JP4228383B2 - Know-how extraction apparatus and method - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a know-how extracting apparatus and method for extracting process know-how.
[0002]
[Prior art]
"Japanese Patent No. 2923552", "Japanese Patent Laid-Open No. 6-44255" and "Japanese Patent Laid-Open No. 62-57023" (references 1 to 3) depend on the field as techniques for describing and classifying process knowledge. Disclosed is a technique for creating a field-specific database by the process description method described above.
[0003]
In addition, the above documents 1, 2 and "Jacobson, I. et. Al .: Object-Oriented Software Engineering-A Use Case Driven Approach, the ACM Press, New York (1992); Toshihiro Nishioka et al. Toppan (1995), “Object-oriented modeling technique“ MELON ”;“ Object-oriented front line ”subtitle,“ Information Processing Society of Japan '96 Symposium ”, Information Processing Society of Japan Software Engineering Study Group, Aoyama Mikio et al., Asakura Shoten, 1996 July 5, 1st edition first edition "and" Japanese Patent Laid-Open No. 11-119987 "(references 4 to 6) describe business processes, particularly information processes and human processes, using an object-oriented model. The technology to do is disclosed.
[0004]
Since the techniques disclosed in these documents are process descriptions assuming target domains, there is a feature that the process descriptions of a plurality of different target domains are not assumed to be integrated.
However, even in processes in different fields, the intrinsic nature of the process may be common across fields.
"Malone, TW and Crowston, K,: The interdisciplinary study of coodination, ACM Computing Survays, 26 (1), pp.87-pp.119, 1994" (Reference 7) 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.
[0005]
In this way, it has been proposed that a network of processes and activities included in a process that can be reused and referenced in common by different entities, and a flow of activities included in the process are repositories and utilized for process construction and redesign.
However, Document 7 only discloses such a repository data structure and a proposal of a usage method, and does not disclose a methodology for constructing the repository itself efficiently, effectively, and practically.
[0006]
[Problems to be solved by 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 reused and referred to by different entities in common, and a network of activities and flows included in the process are made into a repository. It is an object of the present invention to provide a know-how extraction apparatus and method that can be used for construction and redesign.
[0007]
[Means for Solving the Problems]
[Know-how extraction device]
In order to achieve the above object, a know-how extracting apparatus according to the present invention includes a database that holds information on a subject of a plurality of activities in association with a plurality of process information each including one or more of the activities, Know-how extracting means for extracting know-how based on the information of the main subject of the held activity and a plurality of process information.
[0008]
Preferably, the database holds the activities included in the plurality of process information divided into one or more hierarchies, and the know-how extracting means associates less than the first number of subjects in the same hierarchy. The activity included in the processed process information is extracted as know-how of the subject less than the first number.
[0009]
Preferably, the database holds the activities included in the plurality of process information divided into a plurality of hierarchies, and the know-how extracting means is a process associated with the first or more subjects in the same hierarchy. Activities included in the process information that are included in the information and are associated with the second or more entities among different layers are extracted as know-how of the first or more entities.
[0010]
Preferably, the first number and the second number are one.
[0011]
Preferably, the database further holds dependency relationships of activities included in the plurality of process information, and the know-how extracting unit extracts the know-how based on the presence or absence of dependency relationships between activities.
[0012]
Preferably, 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 includes the presence or absence of the resources intervening between the dependency relationships between the activities. Based on the above, the know-how is extracted.
[0013]
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.
[0014]
Preferably, the database further stores why, how and where the activities included in the plurality of process information are performed, and where, or any combination thereof, The know-how extracting means extracts the know-how based on why the activity is performed, how it is performed, where it is performed, or any combination thereof.
[0015]
[Know-how extraction method]
In addition, the know-how extracting method according to the present invention relates to a plurality of activity subjects and a plurality of process information each including one or more of the activities in association with each other in a database, and the retained activity subjects Know-how is extracted based on the process information and the plurality of process information.
[0016]
[program]
Further, the program according to the present invention includes a step of associating and holding in a database a plurality of process information including one or more of the activities, and a plurality of process information including each of the activities. And a step of extracting know-how based on the information and the plurality of process information.
