DATA ANALYSIS SUPPORT APPARATUS AND DATA ANALYSIS SUPPORT METHOD

A data analysis support apparatus is configured to acquire first result information which is result information acquired for a product produced through a prescribed step, and which includes information indicating a first processing time period which is a processing time period of the step for a first number of the products, and second result information which is result information including information indicating a second processing time period which is a processing time of the step for a second number of the products produced through the step, the second number being different from the first number, and convert the first result information and the second result information into a plurality of result information sets by performing time-division of the first result information and/or the second result information such that the result information sets each indicate a time period taken to perform the step for the same unit number of the products.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority pursuant to Japanese patent application No. 2020-094310, filed on May 29, 2020, the entire disclosure of which is incorporated herein by reference.

BACKGROUND

Technical Field

The present invention relates to a data analysis support apparatus and a data analysis method.

Related Art

JP-2017-68816-A discloses a management system that is formed in order to enable detailed production management even in a production site where order-based production management is carried out. The management system is linked with a production line including one or more pieces of equipment, each of the pieces of equipment is formed so as to process each of workpieces according to order information that includes designation of the type of an item to be produced and designation of the number of items to be produced, processing-related event information that is generated in each of the pieces of equipment is collected, the collected event information is classified into groups of event information generated for the identical workpieces, on the basis of the respective generation sources and the details of the event information, data indicating the processing state of each workpiece is generated on the basis of the event information belonging to the corresponding classification group, and the processing progression statuses of workpieces, which are processed according to the order information, are visualized on the basis of the generated data.

In recent years, smart factories are being realized in product producing sites. In a smart factory, various types of data (data on equipment operating statuses, inspections of product qualities, environments, and the like) are acquired through various sensors and equipment, and the acquired data is visualized and analyzed, whereby the productivities and the quality of products are improved.

In a product producing site, it is important to manage the production state. In a case where an abnormality such as a delay in processing at a step has occurred in the production state, a user who is, for example, a manager of a department having charge of the step, needs to promptly get to know the occurrence of the abnormality and the production state. As a method for allowing the user to promptly get to know the state of the site, there has been a mechanism for presenting information that indicates the state of the site (information about the start time and the end time of each production step (a cutting step, an assembling step, or the like) for each product, hereinafter, referred to as “result information”) to a user in real time.

While an abnormality in the production state is detected, the production efficiency (a time period that is taken for a prescribed number of products to undergo a step) in a certain step for a prescribed unit number of products (e.g., one product) is required in some cases. However, information that is transmitted from a production site such as a production factory, does not always include information about the prescribed unit number. Only information indicating a time period taken to perform the step on all the unit number of products may be included. In this case, proper detection of an abnormality in the production state fails, or the information needs to be acquired by another method.

This situation will be specifically explained with reference toFIG. 14. Graphs A and B inFIG. 14each indicate some result information sets in graph form with a progression status of the step indicated by an ordinate and a time axis indicated by an abscissa. In the graphs A and B, a circular mark (plot) and a triangular mark (plot) correspond to the start time of the step and the end time of the step, respectively. Further, the correspondence between the start time and the end time of each of the result information sets is shown by a line connecting the marks. It is noted that the slope of the line represents a step progression degree per unit time.

Here, in the graph A, all the lines are based on result information about the same unit number of products. Thus, a user can easily determine the presence/absence of an abnormality in the production state by comparing the slopes of the lines. That is, in this example, the slope of a line indicating that the start time is “9:40” and the end time is “9:50” is more moderate than those of the remaining lines so that the user can easily determine that the result information corresponding to this line includes an abnormality. However, if a plurality of result information sets acquired from the production site are not based on the same unit number of products, the user cannot properly determine the presence/absence of an abnormality from a graph in which the result information sets are shown by a method the same as the above one. For example, like the graph A, the graph B also shows that three products are produced during a time period from a start time “9:00” to an end time “9:15,” two products are produced during a time period from a start time “9:25” to an end time “9:35,” and two products are produced during a time period from a start time “9:40” to an end time “9:55.” However, the graph B differs from the graph A in that, in the graph B, the line connecting the start time “9:00” to the end time “9:15” indicates a case of producing “three” products, the line connecting the start time “9:25” to the end time “9:35” indicates a case of producing “two” products, and the line connecting the start time “9:40” to the end time “9:55” indicates a case of producing “two” products. The slopes of these lines are different in the meanings thereof. Therefore, the user cannot properly determine the presence/absence of an abnormality even by simply comparing the slopes of these lines.

