Production information processing device, production information processing system, and production information processing method

Abstract

To develop improvement measures based on the analysis of production loss to improve the productivity of an entire production area formed with a plurality of machines.SOLUTION: In a production information processor, a storage device stores resources shared between a plurality of machines belonging to a predetermined manufacturing area, 4M data information as time-series data of working states for each unit time of the machine and the resources related to the machine, and a production loss analysis model in which the reference for determining production loss from a combination of the working states for each unit time of the 4M data information is defined, and a processor uses the 4M data information and the production loss analysis model to specify production loss for each machine and generate production loss information, and then uses the production loss information and shared resources to combine the production loss information of the machine and the 4M data information of the other machine different from the machine but sharing the resources to classify causes of the production loss and generate loss cause information.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a production information processing device, a production information processing system, and a production information processing method.

Background Art

[0002] One of the purposes of production management is to improve productivity. One method for improving productivity is to reduce production losses. Here, "production loss" is a general term for various factors that inhibit the maximization of the output by the production system in production activities. In the present invention, production loss is a concept commonly recognized in general activities such as TPM (Total Productive Maintenance), and as a specific example, it refers to the factors causing the downtime of production activities.

[0003] Also, it has been empirically found that production losses at the manufacturing site are caused not only by machines (Machine), but also by workers (Man) who perform the setup work of the machines, materials (Material) input into the machines, and combinations of states such as procedures and programs (Method) for operating the machines. However, the computational resources for analyzing the data (4M data) obtained for these states in order to detect production losses are enormous. Also, in actual production facilities, resources such as workers and materials are shared among multiple machines, and the productivity of each machine can mutually affect one another.

[0004] Patent Document 1 describes that "the cell controller 13 includes a first communication unit 18 that receives the work program and signal setting information stored in each manufacturing machine 11 from each manufacturing machine 11, a stop detection unit 22 that detects whether the production facility 12 is stopped by referring to the work program and the signal setting information, a manufacturing machine 11 that causes the production facility 12 to stop operating by analyzing the work program and the signal setting information, and a stop cause specifying unit 23 that specifies the cause and the cause."

Prior Art Documents

Patent Documents

[0005] [Patent Document 1] Japanese Patent Publication No. 2018-036713 [Overview of the project] [Problems that the invention aims to solve]

[0006] The technology described in Patent Document 1 above is said to be able to detect whether a production facility consisting of multiple manufacturing machines is experiencing a shutdown, and to automatically identify the manufacturing machine causing the shutdown and the cause of the shutdown. However, the technology described in Patent Document 1 above may fail to identify the cause of losses due to, for example, an operator simultaneously operating multiple machines, or materials with sequential processing relationships between machines, and thus may not lead to countermeasures.

[0007] The objective of this invention is to improve the productivity of the entire production area, which consists of multiple machines, by formulating improvement measures based on the analysis of production losses. [Means for solving the problem]

[0008] This application includes several means to solve at least some of the above problems, and some examples are as follows.

[0009] One aspect of the present invention is a production information processing device comprising a processor and a storage device, wherein the storage device includes shared resources which are resources shared among a plurality of machines belonging to a predetermined manufacturing area, 4M (Machine, Man, Material, and Method) data information which is time-series data of the operating status of the machines and the resources related to the machines per unit time, and a production loss analysis model which defines criteria for determining production loss from combinations of the operating status of the 4M data information per unit time. A loss cause analysis model defines criteria for determining the causes of production loss from a combination of production loss information that identifies the production loss of the machine and the operating status per unit time included in the 4M data information of other machines that share the same resources as the machine,The data is stored, and the processor uses the 4M data information and the production loss analysis model to identify the production loss for each machine and generate production loss information, and the production loss information and the shared resources, The 4M data information of the aforementioned other machine, and the loss occurrence cause analysis model, Using ,before This system is characterized by classifying the causes of production losses and generating information on the causes of those losses. [Effects of the Invention]

[0010] According to the present invention, in an area composed of multiple machines that share resources such as workers and materials, it is possible to provide a technology that classifies the causes of production losses, proposes improvement measures, and improves the productivity of the entire area.

[0011] Other issues, configurations, and effects not mentioned above will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0012] [Figure 1] This figure shows an example of the configuration of a production information processing device. [Figure 2] This figure shows examples of how production information processing equipment can be used. [Figure 3] This figure shows an example of the hardware configuration of a production information processing device. [Figure 4] This figure shows an example of the data structure of the 4M data storage unit. [Figure 5] This figure shows an example of the data structure of the production loss analysis model memory unit. [Figure 6] This figure shows an example of the data structure of the production loss storage unit. [Figure 7] This figure shows an example of the data structure of the shared resource storage unit. [Figure 8] This figure shows an example of the data structure of the loss cause analysis model storage unit. [Figure 9] This figure shows an example of the data structure of the loss source memory unit. [Figure 10] This diagram shows an example of the data structure of the improvement measures memory unit. [Figure 11]This is a diagram showing an example of a flowchart for presenting loss countermeasure plans. [Figure 12] This is a diagram showing an example of the display of loss occurrence factors. [Figure 13] This is a diagram showing an example of a flowchart for the process of deriving improvement measures. [Figure 14] This is a diagram showing an example of the display of improvement measures.

Embodiments for Carrying Out the Invention

[0013] In the following embodiments, although the on-site data is shown as 4M data: Man, Machine, Material, Method, it is not limited to this. For example, the on-site data may be 5M data (4M data + Measurement: Measure), 5M + E data (5M data + Environment: Environment).

[0014] In the following embodiments, for the sake of convenience when necessary, they are divided into multiple sections or embodiments for explanation. However, unless otherwise explicitly stated, they are not unrelated to each other, and one is related to a partial or entire modification example, details, supplementary explanation, etc. of the other.

[0015] Also, in the following embodiments, when referring to the number of elements, etc. (including the number, numerical value, quantity, range, etc.), unless otherwise explicitly stated and in cases where it is clearly limited to a specific number in principle, it is not limited to that specific number, and it may be more than or less than the specific number.

