JP2022066892A - Work analysis system and work analysis method - Google Patents

Abstract

To provide useful information relating to a relation between a work procedure (work processing) and a result thereof (work processing result) from log information relating the work.SOLUTION: A work analysis system stores log information including work processing which is information relating a series work flow regarding a business and a work processing result which is information relating a result of the work processing, generates clustering log information which is information in which the work processing is classified into a plurality of clusters. based on a feature amount acquired by quantifying the information relating the work processing in the log information, and generates information indicating a relation between the work processing and the work processing result for each of the clusters based on the clustering information and the work processing result acquired from the log information.SELECTED DRAWING: Figure 1

Description

The present invention relates to a work analysis system and a work analysis method.

Patent Document 1 describes a business processing flow generation system configured for the purpose of being able to generate a business processing flow from a work log of an application program. The business process flow generator of the business process flow generation system acquires the log of the application program, extracts the log data related to a series of work processes as a block from the acquired log, and is related from the extracted blocks. A pattern in which a plurality of log data are arranged in chronological order is extracted, a pattern in which a predetermined log data is common is extracted from the extracted patterns, and a business processing flow is generated based on the predetermined log data.

Patent Document 2 describes a business evaluation system configured for the purpose of being able to objectively evaluate business contents by eliminating human subjectivity without bothering the manager or the person in charge of business. There is. The business evaluation system collects business data related to business, stores analysis data used to analyze the collected business data, and names the business phase indicating the stage of the business and the name of the process included in the business phase. The collected business data is analyzed using the analysis data, the analysis result data is stored as the evaluation target data, the analysis result data is accumulated as the past data, and the evaluation target data is accumulated. Evaluate by comparing with the past data.

Patent Document 3 describes a sales business management system configured for the purpose of supporting the sales activities necessary at that time in the time from the inquiry to the order decision. The sales business management system manages the business of the order process from inquiry to order determination for each product type of the sales product by dividing it into predetermined multiple processes, and the matter identification information of the matter and the matter of the matter entered according to the inquiry. Create and store a file for each case that associates product information and product type information with customer information for each case, and determine a sales person according to at least the product type of the case based on the data in the person in charge database. The contents of this matter file are updated, the information necessary for the next meeting with the customer is output based on the information in the meeting preparation contents information file, and the order probability information with the achievement / unachieved information of the item information is set. Create in a file.

JP-A-2017-227944 Japanese Unexamined Patent Publication No. 2007-2003808 Japanese Unexamined Patent Publication No. 2018-55268

In organizations such as companies and government offices, historical information (hereinafter referred to as "log information") generated by the information processing system introduced in the organization is analyzed for the purpose of improving business efficiency. ing. The result of the above analysis is visualized and provided to the user, and the user uses the above result for planning improvement measures for the work performed in the business.

When improving a business, it is also referred to as a work procedure (hereinafter, also referred to as "work processing") and a result (hereinafter, "work processing result") such as a work procedure in which the work is likely to succeed or a work procedure in which the work is likely to fail. ) Is useful information about the causal relationship. For example, in sales activities, the order rate increases when work such as customer visits, requirement acquisition, and sales methods is performed by what work procedure (work process), and conversely, what kind of work procedure (work process) is used for such work. Information such as whether the loss rate increases when the work process is performed is useful.

The business process flow generation system described in Patent Document 1 extracts a pattern by arranging a plurality of related log data in chronological order from the log data related to a series of work processes extracted from the log of the application program, and the extracted pattern. Among them, the pattern in which the predetermined log data is common is extracted, and the business processing flow is generated based on the predetermined log data. However, Patent Document 1 does not particularly describe a technique for acquiring information regarding a relationship between a business processing flow and the result of executing the business processing flow.

The business evaluation system described in Patent Document 2 analyzes business data using analysis data, stores the analysis result data as evaluation target data, accumulates the analysis result data as past data, and stores the evaluation target data as past data. Evaluate by comparing with. However, the system does not evaluate business data based on actual results, and does not particularly describe a technique for acquiring information on the relationship between a business processing flow and the result of executing the business processing flow.

