same as that of a designated process in the data model common to all factories.

[0074] Furthermore, the estimation result display area 33 lists in tabular form information on at least "parts" and "finished products" out of the 4M information of products that have been manufactured or are in the process of being manufactured up to the designated process using the suspect lot of suspect raw materials in each factory, which information has been estimated by tracing forward from the suspect raw materials to the designated process using the necessary integrated data models from the all-factory common data model and each individual factory data model under the search conditions set in the search condition setting area 31.

[0075] 10A illustrates an example in which "Process 1" is assumed to be a process for receiving raw materials, and only part of the information on "Parts" and "Finished Products" in that process is displayed, but other 4M information may also be displayed. Also, the type of information displayed as an estimation result in the estimation result display area 33 may be freely set.

[0076] Furthermore, a check box 48 is displayed corresponding to each factory (each record) in the list 47 storing the estimation results displayed in the estimation result display area 33. Then, by clicking on this check box 48 to display a check mark 49 in the check box 48, it is possible to set the scope of the impact of the suspect raw material to be estimated for processes from the specified process onwards only for the factory corresponding to the record that displayed the check mark 49.

[0077] For example, when a list 47 of the scope of influence of the suspect raw material up to "Process 1" is displayed in the estimation result display area 33, if a check mark 49 is displayed in the check box 48 of the record corresponding to the desired factory, clicking the process button 46 for "Process 2" will display a list 47 of the scope of influence of the suspect raw material in "Process 2" of the factory related to the record with the displayed check mark 49 in the estimation result display area 33.

[0078] Similarly, in this state, if a check mark 49 is displayed in the check box 48 of the record corresponding to the desired factory in the list 47 of the scope of the impact of the suspect raw material, and the process button 46 for "Process 3" is clicked, the list 47 of the scope of the impact of the suspect raw material in "Process 3" of the factory related to the record with the displayed check mark 49 will be displayed in the estimation result display area 33. Furthermore, in the same manner thereafter, the scope of the impact of the suspect raw material can be identified while narrowing down the scope of the impact of the suspect raw material for each process.

[0079] The check marks 49 can be simultaneously displayed in multiple check boxes 48. In this case, for processes subsequent to the designated process, the scope of the impact of the suspect raw material is displayed in the list 47 of the estimation result display area 33 for all factories corresponding to each record where the check marks 49 are displayed.

[0080] According to this first condition setting / result display screen 30, the user can switch the specified process to each process in the all-factory common data model in order, and thus easily recognize which process in the factory the product is currently in, even if the product is in the middle of being manufactured in a factory using a questionable lot of questionable raw materials.

[0081] Furthermore, by designating the last process of the all-factory common data model (hereinafter referred to as the final process) as the designated process, the user can display information about the finished products at each factory that were manufactured using the suspect lot of the suspect raw material in the estimation result display area 33. In this case, the information about the finished products at the final process includes the identification information of the pallet on which the products are loaded, so even if the products have already been shipped from the factory, the destination of the products after shipment can be tracked using the identification information of this pallet.

[0082] It should be noted that on the first condition setting / result display screen 30, by clicking the back button 34, the display screen of the client 14 can be returned to a predetermined menu screen (not shown).

[0083] 10B, in which the same reference numerals are assigned to parts corresponding to those in Fig. 10A, shows an example of the first condition setting and result display screen 30 displayed for the individual factory data model described above with reference to Fig. 7A. This first condition setting and result display screen 30 also has the same configuration as the first condition setting and result display screen 30 described above with reference to Fig. 10A.

[0084] However, in this case, on the first condition setting / result display screen 30, the corresponding factory manages data for more processes than the factory corresponding to the all-factory common data model, so a larger number of process buttons 46 are displayed in the process designation area 32. Also, on the first condition setting / result display screen 30 in this case, a list 47 of more detailed contents is displayed in the estimation result display area 33 accordingly.