[0016] Furthermore, in the following embodiments, it goes without saying that the constituent elements (including element steps, etc.) are not necessarily essential unless otherwise explicitly stated and in cases where it is clearly considered essential in principle.

[0017] Similarly, in the following embodiments, when referring to the shape, positional relationship, etc., of components, unless otherwise specifically stated or when it is clearly not the case in principle, it shall include those that substantially approximate or resemble such shapes, etc. The same applies to the numerical values ​​and ranges mentioned above.

[0018] Furthermore, in all the drawings used to illustrate the embodiments, the same reference numerals are generally used for identical components, and repeated explanations are omitted. However, even for identical components, if there is a high risk of confusion due to environmental changes or other reasons causing the same designation as the component before the change, a different reference numeral or name may be used. Hereinafter, each embodiment of the present invention will be described with reference to the drawings.

[0019] In the following description, the "input / output unit," "display unit," and "interface device" may refer to one or more interface devices. These one or more interface devices may be at least one of the following: • One or more I / O (Input / Output) interface devices. An I / O (Input / Output) interface device is an interface device to at least one of the following: an I / O device and a remote display computer. The I / O interface device to the display computer may be a communication interface device. The at least one I / O device may be either a user interface device, such as an input device like a keyboard and a pointing device, or an output device like a display device. • One or more communication interface devices. One or more communication interface devices may be one or more identical communication interface devices (e.g., one or more NICs (Network Interface Cards)) or two or more different communication interface devices (e.g., a NIC and an HBA (Host Bus Adapter)).

[0020] Furthermore, in the following explanation, "memory" refers to one or more memory devices, which are examples of one or more storage devices, and may typically be main memory devices. At least one memory device in memory may be a volatile memory device or a non-volatile memory device.

[0021] Furthermore, in the following explanation, "persistent storage device" may refer to one or more persistent storage devices, which are examples of one or more storage devices. Persistent storage devices are typically non-volatile storage devices (e.g., auxiliary storage devices), and specifically may be, for example, HDDs (Hard Disk Drives), SSDs (Solid State Drives), NVME (Non-Volatile Memory Express) drives, or SCMs (Storage Class Memory).

[0022] Furthermore, in the following explanation, "storage unit" or "storage device" can refer to either memory or persistent storage, or both.

[0023] Furthermore, in the following description, "processing unit" or "processor" may refer to one or more processor devices. At least one processor device may typically be a microprocessor device such as a CPU (Central Processing Unit), but may also be other types of processor devices such as a GPU (Graphics Processing Unit). At least one processor device may be single-core or multi-core. At least one processor device may be a processor core. At least one processor device may be a broad-sense processor device such as a circuit that is a collection of gate arrays according to a hardware description language that performs some or all of the processing (e.g., FPGA (Field-Programmable Gate Array), CPLD (Complex Programmable Logic Device), or ASIC (Application Specific Integrated Circuit)).

[0024] Furthermore, in the following explanation, functions may be described using the expression "yyy section," but a function may be realized by the execution of one or more computer programs by a processor, by one or more hardware circuits (e.g., FPGA or ASIC), or by a combination thereof. When a function is realized by the execution of a program by a processor, the defined processing is carried out using memory and / or interface devices as appropriate, so the function may be at least a part of the processor. Processing described with a function as the subject may be processing performed by the processor or a device having that processor. Programs may be installed from program source. Program source may be, for example, a program distribution computer or a computer-readable recording medium (e.g., a non-temporary recording medium). The description of each function is an example, and multiple functions may be combined into one function, or one function may be divided into multiple functions.

[0025] Furthermore, in the following explanation, processing may be described using "program" or "processing unit" as the subject. However, processing described using "program" as the subject may also be processing performed by a processor or a device having such a processor. Also, two or more programs may be implemented as a single program, or one program may be implemented as two or more programs.

[0026] Furthermore, in the following explanation, the term "xxx table" may be used to describe information from which an output is obtained for a given input. This information can be a table of any structure, or it can be a neural network that generates an output for a given input, or a learning model such as a genetic algorithm or random forest. Therefore, "xxx table" can be referred to as "xxx information." Also, in the following explanation, the structure of each table is just an example; one table may be divided into two or more tables, or all or part of two or more tables may be a single table.

[0027] Furthermore, in the following explanation, "system" may refer to a system composed of one or more physical computers, or a system implemented on a group of physical computing resources (e.g., a cloud infrastructure) (e.g., a cloud computing system). "Displaying" information by the production information processing device may mean displaying the information on a display device owned by the computer, or the computer may transmit the information to a display computer (in the latter case, the display computer will display the information).

[0028] [Example 1] This example describes how to classify the causes of production losses and propose improvement measures in a production area consisting of multiple machines that share resources such as workers and materials.

[0029] Figure 1 shows an example of the configuration of a production information processing device. The production information processing device 100 includes an input / output unit 110, a display unit 120, a processing unit 130, and a storage unit 140. The display unit 120 includes a 4M data display unit 121, a production loss display unit 122, a loss occurrence cause display unit 123, and an improvement measures display unit 124. The display unit 120 is a type of processing unit that performs presentation processing on the information to be displayed on the output screen and outputs it to the screen, and also performs screen control in response to on-screen operation inputs such as scrolling, sorting, and highlighting.

[0030] The processing unit 130 includes a 4M data acquisition unit 131, a production loss analysis unit 132, a loss occurrence cause analysis unit 133, and an improvement measure derivation unit 134.

[0031] The memory unit 140 includes a 4M data memory unit 141, a production loss analysis model memory unit 142, a production loss memory unit 143, a shared resource memory unit 144, a loss occurrence factor analysis model memory unit 145, a loss occurrence factor memory unit 146, and an improvement measures memory unit 147.

[0032] The production information processing device 100 is connected to the manufacturing site 190 via a communication network (e.g., LAN (Local Area Network), WAN (Wide Area Network), or the Internet) 199. The communication network 199 may also be a VPN (Virtual Private Network), a communication network using public lines such as the Internet in part or in whole, a mobile phone communication network, or a combination thereof. The communication network 199 may also be a wireless communication network such as Wi-Fi (registered trademark) or 5G (Generation).

