JP2003131876A - Know-how extraction apparatus and method therefor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To extract know-hows able to be shared between different subjects and display the same. SOLUTION: A data base 20 operates in accordance with an operation by a user input from an input/output portion 24, stores subject information and process information by making the two related to each other and manages the same. A know-how extraction portion 22 operates in accordance with an operation by a user input from the input/output portion 24, extracts know-hows from the subject information and process information stored and managed in the database 20, and outputs the same to the input/output portion 24 and the database 20. The input/output portion 24 outputs subject information and process information input from the outside to the database 20. Also, the input/ output portion 24 displays the know-hows input from the know-how extraction portion 22.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a know-how extracting device and method for extracting process know-how.

[0002]

2. Description of the Related Art "Japanese Patent No. 2923552", "JP-A-6-44255" and "JP-A-62-57".
Japanese Patent Publication No. 023 ”(References 1 to 3) discloses, as a technique for describing and classifying process knowledge, a technique for creating a database specific to a field by a process description method depending on a field.

In addition, the above-mentioned references 1 and 2 and "Jacobson,
I. et. Al.: Object-Oriented Software Engineering-A
Use Case Driven Approach, the ACM Press, New York
(1992); Toshihiro Nishioka and others: Object-oriented software OOSE, Toppan (1995), "Object-oriented modeling method" MELON ";" Object-oriented frontline "subtitle," Information Processing Society of '96 Symposium " Japan Society for Software Engineering, Mikio Aoyama et al., Asakura Shoten, First edition, July 5, 1996, first edition ”
And "Japanese Patent Application Laid-Open No. 11-199987" (Reference 4 to
Reference 6) discloses a technique for describing a business process, particularly an information process or a human process, using an object-oriented model.

Since the techniques disclosed in these documents are process descriptions assuming a target domain, they are not supposed to integrate process descriptions of a plurality of different target domains. However, even in processes in different fields, the essential properties of processes may be common across fields. `` Malone, TW and
Crowston, K,: The interdisciplinary study of coodi
nation, ACM Computing Survays, 26 (1), pp.87-pp.11
9, 1994 ”(reference 7), focusing on such a property,
Coordination science by MIT (Massachusetts Institute of Technology), which describes process knowledge in various fields by a common model and tries to elucidate scientifically, is disclosed.

As described above, it has been proposed to make a repository of networks and flows of processes and activities included in the processes that can be reused or referenced by different entities in common, and utilize them for process construction and redesign. However, Document 7 only discloses such a data structure of the repository and a proposal of a method of using the repository, and does not disclose a methodology for constructing the repository itself efficiently, effectively, and realistically.

[0006]

SUMMARY OF THE INVENTION The present invention has been made in view of the above-mentioned problems of the prior art, and a process that can be commonly reused and referred to by different subjects, and
It is an object of the present invention to provide a know-how extraction device and its method that can be utilized for the construction and redesign of a process by converting the network and flow of activities included in the process into a repository.

[0007]

[Means for Solving the Problems] [Know-how extracting device] In order to achieve the above-mentioned object, a know-how extracting device according to the present invention has a plurality of information on the subject of a plurality of activities and a plurality of activities each including one or more of the activities. And a know-how extracting means for extracting know-how based on the held information about the subject of the activity and a plurality of process information.

[0008] Preferably, the database holds the activities included in the plurality of process information in one or more hierarchies, and the know-how extracting means has less than the first number of entities in the same hierarchies. The activities included in the process information associated with are extracted as know-how of the subject less than the first number.

Preferably, the database holds the activities included in the plurality of process information by dividing them into a plurality of layers, and the know-how extracting means associates with a first number or more of subjects in the same layer. The activities included in the process information included in the process information associated with the second or more main subjects in different layers are extracted as know-how of the first or more main subjects.

Preferably, the first number and the second number are one.

[0012] Preferably, the database further holds the dependency relation of the activities included in the plurality of process information, and the know-how extracting means extracts the know-how based on the presence / absence of the dependency relation between the activities. .

[0012] Preferably, the database further holds resources intervening between the dependency relationships of the activities contained in the plurality of process information, and the know-how extracting means intervenes between the dependency relationships of the activities. The know-how is extracted based on the presence or absence of resources.