[0085] For example, in the example of Figure 10A, only the pallet number of the pallet on which the final product is loaded can be identified as the scope of influence of the suspect raw material, whereas in the example of Figure 10B, the serial number of the final product, the case number of the case in which these products are stored, and even the pallet number of the pallet on which these cases are loaded are displayed in the list 47 of the estimation result display area 33 as the scope of influence of the suspect raw material. In other words, highly accurate tracing can be achieved according to the information granularity held by the manufacturing site. In this way, the estimation device 15 can perform tracing for multiple manufacturing sites with different information granularity according to the information management level of the manufacturing site.

[0086] 11 shows the screen configuration of a second condition setting / result display screen 50 that can be displayed on the client 14 by a predetermined operation. This second condition setting / result display screen 50 is a screen on which the user sets search conditions when, when a defect in a product is discovered, the estimation device 15 estimates the lot of the raw material by traceback processing, and on which the estimation results are displayed.

[0087] The second condition setting / result display screen 50 comprises a search condition setting area 51, an ingredient button display area 52, an estimated result display area 53, and a back button .

[0088] The search condition setting area 51 is an area for setting such search conditions, and includes a product item setting text box 60, a factory identification code setting text box 61, a manufacturing date setting text box 62, a manufacturing time setting text box 63, and a serial number setting text box 64.

[0089] In the search condition setting area 51, by entering available information from the product item, factory identification code, manufacturing date, manufacturing time, and serial number printed on the product itself or its packaging in which a defect has been discovered, into the product item setting text box 60, factory identification code setting text box 61, manufacturing date setting text box 62, manufacturing time setting text box 63, or serial number setting text box 64, this information can be set as search conditions when searching for the raw materials and lot of the product in which a defect has been discovered.

[0090] In addition, in the ingredient button display area 52, product marks 65 displayed in association with the search condition setting area 51 and ingredient buttons 66 corresponding to the ingredients are displayed side by side in the vertical direction.

[0091] On the second condition setting / result display screen 50, by clicking the raw material button 66 displayed in the raw material button display area 52, it is possible to instruct the estimating device 15 to estimate the raw materials and their lots of the product by traceback processing.

[0092] The identification information of the raw material estimated by this traceback process and the lot number of the lot are then displayed in the estimation result display area 53.

[0093] It should be noted that, even on the second condition setting / result display screen 50, by clicking the back button 54, the display screen of the client 14 can be returned to the menu screen described above.

[0094] (4) Trace forward / backward search processing Next, the above-mentioned trace forward process and trace back process FlowThe trace forward process will be described below with reference to the flow of processing when the process button 46 corresponding to the last process of the all-factory common data model in the process designation area 32 (the process button 46 for "Process 4" in the case of FIG. 10) is clicked after the search conditions have been set on the first condition setting / result display screen 30 (FIG. 10) described above.

[0095] (4-1) Trace forward processing 12 shows the procedure for trace forward processing executed by the data access control unit 23 (FIG. 3). This trace forward processing starts when the client 14 notifies the data access control unit 23 via the display unit 24 (FIG. 3) of the estimation device 15 that the process button 46 corresponding to the last process of the all-factory common data model has been clicked in the process designation area 32 after the search conditions have been set in the search condition setting area 31 of the first condition setting / result display screen 30 (FIG. 10).

[0096] When the data access control unit 23 starts this trace forward process, it first accepts as search conditions the identification information of the suspect raw material and the lot number of the suspect lot that have been set as search conditions on the first condition setting / result display screen 30 provided by the client 14 via the display unit 24 of the estimation device 15, and, if a period has been set, the start date and end date of that period (S1).

[0097] Next, the data access control unit 23 identifies the factory that accepted the suspect lot of the suspect raw material (S2). This identification is performed by identifying the all-factory common data model and the individual factory data model that contain the identification information of the suspect raw material and the lot number of the suspect lot among the "parts" information of the 4M information linked to the raw material receiving process, from among the all-factory common data model and each individual factory data model stored in the data model storage unit 26, and then identifying the factory that corresponds to the identified all-factory common data model and individual factory data model.