[0017]
DETAILED DESCRIPTION OF THE INVENTION
[Background technology]
First, in order to facilitate understanding of the present invention, the background art will be described.
"Japanese Patent Application No. 2000-14107" by the same applicant as the present application refers to the above-mentioned coordination theory disclosed in Document 7 and has a unique process description method and classification method that solves practical problems on a computer system. This is an realized invention.
In the present invention, a plurality of model descriptions and components are classified according to the viewpoint of recognition of know-how extraction.
Therefore, with the two overall classification structures in the present invention, the writing speed attribute of the component can be expanded according to a plurality of recognition viewpoints, and a plurality of recognition viewpoints can be used as classification criteria. It can be included and ambiguous.
Furthermore, the present invention introduces a recognition field in order 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 further improves the invention according to the “Japanese Patent Application No. 2000-14107”, and a process that can be reused and referred to by different entities in common, and a network of activities included in the process and a repository of the flow itself. It can be constructed efficiently, effectively, and realistically.
[0018]
[Outline of the present invention]
In the know-how extraction method according to the present invention, “subject information (personal attributes and organization attributes)” stored in a database and “process information” associated with “subject information” are unique to different subject classifications. Know-how (adjustment knowledge) is automatically extracted.
Specifically, in the know-how extraction method according to the present invention, processes executed by different entities intervene in “activities”, “dependencies between activities”, and “dependencies between activities” constituting the processes. For each subject attribute when executing a process based on the presence or absence of “resources”, significant difference in distribution of presence or absence, and differences in hierarchical arrangements such as inclusion relationships of “activity” and “dependency between activities” Know-how (adjustment knowledge) is extracted.
The extracted know-how is explicitly associated with subjects with different attributes, and the level of abstraction of the know-how is raised based on the inclusion relationship of activities, etc., and is shown to different subjects. Served for review.
In the following description, “subject” generally refers to an individual or an organization.
The “subject classification” refers to a set of one or more subjects that are bundled according to the attribute of the subject information attached to the “subject”.
For example, if the “subject” is an employee belonging to a company, the subject information associated with the “subject” includes the year of employment and job title, etc. Is the “subject classification”.
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 △ male”, the “subject name” of this “subject” is “Yoyama △ male”.
Similarly, the name of “subject classification” is also referred to as “subject classification name”. For example, when “subject classification” is bundled in the year of entry, the “subject classification name” is “19 "
[0019]
In order to realize the know-how extraction method according to the present invention, first, “process information” in which “subject information” is associated with “subject information” of the subject executing the process is stored in a database.
This “subject information” may be an individual or an organization that executes the corresponding process.
[0020]
FIG. 1 is a diagram showing the configuration of a computer (PC) 1 that implements the business process display method according to the present invention.
As shown in FIG. 1, the PC 1 includes, for example, a main body 10 including a CPU 102 and a memory 104, a communication device 12, a recording device 14 such as an HDD / CD-RW / FDD, a display including a CRT display device, a keyboard / mouse, and the like. -It is comprised from the input device 16 grade | etc.,.
That is, the PC 1 adopts a configuration as a general computer.
The business process display method according to the present invention is realized by, for example, a program (a know-how extracting program 2 described later with reference to FIG. 17) loaded into the memory 104 via the recording medium 140 and executed.
[0021]
FIG. 2 is a chart illustrating the subject information stored in the database in the form of a table for realizing the know-how extraction method according to the present invention.
As illustrated in FIG. 2, the subject information includes a sales form (new customer development / existing customer) and sales results (numerical value) as attribute items of personal attributes, and these attributes are assigned to each subject. It is associated with a unique number (personal ID).
In other words, the subject information illustrated in FIG. 2 is based on whether the subject indicated by the “personal ID” is engaged in sales in the form of cultivating a new customer, or a case where a case is developed for an existing customer. For example, the achievement rate for the previous sales target assigned to each entity indicated by the “personal ID” is shown as “business results”. It is shown as attribute information.