Preferably, the know-how extracting means extracts the know-how by statistical processing between the information on the subject and the activity included in the process information.

Preferably, the database further stores why the activity included in the plurality of process information is performed, how the activity is performed, and where, or any combination thereof. However, the know-how extracting means extracts the know-how based on why the activity is performed, how the activity is performed, where the activity is performed, or an arbitrary combination thereof.

[Know-how extraction method] Further, in the know-how extraction method according to the present invention, information on the subject of a plurality of activities and a plurality of process information each including one or more of the activities are held in the database in association with each other. , Know-how is extracted based on the held information on the subject of the activity and a plurality of process information.

[Program] Further, the program according to the present invention stores information in the database in association with a plurality of pieces of information on the subjects of a plurality of activities and a plurality of pieces of process information each containing one or more of the above activities. The computer is caused to execute the step of extracting know-how based on the held information on the subject of the activity and the plurality of process information.

[0017]

DESCRIPTION OF THE PREFERRED EMBODIMENTS [Background Art] First, in order to facilitate understanding of the present invention, a background art thereof will be described. "Japanese Patent Application No. 2000-14107" filed by the same applicant as the present application,
This is an invention in which a unique process description method and classification method that solves practical problems are realized on a computer system with reference to the above coordination theory disclosed in Reference 7.
In the present invention, a plurality of model descriptions and components are classified according to the viewpoint of recognition of know-how extraction. Therefore, the two overall classification structures of the present invention allow the writing speed attribute of a component to be expanded according to a large number of recognition viewpoints, and moreover, a plurality of recognition viewpoints can be used as classification criteria. It can be included and ambiguous. Further, the present invention introduces a recognition field to clearly describe a plurality of recognition viewpoints, and can provide a plurality of classification structures corresponding to the recognition viewpoints for each recognition field. . The present invention is directed to the Japanese Patent Application No. 2000-14.
Further improving the invention of No. 107, to efficiently, effectively and realistically construct a process itself that can be reused and referenced by different entities in common, and a repository itself of the network of activities included in the process and the flow. It is something that can be done.

[Outline of the Present Invention] In the know-how extraction method according to the present invention, "subject information (individual attribute or organization attribute)" accumulated in a database and "process information" associated with "subject information" From this, unique know-how (adjustment knowledge) for each different subject classification is automatically extracted.
Specifically, in the know-how extraction method according to the present invention, the processes executed by different subjects are intervened in “activities”, “dependencies between activities” and “dependencies between activities” that constitute the processes. For each subject attribute when executing a process, based on the presence or absence of "resources", the significant difference in the distribution of presence or absence, and the difference in hierarchical arrangement such as the inclusion relationship of "activity" and "dependency between activities" Know-how (adjustment knowledge)
Is extracted. The extracted know-how is explicitly associated with the subjects having different attributes, the degree of abstraction of the know-how is raised based on the inclusion relation of activities, etc., and it is shown to different subjects, and the know-how is diverted. It is used for examination. In the following description, the “subject” generally refers to an individual or an organization. Also, "subject classification"
Is a set of one or more subjects bundled by the attribute of the subject information attached to the “subject”. For example, if the "subject" is an employee who belongs to a certain company, the subject information associated with the "subject" is the year of joining the company, the position, etc. Is the “subject classification”. In addition, the name of “subject” is also referred to as “subject name”. For example, when the “subject” is an individual and the nomination of the subject is “Yama ΔMale”, the “subject name” of this “Subject” is “Yama ΔMale”. Similarly, the name of "principal classification" is also referred to as "principal classification name". For example, when "principal classification" is bundled in the joining year, the "principal classification name" is "19 ○ △ It will be.

In order to realize the know-how extraction method according to the present invention, first, "process information" is associated with "subject information" and "subject information" of a subject who executes a process.
Are stored in the database. This "subject information" may be an individual or an organization that executes the corresponding process.

FIG. 1 is a diagram showing the configuration of a computer (PC) 1 that realizes a business process display method according to the present invention. As shown in FIG. 1, the PC 1 is, for example, a CPU
Main unit 10 including 102 and memory 104, communication device 12, recording device 1 such as HDD / CD-RW / FDD
4 and a display / input device 16 including a CRT display device and a keyboard / mouse. That is, the PC 1 has a configuration as a general computer. The business process display method according to the present invention is, for example,
It is realized by a program (know-how extraction program 2 which will be described later with reference to FIG. 17) loaded into the memory 104 via the recording medium 140 and executed.