[0098] Next, the data access control unit 23 selects one factory from all factories identified in step S2 that has not yet been processed from step S4 onwards (S3).The data access control unit 23 then identifies the input record of the suspect lot of the suspect raw material at the selected factory (hereinafter referred to as the selected factory) (S4).This identification is performed by referring to the business information (the "business" information in FIG. 1(A)) for each process in the integrated data model of the selected factory (the data model common to all factories or the individual factory data model) to identify the process of inputting raw materials into the machine, and then identifying information containing the identification information of the suspect raw material and the lot number of the suspect lot as "part" information from the integrated 4M information 25A or 4M information 25B linked to that process.

[0099] Next, the data access control unit 23 starts from the results identified in step S4 and sequentially identifies the manufacturing results related to the information on the manufacturing results identified in step S4 for each process in the integrated data model of the selected factory along the product manufacturing process (S5).

[0100] This process is performed by taking into consideration that the output components of the preceding process are input components of the succeeding process, as described above. Specifically, the data access control unit 23 identifies the information on the "finished product" in Fig. 1(A) that corresponds to the actual results identified in step S4 (corresponding to the information on the "components" in Fig. 1(A)), identifies the information on the "components" that includes the information on the "finished product" from the integrated 4M information 25A or 4M information 25B of the next process, and further identifies the information on the "finished product" in Fig. 1(A) that corresponds to that information, and so on. This process is repeated.

[0101] The data access control unit 23 then identifies the final process at the selected factory for the product manufactured using the questionable lot of the questionable raw material (hereinafter referred to as the final process) (S6), and obtains information on the "parts" and "finished products" in Figure 1(A) related to the product manufactured using the questionable lot of the questionable raw material at the identified final process from the 4M information linked to that process (S7).

[0102] Thereafter, the data access control unit 23 determines whether or not the processing of steps S4 to S7 has been completed for all factories identified in step S2 (S8). If the determination returns a negative result, the data access control unit 23 returns to step S3, and thereafter repeats the processing of steps S3 to S8 while sequentially switching the factory selected in step S3 to other corresponding factories for which step S4 and subsequent steps have not yet been processed.

[0103] Then, when the data access control unit 23 obtains the process results in step S8 by completing the acquisition of information regarding the "parts" and "finished products" in FIG. 1(A) in the final process of each product manufactured at all factories identified in step S2 using the suspect lot of suspect raw materials, the data access control unit 23 displays the information acquired in step S7 up to that point in the estimation result display area 33 (FIG. 10) of the first condition setting / result display screen 30 (FIG. 10), and then terminates this trace forward process (S9).

[0104] (4-2) Traceback processing 13 shows the procedure for traceback processing executed by the data access control unit 23. This traceback processing starts when the client 14 notifies the data access control unit 23 via the display unit 24 (FIG. 3) of the estimation device 15 that the ingredient button 66 in the ingredient button display area 52 has been clicked after the search conditions have been set in the search condition setting area 51 of the second condition setting / result display screen 50 (FIG. 11).

[0105] When the data access control unit 23 starts this traceback process, it first accepts the product item, factory identification code, production date, and production time of the target product (the product in which a defect has been discovered) that have been set as search conditions on the second condition setting / result display screen 50, which are provided from the client 14 via the display unit 24 of the estimation device 15 (S10). In the case of an individual factory model of a manufacturing base with high information granularity, it is also possible to accept a serial number.

[0106] Next, the data access control unit 23 refers to the integrated 4M information 25A (FIG. 6) linked to the last process of the data model common to all factories and the 4M information 25B (FIG. 7) linked to the last process of each individual factory data model to identify the last manufacturing record of the target product (S11). This identification is performed by identifying the information on the "finished product" in FIG. 1(A) from the integrated 4M information and the 4M information, which includes information such as the product item, factory identification code, manufacturing date, manufacturing time, and / or serial number received in step S10.

[0107] Next, the data access control unit 23 identifies the factory that has the manufacturing history identified in step S11, and identifies the manufacturing history for each process related to the manufacturing history by tracing the integrated data model (data model common to all factories or individual factory data model) associated with that factory back in the direction opposite to the manufacturing process of the target product, starting from the manufacturing history identified in step S11 (S12).