[0022]
FIG. 3 is a chart illustrating the process information accumulated in the database in the form of a table for realizing the know-how extraction method according to the present invention, where (A) shows the process information, and (B) shows ( (A) shows the phase information included in the process information shown in (A), (C) shows the active classification information shown in (B), and (D) shows the small activity classification information shown in (C). .
FIG. 4 is a diagram illustrating “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” includes “resources” intervening in “activity”, “dependency between activities”, and “dependency between activities” as components, and as shown in FIGS. It includes information indicating some inclusive relationship such as abstract / concrete relationship.
[0023]
For example, each piece of process information includes a phase (major activity category) as the highest unit of the process, each phase includes an active category as a substructure, each active category includes a subcategory as a substructure, Take a hierarchical structure.
Further, as shown in FIG. 3A, the “process information” includes a “personal ID” indicating the subject that has 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 managed by a database as shown in FIG. 4 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.
[0024]
[Know-how extraction process]
Hereinafter, a process for extracting unique know-how (adjustment knowledge) from “subject information” and “process information” (FIGS. 2 and 3) in a database by the know-how extraction method according to the present invention will be described.
[0025]
FIG. 6 is a flowchart showing a process (S10) of extracting know-how from the subject information and process information shown in FIGS.
As shown in FIG. 6, in step 100 (S100), the values of variables i, j, x and FLAG_ [i] [x] are initialized (i = 0, j = 0, x = 0, FLAG_ [ i] [x] = 0).
The variable i indicates the personal ID i, the variable j (≠ i) indicates the personal ID j different from the personal ID j, and the variable x indicates the activity x included in the process information.
[0026]
Whether or not the activity x included in the process information corresponding to the subject information indicated by the personal IDi (hereinafter abbreviated as process information of the personal IDi) is included in the process information of the personal IDj in step 102 (S102) Is judged.
If the activity x included in the process information of the personal ID i is not included in the process information of the personal ID j (≠ i), the process of S104 is executed, and otherwise, the process of S106 is executed.
[0027]
In step 104 (S104), the variable j is incremented.
[0028]
In step 106 (S106), the values of the flags FLAG_ [i] and [x] indicating that the activity x included in the process information of the personal ID i is included in the process information of the other personal ID j (≠ i) Set to 1.
[0029]
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 a numerical value N, the process of S110 is executed. In other cases, the process of S102 is executed, and further, the process of S102 is executed.
[0030]
In step 110 (S110), the variable x is incremented.
[0031]
In step 112 (S112), 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 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 In addition, the process of S102 is executed.
[0032]
In step 116 (S116), the variable i is incremented.
[0033]
In step 118 (S118), it is determined whether or not the value of the variable i is a numerical value N.
When the value of the variable i is a numerical value N, the process of S122 is executed, otherwise, the process of S120 is executed, the value of the variable x is set to 0, and the process of S102 is further executed. .
[0034]
In steps 122 (S122) to S128 (S128), an incremental loop process is performed on the subject of the personal IDi and the activity x.
[0035]
In step 124 (S124), it is determined whether or not the value of the flag FLAG_ [i] [x] is zero.
If the value of the flag FLAG_ [i] [x] is 0, the process of S126 is executed, and otherwise, the process of S128 is executed.
[0036]
In step 126 (S126), it is determined that the activity x of the subject (hereinafter abbreviated as subject i) indicated by the personal ID i is know-how (adjustment knowledge).
[0037]
That is, the activity x that is included in the process information of the personal ID i and that is not included in the process information of any other personal ID j is uniquely owned by the subject i by the process of S10 shown in FIG. Judged as know-how (adjustment knowledge).
The activity x included in the process information of the personal IDi and included in the process information of any other personal IDj is uniquely owned by the subject indicated by the personal IDi (hereinafter abbreviated as the subject i). Whether or not it is know-how (adjustment knowledge) is determined by processing S14 described with reference to FIG.
When the process information is not hierarchized (only one hierarchy), only the processing shown in FIG. 6 is performed, and the processing shown in FIG. 7 is not performed.