FIG. 2 is a diagram showing, in a tabular form, subject information accumulated in the database for realizing the know-how extraction method according to the present invention. As illustrated in FIG.
The subject information includes a business form (new customer development / existing customer) and business performance (numerical value) as attribute items of individual attributes.
These attributes are associated with a unique number (personal ID) given to each subject. That is, in the subject information illustrated in FIG. 2, whether each subject indicated by the “individual ID” mainly conducts sales in the form of cultivating new customers,
It is indicated by the information of the attribute of "business type" whether or not the business is mainly conducted to the existing customer, and is assigned to each of the entities indicated by the "personal ID". The achievement rate against the sales target for the previous term is shown as information on the attribute of "sales performance".

FIG. 3 is a diagram showing, in a tabular form, process information accumulated in a database for realizing the know-how extraction method according to the present invention. (A) shows the process information, and (B). Indicates the phase information included in the process information shown in (A), (C) shows the in-activity classification information shown in (B), and (D) shows the activity sub-classification shown in (C). Indicates information. FIG. 4 is a diagram showing an example of “process information” (FIG. 3) stored and managed in the database. FIG. 5 is a diagram showing a specific example of the process information shown in FIG. "Process information" means "resources" that intervene in "activities", "dependencies between activities", and "dependencies between activities".
As a constituent element, as shown in FIGS. 3A to 3D, includes information indicating some inclusive relation such as an abstract / concrete relation.

For example, each of the process information includes a phase (major category of activity) as the highest division unit of the process, each phase includes a category of active category as a substructure, and each of the active category includes a subcategory of active category. , And adopts a hierarchical structure. Further, as shown in FIG. 3A, the “process information” includes a “personal ID” indicating the subject who executed the process in the data structure, and is associated with the “subject information” shown in FIG. ing. Such "subject information" and "process information" associated with the "subject information" are stored and stored in a database as shown in FIG.
It is managed and used for know-how extraction according to the present invention.
A specific example of the contents of the process information stored in the database is as shown in FIG.

[Know-how extraction process] In the following, the know-how extraction method according to the present invention extracts unique know-how (adjustment knowledge) from "subject information" and "process information" (FIGS. 2 and 3) in the database. The processing to be performed will be described.

FIG. 6 is a process for extracting know-how from the subject information and process information shown in FIGS. 2 to 4 (S1).
It is a flowchart which shows 0). As shown in FIG.
In step 100 (S100), variables i, j, x
And the values of FLAG_ [i] [x] are initialized (i
= 0, j = 0, x = 0, FLAG_ [i] [x] =
0). The variable i indicates the personal ID i, and the variable j (≠
i) indicates an individual IDj different from the individual IDj, and the variable x
Indicates an activity x included in the process information.

In step 102 (S102), process information corresponding to the subject information indicated by the individual IDi (hereinafter abbreviated as process information of the individual IDi).
It is determined whether or not the activity x included in (1) is included in the process information of the individual ID j. If the activity x included in the process information of the individual ID i is not included in the process information of the individual ID j (≠ i), the process of S104 is executed, and in other cases, the process of S106 is executed.

In step 104 (S104), the variable j is incremented.

In step 106 (S106), flags FLAG_ [i], [x] indicating that the activity x included in the process information of the individual IDi is included in the process information of another individual IDj (≠ i). Is set to 1.

In step 108 (S108), it is determined whether or not the value of the variable j is a numerical value N (N is the number of all subjects). When the value of the variable j is the numerical value N, S110
Process is executed, otherwise the process of S102 is executed, and further the process of S102 is executed.

In step 110 (S110), the variable x is incremented.

In step 112 (S112), it is judged whether or not the value of the variable x is a numerical value Ni (Ni is the number of all activities included in the process information). If the value of the variable x is a numerical value Ni (Ni is the number of all activities included in the process information), the process of S116 is executed, otherwise the process of S114 is executed, and the value of the variable j is It is set to 0, and the process of S102 is further executed.

In step 116 (S116), the variable i is incremented.