The sales business management system described in Patent Document 3 obtains order accuracy based on the sales position information input from the sales staff, and asks the sales staff about the next sales activity to be executed and a template of information to be provided or acquired. To present. However, the system can be configured on the premise that the success pattern of sales activities is established in advance, and it is not a system that automatically extracts information on the relationship between work procedures and work results from log information. not.

The present invention has been made in view of such a background, and is a work analysis system and a work analysis method capable of providing useful information on the relationship between a work procedure and the result from log information on the work. The purpose is to provide.

One of the present inventions for achieving the above object is a work analysis system, which is configured by using an information processing device having a processor and a storage device, and is information on a series of work flows related to work. The work is based on a storage unit that stores log information including the work process result, which is information about the result of the work process, and a feature amount acquired by quantizing the information about the work process in the log information. Based on the clustering unit that generates the clustering log information, which is the information that classifies the processing into a plurality of clusters, and the clustering log information and the work processing result acquired from the log information, each of the clusters It is provided with an analysis unit that generates information indicating the relationship between the work process and the work process result.

In addition, the problems disclosed in the present application and the solutions thereof will be clarified by the column of the form for carrying out the invention and the drawings.

According to the present invention, it is possible to provide useful information about the relationship between the work procedure and the result from the log information about the work.

It is a figure which shows the schematic structure of the work analysis system which concerns on 1st Embodiment. This is an example of the hardware configuration of the information processing device used to configure the work analysis system. This is an example of log information. This is an example of clustering log information. This is an example of the analysis result. This is an example of a work flow diagram. It is a system flow diagram which shows the schematic structure of the work analysis system which concerns on 2nd Embodiment. It is a flowchart explaining the work process analysis process. This is an example of the analysis result for a certain cluster. This is an example of the analysis result for a certain cluster. It is a figure which shows the schematic structure of the work analysis system which concerns on 3rd Embodiment. This is an example of the history analysis result display screen.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings as appropriate. The following description and drawings are merely examples for explaining the present invention, and are appropriately omitted and simplified for the sake of clarification of the description. The present invention can also be implemented in various other forms. Unless otherwise specified, each component may be singular or plural.

In the following description, the same or similar configurations may be designated by the same reference numerals and duplicate description may be omitted. Further, in the following description, the letter "S" added before the reference numeral means a processing step. Further, various information (data) in the following description may be expressed or managed by a method other than the illustrated data structure. Further, in the following description, when various identification information is described, expressions such as "identifier" and "ID" are appropriately used, but these can be replaced with each other.

[First Embodiment]
FIG. 1 is a system flow diagram showing a schematic configuration of an information processing system (hereinafter, referred to as “work analysis system 1”) shown as a first embodiment. The work analysis system 1 is configured by using one or more information processing devices (computers).

The work analysis system 1 is based on log information which is text data including information on a series of work flow (hereinafter referred to as "work process") related to work, and information on work process (work subject, work content, work order). , Work date and time, information indicating the result of the work process (hereinafter referred to as "work process result"), etc.), and the work process is referred to as a plurality of types (hereinafter referred to as "cluster") based on the acquired information. ), Analyze the relationship between work processing and work processing results for each cluster, and output the results.

The work that constitutes the work process, which is the target of the work analysis system 1, is not necessarily limited, but is, for example, a work performed by a work entity such as an employee or an employee regarding work in an organization such as a company, a government office, or a local government. The work subject does not necessarily have to be a human being, and may be a robot or the like. The log information is acquired from, for example, an information processing system operated in the above-mentioned organization. The information processing system is, for example, a sales management system, a travel expense settlement system, an attendance / leaving management system, an inventory management system, an order management system, a customer management system, a personnel management system, a distribution management system, an accounting system, and a settlement system. In this embodiment, the case where the above information processing system is a business management system will be described as an example.