[0038]
FIG. 7 is based on the process S10 shown in FIG. Therefore, the activity x determined to be included in the process information of the personal IDi and included in the process information of any other personal IDj Is a flowchart showing a process (S14) for determining whether or not it is know-how (adjustment knowledge).
As shown in FIG. 7, in step 140 (S140), the values of variables i, j, x and FLAG_ [i] [x] are initialized (i = 0, j = 0, x = 0, FLAG_ [ i] [x] = 0).
[0039]
In step 142 (S142), it is determined whether or not the activity x included in the process information of the personal ID i is included in another classification (other hierarchy) of the process information of the other personal ID j.
That is, in S142, for example, the activity x included in the middle classification of the process information of personal IDi (FIG. 3C) is changed to the minor classification (FIG. 3D) of the process information of other personal IDj. Whether it is included in the major classification (phase; FIG. 3A) is determined.
If the activity x included in the process information of the personal IDi is not included in another classification of the process information of the personal IDj, the process of S144 is executed, and otherwise the process of S146 is executed.
[0040]
In step 144 (S144), the variable j is incremented.
[0041]
In step 146 (S146), the values of the flags FLAG_ [i] and [x] indicating that the activity x included in the process information of the personal IDi is included in another classification of the process information of the other personal IDj are set. Set to 1.
[0042]
In step 148 (S148), it is determined whether or not the value of the variable j is a numerical value N.
If the value of the variable j is a numerical value N, the process of S150 is executed, otherwise the process of S142 is executed, and further the process of S142 is executed.
[0043]
In step 150 (S150), the variable x is incremented.
[0044]
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 further executed. .
[0045]
In step 156 (S156), the variable i is incremented.
[0046]
In step 158 (S158), it is determined whether or not the value of the variable i is a numerical value N.
When the value of the variable i is the numerical value N, the process of S162 is executed, otherwise, the process of S160 is executed, the value of the variable x is set to 0, and the process of S142 is further executed. .
[0047]
In steps 162 (S162) to S168 (S168), an incremental loop process is executed for the subject of the personal IDi and the activity x.
[0048]
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 otherwise, the process of S168 is executed.
[0049]
In step 166 (S166), it is determined that the activity x of the subject i is know-how (adjustment knowledge).
[0050]
[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, and shows a method for extracting know-how from subject classifications according to differences in business forms.
As shown in FIG. 8, when executing the same process (process name), whether or not there is a certain activity (in this case, only active category and small activity category) is determined based on whether a new customer is cultivated or existing Know-how (adjustment knowledge) can be drawn for each subject classification as a customer.
[0051]
For example, if there is an activity that includes the subject classification process of sales form (existing customers) but is not included in the subject classification process of sales form (new customer development), these activities are Is likely to be know-how (adjustment knowledge) for each subject.
On the contrary, activities that are not included in the process of subject classification called sales form (existing customers) are included in the know-how (adjustment knowledge) for the same reason. It can be determined that there is a high possibility.
[0052]
Also, if the business category (existing customers) is included in the business category (new customer development), but the phases in which the activities are categorized (major categories of activities) are different. There is a high possibility that the timing and order of execution of the activities will differ depending on the business form, and it can be determined as know-how (adjustment knowledge) for each business form.
[0053]
FIG. 9 is a second diagram showing a first application example of the know-how extraction method according to the present invention, and shows a display in a matrix format 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, activities α-C3 and activity θ-G1 are included in the sales form (existing customers) but are included in the sales form (new customer development) as know-how (adjustment knowledge) specific to each sales form. The activity α-D4 and activity θ-B2 are included in the sales form (new customer development) but are determined as activities not included in the sales form (existing customers). .
[0054]
On the other hand, although it is included in the sales form (new customer development) and sales form (existing customer), the activity name “confirm the budget to the person with the decision-making authority” is the activity α In A2, in the case of a sales form (new customer development), since it is located in activity γ-A3, it is determined and displayed as know-how (adjustment knowledge) specific to each sales form.
[0055]
[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 extraction method according to the present invention, and shows a method for extracting know-how (adjustment knowledge) based on the presence or absence of a certain activity.