In step 118 (S118), it is determined whether or not the value of the variable i is the numerical value N. When the value of the variable i is the numerical value N, the processing of S122 is executed,
In other cases, the process of S120 is executed and the variable x
Is set to 0, and the process of S102 is further executed.

Steps 122 (S122) to S128
Regarding (S128), an incremental loop process is executed for the subject of the individual IDi and the activity x.

In step 124 (S124), it is determined whether or not the value of the flag FLAG_ [i] [x] is 0. When the value of the flag FLAG_ [i] [x] is 0, the process of S126 is executed, and in other cases, the process of S128 is executed.

In step 126 (S126), the activity x of the subject (hereinafter abbreviated as subject i) indicated by the individual IDi is determined to be know-how (adjustment knowledge).

In other words, the activity x included in the process information of the individual IDi and not included in the process information of any other individual IDj is uniquely identified by the subject i by the process of S10 shown in FIG. Know-how (adjustment knowledge) we have
Is determined. Further, the activity x included in the process information of the individual IDi and included in the process information of any other individual IDj is uniquely owned by the subject (hereinafter, abbreviated as the subject i) indicated by the individual IDi. Whether or not it is the know-how (adjustment knowledge) that is being determined is determined by the process S14 described with reference to FIG. When the process information is not hierarchized (there is only one hierarchy), only the processing shown in FIG. 6 is performed and the processing shown in FIG. 7 is not performed.

[Determination of know-how] FIG.
7 is a flowchart showing a process (S14) of determining whether or not the activity determined to be included in the process information of only one subject by the process S10 shown in FIG. 6 is know-how (adjustment knowledge). As shown in FIG. 7, step 14
0 (S140), variables i, j, x and FLA
The value of G_ [i] [x] is initialized (i = 0, j =
0, x = 0, FLAG_ [i] [x] = 0).

In step 142 (S142), it is determined whether the activity x included in the process information of the individual IDi is included in another class (other layer) of the process information of another individual IDj. That is, in S142, for example, the middle classification of the process information of the individual IDi (see FIG.
Whether or not the activity x included in (C)) is included in the small classification (FIG. 3D) or the large classification (phase; FIG. 3A) of the process information of another individual IDj. To be judged.
The activity x included in the process information of the individual IDi is the individual I.
If it is not included in the other classification of the process information of Dj, the process of S144 is executed, otherwise, S1.
The processing of 46 is executed.

In step 144 (S144), the variable j is incremented.

In step 146 (S146), flags FLAG_ [i], [x] indicating that the activity x included in the process information of the individual IDi is included in the other classification of the process information of the other individual IDj. Is set to 1.

In step 148 (S148), it is determined whether the value of the variable j is the numerical value N. When the value of the variable j is the numerical value N, the processing of S150 is executed,
In other cases, the process of S142 is executed, and further the process of S142 is executed.

In step 150 (S150), the variable x is incremented.

In step 152 (S152), it is determined whether or not the value of the variable x is a numerical value Ni (Ni is the number of all activities included in the process information). If the value of the variable x is the numerical value Ni, the process of S156 is executed, otherwise the process of S154 is executed, the value of the variable j is set to 0, and the process of S142 is executed. .

In step 156 (S156), the variable i is incremented.

In step 158 (S158), it is determined whether or not the value of the variable i is the numerical value N. When the value of the variable i is the numerical value N, the processing of S162 is executed,
In other cases, the process of S160 is executed and the variable x
Is set to 0, and the process of S142 is further executed.

Steps 162 (S162) to S168
Regarding (S168), an incremental loop process is executed for the subject of the individual IDi and the activity x.

In step 164 (S164), it is determined whether or not the value of the flag FLAG_ [i] [x] is 1. When the value of the flag FLAG_ [i] [x] is 1, the process of S166 is executed, and in other cases, the process of S168 is executed.

In step 166 (S166), the activity x of the subject i is determined to be know-how (adjustment knowledge).

[First Application Example] A first application example of the know-how extraction method according to the present invention to which the processing shown in FIGS. 6 and 7 is applied will be described. FIG. 8 is a first diagram showing a first application example of the know-how extracting method according to the present invention, showing a method of extracting know-how from subject classification according to a difference in business form. As shown in FIG. 8, when executing the same process (process name), the presence or absence of a certain activity (here, only the active category and the activity sub-category) is used as a criterion, and new business is cultivated or existing for the business form. Know-how (adjustment knowledge) can be visualized for each subject classification as a customer.