In JP-2017-68816-A, even in a case where one result information set (particularly, a start time and an end time) is acquired after a plurality of products are produced, equipment on/off information is additionally acquired and used to estimate a pseudo result information set to be acquired when one product is produced. Then, the production state is displayed. However, in order to acquire the equipment on/off information, a special device such as a programmable logic controller (PLC) needs to be installed, a special communication environment needs to be prepared, or a system needs to be greatly repaired, for example. Thus, there is a problem that a big burden is required to acquire the equipment on/off information.

SUMMARY

The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a data analysis support apparatus and a data analysis support method by which the presence/absence of an abnormality in a product producing state can be properly determined on the basis of a plurality of result information sets that are provided in different forms from a production site.

One aspect of the present invention for achieving the aforementioned object, is a data analysis support apparatus that is formed of an information processing apparatus. The data analysis support apparatus comprises a result information acquisition unit configured to acquire first result information which is result information acquired for a product produced through a prescribed step, the first result information including information that indicates a first processing time period which is a processing time period of the step for a first number of the products, and second result information which is result information including information that indicates a second processing time period which is a processing time of the step for a second number of the products produced through the step, the second number being different from the first number, and a result information conversion unit configured to convert the first result information and the second result information into a plurality of result information sets by performing time-division of the first result information and/or the second result information such that the result information sets each indicate a time period taken to perform the step for a same unit number of the products.

According to the present invention, the presence/absence of an abnormality in a production state can be properly determined on the basis of a plurality of result information sets in different forms that are provided from a production site.

It is to be noted that problems, configurations, and effects other than the aforementioned ones will be apparent from an embodiment explained below.

DESCRIPTION OF EMBODIMENTS

Hereinafter, an embodiment of the present invention will be explained with reference to the drawings. It is to be noted that the following description and drawings exemplify the present invention. Omission and simplification are included, as appropriate, for clarification of the description. The present invention can be implemented by various other embodiments. Unless otherwise defined, the number of components may be one, or may be two or more.

In addition, in the following explanation, a term “information” is used to explain various types of data. The various types of data may be expressed by another data structure such as a table or a list. Further, terms “identifier,” “ID,” and the like are used to explain identification information. These terms can be replaced with each other. In addition, in the following explanation, the character “S” before a reference numeral means a process step.

FIG. 1schematically illustrates the configuration of an information processing system (hereinafter, referred to as “data analysis support system1”) which is shown as one embodiment of the present invention. As illustrated inFIG. 1, the data analysis support system1includes a data analysis support apparatus100, a result information management apparatus200, and a user apparatus300(data analysis apparatus). These apparatuses are information processing apparatuses (computers), and are connected to one another via a communication network5such that mutual communication can be performed among these apparatuses. The communication network5is a data communication network such as a local area network (LAN) or a wide area network (WAN), or is a dedicated line, for example.

The result information management apparatus200is an information processing apparatus that is operated by an organization such as a product producing site such as a factory or a company for managing the site, for example, and is an IoT server of an IoT system or edge computing, for example. The result information management apparatus200manages (stores) result information111which is acquired through a sensor, production equipment, or the like, installed in the production site. The result information111is sensor data or IoT data, for example, and includes information about the production state of the site (e.g., the number of produced products, the start time and the end time of each step, a worker who carries out each step).

The user apparatus300is operated by an organization such as a company for managing the site, for example, and is manipulated by a user such as a manager of the organization. The user apparatus300presents, to the user, information (e.g., result information or an analysis result of the result information) transmitted from the data analysis support apparatus100. Further, the user apparatus300transmits information acquired from the user to the data analysis support apparatus100.

The data analysis support apparatus100performs information processing concerning management of the product producing state. The data analysis support apparatus100provides, to the user apparatus300, information about the production state of the site (e.g., result information, or information indicating an abnormality in the production state of the site) on the basis of the result information111transmitted from the result information management apparatus200.

FIG. 2illustrates a hardware configuration example of the information processing apparatuses (the data analysis support apparatus100, the result information management apparatus200, and the user apparatus300) constituting the data analysis support apparatus1. As illustrated inFIG. 2, an information processing apparatus10includes a processor11, a main storage device12, an auxiliary storage device13, an input device14, an output device15, and a communication device16. The information processing apparatus10may be realized by using a virtual information processing resource that is provided by a virtualization technology, a processing space separation technology, or the like. For example, the virtual information processing resource is a virtual server a part or the entirety of which is provided by a cloud system. Further, all or some of the functions provided by the information processing apparatus10may be implemented by, for example, a service which a cloud system provides via an application programming interface (API) or the like. Moreover, the data analysis support apparatus100, the result information management apparatus200, and the user apparatus300may be formed of a plurality of the information processing apparatuses10that are connected such that communication thereamong can be performed.