As shown in FIG. 10, when executing the same process (process name), the presence or absence of a certain activity (in this case, active category or activity only) is determined for each subject category based on the difference in business results. Know-how (adjustment knowledge) can be extracted.
In the example shown in FIG. 10, the subject is classified into two depending on whether the target value of the past sales is achieved (100% or more) or not (less than 100%).
Use the same method as in the first application example to determine the difference in activities performed in the process between a sales entity that has not achieved its sales target and a sales entity that has achieved its sales objective. can do.
[0056]
Since it is generally better for the business results to have a high achievement rate, here, it is possible to give a meaningful level between the classified subjects (subject categories).
Therefore, activities that are included in the activities of sales entities that have achieved sales objectives but are not included in activities of sales entities that do not achieve sales objectives are know-how (adjustment knowledge) for achieving sales targets. It can be judged.
In addition, activities included in either subject classification and included in different phases can be determined as know-how (adjustment knowledge) such as the timing and order of execution of the activities for achieving the sales target.
As described above, according to the know-how extraction method according to the present invention, whether or not it is know-how (adjusted knowledge) can be automatically determined and extracted based on the presence or absence of “activity” for different subject classifications. .
[0057]
FIG. 11 is a diagram showing a basic dependency relationship among a plurality of activities included as components in a process.
In addition, as a method for extracting this know-how (adjustment knowledge), not only the determination based on the presence or absence of a certain activity included in the “process information” but also a basic “dependency between activities as shown in FIG. ”For activities and“ dependencies between activities ” 6 and 7 By applying the extraction process shown in the above, know-how (adjustment knowledge) about “dependency between activities” can be automatically extracted.
[0058]
FIG. 12 is a diagram showing a specific example of the basic dependency relationship between the plurality of activities shown in FIG.
As shown in FIG. 12, the topmost activity “planning” and the activity “proposing” have the most basic flow dependency.
This flow dependency relationship is different from the middle and lower dependency relationships, but can be understood as a chain of activities starting from a certain activity.
For example, if the activity “Keep a plan” is the starting point, it can be understood as a dependency similar to the dependency between the activity at the top and the activity at the bottom.
[0059]
In addition, the activity “Propose” at the top is the activity “Convince the person with the decision-making authority” and the activity “Value of the product to the person in charge” in order to smoothly advance the activity “Propose” at the bottom. It is replaced by two activities that "convince you".
In other words, when such a way of understanding “dependency between activities” is different depending on the subject classification, such a difference in dependency can be determined as know-how (adjustment knowledge).
[0060]
Similarly, the dependency relationship between the activities of the top and middle stages can be regarded as a similar dependency relationship starting from the activity “propose”.
Concerning the dependency between activities in the middle row, before carrying out the activity “Propose”, the activities “Confirm the budget to the person with the decision-making authority” and the activity “Search for problems and issues from the person in charge” agreed in parallel It can be regarded as know-how (coordination knowledge) for making a more reliable plan when “exercising unplanned activities”.
In this way, it is possible to determine, extract, and display as know-how (adjustment knowledge) based on different subject classifications based on the presence or absence of “dependency between activities”.
[0061]
FIG. 13 is a diagram illustrating the dependency relationship of activities included as components in a process and resources included between activities.
Here, “resource” means “activity” Offer From an object or "activity" use Means an object to be created.
Furthermore, when the basic “activity dependency” as shown in FIG. 13 and the “resource” intervening between the activities are stored and managed in the “process information”, The “activity” or “dependency between activities” as shown in FIG. 11 and FIG. 12, depending on the judgment criteria such as the presence or difference of “resources” intervening in “dependency between”. Know-how (adjustment knowledge) for providing or using some kind of “resource” can be automatically extracted.
[0062]
FIG. 14 is a diagram illustrating a specific example of the dependency relationship between the activities illustrated in FIG. 13 and the resources included between the activities.
For example, as shown at the top of FIG. 14, when “resources” is specified for “dependency between activities”, the activity “planning” and the activity “suggestion” shown at the top of FIG. This indicates that the resource “target customer” is interposed between “Yes” and the activity “suggest” using the information of the resource “target customer”.