The log information to be analyzed by the work analysis system 1 is appropriately preprocessed such as shaping and cleansing based on the characteristics of each log by using a log parser, a log adapter, or the like. In addition, in the log information, by the above preprocessing, a series of finite work from the start work of the work in the work to the completion work to the last work performed in the work is a series of finite work based on the actual execution order. It shall be in a state that can be identified as a work process. The work process can be obtained by using a known technique such as the method described in Patent Document 1 (Japanese Unexamined Patent Publication No. 2017-227944).

Further, it is assumed that the work processing result is given to each work processing in the preprocessing. The mode of the work processing result is not necessarily limited, and may be various, for example, when it is expressed as binary (success / failure) or when it is classified by a threshold value (for example, rapid, delay, failure, etc.). The work process result is acquired or calculated based on, for example, the content of each work (each work step) included in the work process, the work time obtained from the log that is the source of the work process, and the like. The work processing result may be obtained, for example, by merging the contents from another file in which the work processing result is defined. The work analysis system 1 analyzes the work process consisting of various combinations of work included in the log information, and automatically classifies the work process into clusters based on the characteristics of the work process obtained by the analysis. The method of classifying work processes into clusters is not always limited, but for example, the work analysis system 1 has information on work processes (results of individual work constituting the work, work subject, elapsed time, set of work, and order of work. Etc.) are quantized to classify work processes into clusters based on the features (feature values) obtained. More specifically, the work analysis system 1 provides information expressed in the specific work processing of each business (difference in skill level of the work subject, status of sharing of different work processing with a common information processing system, customer). The work process is divided into clusters according to the information implicitly embodied (expressed) in the work process, such as whether or not it is the work process on the side, information such as differences in work objects (products and fields handled, etc.). Classify.

FIG. 2 shows a hardware configuration example of the information processing apparatus constituting the work analysis system 1. The information processing device 10 exemplified includes a processor 11, a main storage device 12, an auxiliary storage device 13, an input device 14, an output device 15, and a communication device 16. It should be noted that the information processing apparatus 10 illustrated is virtual information in which all or part of the information processing apparatus 10 is provided by using virtualization technology, process space separation technology, or the like, for example, like a virtual server provided by a cloud system. It may be realized by using processing resources. Further, all or a part of the functions provided by the information processing apparatus 10 may be realized by, for example, a service provided by a cloud system via an API (Application Program Interface) or the like. In addition, work analysis system 1
May be configured by using a plurality of information processing devices 10 connected so as to be communicable.

In the figure, the processor 11 is, for example, a CPU (Central Processing Unit), M.
PU (Micro Processing Unit), GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), ASIC (Application Specific Integrated Circuit)
), AI (Artificial Intelligence) chip and the like.

The main storage device 12 is a device for storing programs and data, and is, for example, a ROM (Read).
Only Memory), RAM (Random Access Memory), non-volatile memory (NVRAM (Non Volatile RAM)) and the like.

The auxiliary storage device 13 is, for example, an SSD (Solid State Drive), a hard disk drive, an optical storage device (CD (Compact Disc), DVD (Digital Versatile Disc), etc.), a storage system, an IC card, an SD card, or an optical recording device. A reading / writing device for a recording medium such as a medium, a storage area for a cloud server, and the like. The auxiliary storage device 13 can read a program or data via a reading device of a recording medium or a communication device 16. Programs and data stored (stored) in the auxiliary storage device 13 are read into the main storage device 12 at any time.

The input device 14 is an interface that accepts input from the outside, and is, for example, a keyboard, a mouse, a touch panel, a card reader, a pen input type tablet, a voice input device, and the like.

The output device 15 is an interface that outputs various information such as processing progress and processing results. The output device 15 is, for example, a display device (liquid crystal monitor, LCD (Liquid Crystal Display), graphic card, etc.) that visualizes the above-mentioned various information, and a device (voice output device (speaker, etc.)) that visualizes the above-mentioned various information. , A device (printing device, etc.) that converts the above various information into characters. In addition, for example, the information processing device 10 may be configured to input and output information to and from another device via the communication device 16.

The input device 14 and the output device 15 form a user interface for receiving and presenting information with the user.