[0033] The manufacturing site 190 contains a manufacturing system (for example, a line manufacturing system, a job shop manufacturing system, or a cell manufacturing system). The manufacturing system is equipped with, for example, one or more manufacturing facilities 191.

[0034] Furthermore, the manufacturing equipment 191 is equipped with a Machine management device for managing elements belonging to Machine (e.g., machine tools and robots), a Material management device for managing elements belonging to Material (e.g., workpieces), a Method management device for managing elements belonging to Method (e.g., tools), and a Man management device for managing elements belonging to Man (e.g., workers). In this embodiment, for the sake of simplicity, we will assume that the elements belonging to Material are workpieces, the elements belonging to Method are tools, and the elements belonging to Man are workers.

[0035] Data measured by various control devices at the manufacturing site 190 (for example, data including measurement time and measured value) is sent from each of the multiple devices through a gateway to the 4M data storage unit 141 and stored therein.

[0036] Figure 2 shows an example of how the production information processing device is used. The production information processing device 100 on the cloud environment 200 receives input information related to production at all production-capable factories 211, 212, and 213 via a network 220 such as the Internet, and outputs (transmits) improvement measure information suitable for each production line at all production-capable factories 211, 212, and 213 via the network 220, thereby enabling the device to instruct optimal improvement measures considering the production lines of all production-capable factories 211, 212, and 213. Note that "all production-capable factories" may include the company's own factories, other companies' factories, or both the company's and other companies' factories.

[0037] Figure 3 shows an example of the hardware configuration of a production information processing device. The production information processing device 100 can be realized as a general-purpose computer 300, or as a network system comprising multiple such computers 300, which includes a processor 301, memory 302, storage 303 such as a hard disk drive (HDD), a storage medium read / write device 305 for reading or writing information to or from a portable storage medium 304 such as a CD (Compact Disk) or DVD (Digital Versatile Disk), an input device 306 such as a keyboard, mouse, or barcode reader, an output device 307 such as a display, and a communication device 308 for communicating with other computers via a communication network such as the Internet.

[0038] For example, the processing unit 130 can be implemented by loading a predetermined program stored in the storage 303 into the memory 302 and executing it with the processor 301; the input / output unit 110 can be implemented by the processor 301 utilizing the input device 306 and the output device 307; and the storage unit 140 can be implemented by the processor 301 utilizing either the memory 302 or the storage 303.

[0039] This predetermined program may be downloaded to the storage 303 from the storage medium 304 via the storage medium read / write device 305, or from the network via the communication device 308, and then loaded onto the memory 302 and executed by the processor 301.

[0040] Alternatively, a predetermined program may be loaded directly onto the memory 302 from the storage medium 304 via the storage medium read / write device 305, or from the network via the communication device 308, and executed by the processor 301.

[0041] However, the production information processing device 100 is not limited to this, and may also be a wearable computer that can be worn by a worker, such as a headset, goggles, glasses, or intercom.

[0042] Figure 4 shows an example of the data structure of the 4M data storage unit. 4M data is time-series data of the operating status per unit time of multiple machines belonging to a manufacturing area and the resources associated with those machines. For example, the 4M data storage unit 141 periodically (for example, every minute) collects and records data representing the status of each of the 4Ms associated with each target machine (for example, "machine 001" and "machine 002") (for example, the acquired status and acquisition method for each 4M type and element name).

[0043] Each record 141a of the 4M data storage unit 141 associates time 141b, target machine 141c, 4M type 141d, element name 141e, state 141f, and acquisition method 141g. Time 141b is information that identifies the start time of the period during which the 4M data was acquired. Target machine 141c is information that identifies the machine of interest. 4M type 141d is information that indicates which of the 4Ms it is. Element name 141e is information that identifies the name of the element belonging to that M. State 141f is information that identifies the state of the element. Acquisition method 141g is information that identifies the method of data acquisition.

[0044] For example, in the example in data row #1, it is shown that for Man, the worker was "present" (working on "Machine 001") between 10:00 and 10:01 on September 1st. Also, in the example in data row #2, it is shown that for Machine, "Machine 001" was in a stopped state between 10:00 and 10:01 on September 1st.

[0045] According to the 4M data storage unit 141, the combination of resource states for each time interval (for example, every minute) is identified. For each of the 4M perspectives, the state of the elements belonging to that M perspective may be the state described in the data collected from the manufacturing site 190, or the state identified using the measured values ​​described in the collected data.

[0046] Figure 5 shows an example of the data structure of the production loss analysis model storage unit. The production loss analysis model defines criteria for determining production loss from combinations of operating states of 4M data information per unit time. Specifically, it is a model that defines the production loss of machine X from combinations of machine X and related 4M states, with machine X as the main component. More specifically, the production loss analysis model storage unit 142 has a record for each record number 142a that represents a pre-analyzed correspondence between 4M142b related to machine X, which is a combination of state (combination of 4M states), and the production loss 142c of machine X. One row in the production loss analysis model storage unit 142 represents the correspondence between one state combination and one production loss. Note that the state "-" means an unspecified state.

[0047] A state combination is data that aggregates the acquired 4M data for each identical time period (a unit of time treated as the same), i.e., in a time series. As shown in the example in Figure 5, for "Man," the presence or absence of workers in the work area of ​​machine X is used; for "Machine," the operation or stoppage of machine X and the transport robot is used; for "Material," the surplus or shortage of material processed by machine X is used; and for "Method," the surplus or shortage of tools and the operating status of the machine program (e.g., in the process of running the in-machine cleaning program) are used as 4M data.

[0048] For example, according to the example in data row #1, "setup loss" refers to a combination of the following states: Machine X is stopped and Man is present (working on Machine X). Similarly, according to the example in data row #2, even if Machine X is stopped, if Man is absent, it results in "waiting for manpower loss."

[0049] In this embodiment, the correspondence between production losses and state combinations is shown in a table format, but it may be defined in another format. For example, it may be defined in XML format according to the DMN (Decision Model and Notation) standard. This method facilitates implementation by computer programs.

[0050] Furthermore, regarding state combinations, each of the 4M states can be defined using a decision tree where the final destination is the production loss. This method facilitates the visual interpretation of the production loss analysis model.