Further, in the dependency shown in the middle of FIG. 12, the resource “target customer” is obtained from the two activities of the activity “confirm the budget to the person with the decision-making authority” and the activity “search for problems and issues from the person in charge”. It is clearly stated that
[0063]
Furthermore, in the dependency shown at the bottom of FIG. 12, the resource “target customer” obtained from the activity “planning a plan” has two activities “persuade a person with a decision-making authority” and the activity “ It is clearly stated that it is used in common to “convince and value the value of the product”.
[0064]
Based on such “resources” intervening in “dependency between activities”, there is a possibility that the resources recognized as being input / output are different for each subject classification.
Therefore, this can be determined as know-how (adjustment knowledge) including “dependency between sliding”, extracted, and displayed.
[0065]
FIG. 15 is a diagram exemplifying a mode in which “recognition of resource j is different for each subject” intervening in “dependency between activities”.
For example, as shown in FIG. 15, since the recognition of “resources” intervening in “dependency between activities” differs for each subject category, no resource is described even if the dependency is between the same activities. , It is described that the previous two activities are extracting the resource “target customer”, or the same target customer is output with the priority viewpoint added, and is input to the next Hiroki “Propose” The resource “proposal” is created directly from the previous two activities.
It can be determined as know-how (adjustment knowledge) on the basis of recognition and different descriptions made for each subject.
[0066]
So far, the method of automatically extracting know-how (coordination knowledge) based on the existence of “resources” intervening in “activity”, “dependency between activities” and “dependency between activities” has been described. If there is a sufficient number of “subject information” in addition to the binary information of presence / absence, and the number of activities included in each subject classification is large enough to withstand statistical processing as a sample, Differences between principal information A determination of whether or not is statistically significant can be displayed.
For example, a person who has achieved the sales target performs activity A (for example, grasps the customer's management policy / department policy) with a significant difference of 5% compared to the person who has not achieved the sales target. It is possible to display a determination that the user is performing.
[0067]
FIG. 16 is a diagram illustrating an expanded data structure of activities included in the process information.
Furthermore, in the above explanation, “activity”, “dependency between activities”, and dependency relations for different subject classifications based on activity names (phase names, active activity classification names, activity minor classification names). The presence or absence of "resources" that intervene between activities Existence of statistical significance In the data structure of the activities (phase name, active category name, and activity minor category name) shown in FIGS. 3B to 3E, as shown in FIG. “Why do you do this activity? (Why?)”, “How do you do this activity? (How?)”, “Yes One Do you do this activity? (When?) "," Where do you do this activity? (Where?) "," Where do you do this activity? (Where?) ", Etc., analyze the process using a database that stores and manages information You can also
[0068]
For example, when the know-how extraction method according to the present invention is applied to “Why do this activity? (Why)”, when executing an activity having the same process (process name), each subject has a different purpose (Why). ) Is extracted.
In addition, when the know-how extraction method according to the present invention is applied to “How to perform this activity (How)”, the same purpose (Why) is obtained when an activity having the same process (process name) is executed. However, it is extracted that each subject has different means (How).
[0069]
[Know-how extraction program]
FIG. 17 is a diagram showing a configuration of the know-how extracting program 2 that realizes the know-how extracting method according to the present invention.
As shown in FIG. 17, the know-how extracting program 2 includes a database 20, a know-how extracting unit 22, and an input / output unit 24.
The know-how extraction program 2 executes the know-how extraction method according to the present invention described above with reference to FIGS.
[0070]
[Database 20]
The database 20 operates in accordance with user operations input from the input / output unit 24, and associates the subject information shown in FIG. 2 with the process information shown in FIGS. 4 and FIG. 5 are stored and managed.
In addition, when know-how extraction based on the dependency relationships between activities shown in FIGS. 11 and 12 is performed in the know-how extraction program 2, the database 20 further stores and manages information indicating the dependency relationships between activities. To do.