The communication device 16 is a device that realizes communication with other devices. The communication device 16 is a wired or wireless communication interface that realizes communication with another device via the communication network 5, and is, for example, a NIC (Network Interface Card), a wireless communication module, a USB module, or the like. Is.

For example, an operating system, a file system, a DBMS (DataBase Management System) (relational database, NoSQL, etc.), a KVS (Key-Value Store), or the like may be introduced in the information processing apparatus 10.

Each function provided in the work analysis system 1 is performed by the processor 11 reading and executing a program stored in the main storage device 12, or by hardware (FPGA, ASIC, AI chip) constituting the work analysis system 1. Etc.). The work analysis system 1 stores the various information (data) described above as, for example, a database table or a file managed by the file system.

As shown in FIG. 1, the work analysis system 1 includes the functions of the quantization unit 120, the clustering unit 130, and the analysis unit 150. Further, the work analysis system 1 includes a storage unit 110 (not shown) for storing log information 111, quantization log information 112, clustered log information 114, and analysis result 115.

FIG. 3 shows an example of the log information 111. The example log information 111 is composed of a plurality of records (entries) having each item of work processing ID 1111, order 1112, work ID 1113, work result 1114, work main body ID 1115, and work date and time 1116. One record of log information 111 corresponds to one work process.

A work process ID, which is an identifier of the work process, is set in the work process ID 1111. Information indicating the order in which each work constituting the work process is performed is set in the order 1112. In this example, the work is performed in ascending order of natural numbers. In this example, the above information is a natural number, and the work is performed in order from the smallest number. In the work ID 1113, a work ID which is information for specifying each work constituting the work process is set. The result of the work (hereinafter referred to as "work result") is set in the work result 1114. A work subject ID, which is an identifier of the work subject of the work, is set in the work subject ID 1115. The work date and time, which is the date and time when the work was performed, is set in the work date and time 1116.

Returning to FIG. 1, the quantization unit 120 generates the quantization log information 112, which is the information obtained by quantizing the information related to each work process, which is acquired from the log information 111. The quantization method is not always limited, but the quantization can be, for example, BoW (Bag of Words) or TF-IDF (Term).
Frequency --Invers Document Frequency), Word2Vec (CBOW (Continuous)
It is performed using Bag of Word), Skip-Gram), etc.

Returning to FIG. 1, the clustering unit 130 classifies the work process into a plurality of clusters based on the quantized log information calculated by the quantized unit 120, and enables analysis in cluster units. Due to this clustering (cluster classification process), the work process is, for example, the difference in skill level of the work subject described above, the status of carpooling of different work processes to a common information processing system, and whether or not the work process is a work process on the customer side. Alternatively, work processing groups with different assumptions, such as differences in work objects, can be handled separately based on different assumptions, and the user's analysis work can be facilitated.

The number of clusters to be classified into the work process may be set in advance, or may be accepted from a user (analyst) who uses the analysis result 115, for example. The number of clusters is, for example, a number that the user can handle (for example, about 20). Clustering can be performed using, for example, K-means, a mixed Gauss model, or the like. When the number of dimensions increases, for example, latent semantic analysis (LSI (Latent Semantic Indexing), LSA (Latent Semantic Analysis)) or singular value decomposition (SVD (Singular Value Decomposition)) is used for dimensional compression. You may do so.

FIG. 4 shows an example of the clustered log information 114. The exemplified clustering log information 114 is composed of a plurality of records (entries) having each item of the cluster ID 1141, the work processing ID 1142, and the work processing result 1143.

Among the above items, the cluster ID 1141, which is an identifier of the cluster, is set in the cluster ID 1141. The work process ID of the work process constituting the cluster is set in the work process ID 1142. Each work process that constitutes a cluster is one of the features of the cluster. The work process result of the work process specified by the work process ID is set in the work process result 1143.

Returning to FIG. 1, the analysis unit 150 generates and outputs the analysis result 115 for each cluster by performing statistical processing for each cluster of the clustered log information 114.