For example, if there is an activity that is included in the process of subject classification called sales form (existing customer) but not included in the process of subject classification called sales form (development of new customers), such an activity is performed. Can be judged to be highly likely to be know-how (adjustment knowledge) for each subject. On the other hand, activities that are included in the process of subject classification called sales form (development of new customers), but not included in the process of subject classification called sales form (existing customers) are also know-how (adjustment knowledge) for the same reason. It can be determined that there is a high possibility.

Further, although it is included in both the business type (existing customer) subject classification and the business type (new customer development) subject classification, there is a phase (major category of activity) in which the activity is categorized. If they are different, there is a high possibility that the timing and order of execution of the activities will differ depending on the business type, and it can be determined as know-how (adjustment knowledge) for each business type.

FIG. 9 is a second diagram showing a first application example of the know-how extraction method according to the present invention, showing a matrix display of activities determined as know-how (adjustment knowledge). As shown in FIG. 8, the determined know-how (adjustment knowledge) can be displayed on an arbitrary graphical user interface (GUI). In FIG. 9, activity α-C3 and activity θ-G1 are included in the sales form (existing customers) but are included in the sales form (development of new customers) as know-how (adjustment knowledge) peculiar to each sales form. It is judged that there is no activity and is displayed. Activity α-D4, activity θ-B
2 is included in the business form (development of new customers), but is determined and displayed as an activity that is not included in the business form (existing customer).

On the other hand, although it is included in both the sales form (development of new customers) and the sales form (existing customers), the activity name "Check budget with person having approval right" is for the sales form (existing customer). Is located in the activity α-A2, and in the case of the business form (development of new customers), it is located in the activity γ-A3. Therefore, the know-how (adjustment knowledge) unique to each business form is determined and displayed.

[Second Application Example] A second application example of the know-how extraction method according to the present invention to which the processing shown in FIGS. 6 and 7 is applied will be described. FIG. 10 is a first diagram showing a second application example of the know-how extracting method according to the present invention, and shows a method of extracting know-how (adjustment knowledge) based on the presence or absence of a certain activity. As shown in FIG. 10, when the same process (process name) is executed, the presence or absence of a certain activity (here, the active classification or only the small activity) is used as a judgment criterion for each subject classification according to the difference in business performance. , Know-how (adjustment knowledge) can be extracted. In the example shown in FIG. 10, has the target value of past sales been achieved (100%
Above), the main subjects are classified into two depending on whether they have not been achieved (less than 100%). By the same method as in the first application example, determine what kind of difference there is in the activities executed in the process between the sales entity that has not achieved the sales target and the business entity that has achieved the sales purpose can do.

Since it is generally better for the business performance to have a higher achievement rate, it is possible to make a significant difference between the classified subjects (subject classification) here. Therefore, an activity that is included in the activities of the sales entity that has achieved the sales purpose and is not included in the activities of the sales entity that has not achieved the sales purpose is know-how (coordination knowledge) for achieving the sales goals. Can be judged. In addition, the activities included in both subject classifications and included in different phases can be determined as know-how (adjustment knowledge) such as the timing and order of execution of activities for achieving the sales target. in this way,
According to the know-how extraction method according to the present invention, whether or not it is know-how (adjusted weaving) can be automatically determined and extracted based on the presence or absence of “activity” for different subject classifications.

FIG. 11 is a diagram showing a basic dependency relationship among a plurality of activities included as components in a process. As a method of extracting this know-how (adjustment knowledge), not only judgment based on the presence / absence of a certain activity included in the “process information” but also basic “dependency between activities as shown in FIG. For the database having “” as a constituent element, the extraction process shown in FIGS. 7 and 8 is applied to “activity” and “dependency between activities”, and know-how (coordination between activities) (adjustment) is applied. Chiori) can be automatically extracted.

FIG. 12 is a diagram showing a concrete example of the basic dependency relationships among the plurality of activities shown in FIG. As shown in FIG. 12, the activities “planning” and activities “proposing” at the top are in the most basic flow dependency relationship. The dependency relationship of this flow is different from the dependency relationship in the middle and lower parts, but can be understood as a chain of activities starting from a certain activity. For example, when the activity “form a plan” is used as a starting point, it can be understood as a dependency relationship similar to the dependency relationship between the activity at the top and the activity at the bottom.