In addition, when know-how extraction based on the presence / absence of resources intervening in the dependency relationships between activities shown in FIGS. 13 and 14 is performed, the database 20 stores information indicating resources intervening in the dependency relationships between activities. Further remember and manage.
In addition, the database 20 further stores and manages information indicating why, how, and where activities are performed when know-how extraction is performed based on why, how, and where actions are performed. To do.
The database 20 stores and manages the know-how extracted by the know-how extracting unit 22.
[0071]
[Know-how extraction unit 22]
The know-how extracting unit 22 operates in accordance with a user operation or the like 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 shown in FIGS. 8 and 10 is extracted and output to the input / output unit 24 and the database 20.
[0072]
[Input / output unit 24]
The input / output unit 24 outputs subject information and process information input from the display / input device 16 and the recording device 14 (FIG. 1) to the database 20.
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.
The input / output unit 24 accepts a user operation on the display / input device 16 and outputs it to the know-how extracting unit 22 and the database 20.
[0073]
[effect]
By using the know-how extraction method described above, unique and multi-faceted know-how (adjustment knowledge) can be extracted and displayed for each different subject classification.
Also, using this know-how extraction method, know-how (adjustment knowledge) is extracted by focusing on “activities” that differ for each subject, “dependency between activities”, and intervening “resources”. It is possible to raise the level of abstraction of “activities”, clarify that the same “activities” have different substructures (“dependencies between activities” and intervening “resources”), and refer to know-how between different entities it can.
Further, according to this know-how extraction method, it is possible to construct a database relating to know-how (adjustment knowledge) that can be cross-referenced between different fields (subjects).
[0074]
【The invention's effect】
As described above, according to the know-how extraction apparatus and method therefor according to the present invention, a process that can be reused and referred to by different entities, and a network of activities and flows included in the process are repositories, and the process Can be built and redesigned.
[Brief description of the drawings]
FIG. 1 is a diagram showing a configuration of a computer (PC) that realizes a business process display method according to the present invention.
FIG. 2 is a chart illustrating subject information stored in a database in tabular form for realizing the know-how extraction method according to the present invention.
FIGS. 3A and 3B are diagrams illustrating process information accumulated in a database in a table format for realizing the know-how extraction method according to the present invention, wherein FIG. 3A shows process information, and FIG. (A) shows the phase information included in the process information shown in (A), (C) shows the active classification information shown in (B), and (D) shows the small activity classification information shown in (C). .
FIG. 4 is a diagram illustrating “process information” (FIG. 3) stored and managed in a database.
FIG. 5 is a diagram showing a specific example of process information shown in FIG. 4;
6 is a flowchart showing a process (S10) of extracting know-how from the subject information and process information shown in FIGS.
7 is a flowchart showing a process (S14) for determining whether or not the activity determined to be included in only one process information by the process S10 shown in FIG. 6 is know-how (adjustment knowledge). is there.
FIG. 8 is a first diagram showing a first application example of the know-how extracting method according to the present invention, and shows a method for extracting know-how from subject classifications according to differences in business forms;
FIG. 9 is a second diagram showing a first application example of the know-how extraction method according to the present invention, and shows a display in a matrix format of activities determined as know-how (adjustment knowledge).
FIG. 10 is a first diagram showing a second application example of the know-how extraction method according to the present invention, and shows a method for extracting know-how (adjustment knowledge) based on the presence or absence of a certain activity.
FIG. 11 illustrates basic dependencies between multiple activities included as components in a process.
12 is a diagram showing a specific example of a basic dependency relationship among a plurality of activities shown in FIG.
FIG. 13 is a diagram illustrating dependency relationships of activities included as components in a process and resources included between activities.
14 is a diagram showing a specific example of the dependency relationship of the activities shown in FIG. 13 and the resources included between the activities.
FIG. 15 is a diagram illustrating a mode in which “recognition of resource j is different for each subject” intervening in “dependency between activities”;
FIG. 16 is a diagram illustrating an expanded data structure of activities included in process information.
FIG. 17 is a diagram showing a configuration of a know-how extracting program for realizing the know-how extracting method according to the present invention.