[0108] This process is performed by taking into consideration that the input parts of the subsequent process are output parts of the preceding process. Specifically, the data access control unit 23 identifies the "parts" information in Fig. 1(A) that corresponds to the manufacturing performance identified in step S11 (corresponding to the "finished product" information in Fig. 1(A)), identifies the "finished product" information that includes the "parts" information from the integrated 4M information 25A or 4M information 25B of the immediately preceding process, and further identifies the "parts" information in Fig. 1(A) that corresponds to that information, and so on.

[0109] Then, the data access control unit 23 traces back to the first process through the above processing, identifies the raw materials and their lots of the target product based on the "parts" information of that process (S13), displays the identified raw materials and their lot information in the estimation result display area 53 (Fig. 11) of the second condition setting / result display screen 50 (Fig. 11) (S14), and then terminates this traceback processing. Note that in the case of a factory in the entire factory data model, the manufacturing date and time, etc. are input for tracing, so the suspicious range is identified (lot 1 to lot 3, etc.), whereas in the case of a factory in the individual factory data model, serial numbers are input for tracing, so the raw materials and their lot information can be pinpointed.

[0110] (5) Effects of this embodiment As described above, in the estimation system 10 of this embodiment, for each process carried out to manufacture a product in each factory, a business data model 1 that models that process is defined, and an integrated data model 2 is defined by associating these business data models 1 with the order of the manufacturing processes.Based on information about a raw material in which a problem has been discovered, the integrated data model 2 is used to estimate the range of products manufactured using that raw material, or to estimate the raw materials of a product in which a defect has been discovered.

[0111] Therefore, the estimation system 10 can realize tracing for multiple manufacturing bases with different information granularity according to the information management level of the manufacturing base. It can quickly estimate the extent of the impact of a suspected raw material or the raw material of a defective product, thereby reducing the loss costs due to the production of defective products and the risk of loss due to the recall of defective products.

[0112] (6) Other embodiments In the above embodiment, the case where the same product is manufactured using the same raw materials at all factories is described, but the present invention is not limited to this. The present invention can also be applied to cases where the same raw materials are used to manufacture different products, and when a defect in the raw materials is discovered, the extent of the impact is estimated.

[0113] Furthermore, in the above-described embodiment, the data model definition unit 22, the data access control unit 23, and the display unit 24 are arranged within the estimation device 15, which is a single computer device. However, the present invention is not limited to this, and the data model definition unit 22, the data access control unit 23, and the display unit 24 may be arranged in a distributed manner across multiple computer devices that constitute a distributed computing system.

[0114] Furthermore, in the above embodiment, the case where the data extraction device 13 is provided separately from the estimation device 15 has been described, but the present invention is not limited to this, and a functional unit having the same function as the data extraction device 13 may be provided within the estimation device 15, and the data extraction device 13 may be omitted. [Industrial Applicability]

[0115] The present invention can be widely applied to estimation systems of various configurations that estimate the extent of the impact when a defect is discovered in raw materials commonly used in the manufacture of products at multiple manufacturing sites, and / or estimate raw materials when a defect is discovered in a product. [Explanation of symbols]

[0116] 1...Business data model, 2...Integrated data model, 10...Estimation system, 11...Manufacturing system / equipment, 12...General management device, 13...Data extraction device, 14...Client, 15...Estimation device, 16...Common management master information, 17...Common system information, 18...Master data storage unit, 19...Master data, 20...Transaction data storage unit, 21...Transaction data, 22...Data model definition unit, 23...Data access control unit, 24...Display unit, 25A...Integrated 4M information, 25B...4M information, 26...Data model storage unit, 30...First condition setting / result display screen, 50...Second condition setting / result display screen.