At the bottom, the activity of "suggest" is to "persuade a person who has the right to approve" and to "require the person in charge" in order to smoothly proceed with the activity of "suggest". It has been replaced by two activities, "convince the value of the product." In other words, when the way of capturing such "dependency between activities" differs depending on the subject classification, such difference in dependence can be determined as know-how (adjustment knowledge).

Similarly, the dependency relationship between the activities in the uppermost part and the middle part can be grasped as a similar dependency relationship starting from the activity "suggest". Regarding the dependency relationship between activities in the middle row, before carrying out the activity “Suggest”, the activity “Check the budget with a person who has the right to approve” and the activity “Search for problems and issues from the person in charge” are agreed in parallel. It can be understood that the implementation is know-how (coordination knowledge) for making a more reliable plan when "planning an activity plan". As described above, it is possible to judge, extract, and display as know-how (adjustment knowledge) by different subject classification based on the presence or absence of “dependency between activities”.

FIG. 13 is a diagram showing dependency relationships among activities included as constituent elements in a process and resources included between activities. Further, in the “process information”, when the basic “activity dependency” as shown in FIG. 13 and the “resource” intervening between the activities are stored and managed in the database, “activity” is displayed. Depending on the judgment criteria such as the presence or difference of "resource" intervening in the "dependency of question", the "activity" or the "dependency between activities" as shown in FIG. 11 and FIG. Know-how (adjustment knowledge) for providing or using some kind of "resource" can be automatically extracted.

FIG. 14 is a diagram showing a specific example of the dependency relationships among the activities shown in FIG. 13 and the resources included between the activities. For example, as shown in the uppermost part of FIG. 14, when “resource” is specified for “dependency between activities”, the activity “plan up” and the activity “suggest” shown in the uppermost part of FIG. It means that the resource “target customer” is present between “do” and the activity “suggest” is being performed using the information of the resource “target customer”. In the dependency shown in the middle part of FIG. 12,
It is stipulated that the resource "target customer" can be obtained from two activities: the activity "check the budget with a person who has the right to approve" and the activity "search for problems and issues from the person in charge".

Further, in the dependency relationship shown at the bottom of FIG. 12, the resource "target customer" obtained from the activity "plan"
However, it is clearly stated that the two activities are commonly used in two activities, "persuade a person who has the right to approve" and an activity "to convince the person in charge of the value of the product."

Based on the “resource” intervening in such “dependency between activities”, the resources recognized as input / output may differ for each subject classification. Therefore, know-how (adjustment knowledge) including "dependency between slides"
Can be extracted and displayed.

FIG. 15 is a diagram exemplifying an aspect in which the recognition of the resource j is different for each subject intervening in the “dependency between activities”. For example, as shown in FIG. Since the recognition of "resource" intervening in "is different for each subject classification, even if there is a dependency between the same activities, the resource is not described or the previous two activities extract the resource" target customer ". It is described that it is described, or even the same target customer is output with the priority position added and is input to the next Koudo “Suggest”, or the resource “Proposal” directly from the previous two activities. Are created. It can be judged as know-how (adjustment knowledge) based on the fact that each subject is recognized and described differently.

The method for automatically extracting know-how (coordination knowledge) based on the presence or absence of "resources" intervening in "activity", "activity dependency" and "activity dependency" has been described above. However, not only the binary information of presence or absence,
If a sufficient number of “subject information” is accumulated and the number of samples included in each subject classification is large enough to withstand statistical processing, determine whether or not there is a statistically significant difference. Can be displayed. For example, a person who has achieved the sales target is 5
It is possible to display the judgment that the activity A (for example, grasping the business policy / division policy of the customer) is performed with a significant difference of%.