FIG. 5 shows an example of the analysis result 115 for each cluster. Each record (entry) of the illustrated analysis result 115 corresponds to one cluster. The analysis result 115 includes a plurality of entries (records) having the cluster ID 1151, the number of executions 1152, the order odds 1153, the order / lost order 1154, and the classification result 1155. The contents of the order odds 1153 and the order / lost order 1154 correspond to the statistical processing of the work processing result. Further, the content of the classification result 1155 corresponds to the analysis result.

A cluster ID is set in the cluster ID 1151. The number of work processes belonging to the cluster is set in the number of executions 1152. The order odds 1153 are set to the odds values obtained by a predetermined formula for the cluster. In the order / lost order 1154, the number of orders (successful number) and the number of lost orders (failure number) are set. In the classification result 1155, information indicating the classification destination (promising group, possible group, despair group) of the cluster according to the value of the order odds 1153 is set. In this example, "3.0" is set as the value of the order odds 1153 that draws the line between the promising group and the possible group (hereinafter, referred to as "first threshold value"), and the value of the order odds 1153 that draws the line between the possible group and the despair group. (Hereinafter referred to as "second threshold value"), "0.10" is set.

By providing the exemplary analysis result 115, the user (analyst) can obtain, for example, an index as to which type (cluster) of work processing should be followed. For example, the user can effectively improve the restructuring of business processes, the method of instructing workers, and the like.

The analysis result 115 shown in the figure is only an example, and may be, for example, a scatter plot (distribution map) or the like. Conventionally, in order to create a work flow diagram, a scatter diagram, etc., for example, it was necessary to track individual cases of work and conduct interviews with business personnel, but the work analysis system 1 of this embodiment is used. By using it, the user can easily obtain such a diagram.

By the way, the analysis unit 150 may generate, for example, a work flow diagram showing a work flow for each cluster as an analysis result 115 and present it to the user.

FIG. 6 shows an example of a work flow diagram. The exemplary work flow diagram includes a node (rectangular frame shown in the figure) indicating each work constituting the work process, and information indicating the work order (arrow line shown in the figure). In the figure, for example, the work “order” at the end corresponds to information indicating a work processing result. By presenting a work flow diagram as an example to the user together with the analysis result 115 shown in FIG. 5, the user can visually and efficiently grasp, for example, the contents of the work process of a promising cluster, and the work can be performed. It can be used for planning improvement policies for procedures.

[Second Embodiment]
FIG. 7 is a system flow diagram showing a schematic configuration of the work analysis system 1 shown as the second embodiment. The basic configuration of the work analysis system 1 of the second embodiment is the same as that of the first embodiment, but the analysis unit 150 acquires and acquires the work process of the cluster for each cluster of the clustered log information 114. Based on the log information 111, the work processing performed is a group of work processing in which the work is successful (hereinafter referred to as "successful work processing") (hereinafter referred to as "successful work processing group") and work in which the work is unsuccessful. It is classified into a group of processes (hereinafter referred to as "failed work process") (hereinafter referred to as "failed work process group"), and frequently appears in the successful work process group (hereinafter referred to as "success series"). (Hereinafter referred to as), a frequent series appearing only in the failed work processing group (hereinafter referred to as “failure series”), and a frequent series appearing in both the successful work processing group and the failed work processing group (hereinafter referred to as “common”). The configuration is different from that of the work analysis system 1 of the first embodiment in that the analysis result 115, which is the result of performing the analysis based on the specified frequent sequence, is generated and output.

FIG. 8 is a flowchart illustrating a process (hereinafter referred to as “work process analysis process S800”) performed by the analysis unit 150 of the work analysis system 1 of the second embodiment when analyzing the work process. Hereinafter, the work process analysis process S800 will be described with reference to the figure.

First, the analysis unit 150 selects one cluster from the clustering log information 114 (S811).

Subsequently, the analysis unit 150 selects one work process of the selected cluster (S812).

Subsequently, the analysis unit 150 refers to the log information 111 and determines whether the selected work process is a successful work process or a failed work process (S813). If the selected work process is a successful work process (S813: success), the analysis unit 150 stores the selected work process as a successful work process (S814). After that, the process proceeds to S816. On the other hand, if the selected work process is a failed work process (S813: failure), the analysis unit 150 stores the selected work process as a failed work process (S815). After that, the process proceeds to S816.