Claims

1. In this estimation system, the extent of the impact is estimated when a problem is discovered with raw materials used in the manufacture of products at multiple manufacturing sites, and / or when a product defect is discovered, the raw materials are estimated. a data collection unit that collects on-site data of each process performed at each of the manufacturing bases; a data model definition unit that defines a first data model that models each of the processes performed at the manufacturing base based on the collected on-site data, and that defines a second data model that models the manufacturing process of the product by sequentially associating the first data model for each of the processes with the order of the manufacturing process of the product; a data access control unit that uses the second data model to estimate the range of the products manufactured using the raw materials in which doubt has been discovered, based on information about the raw materials in which doubt has been discovered, or that uses the second data model to estimate the raw materials of the product in which a defect has been discovered, based on information about the product in which a defect has been discovered; Equipped with The data model definition unit The first data model of each process, for which the on-site data is managed in common among the plurality of manufacturing bases, is sequentially associated with the order of the manufacturing processes of the product, and the on-site data of each of the manufacturing bases is collectively linked to each of the processes, thereby defining a second data model that models the manufacturing processes of the product. An estimation system comprising:

2. The data model definition unit For the manufacturing bases that manage the on-site data of the processes other than the processes for which the on-site data is managed in common among the plurality of manufacturing bases, the first data models that model the processes for which the on-site data is managed at the manufacturing bases are sequentially associated in the order of the manufacturing processes, and the second data model is defined by linking the on-site data of the manufacturing bases to each of the processes. The estimation system according to claim 1 .

3. The data access control unit Based on information about the product in which a defect has been discovered, the second data model is traced in a reverse direction to the flow of the manufacturing process to estimate the raw materials of the product.

3. The estimation system according to claim 1 or 2.

4. The data access control unit Based on the information on the suspected raw material, the second data model is traced in a direction along the flow of the manufacturing process to estimate the range of the products manufactured using the suspect raw material.

3. The estimation system according to claim 1 or 2.

5. The data model definition unit The second data model is defined by sequentially associating the first data model with the order of the manufacturing process of the product so that the finished product and the lot of the finished product in the preceding process match the input part and the lot of the input part in the subsequent process. The estimation system according to claim 1 .

6. The system further includes a display unit that displays the extent of the influence of the suspected raw material or the raw material of the product in which a defect has been discovered, estimated by the data access control unit. The estimation system according to claim 1 .

7. An estimation method executed in an estimation system that estimates the extent of impact when a problem is discovered with raw materials used in the manufacture of products at multiple manufacturing sites, and / or estimates raw materials when a defect in a product is discovered, A first step of collecting on-site data of each process performed at each of the manufacturing sites; a second step of defining a first data model that models each of the processes performed at the manufacturing site based on the collected on-site data, and defining a second data model that models the manufacturing process of the product by sequentially associating the first data model for each of the processes with the order of the manufacturing process of the product; a third step of estimating the range of the product manufactured using the suspected raw material using the second data model based on information about the suspected raw material, or estimating the raw material of the product in which a defect has been discovered using the second data model based on information about the product in which a defect has been discovered; Equipped with In the second step, the estimation system The first data model of each process, for which the on-site data is managed in common among the plurality of manufacturing bases, is sequentially associated with the order of the manufacturing processes of the product, and the on-site data of each of the manufacturing bases is collectively linked to each of the processes, thereby defining a second data model that models the manufacturing processes of the product. An estimation method characterized by:

8. In the second step, the estimation system For the manufacturing bases that manage the on-site data of the processes other than the processes for which the on-site data is managed in common among the plurality of manufacturing bases, the first data models that model the processes for which the on-site data is managed at the manufacturing bases are sequentially associated in the order of the manufacturing processes, and the second data model is defined by linking the on-site data of the manufacturing bases to each of the processes. The estimation method according to claim 7 .

9. In the third step, the estimation system Based on information about the product in which a defect has been discovered, the second data model is traced in a reverse direction to the flow of the manufacturing process to estimate the raw materials of the product.

9. The estimation method according to claim 7 or 8.

10. In the third step, the estimation system Based on the information on the suspected raw material, the second data model is traced in a direction along the flow of the manufacturing process to estimate the range of the products manufactured using the suspect raw material.

9. The estimation method according to claim 7 or 8.

11. In the second step, the estimation system The second data model is defined by sequentially associating the first data model with the order of the manufacturing process of the product so that the finished product and the lot of the finished product in the preceding process match the input part and the lot of the input part in the subsequent process. The estimation method according to claim 7 .

12. The method further includes a fourth step of displaying the extent of the influence of the suspected raw material or the raw material of the product in which a defect has been discovered, which is estimated in the third step. The estimation method according to claim 7 .

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