[0051] Furthermore, in this embodiment, a production loss analysis model storage unit 142 is provided in advance, which takes state combinations as input and outputs production losses. However, instead, a trained model (for example, a neural network) which takes state combinations as input and outputs production losses may be used.

[0052] Figure 6 shows an example of the data structure of the production loss storage unit. The production loss storage unit 143 has a record for each target machine 143c, with a record number 143a that associates the 4M state 143d and the determination result of the production loss 143e for each time interval 143b.

[0053] The production loss analysis unit 132 receives the 4M data information from the 4M data storage unit 141 as input, and outputs the analysis results of production loss for each unit of time (e.g., 1 minute) based on the production loss analysis model information in the production loss analysis model storage unit 142. The production loss analysis unit 132 stores the analysis results in the production loss storage unit 143.

[0054] For example, according to the example in data row #1, information on the target machine "Machine 001" at the time "2022 / 9 / 1 10:00" is obtained from data rows #1 to #5 of the 4M data storage unit 141 in Figure 4. The production loss analysis unit 132 then analyzes that the state of this 4M data corresponds to the "setup loss" defined in data row #1 of the production loss analysis model storage unit 142 in Figure 5. Furthermore, the data "-" in the 4M state 143d of the production loss memory unit 143 means that the corresponding data does not exist for the target machine.

[0055] Figure 7 shows an example of the data structure of the shared resource storage unit 144. Shared resources are information that associates resources shared by two or more machines with each combination of machines. Here, using the production area 144e as an example, an example of the shared resource storage unit 144 that defines the relationships between machines that have shared resources is shown.

[0056] Here, production area 144e is assumed to contain three machines: "Machine 001," "Machine 002," and "Machine 003." Additionally, one transport robot R (Machine) performs setup work for "Machine 002" and "Machine 003." Furthermore, one worker (Man) manages these facilities. The material processed by "Machine 001" will undergo further processing by either "Machine 002" or "Machine 003."

[0057] In such examples, resources shared by multiple machines include workers, robots, and materials. The shared resource storage unit 144 defines the resources to be shared for a combination of two machines.

[0058] Specifically, the shared resource storage unit 144 has a record for each record number 144a that associates a shared resource 144d with a combination of target machine X 144b and target machine Y 144. Here, the target machines, which are target machine X and target machine Y, are machines that add value. In other words, machines that directly contribute to productivity become target machines. Machines that perform auxiliary functions, such as transport robots, do not become target machines and become shared resources.

[0059] For example, in the example in data row #1, "worker" is defined as a shared resource for the combination of "machine 001" and "machine 002". In this example, the worker may work on "machine 002" after working on "machine 001", or work on "machine 001" again after working on "machine 002". Therefore, data with the target machines X and Y swapped (data row #3) is also defined. Furthermore, the worker is responsible for all of "machine 001", "machine 002", and "machine 003". Therefore, the worker is defined as a shared resource not only for the combination of "machine 001" and "machine 002", but also for the combination of "machine 001" and "machine 003" (data rows #2, #5) and "machine 002" and "machine 003" (data rows #4, #6).

[0060] Furthermore, according to the example in data row #9, "Machine 002" shares materials with "Machine 001". In this example, the material is processed in "Machine 001" (Target Machine Y) before being processed in "Machine 002" (Target Machine X), and the operating status of "Machine 002" is affected by the processing status of the material in "Machine 001". On the other hand, "Machine 001" is not affected by the processing status of the material in "Machine 002". Therefore, the data in data row #9 with Target Machine X and Target Machine Y swapped is not defined. In other words, from the perspective of "Machine 002", it is necessary to define that it shares materials with "Machine 001" because it is affected by the processing status of the material in "Machine 001", but the reverse is not true, so no definition is needed.

[0061] All of the 4M elements of a production site are potential resources to be shared between machines. For example, Machine could include robots and AGVs, Man could be workers, Material could be materials, and Method could be tools or machine programs.

[0062] Figure 8 shows an example of the data structure of the loss cause analysis model storage unit. The loss cause analysis model storage unit 145 stores a loss cause analysis model that defines criteria for determining the causes of production loss from a combination of production loss information of a machine and the operating status per unit time of another machine that shares resources but is different from the machine in question. In other words, the loss cause analysis model storage unit 145 defines the causes of loss for a combination of two machines, X and Y. More specifically, the loss cause analysis model storage unit 145 pre-associates a loss cause 145e for each shared resource 145b, corresponding to a combination of the production loss 145c of machine X and the 4M state 145d of machine Y. Note that the data "-" included in the 4M state 145d of machine Y means an unspecified state.

[0063] For example, according to the example in data row #1, when the production loss detected in machine X is "waiting for workers loss," and the 4M state for machine Y is in a predetermined state, namely Machine Y is running and Man is present (working on machine Y), then the cause of the "waiting for workers loss" in machine X is defined as "machine Y's work priority error." Here, "machine Y's work priority error" refers to a situation where a worker, a shared resource, prioritized work on the running machine Y over work to start the stopped machine X, resulting in a waiting for workers loss in machine X.

[0064] In the example of the loss cause analysis model storage unit 145, in addition to "machine Y's work priority error," the following are defined as loss cause 145e: "work overlap between machines X and Y," "work incomplete by machine Y," "overlapping transport by machines X and Y," "waiting for transport from machine Y," and "causes other than machine Y." For example, "work overlap between machines X and Y" indicates a state where both machine X and machine Y are stopped and require setup work by an operator, meaning that the states requiring work overlap at the same time. Furthermore, loss cause factors other than those described in this example may be defined.

[0065] In other words, the model definition stored in the loss cause analysis model memory unit 145 allows for the unique classification of the factors causing production losses in machine X, according to the 4M states of different machines Y that share the same resources.

[0066] In this embodiment, the model definition stored in the loss-causing factor analysis model storage unit 145 defines the factors causing loss using the production loss for machine X and the 4M state data for machine Y. However, it is not limited to this, and the model definition stored in the loss-causing factor analysis model storage unit 145 may use the 4M state data for machine X, which is the raw data for analyzing production loss, instead of the production loss for machine X. Alternatively, the model definition stored in the loss-causing factor analysis model storage unit 145 may use the production loss analyzed from the 4M state data for machine Y, instead of the production loss for machine Y. Furthermore, the model definition stored in the loss-causing factor analysis model storage unit 145 may define three or more machines in terms of machine combinations.