In S816, the analysis unit 150 determines whether or not all the work processes of the selected cluster have been selected. If all the work processes of the selected cluster have not been selected (S816: NO), the process returns to S812. On the other hand, if all the work processes of the selected cluster have been selected (S816: YES), the process proceeds to S817.

In S817, the analysis unit 150 identifies the frequent occurrence sequence for each of the stored successful work processing group and the unsuccessful work processing group by an algorithm for specifying the frequent occurrence sequence. The frequent occurrence sequence here is a work and a sequence of work that appear frequently in the set. The high frequency judgment is made by a certain threshold, for example, if the threshold is 0.8 and the node "initial visit_not good" appears in the set with a frequency of 0.9, "initial visit_not good". Is considered to be a frequent sequence of length 1. Also, if the node "Determining the proposal system" appears after "Initial visit_Not good" with a frequency of 0.9, it is a frequent sequence of the order relationship from "Initial visit_Not good" to "Determining the proposal system". It is regarded. The frequent occurrence series expresses the degree of frequent occurrence in the relative order relationship between the nodes. Unless the upper limit of the interval between nodes is specified, the order relationship is established in the number of cases exceeding the threshold value, no matter how many other nodes are included between the nodes when compared with the actual cases. If so, it is judged to be frequent. The frequent occurrence sequence can be specified by using, for example, a known frequent occurrence sequence extraction method (frequent pattern mining or the like).

Subsequently, the analysis unit 150 identifies the success sequence, the failure sequence, and the common sequence, respectively, and outputs the identified result (S818).

In S819, the analysis unit 150 determines whether or not all the clusters have been selected. If there is an unselected cluster (S819: NO), the process returns to S811. If all the clusters have been selected (S819: YES), the work process analysis process S800 ends.

9A and 9B show an example of the analysis result 115 for a cluster. FIG. 9A is a work flow diagram depicting work processing in the cluster, and FIG. 9B is a tabular representation of the common sequence, success sequence, and failure sequence specified for the cluster.

In FIG. 9A, the work and the work result of the work (the work result may not always be obtained) are described in the circle representing each node. The arrow connecting each node represents the relative order relationship between the series. The arrow line shown by the solid line represents the common sequence, the arrow line indicated by the broken line represents the success sequence, and the arrow line indicated by the alternate long and short dash line represents the failure sequence. As described above for the characteristics of the frequent series, in individual cases, different operations may be performed a plurality of different operations between the arrows.

By visually displaying the common sequence, the success sequence, and the failure sequence in this way, the user can easily obtain information about the work process that caused the success and information about the work process that caused the failure. It can be obtained and used for business improvement plans. For example, the user can easily obtain information such as how to proceed with each activity to lead to success. Further, for example, the user can easily acquire information such as the characteristics of sales activities that are likely to lead to success or sales activities that are likely to fail in a certain domain or region.

[Third Embodiment]
The work analysis system 1 of the third embodiment is a work performed by the user based on the clustered log information 114 and the analysis result 115 generated in the second embodiment in addition to the configuration of the work analysis system 1 of the second embodiment. It has a configuration for generating and outputting information obtained by analyzing the history information 118. Provenance information 118 includes information about work processes performed by the user so far. The acquisition method of the history information 118 is not necessarily limited, but is acquired from, for example, manual input by the user or an existing information processing system used by the user.

FIG. 10 is a system flow diagram showing a schematic configuration of the work analysis system 1 shown as the third embodiment. The work analysis system 1 of the third embodiment further includes a cluster estimation unit 160 and a history analysis processing unit 170. Further, the storage unit 110 of the work analysis system 1 of the third embodiment further stores the history information 118 and the history analysis result 119. In the figure, the configuration described in the second embodiment is omitted or simplified.