InFIG. 2, the processor11is formed by using a central processing unit (CPU), a micro processing unit (MPU), a graphics processing unit (GPU), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or an artificial intelligence (AI) chip, for example.

The main storage device12stores a program and data, and is a read only memory (ROM), a random access memory (RAM), or a nonvolatile memory (non-volatile RAM (NVRAM)), for example.

For example, the auxiliary storage device13is a solid state drive (SSD), a hard disk drive, an optical storage device (e.g., a compact disc (CD) or a digital versatile disc (DVD)), a storage system, a reading/writing device for recording mediums such as integrated circuit cards (IC cards), secure digital cards (SD cards), and optical recording mediums, or a recording region in a cloud server. Programs and data can be read into the auxiliary storage device13via a recording medium reading device or the communication device16. Programs and data stored in the auxiliary storage device13are sequentially read into the main storage device12. It is to be noted that the auxiliary storage device13forms a function (hereinafter, referred to as “storage unit”) for storing various types of data.

The input device14is an interface that receives an input from the outside, and is a keyboard, a mouse, a touch panel, a card reader, a pen input type tablet, or a sound input device, for example.

The output device15is an interface that outputs various information about processing progression, process results, and the like. The output device15is a display device (e.g., a liquid crystal monitor, a liquid crystal display (LCD), or a graphics card) that visualizes the aforementioned various information, a device (a sound output device (e.g., a loudspeaker)) that converts the aforementioned various information into voice, or a device (e.g., printing device) that converts the aforementioned various information into texts, for example. It is to be noted that, for example, the information processing apparatus10may be configured to exchange information with a separate apparatus via the communication device16. For example, the information processing apparatus10may exchange information with a separate apparatus over the internet.

It is to be noted that the input device14and the output device15constitute a user interface to receive information from a user, and present information to the user.

The communication device16implements communication with a separate apparatus. The communication device16is a wired or wireless type communication interface that implements communication with a separate apparatus over a communication network (e.g., the internet, a LAN, a WAN, a dedicated line, or a public communication network). The communication device16is a network interface card (NIC), a wireless communication module, or a USB module, for example.

For example, an operating system, a file system, a database management system (DBMS) (e.g., relational database or NoSQL), a key-value store (KVS), and any other type of software (e.g., software, middleware, and various applications for implementing a user interface through graphical user interface (GUI) by the input device14and the output device15) may be installed in the information processing apparatus10.

Each of the functions of each of the information processing apparatuses (the data analysis support apparatus100, the result information management apparatus200, and the user apparatus300) constituting the data analysis support system1are implemented by the processor11reading out and executing a program stored in the main storage device12, or is implemented by hardware (e.g., an FPGA, an ASIC, or an AI chip) constituting the apparatus. In addition, the information processing apparatuses each store various types of information (data) as a table of a database that is provided by a DBMS, or as a file being managed by a file system, for example.

FIG. 3illustrates main functions included in the data analysis support apparatus100. As illustrated inFIG. 3, the data analysis support apparatus100includes functions of a storage unit110, an information management unit120(result information acquisition unit), a conversion necessity/unnecessity determination unit130, a result information conversion unit140, a data analysis unit150, an information presentation unit160, and a communication processing unit170. Each of these functions is implemented by the processor11of the data analysis support apparatus100reading out and executing a program stored in the main storage device12of the data analysis support apparatus100, or is implemented by hardware (e.g., an FPGA, an ASIC, or an AI chip) of the data analysis support apparatus100.

The storage unit110stores the result information111, production management information112, and result information (after conversion)113. The storage unit110stores these types of information as a table of a database or as a file being managed by a file system, for example.

The information management unit120acquires the result information111from the result information management apparatus200via the communication network5. Further, the information management unit120updates the production management information112by using the acquired result information111. A method for updating the production management information112will be described later in detail.

The conversion necessity/unnecessity determination unit130determines whether conversion of the result information111is necessary or unnecessary (whether conversion of the result information111to information for a prescribed unit number of products (the result information (after conversion)113) is necessary or unnecessary). Determination on whether conversion of the result information111is necessary or unnecessary will be described later in detail.

The result information conversion unit140creates the result information (after conversion)113by converting the result information111for which the necessity to be converted has been determined by the conversion necessity/unnecessity determination unit130.

The data analysis unit150determines the presence/absence of an abnormality in the production state of a production site on the basis of the result information111/the result information (after conversion)113and the production management information112. It is to be noted that, in a case where the result information111has been converted, the data analysis unit150determines the presence/absence of an abnormality in the production state of the production site on the basis of the result information (after conversion)113, and, in a case where the result information111has not been converted, the data analysis unit150determines the presence/absence of an abnormality in the production state of the production site on the basis of the result information111.