[0067] Furthermore, in this embodiment, the model definition stored in the loss cause analysis model storage unit 145 defines the combination of shared resources 145b, production losses 145c of machine X, 4M states 145d related to machine Y, and loss cause factors 145e in a table format, but it may be defined in a different format. For example, the model definition stored in the loss cause analysis model storage unit 145 may be defined in XML format according to the DMN (Decision Model and Notation) standard. This method makes implementation by computer programs easier.

[0068] Furthermore, the model definition stored in the loss cause analysis model memory unit 145 may be defined as a decision tree that branches out the shared resources 145b of machines X and Y, the production losses 145c of machine X, and the 4M states 145d related to machine Y, with the final destination being the loss cause 145e. This method facilitates the visual interpretation of the loss cause analysis model.

[0069] Furthermore, in this embodiment, the model definition stored in the loss-causing factor analysis model storage unit 145 is pre-prepared, taking a combination of shared resources 145b of machines X and Y, production losses 145c of machine X, and 4M states 145d related to machine Y as input, and outputting loss-causing factors 145e. However, instead, a trained model (e.g., a neural network) that takes a combination of shared resources 145b of machines X and Y, production losses 145c of machine X, and 4M states 145d related to machine Y as input and outputs loss-causing factors 145e may be used.

[0070] Figure 9 shows an example of the data structure of the loss occurrence cause storage unit. The loss occurrence cause storage unit 146 stores the loss occurrence causes for combinations of target machines that share resources (for example, "machine 001" and "machine 002") as statistics of their frequency of occurrence during the analysis period (for example, 24 hours).

[0071] More specifically, the loss occurrence factor storage unit 146 has a record for each record number 146a that associates the loss occurrence factor 146b with the combination of target machine X146c and target machine Y146d and the occurrence frequency 146e. The loss occurrence factor storage unit 146 stores the results of the loss occurrence factor analysis unit 133's analysis of loss occurrence factors for each unit of analysis time (e.g., 1 minute). The loss occurrence factor analysis unit 133 receives production loss information from the production loss storage unit 143, 4M data information from the 4M data storage unit 141, and shared resource information from the shared resource storage unit 144 as input, and analyzes the loss occurrence factors for each shared resource based on the loss occurrence factor analysis model information from the loss occurrence factor analysis model storage unit 145. In addition, the loss occurrence factor analysis unit 133 aggregates the loss occurrence factors for each combination of target machines during the analysis period and stores them in the loss occurrence factor storage unit 146.

[0072] For example, according to the example in data row #1, in the combination of "Machine 001" and "Machine 002" as the target machines, it is shown that 11 percent of the production losses for "Machine 001" occurred during the analysis period, with "overlap in operations between Machines X and Y" being the cause of the loss. Note that the frequency of occurrence is the value obtained by dividing the machine downtime due to losses by the total period, but it is not limited to this, and may also be the number of downtimes that occurred during the period.

[0073] Figure 10 shows an example of the data structure of the improvement measure storage unit 147. The improvement measure storage unit 147 defines the target production loss and proposed improvement measures for the causes of that production loss. For each type of production loss, one or more improvement measures are defined, and the type and priority of the improvement measures are defined.

[0074] More specifically, the improvement measure storage unit 147 defines the target production loss 147b, the cause of the loss 147c, the improvement measures 147d for combinations thereof, the type of improvement measure 147e, and the priority of the measures 147f. Figure 10 shows two examples of improvement measure type 147e: (A) Real-time instruction at the time of data acquisition, which allows for immediate action when a production loss is detected, and (B) Plan update after data accumulation, which is reflected in future plans based on statistics during the analysis period.

[0075] Priority 147f indicates the priority order for application when there are multiple improvement measures for the same combination of production loss, loss cause, and type of improvement measure (for example, data rows #3-5).

[0076] Improvement measures may overlap for different combinations of production losses and loss-causing factors (for example, data rows #4 and #6). This is because effective improvement measures exist for different loss-causing factors.

[0077] The 4M data acquisition unit 131 acquires 4M data from the 4M data storage unit 141 for each machine to be analyzed.

[0078] The production loss analysis unit 132 uses the information from the 4M data storage unit 141 and the information from the production loss analysis model storage unit 142 to identify the production loss for each machine, generate production loss information, and store it in the production loss storage unit 143.

[0079] The loss cause analysis unit 133 uses the information from the production loss storage unit 143 and the information from the shared resource storage unit 144 to combine the production loss information of the machine and the 4M data information of other machines that share resources with the machine in question, classify the causes of production loss, generate loss cause information, and store it in the loss cause storage unit 146. In other words, the loss cause analysis unit 133 classifies the causes of production loss using a loss cause analysis model.

[0080] The improvement measure derivation unit 134 performs the improvement measure derivation process described later and, based on the analyzed loss occurrence factors, presents improvement measures for combinations of multiple machines with priority.

[0081] Figure 11 shows an example flowchart of the loss mitigation proposal presentation process. The loss mitigation proposal presentation process starts when it receives a start command from the user via the interface device.

[0082] First, the 4M data acquisition unit 131 acquires 4M data for each machine to be analyzed from the 4M data storage unit 141 (step S101). At this point, the 4M data acquisition unit 131 may also allow the user to confirm the acquired 4M data for each machine by displaying it via the 4M data display unit 121.

[0083] The production loss analysis unit 132 then retrieves the production loss analysis model from the production loss analysis model storage unit 142, analyzes the production loss of each machine against the 4M data acquired in step S101, and stores the analysis results in the production loss storage unit 143 (step S102). Specifically, if the acquired 4M data contains conditions that satisfy the 4M conditions defined in the production loss analysis model, the production loss analysis unit 132 determines that there is a production loss and stores it in the production loss storage unit 143. At this point, the production loss analysis unit 132 may also allow the user to confirm the analysis results of the production loss of each machine by displaying them via the production loss display unit 122.