The cluster estimation unit 160 controls a cluster having work processing similar to the history information 118 by comparing all or a part of the work processing of each cluster of the work history information 118 and the clustering log information 114 performed by the user. The information indicating the specified and specified cluster is output as the clustering estimation result 117. The above specification is performed, for example, by selecting the one with the maximum probability based on the probability (certainty) assigned to each cluster, or sequentially selecting the clusters with high likelihood until a certain degree of certainty is reached. Further, the determination of whether or not they are similar is determined by, for example, a method based on various similarities (similarity of each work constituting the work process, similarity of the work order, similarity of the work name of each work, etc.), a neural network. It is performed by a method using machine learning such as, a known work (work) flow judgment method, or the like.

The Provenance Analysis Processing Unit 170 outputs the clustering estimation result 117 as the Provenance Analysis Result 119. Further, the provenance analysis processing unit 170 contrasts each work process constituting the cluster of the clustering estimation result 117 with the success series or the failure series in the analysis result 115 obtained by the work analysis system 1 of the second embodiment. By doing so, for example, information indicating work processing that will lead to success by performing in the future and information indicating work processing that will lead to failure by performing in the future are acquired, and the acquired information is generated and output as a history analysis result 119.

FIG. 11 shows an example in which the contents of the history analysis result 119 are displayed on the screen. The screen illustrated (hereinafter referred to as "provenance analysis result display screen 1100") is a display column of the current work 1271, a display column of the person in charge of the work 1272, a display column of the predicted frequent path 1273, and a display column of advice. 1274 is provided.

In the example history analysis result display screen 1100, from the history information 118 of the person in charge "Patent Taro", the work currently (currently) performed by the person in charge and the work result are "small budget acquisition". In order to succeed, it is displayed that "exclusion of competition" is performed next and this is successful, and that "decision of responsibility system" as the next work may lead to business failure.

By confirming the history analysis result display screen 1100, the user confirms the background of the work that he / she has performed. You can get information such as whether it exists. In addition, the user can obtain information such as what kind of work should be done in order to succeed in the work and what kind of work should not be done, which is effectively useful for planning work plans. Can be done.

Although one embodiment of the present invention has been described above, it is needless to say that the present invention is not limited to the above embodiment and can be variously modified without departing from the gist thereof. For example, the above-described embodiment has been described in detail in order to explain the present invention in an easy-to-understand manner, and is not necessarily limited to the one including all the described configurations. Further, it is possible to add, delete, or replace a part of the configuration of the above embodiment with another configuration.

Further, each of the above configurations, functional units, processing units, processing means and the like may be realized by hardware by designing a part or all of them by, for example, an integrated circuit. Further, each of the above configurations, functions, and the like may be realized by software by the processor interpreting and executing a program that realizes each function. Information such as programs, tables, and files that realize each function can be stored in memory, hard disks, and recording devices such as SSDs (Solid State Drives).
It can be placed on a recording medium such as an IC card, SD card, or DVD.

Further, the arrangement form of various functional units, various processing units, and various databases of each information processing apparatus described above is only an example. The arrangement form of various function units, various processing units, and various databases can be changed to the optimum arrangement form from the viewpoint of the performance, processing efficiency, communication efficiency, and the like of the hardware and software included in these devices.

Further, the configuration of the database (schema, etc.) for storing various data described above can be flexibly changed from the viewpoints of efficient use of resources, improvement of processing efficiency, improvement of access efficiency, improvement of search efficiency, and the like.

1 Work analysis system, 110 storage, 111 log information, 112 quantization log information, 114 clustering log information, 115 analysis result, 117 clustering estimation result, 118
Provenance information, 119 Provenance analysis results, 120 quantization unit, 130 clustering unit, 150
Analysis unit, 160 cluster estimation unit, 170 history analysis processing unit, S900 work processing analysis processing, 1200 history analysis result display screen

Claims (14)