The information presentation unit160provides (transmits), to the user apparatus300, information indicating the state of the production site or information indicating the presence/absence of an abnormality in the production state of the site (hereinafter, referred to as “analysis result”). For example, the information presentation unit160creates a screen showing the analysis result (hereinafter, referred to as “analysis result presentation screen”), and transmits the generated analysis result presentation screen to the user apparatus300.

The communication processing unit170exchanges various types of information with a separate apparatus via the communication network5. For example, the communication processing unit170acquires the result information111from the result information management apparatus200. Further, for example, the communication processing unit170transmits the analysis result presentation screen to the user apparatus300.

FIG. 4shows one example of the result information111. As shown inFIG. 4, the result information111is formed of one or more entries (records) each including a result information ID411, a product ID412, a step ID413, a worker ID414, a start time415, an end time416, and the number of processed products417. One entry in the result information111corresponds to one result information set acquired from the result information management apparatus200.

In the above items, a result information identifier (hereinafter, referred to as “result information ID”) is set in the result information ID411. Information (an identifier of a product (product type) in the present embodiment, and hereinafter, referred to as “product ID”) indicating about which product (product type) the corresponding result information indicates, is set in the product ID412. It is to be noted that the concept of a product or a product type is optionally set in the production site, for example. Information (an identifier of a step in the present embodiment, hereinafter referred to as “step ID”) indicating which step for this product the result information is about is set in the step ID413. Information (hereinafter, referred to as “worker ID”) indicating a worker who carries out this step is set in the worker ID414. A clock time at which the step for this product (all products, if the number of products to be produced is two or more) is started, is set in the start time415. A clock time at which the step for this product (all products, if the number of products to be produced is two or more) is ended, is set in the end time416. It is to be noted that the start time415and the end time416may include information about a date. Information indicating the number of the products processed in this step is set in the number of processed products417.

For example, inFIG. 4, an entry having “1” set as the result information ID indicates that a step the step ID of which is “press” for a production the production ID of which is “door A” is performed by a worker the worker ID of whom is “worker A” during a time period from “10:08” to “10:20,” and that the number of the processed products is “three.”

It is to be noted that the result information111may further include information about equipment for carrying out a step, and various information about the production state such as a material to be processed, for example. In addition, an entity of providing the result information111is not necessarily limited to the result information management apparatus200.

FIG. 5shows one example of the production management information112. As shown inFIG. 5, the production management information112is formed of one or more entries (records) each including a production management information ID511, a production ID512, a step ID513, a worker ID514, a relation515between the number of processed products and a processing time period, and a reference time516. One entry in the production management information112corresponds to one production management information set.

An identifier of production management information (hereinafter, referred to as “production management information ID”) is set in the production management information ID511. The aforementioned production ID is set in the production ID512. The aforementioned step ID is set in the step ID513. The aforementioned worker ID is set in the worker ID514. Information (e.g., “linear,” “fixed”) indicating the relation between the number of processed products and the processing time period in this step, for the combination of this product, this step, and this worker, is set in the relation515between the number of processed products and the processing time period. A reference (standard) processing time period (hereinafter, referred to as “reference time”) that is taken to process one product in this step is set in the reference time516. As shown inFIG. 5, the reference time Y is expressed by an expression (e.g., “Y=4X,” “Y=2X”) that represents the relation with a number X of the products, in the present example.

For example, inFIG. 5, an entry having “1” set as the production management information ID indicates that, in a case where a step the step ID of which is “press” for a product the production ID of which is “door A” is performed by a worker the worker ID of whom is “worker A,” the processing time period of this step is increased “linearly” in proportion to the number of processed products so that the reference time Y is “4X.”

It is to be noted that the production management information112may include information other than the shown information. For example, the production management information112may include information about equipment to be used in the step, information about a product producing option, and the like. In addition, the relation between the number of processed products and the processing time period is not limited to those shown in the drawing, and may be set in such a way that “the number of processed products increases stepwise by a prescribed number of processed products,” or “the processing time period increases exponentially with the number of processed products,” for example.

FIG. 6shows one example of the result information (after conversion)113. The result information (after conversion)113is obtained by converting the result information111(by performing time-division of entries), and includes information obtained by converting (time-dividing) the result information111into information that indicates the production efficiency (processing time period taken for a step) for a unit number (e.g., one) of products. As shown inFIG. 6, the result information (after conversion)113is formed of one or more entries (records) each including a result information ID611, a production ID612, a step

ID613, a worker ID614, a start time615, an end time616, and a number of processed products617. One entry in the result information (after conversion)113corresponds to one of information sets (hereinafter, referred to as “result information (after conversion”) obtained by time-division of result information into information about a unit number (e.g., one).