[0084] Then, the loss cause analysis unit 133 initializes the combination of machines X and Y to be analyzed for loss causes (step S103). For example, the loss cause analysis unit 133 sets "machine 001" as machine X and "machine 002" as machine Y.

[0085] Then, the loss cause analysis unit 133 retrieves the shared resources of machine X and machine Y from the shared resource storage unit 144 (step S104). For example, if machine X is set to "machine 001" and machine Y is set to "machine 002", in the example of the shared resource storage unit 144 in Figure 7, only the worker (data row #1) is retrieved as a shared resource. Alternatively, if machine X is set to "machine 003" and machine Y is set to "machine 001", in the example of the shared resource storage unit 144 in Figure 7, the worker (data row #5) and material (data row #10) are retrieved as shared resources.

[0086] The loss cause analysis unit 133 then combines the production loss of machine X and the 4M data of machine Y to classify the causes of production loss (step S105). Specifically, the loss cause analysis unit 133 obtains a loss cause analysis model from the loss cause analysis model storage unit 145. The loss cause analysis unit 133 then queries the loss cause analysis model to analyze the loss causes corresponding to the combination of the 4M data obtained in step S101, the production loss data output in step S102, and the shared resource data obtained in step S104. The loss cause analysis unit 133 stores the analysis results in the loss cause storage unit 146.

[0087] Here, the loss cause analysis unit 133 may allow the user to confirm the results of the loss cause analysis by displaying them via the loss cause display unit 123. The loss cause display unit 123 displays the production loss and the cause of the loss of the target machine in parallel in chronological order, along with the status of other machines and shared resources that share resources with the machine.

[0088] Figure 12 shows an example of displaying the causes of loss. The loss cause display screen 500 is an example in which the loss cause display unit 123 displays the production loss and loss causes 510 of the target machine in parallel in chronological order, along with the machines that share resources and the status of the shared resources 520. In the example of the loss cause display screen 500, the target machine X is "machine 002" and the target machine Y is "machine 001".

[0089] In this example, it is shown that "Machine 002" is experiencing a labor waiting loss between "10:00 and 12:00". In the example of the shared resource storage unit 144 in Figure 7, "Machine 002" shares workers and materials as resources with "Machine 001". According to the example of the loss cause analysis model storage unit 145 in Figure 8, if workers are a shared resource and a labor waiting loss is occurring in Machine X ("Machine 002"), then when Machine Y ("Machine 001")'s Machine is "Stopped" and Man is "Available" (i.e., data row #3), the loss cause analysis unit 133 analyzes that the loss cause for Machine X ("Machine 002") is "work overlap between Machines X and Y". As a result, in the loss cause display screen 500, "work overlap between Machines 002 and 001" is displayed as the loss cause between "10:00 and 11:00" of the labor waiting loss between "10:00 and 12:00".

[0090] Similarly, if a worker is a shared resource and a worker waiting loss occurs at machine X ("machine 002"), and machine Y ("machine 001") is "operating" and has a "worker" (i.e., data row #1), the loss cause analysis unit 133 analyzes that the cause of the loss at machine X ("machine 002") is "machine Y's work priority error". As a result, the loss cause display screen 500 shows "machine 001's work priority error" as the cause of the loss between "11:00-12:00" in the worker waiting loss between "10:00-12:00".

[0091] The Loss Cause Display Screen 500 can classify and display the production losses of the target machine by the cause of the loss. Furthermore, the Loss Cause Display Screen 500 can display the reasons for classifying the loss causes in parallel with the 4M data of the machines sharing the same resources. In other words, the Loss Cause Display Screen 500 makes it easy for the user to understand the situation by showing the user the occurrence of production losses due to the relationships between multiple machines and their causes, along with the 4M data. Note that the example of the Loss Cause Display Screen 500 shows the loss causes between "Machine 002" and "Machine 001," but it is not limited to this; it could also show the loss causes between "Machine 002" and "Machine 003," or display all of them in a list. Returning to the explanation of the flowchart in Figure 11.

[0092] The loss cause analysis unit 133 then determines whether it has selected all machines that can be combined with machine X (step S106). If there are any unselected machines (if the answer is "No" in step S106), the loss cause analysis unit 133 updates machine Y to an unselected machine and returns control to step S104. If all candidate machines for machine Y have already been selected (if the answer is "Yes" in step S106), the loss cause analysis unit 133 proceeds control to step S107.

[0093] The loss cause analysis unit 133 then determines whether all machines have been selected as machine X (step S107). If there are any unselected machines (if the answer is "No" in step S107), the loss cause analysis unit 133 updates machine X to the selected machines and returns control to step S104. If all candidate machines for machine X have been selected (if the answer is "Yes" in step S107), the loss cause analysis unit 133 proceeds control to step S108.

[0094] Then, the improvement measure derivation unit 134 executes the improvement measure derivation process (step S108). Then, the improvement measure derivation unit 134 terminates the loss countermeasure proposal presentation process. The improvement measure derivation process will be explained using the flowchart in Figure 13.

[0095] The above is an example of the loss reduction proposal presentation process flow. According to the loss reduction proposal presentation process, in a production area consisting of multiple machines that share resources such as workers and materials, it is possible not only to identify the production loss of a single machine, but also to classify the causes of production loss due to correlation factors of multiple machines and resources by cross-analyzing the production loss of multiple machines that share resources with 4M data. Furthermore, based on the classified causes, improvement measures can be presented preferentially to combinations of multiple machines. This allows for the sequential implementation of improvement measures starting with the most effective ones, and efficiently improves the productivity of the entire production area consisting of multiple machines that share resources.

[0096] Figure 13 shows an example of a flowchart for the process of deriving improvement measures. This flowchart example shows a process of presenting the user with a proposal for "(B) updating the plan after data accumulation," which is one of the types of improvement measures, based on statistical data on the causes of loss.

[0097] First, the improvement measure derivation unit 134 obtains statistical data on the factors causing losses during the analysis period from the loss cause storage unit 146 (step S201).