It is configured using an information processing device that has a processor and a storage device.
A storage unit that stores log information including a work process that is information on a series of work flows related to business and a work process result that is information on the result of the work process.
A clustering unit that generates clustered log information, which is information obtained by classifying the work process into a plurality of clusters, based on the feature amount acquired by quantizing the information related to the work process in the log information.
An analysis unit that generates information indicating the relationship between the work process and the work process result for each of the clusters based on the clustered log information and the work process result acquired from the log information.
A work analysis system. The work analysis system according to claim 1.
The clustering unit expresses the difference in the skill level of the work subject, the situation of sharing the different work processes with the common information processing system, whether or not the work process is on the customer side, and whether or not the work process is performed by the customer. Classify the work processes into clusters based on at least one of the information of the difference in work objects.
Work analysis system. The work analysis system according to claim 1.
The information indicating the relationship between the work process and the work process result is either a successful work process that is a work process that leads to success or a failed work process that is a work process that leads to failure. Includes information indicating
Work analysis system. The work analysis system according to claim 3.
The analysis unit is a success series that appears only in the success work processing group that is the success work processing group, or a failure series that appears only in the failure work processing group that is the failure work processing group. Identify the sequence and generate information indicating the identified frequent sequence,
Work analysis system. The work analysis system according to claim 3.
The storage unit stores work history information and stores it.
By comparing the history information with the clustering log information, a clustering estimation unit that identifies the cluster including the work process similar to or matches the history information and generates information indicating the identified cluster. Further prepare
Work analysis system. The work analysis system according to claim 5.
The analysis unit is a success series that appears only in the success work processing group, which is a group consisting of the success work processing, and a frequent occurrence series that appears only in the failure work processing group, which is a group consisting of the failure work processing. Identify a failure sequence and
By comparing the work process constituting the cluster estimated by the clustering estimation unit with the success sequence or the failure sequence in the cluster, the work process result corresponding to the work process is specified and specified. Further provided with a history analysis processing unit that generates information indicating the work processing result.
Work analysis system. The work analysis system according to any one of claims 1 to 6.
Further provided with a user interface for outputting the generated information.
Work analysis system. An information processing device having a processor and a storage device
A step of storing log information including a work process which is information about a series of work flow related to a business and a work process result which is information about the result of the work process.
A step of generating clustered log information, which is information obtained by classifying the work process into a plurality of clusters, based on the feature amount acquired by quantizing the information related to the work process in the log information.
A step of generating information indicating the relationship between the work process and the work process result for each of the clusters based on the clustered log information and the work process result acquired from the log information.
How to perform a work analysis method. The work analysis method according to claim 8.
The information processing apparatus is expressed in the work process, such as the difference in skill level of the work subject, the status of carpooling of different work processes to a common information processing system, whether or not the work process is on the customer side, and. A step of classifying the work process into clusters based on information of at least one of the differences in work objects.
Work analysis method to further execute. The work analysis method according to claim 8.
The information indicating the relationship between the work process and the work process result is either a successful work process that is a work process that leads to success or a failed work process that is a work process that leads to failure. Includes information indicating
Work analysis method. The work analysis method according to claim 10.
The information processing apparatus is a success series that appears only in the success work processing group that is the success work processing group, or a frequent occurrence series that appears only in the failure work processing group that is the failure work processing group. A step of identifying a failed sequence and generating information indicating the identified frequent sequence,
Work analysis method to further execute. The work analysis method according to claim 10.
The information processing device
Steps to memorize work history information and
A step of identifying the cluster including the work process similar to or matching the history information by comparing the history information with the clustered log information, and generating information indicating the identified cluster.
Work analysis method to further execute. The work analysis method according to claim 11.
The information processing device
Identify the success series, which is a frequent sequence that appears only in the successful work process group, which is a group consisting of the successful work processes, and the failure series, which is a frequent sequence that appears only in the failed work process group, which is a group consisting of the failed work processes. Steps to do and
By comparing the work process constituting the estimated cluster with the success sequence or the failure sequence in the cluster, the work process result corresponding to the work process is specified, and the specified work process result is obtained. Steps to generate the information shown and
Work analysis method to further execute. The work analysis method according to any one of claims 8 to 13.
A work analysis method in which the information processing apparatus further executes a step of outputting generated information via a user interface.

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