An identifier of result information (hereinafter, referred to as “result information ID”) is set in the result information ID611. In the present example, in the result information ID611of an entry obtained by conversion of the result information111(division of the result information111into entries), a result information ID obtained by adding a branch number (sub-number) to the original result information ID of the result information111is set. For example, three entries having “1-1,” “1-2,” and “1-3” set as the result information ID611of the result information (after conversion)113inFIG. 6, are obtained as a result of conversion (time-division into three parts) of the entry having “1” set as the result information ID411inFIG. 4. The aforementioned production ID is set in the production ID612. The aforementioned step ID is set in the step ID613. A clock time at which this step for the product is started is set in the start time615. A clock time at which this step for the product is set in the end time616. It is to be noted that the start time615and the end time616may include information about a date. Information that indicates the number of products processed in this step is set in the number of processed products617.

Next, processes which are performed by the data analysis support apparatus100will be specifically explained.

FIG. 7is a flowchart for explaining a process (hereinafter, referred to as “data analysis process S700”) which is performed by the data analysis support apparatus100. Upon receiving an instruction to execute this process from the user apparatus300, or upon arrival of the start time (e.g., at regular intervals (every hour, a prescribed clock time of every day, or the like)), for example, the data analysis support apparatus100starts the data analysis process S700.

In the data analysis process S700, the data analysis support apparatus100first performs a process for determining whether conversion of the result information111is necessary or unnecessary (hereinafter, referred to as “conversion necessity/unnecessity determination process S711”). The details of the conversion necessity/unnecessity determination process S711will be explained later.

Next, the data analysis support apparatus100creates the result information (after conversion)113by performing a process regarding conversion (time division) of the result information111(hereinafter, referred to as “result information conversion process S712”). The details of the result information conversion process S712will be explained later.

Next, the data analysis support apparatus100determines whether an abnormality has occurred in the production state of the production site on the basis of the result information111/the result information (after conversion)113and the production management information112, and performs a process of presenting the details of the result information111/the result information (after conversion)113and the result of the above determination to the user (hereinafter, referred to as “abnormality determination process S713”). The details of the abnormality determination process S713will be explained later. Thereafter, the data analysis process S700is ended.

FIG. 8is a flowchart for explaining the details of the conversion necessity/unnecessity determination process S711in FIG.

7. Hereinafter, the conversion necessity/unnecessity determination process S711will be explained with reference toFIG. 8.

First, the data analysis support apparatus100receives, from a user via the user apparatus300, a search condition for the result information111to be analyzed (S811). Here, it is assumed that the data analysis support apparatus100receives a time section from “10:00” to “11:00” of the step “press” for the product “door A” performed by the worker “A,” as the search condition including a product, a step, a worker, and a time section. It is to be noted that, since the search condition is just one example, a material or the like to be processed in a step may be designated, for example.

Next, the data analysis support apparatus100acquires the result information111corresponding to the search condition, from the result information management apparatus200(S812).

Next, the data analysis support apparatus100determines whether or not the relation between the number of processed products and the processing time period is fixed in each entry of the acquired result information111(S813). Specifically, for each entry of the acquired result information111, the data analysis support apparatus100determines whether or not the relation between the processed products and the processing time period is fixed by confirming the relation515between the number of processed products and the processing time period in the production management information112inFIG. 5. For example, regarding the entries having “1,” “2,” and “3” set as the result information ID411of the result information111inFIG. 4, the relation515between the number of processed products and the processing time period in an entry having “1” set as the production management information ID511of the production management information112is “linear.” Thus, the relation between the number of processed products and the processing time period is determined to be not “fixed.” When the relation between the number of processed products and the processing time period is determined to be fixed (S813: YES), the data analysis support apparatus100determines that conversion of the result information111is unnecessary, and stores this determination result (S820). Then, the conversion necessity/unnecessity determination process S711is ended. When the relation between the number of processed products and the processing time period is determined to be not fixed (S813: NO), the data analysis support apparatus100executes the process from S814.

At S814, the data analysis support apparatus100selects one of the entries acquired at S812.

Next, the data analysis support apparatus100selects, from among the entries acquired at S812, another one different from the entry selected at S814(S815).

Next, the data analysis support apparatus100determines whether the number of processed products417in the entry selected at S814is equal to that in the entry selected at S815(S816). For example, the data analysis support apparatus100selects an entry having “1” set as the result information ID411, and an entry having “2” as the result information ID411from the result information111inFIG. 4. In the former entry, the number of processed products is “3.” In the latter entry, the number of processed products is “2.” Accordingly, the data analysis support apparatus100determines that these entries are different in the number of processed products. When these entries have the same number of processed products417(S816: YES), the data analysis support apparatus100executes S817. When these entries are different in the number of processed products417(S816: NO), the data analysis support apparatus100determines that conversion of the result information111is necessary, and stores this determination result (S821). Then, the conversion necessity/unnecessity determination process S711is ended.