[0098] Then, the improvement measure derivation unit 134 selects one unselected data from the acquired statistical data of loss occurrence factors (step S202). For example, the improvement measure derivation unit 134 selects data row #1 of the loss occurrence factor storage unit 146 in Figure 9.

[0099] Then, the improvement measure extraction unit 134 refers to the improvement measure storage unit 147 to find all corresponding improvement measures for the selected data loss cause 146b, and assigns the occurrence frequency corresponding to the selected improvement measures (step S203).

[0100] For example, if the data acquired in step S202 is data row #1 of the loss cause storage unit 146 in Figure 9, then the loss cause 146b for machine X is "work overlap between machines X and Y", and its occurrence frequency 146e is "11%". According to the example of the improvement measure storage unit 147 in Figure 10, the improvement measures 147d that correspond to the type 147e of the improvement measures for "(B) Plan update after data accumulation" which corresponds to the loss cause 147c being "work overlap between machines X and Y", are the three (#3, #4, #5) "Planning of setup work priorities", "Review of product input plan", and "Review of personnel plan". The improvement measure derivation unit 134 selects all three of these improvement measures and assigns an occurrence frequency of "11%" to each of them.

[0101] Similarly, if the data acquired in step S202 is data row #3 of the loss cause storage unit 146 in Figure 9, then the loss cause 146b for machine X is "incomplete work by machine Y", and its occurrence frequency 146e is "3%". According to the example of the improvement measure storage unit 147 in Figure 10, the improvement measure 147d corresponding to the type 147e of the improvement measure "(B) Plan update after data accumulation" that corresponds to the loss cause 147c which is "incomplete work by machine Y", is "Review of product input plan" (#6). The improvement measure derivation unit 134 selects this improvement measure and assigns it an occurrence frequency of "3%".

[0102] The improvement measure deriving unit 134 then determines whether all the data acquired in step S201 was selected in step S202 (step S204). If there is any unselected data (if "No" is selected in step S204), the improvement measure deriving unit 134 returns control to step S202. If all data has been selected (if "Yes" is selected in step S204), the improvement measure deriving unit 134 proceeds to step S205.

[0103] Then, the improvement measure derivation unit 134 integrates the data for the same combination of improvement measures and target machines drawn in step S203, and sums up the occurrence frequencies assigned to the integrated data (step S205). For example, in the loss occurrence factor storage unit 146 in Figure 9, for the combination of "machine 001" and "machine 002", the improvement measure "review of product input plan" is assigned an occurrence frequency of 11% from data row #1 as described above by the processing in step S203. On the other hand, as described above, the improvement measure "review of product input plan" is assigned an occurrence frequency of 3% from data row #3.

[0104] For the improvement measure "Review of product input plan" applied to the combination of machine X "Machine 001" and machine Y "Machine 002", adding up the occurrence frequencies of these two measures reveals that the effective measure occurs 11% + 3% = 14% of the time. As in this example, when the same improvement measure is effective for multiple different production losses, integrating the occurrence frequency data from the perspective of the improvement measure allows for a quantitative evaluation of the priority of the improvement measure. In other words, improvement measures that are effective for multiple loss-causing factors can be integrated and reflected in order of priority.

[0105] The improvement measure derivation unit 134 then sorts the combinations of improvement measures and target machines in order of frequency of occurrence (step S206). By sorting from the perspective of improvement measures rather than loss-causing factors, the improvement measure derivation unit 134 can present a priority order for improvement measures. If there are improvement measures with the same frequency of occurrence, the improvement measure derivation unit 134 refers to the definition of priority order 147f in the improvement measure storage unit 147 in Figure 10 to determine the priority order.

[0106] Then, the improvement measure derivation unit 134 displays the sorted results on the screen using the improvement measure display unit 124, and terminates the improvement measure derivation process (step S207). The improvement measure display unit 124 selects and outputs improvement measures corresponding to the production loss information and loss occurrence cause information. An example of this screen display is shown in Figure 14.

[0107] Figure 14 shows an example of how improvement measures are displayed. The improvement measures display screen 600 is an example of displaying the results of summing up the occurrence frequencies and sorting them when the improvement measures for the loss-causing factors are the same and the combination of target machines is the same, for one of the types of improvement measures, "(B) Plan update after data accumulation". In other words, the improvement measures display unit 124 sorts the output improvement measures in order of occurrence frequency and displays them as the priority of improvement measures, and for identical improvement measures, if the combination of target machines is the same, it eliminates duplicates and sums up the occurrence frequencies.

[0108] In the example of the improvement measures display screen 600, the highest priority countermeasure is presented as "Review of product input plan" for the combination of "Machine 001" and "Machine 002". The corresponding loss-causing factors are data rows #1 and #3 of the loss-causing factor storage unit 146 in Figure 9, and the total occurrence frequency is displayed as 14% (as in the example above, the breakdown may be 11% + 3%).

[0109] Although the occurrence frequency is the same at 11% for both the 2nd and 3rd priority countermeasures, according to the definition in the improvement measure memory unit 147 in Figure 10, "planning the priority of setup work" has a higher priority than "revising the product input plan," so "planning the priority of setup work" is displayed as having a priority of 2nd place.

[0110] In this way, the improvement measure derivation process can prioritize and present improvement measures for combinations of multiple machines based on the analyzed loss-causing factors. This allows for the sequential implementation of the most effective improvement measures, efficiently improving the productivity of the entire production area composed of multiple machines sharing resources.

[0111] The above describes the production information processing device to which Embodiment 1 of the present invention is applied. According to the embodiment of Embodiment 1 of the present invention, it is possible to plan improvement measures based on the analysis of production losses and improve the productivity of the entire production area composed of multiple machines.

[0112] [Example 2] This example basically has the same configuration as Example 1. However, it differs in that it not only presents improvement measures but also carries out specific improvement operations. A specific example of this example will be explained.

[0113] In Embodiment 2, the improvement measure storage unit 147 stores improvement programs in addition to improvement measures 147d. The improvement measure extraction unit 134 then retrieves and implements the corresponding improvement program for the improvement measure selected on the screen from among the improvement measures derived by the improvement measure extraction process. Here, the improvement program is, for example, a program that changes the machine's settings or operating rules.