FIG. 16 is a diagram illustrating an extended data structure of the activities included in the process information. Furthermore, in the above explanation, based on the activity name (phase name, active category name, activity minor category name), "activity", "dependency between activities", and dependency relationship are defined for different subject classifications. I explained how to judge by the presence or absence of "resources" intervening in a certain activity, or the statistical significance of existence.
In addition to the data structure of the activities (phases, activity classification, activity minor classification) shown in FIGS. 3 (B) to 3 (E), as shown in FIG. 16, “why do this activity? (Why)” A database that stores and manages information such as "How do you do this activity? (How)", "Who do you do this activity?" (When), and "Where do you do this activity?" Can also be used to analyze the process.

For example, "Why do you do this activity? (Wh
When the know-how extraction method according to the present invention is applied to "y)", it is extracted that each subject has a different purpose (Why) when executing an activity having the same process (process name). Further, when the know-how extraction method according to the present invention is applied to "how to carry out this activity? (How)", the same process (process name)
When performing a certain activity, it is extracted that each subject has different means (How) even though they have the same purpose (Why).

[Know-how extraction program] FIG. 17 is a diagram showing the structure of a know-how extraction program 2 for realizing the know-how extraction method according to the present invention. As shown in FIG. 17, the know-how extraction program 2 is composed of a database 20, a know-how extraction unit 22, and an input / output unit 24.
The know-how extracting program 2 executes the know-how extracting method according to the present invention described above with reference to FIGS.

[Database 20] Database 20
Operates according to the user's operation input from the input / output unit 24, and the subject information shown in FIG.
The process information shown in (D) is associated and stored and managed as shown in FIGS. 4 and 5. Further, the database 20 further stores and manages information indicating the dependency relationship between the activities when the know-how extraction program 2 performs the know-how extraction based on the dependency relationship between the activities shown in FIGS. 11 and 12. To do. Further, the database 20 stores information indicating the resources intervening in the dependency relationship between the activities when the know-how extraction based on the presence or absence of the resource intervening in the dependency relationship between the activities shown in FIGS. 13 and 14 is performed. Further store and manage. Also, the database 20
Further memorizes information indicating why, how, and where the activity is performed when know-how extraction based on why, how, and where the activity is performed is performed.
to manage. The database 20 also stores and manages the know-how extracted by the know-how extracting unit 22.

[Know-how extracting section 22] Know-how extracting section 2
2 operates according to a user's operation input from the input / output unit 24, performs the processing shown in FIGS. 6 and 7, and from the subject information and process information stored and managed in the database 20, The know-how as shown in FIG. 10 is extracted and output to the input / output unit 24 and the database 20.

[Input / output unit 24] The input / output unit 24 displays
The database 2 stores subject information and process information input from the input device 16 and the recording device 14 (FIG. 1).
Output for 0. Further, the input / output unit 24 displays the know-how input from the know-how extracting unit 22 on the screen of the display / input device 16 as shown in FIG. Further, the input / output unit 24 accepts a user operation on the display / input device 16 and outputs the operation to the know-how extraction unit 22 and the database 20.

[Effect] By using the know-how extraction method described above, it is possible to extract and display know-how (adjustment knowledge) that is unique and multifaceted for each different subject classification. In addition, if this know-how extraction method is used, the know-how (coordination knowledge publication) is extracted by focusing on the “activities” that differ for each subject, the “dependencies between activities”, and the intervening “resources”. It is possible to raise the abstraction level of “activities” and clarify that even the same “activities” have different substructures (“dependencies between activities” and “resources” that intervene) and refer to know-how between different actors. it can. Further, according to this know-how extraction method, it is possible to build a database of know-how (adjustment knowledge) that can be cross-referenced between different fields (main bodies).

[0074]

As described above, according to the know-how extracting apparatus and method according to the present invention, a process that can be reused or referenced by different entities in common, and a network or flow of activities included in the process can be realized. Can be used as a repository for process construction and redesign.

[Brief description of drawings]

FIG. 1 is a diagram showing a configuration of a computer (PC) that implements a business process display method according to the present invention.

FIG. 2 is a diagram illustrating, in a table format, subject information accumulated in a database for realizing a know-how extraction method according to the present invention.

FIG. 3 is a diagram illustrating, in a table format, process information accumulated in a database for realizing the know-how extraction method according to the present invention, in which (A) shows the process information,
(B) shows the phase information included in the process information shown in (A), (C) shows the active classification information shown in (B), and (D) shows it in (C). Indicates the activity sub-classification information.

FIG. 4 is a diagram illustrating “process information” (FIG. 3) stored and managed in a database.