At S817, the data analysis support apparatus100determines whether or not all the entries selected at S812(excluding the entry selected at S814) have been selected at S815. When not all the entries have been selected (S817: NO), the process returns to S815, and the process from S816is executed for the unselected entries. When all the entries have been selected (S817: YES), the data analysis support apparatus100determines that conversion of the result information111is unnecessary, and stores the determination result (S820). Then, the conversion necessity/unnecessity determination process S711is ended.

In the aforementioned manner, when there is a combination of entries different in the number of processed products417in each of the entries of the result information acquired at S812, the data analysis support apparatus100determines that “conversion of the result information is necessary.” When the entries of the result information acquired at S812have the same values of the number of processed products417, the data analysis support apparatus100determines that “conversion of the result information is unnecessary.”

FIG. 9is a flowchart for explaining the details of the result information conversion process S712inFIG. 7. Hereinafter, the result information conversion process S712will be explained with reference toFIG. 9.

As shown inFIG. 9, the data analysis support apparatus100first determines whether conversion of the result information111has been determined to be necessary as a result of the conversion necessity/unnecessity determination process S711inFIG. 8(S911). When conversion of the result information111has been determined to be necessary (S911: YES), the process proceeds to S912. When conversion of the result information111has been determined to be unnecessary (S911: NO), the data analysis support apparatus100ends the result information conversion process S712.

At S912, the data analysis support apparatus100acquires the values of the numbers of produced products417in the respective entries of the result information111acquired at S812of the conversion necessity/unnecessity determination process S711inFIG. 8. In the above example, from entries having “1,” “2,” and “3” as the result information ID411of the result information111inFIG. 4, “3,” “2,” and “2” are acquired as the number of processed products417.

Next, the data analysis support apparatus100obtains the granularity (a unit number of products) of the number of processed products417in each entry for use in conversion of the result information111(S913). In the present example, the greatest common divisor of the numbers of processed products417in the respective result information sets acquired at S912is defined as the granularity. In the above example, the data analysis support apparatus100obtains, as the granularity, “1” which is the greatest common divisor of “3,” “2,” and “2.” It is to be noted that a method for defining the granularity is not limited to a particular one. For example, the granularity may be a fixed value (e.g., “1”), or may be previously set by a user or the like.

Next, the data analysis support apparatus100performs conversion (time-division) of each of the entries in the result information111acquired at S812of the conversion necessity/unnecessity determination process S711inFIG. 8, by using the granularity decided at S913. Specifically, the data analysis support apparatus100performs time-division of each of the entries such that the entry is divided on the basis of each granularity, and reflects the result of the time-division in the result information (after conversion)113(S914). Thereafter, the data analysis support apparatus100ends the result information conversion process S712.

For example, regarding the entry having “1” set as the result information ID411of the result information111inFIG. 4, the number of processed products417is “3.” The data analysis support apparatus100performs time-division of this entry into three entries, which is obtained by dividing “3” by the granularity of “1.” The entries obtained by this time-division correspond to three entries having “1-1,” “1-2,” and “1-3” set as the result information ID611of the result information (after conversion)113inFIG. 6. It is to be noted that, in the present example, the data analysis support apparatus100sets the start time615and the end time616in each of the three entries such that the time period from the start time “10:08” to the end time “10:20” in the entry which has not been time-divided, is divided into three sections.

FIG. 10is a flowchart for explaining the details of the abnormality determination process S713inFIG. 7. Hereinafter, the abnormality determination process S713will be explained with reference toFIG. 10.

First, the data analysis support apparatus100determines whether or not an abnormality has occurred in the production state of the production site by using the production management information112(S1011). Specifically, the data analysis support apparatus100determines whether or not the relation between the number of processed products and the processing time period (step progression degree per unit time) in the result information111or the result information (after conversion)113satisfies a relation set in the reference time516in the production management information112. When satisfaction is determined, the data analysis support apparatus100determines that no abnormality has occurred in the production state of the production site. When satisfaction is not determined, the data analysis support apparatus100determines that an abnormality has occurred in the production state of the production site.

For example, regarding entries having “1-1,” “1-2,” “1-3,” “2-1,” and “2-2” set as the result information ID611of the result information (after conversion)113inFIG. 6, the number of processed products617is “1” and the difference (processing time period) between the start time615and the end time616is “4 minutes.” Thus, these entries satisfy “Y=4X” which is the reference time516in the production management information112. Therefore, the data analysis support apparatus100determines that no abnormality has occurred in the production state of the production site.