[0114] According to the embodiment of Example 2 of the present invention, it is possible not only to formulate improvement measures based on the analysis of production losses, but also to automatically apply these measures in practice, thereby improving the productivity of the entire production area composed of multiple machines.

[0115] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are included. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations.

[0116] Furthermore, each of the above configurations, functions, processing units, and processing means may be implemented in hardware, either partially or entirely, by designing them as integrated circuits, for example. Alternatively, each of the above configurations and functions may be implemented in software by having the processor interpret and execute programs that implement each function. Information such as programs, tables, and files that implement each function can be stored in memory, a recording device such as a hard disk or SSD, or a recording medium such as an IC card, SD card, or DVD.

[0117] Furthermore, the control lines and information lines shown are those deemed necessary for explanatory purposes, and not all control lines and information lines are necessarily shown in the actual product. In reality, it is safe to assume that almost all components are interconnected. [Explanation of symbols]

[0118] 100: Production information processing device, 110: Input / output unit, 120: Display unit, 121: 4M data display unit, 122: Production loss display unit, 123: Loss occurrence factor display unit, 124: Improvement measure display unit, 130: Processing unit, 131: 4M data acquisition unit, 132: Production loss analysis unit, 133: Loss occurrence factor analysis unit, 134: Improvement measure derivation unit, 140: Storage unit, 141: 4M data storage unit, 142: Production loss analysis model storage unit, 143: Production loss storage unit, 144: Shared resource storage unit, 145: Loss occurrence factor analysis model storage unit, 146: Loss occurrence factor storage unit, 147: Improvement measure storage unit, 190: Manufacturing site, 191: Manufacturing equipment, 199: Communication network.

Claims

1. A processor and a memory device are included. The aforementioned storage device includes: Shared resources are resources shared among multiple machines belonging to a designated manufacturing area, 4M (Machine, Man, Material, and Method) data information, which is time-series data of the operating status of the machine and the resources related to the machine per unit time, A production loss analysis model that defines criteria for determining production loss from the combination of operating states for each unit time of the 4M data information, Production loss information that identifies the production loss of the machine, and a loss cause analysis model that defines criteria for determining the cause of the production loss from the combination of the operating status per unit time included in the 4M data information of the machine and other machines that share the same resources, It is remembered, The aforementioned processor, Using the aforementioned 4M data information and the production loss analysis model, the production loss for each machine is identified and production loss information is generated. Using the aforementioned production loss information, the aforementioned shared resources, the aforementioned 4M data information of other machines, and the aforementioned loss occurrence cause analysis model, the causes of the production loss are classified and loss occurrence cause information is generated. A production information processing device characterized by the following features.

2. A production information processing apparatus according to claim 1, As the shared resource, for each combination of two or more machines, the resource shared by the machine is associated with that machine. A production information processing device characterized by the following features.

3. A production information processing apparatus according to claim 1, It has a display unit, The display unit displays the production loss of the target machine and the causes of the loss in parallel in chronological order, along with the status of other machines that share resources with the target machine and the shared resources. A production information processing device characterized by the following features.

4. A production information processing device according to any one of claims 1 to 3, The aforementioned storage device includes: The aforementioned production loss and the improvement measures corresponding to the cause of the loss are stored. The aforementioned processor, Select and output the improvement measures corresponding to the production loss information and the loss-causing factors information. A production information processing device characterized by the following features.

5. A production information processing apparatus according to claim 4, The aforementioned improvement measures define one or more improvement measures for each type of production loss. A production information processing device characterized by the following features.

6. A production information processing device according to claim 5, The aforementioned improvement measures define one or more improvement measures for each type of production loss, and the types of such improvement measures are also defined. A production information processing device characterized by the following features.

7. A production information processing device according to claim 5, The aforementioned improvement measures define one or more improvement measures for each type of production loss, and the priority of these improvement measures is defined. A production information processing device characterized by the following features.

8. A production information processing apparatus according to claim 4, It has a display unit that displays the improvement measures to be output, The display unit sorts the output improvement measures in order of frequency of occurrence and displays them as the priority of the improvement measures. For identical improvement measures, if the combination of target machines is the same, it eliminates duplicates and sums the occurrence frequencies. A production information processing device characterized by the following features.

9. It comprises a processing unit and a storage unit, The aforementioned storage unit includes: Shared resources are resources shared among multiple machines belonging to a designated manufacturing area, 4M (Machine, Man, Material, and Method) data information, which is time-series data of the operating status of the machine and the resources related to the machine per unit time, A production loss analysis model that defines criteria for determining production loss from the combination of operating states for each unit time of the 4M data information, A loss cause analysis model defines criteria for determining the causes of production loss from a combination of production loss information that identifies the production loss of the machine and the operating status per unit time included in the 4M data information of other machines that share the same resources as the machine, It is remembered, The aforementioned processing unit, Using the aforementioned 4M data information and the production loss analysis model, the production loss for each machine is identified and production loss information is generated. Using the aforementioned production loss information, the aforementioned shared resources, the aforementioned 4M data information of other machines, and the aforementioned loss occurrence cause analysis model, the causes of the production loss are classified and loss occurrence cause information is generated. A production information processing system characterized by the following features.

10. A production information processing method using an information processing device, The aforementioned information processing device comprises a processor and a memory device, The aforementioned storage device includes: Shared resources are resources shared among multiple machines belonging to a designated manufacturing area, 4M (Machine, Man, Material, and Method) data information, which is time-series data of the operating status of the machine and the resources related to the machine per unit time, A production loss analysis model that defines criteria for determining production loss from the combination of operating states for each unit time of the 4M data information, A loss cause analysis model defines criteria for determining the causes of production loss from a combination of production loss information that identifies the production loss of the machine and the operating status per unit time included in the 4M data information of other machines that share the same resources as the machine, It is remembered, The aforementioned processor, Using the aforementioned 4M data information and the production loss analysis model, the steps include: identifying the production loss for each machine and generating production loss information; Using the production loss information, the shared resources, and the loss cause analysis model, the steps include: classifying the causes of production loss and generating loss cause information; A production information processing method characterized by implementing the following.

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