5 is a diagram showing a specific example of the process information shown in FIG.

FIG. 6 is a flowchart showing a process (S10) of extracting know-how from the subject information and process information shown in FIGS.

FIG. 7 is a process (S14) of determining whether or not the activity determined to be included in the process information of only one subject by the process S10 shown in FIG. 6 is know-how (adjustment knowledge).
It is a flowchart showing.

FIG. 8 is a first diagram showing a first application example of the know-how extracting method according to the present invention, showing a method of extracting know-how from subject classification according to a difference in business form.

FIG. 9 is a second diagram showing a first application example of the know-how extraction method according to the present invention, showing a matrix-type display of activities determined as know-how (adjustment knowledge).

FIG. 10 is a first diagram showing a second application example of the know-how extracting method according to the present invention, showing a method of extracting know-how (adjustment knowledge) based on the presence or absence of a certain activity.

FIG. 11 is a diagram showing basic dependency relationships among a plurality of activities included as components in a process.

FIG. 12 is a diagram showing a specific example of a basic dependency relationship among the plurality of activities shown in FIG. 11.

FIG. 13 is a diagram showing dependency relationships among activities included as constituent elements in a process and resources included between activities.

FIG. 14 is a diagram showing a specific example of the dependency relationships among the activities shown in FIG. 13 and the resources included between the activities.

FIG. 15 is a diagram exemplifying an aspect in which “recognition of a resource j is different for each subject, which is interposed in“ dependency between activities ”.

FIG. 16 is a diagram illustrating an extended data structure of activities included in process information.

FIG. 17 is a diagram showing the configuration of a know-how extraction program that realizes the know-how extraction method according to the present invention.

[Explanation of symbols]

1 ... PC 10 ... Main body 102 ... CPU 104 ... Memory 12 ... communication device 14 ... Recording device 140 ... Recording medium 16 ... Display / input device 2 ... Know-how extraction program 20 ... Database 22 ... Know-how extraction unit 24: Input / output section

Claims (10)

[Claims] 1. A database that holds information on the subjects of a plurality of activities in association with a plurality of process information each containing one or more of the activities, and information on the subjects of the held activities and a plurality of processes. A know-how extracting device having know-how extracting means for extracting know-how based on information. 2. The database holds the activities included in the plurality of process information in one or more hierarchies, and the know-how extracting means corresponds to less than the first number of subjects in the same hierarchies. The know-how extracting device according to claim 1, wherein the activities included in the attached process information are extracted as know-how of an entity smaller than the first number. 3. The database holds the activities included in the plurality of process information by dividing them into a plurality of layers, and the know-how extracting means is associated with a first number or more of subjects in the same layer. The activity included in the process information, which is included in the process information and is associated with the second or more main subjects in different layers, is extracted as know-how of the first or more main subjects. Know-how extraction device. 4. The know-how extraction device according to claim 2, wherein the first number and the second number are one. 5. The database further holds a dependency relationship of activities included in the plurality of process information, and the know-how extracting means extracts the know-how based on the presence / absence of a dependency relationship between activities. The know-how extraction device described in 1. 6. The database further holds resources intervening between the dependency relationships of the activities included in the plurality of process information, and the know-how extracting means stores the resources intervening among the dependency relationships between the activities. The know-how extraction device according to claim 4, wherein the know-how is extracted based on presence or absence. 7. The know-how extracting device according to claim 1, wherein the know-how extracting means extracts the know-how by statistical processing between the information of the subject and the activity included in the process information. 8. The database further stores why, how, and where the activities included in the plurality of process information are performed, or any combination thereof, 2. The know-how according to claim 1, wherein the know-how extracting means extracts the know-how based on why the activity is performed, how the activity is performed, where the activity is performed, or an arbitrary combination thereof. Extractor. 9. Information on the subject of a plurality of activities and a plurality of process information each including one or more of the activities are held in a database in association with each other, and information on the held subject of the activities and a plurality of processes are held. A know-how extraction method that extracts know-how based on information. 10. A step of associating information of a plurality of activity subjects with a plurality of process information each including one or more of the activities in a database, and a plurality of information of the retained activity subjects and a plurality of the information. A program that causes a computer to execute a step of extracting know-how based on the process information of.