For example, regarding entries having “3-1” and “3-2” set as the result information ID611of the result information (after conversion)113inFIG. 6, the number of processed products617is “1,” and the difference (processing time period) between the start time615and the end time616is “6 minutes” so that “Y=4X” which is the reference time516in the production management information112is not satisfied. Thus, the data analysis support apparatus100determines that a certain abnormality has occurred in the production state of the production site. It is to be noted that a calculation method for the abnormality determination is not limited to the aforementioned method. For example, a buffer time for allowing a certain degree of variation of a processing time may be provided for the reference time so that, in a case where the processing time period is deviated from the reference time even when the buffer time is taken into consideration, occurrence of an abnormality in the production state of the production site may be determined.

Next, the data analysis support apparatus100creates an analysis result presentation screen showing the determination result about the presence/absence of an abnormality, and the like, and presents the created analysis result presentation screen to the user via the user apparatus300(S1012).

For example, regarding the entries having “1” and “2” set as the result information ID411of the result information111, the data analysis support apparatus100displays on the analysis result presentation screen to indicate that the entries having “1-1,” “1-2,” “1-3,” “2-1,” and “2-2” set as the result information ID611of the result information (after conversion)113are normal. In addition, regarding the entry having “3” set as the result information ID611of the result information111, the data analysis support apparatus100displays the analysis result presentation screen indicating that both the entries having “3-1” and “3-2” set as the result information ID611of the result information (after conversion)113are abnormal.

FIG. 11shows one example of the analysis result presentation screen. An analysis result presentation screen1100inFIG. 11includes visualized information in graph form about seven entries having “1-1,” “1-2,” “1-3,” “2-1,” “2-2,” “3-1,” and “3-2” set as the result information ID611of the result information (after conversion)113. InFIG. 11, each circular mark represents a point (position) corresponding to the start time of the step, and each triangular mark represents a point (position) corresponding to the end time of the step. Further, each line connects the start time to the end time for the same products or the same product group. These entries are obtained by dividing the entries “1,” “2,” and “3” set as the result information ID411of the result information111, into three parts, two parts, and two parts, respectively. Each marks-line combination in this graph indicates result information for the same unit number (one).

Therefore, through comparison of the slopes (the step progression degree per unit time) of the respective combinations, the user can easily and properly determine the presence/absence of an abnormality in the production state of the production site. In the present example, the slope of the line of display information about the entry having “3” set as the result information ID411of the result information111, that is, result information (after conversion) in which “3-1” and “3-2” are set as the result information ID611of the result information (after conversion)113is more moderate than those of the lines of the any other result information (after conversion). Therefore, the user can determine that an abnormality has occurred in the production state of the production site.

FIG. 12shows another example of the analysis result presentation screen. An analysis result presentation screen1200shown inFIG. 12is obtained by graphing the entries having “1,” “2,” and “3” set as the result information ID411of the result information111, which is the original of the result information (after conversion)113, in the same manner as that inFIG. 11. As shown inFIG. 12, in the present example, the marks-line combination of an entry including occurrence of an abnormality is highlighted (is displayed by a broken line in this example), and those of the remaining entries are displayed in a normal way (are displayed by solid lines in the present example). In this manner, the marks-line combination of an entry including the occurrence of an abnormality is highlighted so that a user can easily get to know that an abnormality in the production state of the production site has occurred for this entry. It is to be noted that a method for highlighting is not necessarily limited to a particular one. For example, highlighting may be achieved by using different colors, or different line weights. In addition, the graph inFIG. 11and the graph inFIG. 12may be combined (superimposed) and displayed in different forms (line types, colors, line weights, or the like).

FIG. 13is a flowchart for explaining a process (hereinafter, referred to as “production management information updating process S1300”) which the data analysis support apparatus100performs when updating the production management information112. Upon acquiring the result information111from the result information management apparatus200, or upon arrival of a prescribed timing (at regular intervals, (e.g., every hour or at a prescribed clock time of each day), for example), for example, the data analysis support apparatus100executes the production management information updating process S1300. Hereinafter, the production management information updating process S1300will be explained with reference toFIG. 13.

First, the data analysis support apparatus100acquires, from the result information111, entries having the same information stored in the step ID413and the worker ID414(S1311). For example, the data analysis support apparatus100acquires, as entries having “door A” set as the production ID412, “press” set as the step ID413, and “A” set as the worker ID414in the result information111inFIG. 4, the entries having “1” and “2” set as the result information ID411. It is noted that a method for acquiring the result information111is not necessarily limited to a particular one. For example, the result information111sets having the same information stored in the production ID412and the step ID413in the result information111may be acquired, or entries of a previously designated arbitrary time section (e.g., each day, each month) may be acquired.