[Explanation of symbols]
1 ... PC
10 ... 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 extractor
24 ... I / O section

Claims (5)

A know-how extraction device that extracts know-how by a computer executing a program,
The program is
A storage process for storing information of a plurality of subjects and a plurality of process information in association with an identifier that uniquely identifies the subject, wherein the subject is one of an individual and an organization, and the plurality of process information Each of which is composed of one or more activities performed by each of the plurality of subjects, and each of the one or more activities is classified into a major classification, a middle classification, and a minor classification;
Of the one or more activities performed by the first entity among the plurality of entities, the first activity is the first entity using the stored information of the plurality of entities and the plurality of process information. The first activity is extracted as know-how that is adjustment knowledge peculiar to the first entity when it is determined that it is different from any one or more activities performed by the plurality of entities except for the first entity Extraction process,
Using the stored information of the plurality of subjects and the plurality of process information, the first activity is the same as any one or more activities performed by the plurality of subjects excluding the first subject. And the first activity is one or more activities performed by the plurality of entities excluding the first entity, and the classification is different from the classification of the first activity. A know-how extraction device that causes the computer to execute a second extraction process for extracting the first activity as the know-how when it is determined that the activity is the same as one of the two or more activities. The storing process includes
Further storing dependency information that at least one of the activities is performed starting from one of the activities performed by each of the plurality of entities;
The program is
Further using the stored dependency relationship information, the first activity is in a first dependency relationship with any of the activities included in the process information of the plurality of entities excluding the first entity. The second activity included in the process information of the second entity among the plurality of entities is a second dependency relationship with any of the activities included in the process information of the plurality of entities excluding the second entity. The activity that is the starting point of the first dependency relationship is different from the activity that is the starting point of the second dependency relationship, and the activity that is performed by the activity that is the starting point of the first dependency relationship is A third extraction for extracting the difference between the first dependency relationship and the second dependency relationship as the know-how when it is determined that the activity is the same as the activity performed by the activity that is the starting point of the second dependency relationship; Processing the computer The know-how extracting device according to claim 1. The storing process includes
Further stores information on resources that are objects provided or used by the activity that is the starting point of the dependency or the activity that is performed by the activity, and
The program is
Further using the stored information of the resource, the content of the first resource provided or used in the first dependency relationship is the second of the resource provided or used in the second dependency relationship. The computer according to claim 2, wherein when it is determined that the content is different from the content, the computer executes a fourth extraction process for extracting a difference between the content of the first resource and the content of the second resource as the know-how. Know-how extraction equipment. The storing process includes
The plurality of entities further stores information indicating the purpose, means and place of performing the activities included in the process information of the plurality of entities or a combination thereof;
The program is
Further using the stored information, when determining that the purpose of performing the first activity is different from the purpose of performing the second activity, the difference is extracted as the know-how,
Further using the stored information, the purpose of the first activity is the same as the purpose of the second activity, and the means for performing the first activity is the means for performing the second activity The know-how extracting apparatus according to claim 1, wherein, when determining that the difference is different, the computer executes a sixth extraction process for extracting the different thing as the know-how. A program executed by a computer,
A storage step of storing information of a plurality of subjects and a plurality of process information in association with an identifier that uniquely identifies the subject, wherein the subject is one of an individual and an organization, and the plurality of process information Each comprising one or more activities performed by each of the plurality of subjects, each of the one or more activities being classified as a major classification, a middle classification and a minor classification;
Of the one or more activities performed by the first entity among the plurality of entities, the first activity is the first entity using the stored information of the plurality of entities and the plurality of process information. The first activity is extracted as know-how that is adjustment knowledge peculiar to the first entity when it is determined that it is different from any one or more activities performed by the plurality of entities except for the first entity An extraction step;
Using the stored information of the plurality of subjects and the plurality of process information, the first activity is the same as any one or more activities performed by the plurality of subjects excluding the first subject. And the first activity is one or more activities performed by the plurality of entities excluding the first entity, and the classification is different from the classification of the first activity. A second extraction step of extracting the first activity as the know-how and a program to be executed by a computer when it is determined that the activity is the same as one of the two or more activities;

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