Next, the data analysis support apparatus100obtains the relation between the number of processed products and the processing time period, and a reference time on the basis of the acquired entries in the result information111(S1312). Specifically, by using the acquired result information111sets, the data analysis support apparatus100obtains an average processing time period of producing one product. For example, in the entries having “1” and “2” set as the result information ID411of the result information111inFIG. 4, “12minutes” are taken to produce “three” products, and “8 minutes” are taken to produce “two” products. That is, in this case, “20 minutes” are taken to produce “five” products. Thus, the data analysis support apparatus100obtains the average processing time period by “4minutes/one product =20minutes/5products.” Then, since “Y=4X” in which X represents the number of processed products and Y represents the reference time for the processing time period, the data analysis support apparatus100identifies that production is carried out while the relation between the number of processed products and the processing time period relation shows a “linear” proportion. It is to be noted that a calculation method for the relation between the number of processed products and the processing time period, and the reference time is not necessarily limited to a particular one. For example, the relation between the number of processed products and the processing time period and the reference time may be obtained after the result information111that seems to include an abnormality is previously excluded. In addition, through comparison with a prepared function (e.g., a quadratic function or a logarithmic function), the most approximate function may be defined as the relation between the number of processed products and the processing time period. A buffer time period may be taken into consideration for a certain level of variation of the reference time.

Next, the data analysis support apparatus100reflects, in the production management information112, the obtained relation between the number of processed products and the processing time period and the obtained reference time (S1313). For example, regarding the step “press” for the product “door A” by the worker “A” obtained at S1312, “linear” is set for the relation515between the number of processed products and the processing time period, and “Y=4X” is set for the reference time516. As explained so far, when acquiring a plurality sets of the result information111in different forms (the result information111including information that indicates step processing time periods for different unit numbers of products) from a production site, the data analysis support apparatus100according to the present embodiment performs time-division of at least any one of the result information111sets so that the result information set is divided into a plurality of result information sets each indicating a time period taken to perform the process for the same unit number of products. Accordingly, the presence/absence of an abnormality in the product producing state of the production site can be easily and properly determined.

Further, the data analysis support apparatus100creates a screen showing the plurality of result information sets in graph form with a time axis indicated by an abscissa and a progression status of the step indicated by an ordinate, and presents the screen to a user. Accordingly, the user can easily and properly determine the presence/absence of an abnormality in the production state of the production site by comparing the respective result information sets shown in the screen (the slopes of the lines (graph) of the respective result information sets).

In addition, the data analysis support apparatus100determines the presence/absence of an abnormality in each of result information sets (the production state of the production site) by comparing the progression degree of the result information set per unit time with a reference progression degree, and then, highlights a line (graph) of the result information set in which the presence of an abnormality has been determined. Accordingly, a user can easily get to know which result information set includes an abnormality, and can promptly take measures against the abnormality.

In the aforementioned mechanism, it is not necessary to separately provide a special mechanism such as a programmable logic controller (PLC). The aforementioned mechanism can be achieved at low cost with a light burden.

One embodiment of the present invention has been explained so far. However, the present invention is not limited to the above embodiment. It goes without saying that various modification can be made within the gist of the present invention. For example, the above embodiment exemplifies the present invention in detail in an easy-to-understand manner, and thus, the present invention is not necessarily limited to an apparatus including all the components explained above. In addition, any one of the components in the above embodiment may be deleted or replaced with any other component, or any other component may be added thereto.

Further, the aforementioned components, function units, processing units, processing means, and the like may be partially or entirely implemented by hardware that is, for example, designed on an integrated circuit. In addition, the aforementioned components, functions, and the like, may be implemented by software by a processor interpreting and executing a program for implementing these functions. Information about the program for executing the functions, a table, a file, and the like can be put in a recording device such as a memory, a hard disk, or an SSD, or in a recording device such as an IC card, an SD card, or a DVD.

Further, the aforementioned arrangement of the function units, the processing units, and the databases in each of the information processing apparatuses is just one example. The aforementioned arrangement of the function units, the processing units, and the databases may be changed to an optimum arrangement form from the viewpoint of the performance of the hardware/software of the apparatuses, the processing efficiency, the communication efficiency, and the like.

Moreover, the configuration (e.g., schema) of each database for storing the aforementioned various data can be flexibly changed from the view point of the efficient use of resources, improvement of the processing efficiency, improvement of the access efficiency, improvement of the search efficiency, and the like.