JP6446903B2 - Simulation device, simulation method, and simulation program - Google Patents

Description

  The present invention relates to a simulation apparatus, a simulation method, and a simulation program.

  In sales and customer support operations, operations corresponding to individual requests of customers that are difficult to make manuals are performed. Such operations are called atypical operations, and the knowledge of employees is an important asset. For this reason, companies and the like need to consider measures for increasing the knowledge of employees and the entire organization.

  2. Description of the Related Art Conventionally, a knowledge learning model has been proposed in which a certain amount of knowledge is acquired after business execution depending on the difficulty level of the business content (see, for example, Non-Patent Document 1). Thereby, before actually implementing a measure, it is possible to analyze a change in knowledge of an operator caused by the measure.

Noriko Hanakawa, Kenichi Matsumoto, Koji Torii: “Evaluation of Software Development Simulation Model Considering Learning Proficiency”, IEICE Technical Report, KBSE98-28, pp.49-55, Nov. 1998.

  However, although the knowledge acquisition model described above can express the knowledge acquisition behavior of individual employees, it cannot express the behavior of acquiring knowledge from other employees, so it can simulate situations that can occur in atypical work. Can not.

  In one aspect, an object of the present invention is to provide a simulation apparatus, a simulation method, and a simulation program that can calculate a change in the knowledge amount of a worker in an atypical task by simulation.

In one aspect, in a simulation device that simulates a change in knowledge held by each of a plurality of businessmen corresponding to a customer's request, the request held by the customer and each of the plurality of businessmen A reception unit that receives knowledge for solving the request and rules for acquiring knowledge among the plurality of business workers, a request held by the customer, and the customer is assigned among the plurality of business workers. Compared with the knowledge held by any of the business personnel, the identification unit for identifying the knowledge that is insufficient for solving the request, and the rules for acquiring the knowledge are insufficient for solving the request. And a receiving unit that allows any one of the employees to enjoy the knowledge of the request, and the request held by the customer is expressed as a structure including a plurality of sub-requests for which a resolution order is determined. The

  It is possible to calculate a change in the knowledge amount of an atypical business worker by simulation.

Fig.1 (a)-FIG.1 (c) are the figures for demonstrating the characteristic of atypical work. It is a figure which shows the outline | summary of a simulation apparatus typically. It is a conceptual diagram of the model operation | movement performed within a simulation apparatus. It is a figure which shows the outline | summary of the operation process of a model. FIG. 5A is a diagram illustrating a hardware configuration of the simulation apparatus, and FIG. 5B is a functional block diagram of a CPU of the simulation apparatus. It is a flowchart which shows the operation | movement process of the model which a simulation execution part performs. Fig.7 (a)-FIG.7 (c) are the figures for demonstrating a customer generation | occurrence | production pattern. It is a figure shown about an example of the structure of a customer request. FIG. 9A and FIG. 9B are diagrams for explaining an input example of a customer request. It is a figure for demonstrating the customer request | requirement of each customer. FIG. 11A to FIG. 11C are diagrams for explaining the request determination probability. FIG. 12A is a table showing associations between business workers and teams, and FIG. 12B is a table showing associations between teams and customer request categories. It is a figure for demonstrating the knowledge which a business employee has. FIG. 14A and FIG. 14B are diagrams for explaining a method (first) for determining a path held by a business worker. It is a figure for demonstrating the method (2nd) which determines the path | pass which an employee holds. It is a table | surface which shows the time which request processing by each business worker requires. It is a table | surface which shows shared knowledge. It is a table | surface which shows the example of an assignment rule. It is a flowchart which shows the specific process of step S3 of FIG. FIG. 20A is a flowchart showing the request processing in step S31, and FIGS. 20B and 20C are diagrams for explaining the success or failure of matching between the requested knowledge and the customer request. It is a table | surface which shows the priority of a knowledge acquisition method. It is a flowchart which shows the specific process of step S313 of Fig.20 (a). FIG. 23A is a table showing the customer correspondence change probability, and FIG. 23B is a table showing the shared knowledge search rule. 24A is a table showing an input example of a human resource resource allocation scenario, FIG. 24B is a table showing an input example of a review measure scenario, and FIG. 24C is a customer assignment measure scenario. It is a table | surface which shows the example of input. FIG. 25A is a diagram illustrating an input example of a Know-how sharing measure scenario, and FIG. 25B is a diagram illustrating an input example of a Know-who search measure scenario. It is a figure which shows an example of a simulation setting. Fig.27 (a)-FIG.27 (c) are figures which show an example of a simulation result. It is a graph which shows the change of the response time per customer's satisfaction rate, knowledge retention rate, before execution of a measure scenario. It is a graph which shows the change of the response time per needs customer satisfaction rate after knowledge scenario execution (after transfer), knowledge retention rate, and one customer.

  Hereinafter, an embodiment will be described in detail with reference to FIGS.

  The simulation apparatus according to the present embodiment analyzes service evaluation organizations such as government offices and customer support centers, and analyzes and evaluates knowledge sharing measures for employees in the organization to acquire knowledge necessary for solving customer requirements. Enable.

  Here, in the service business organization, a business for responding to customer's individual request that is difficult to make a manual, that is, an “atypical business” is performed. Atypical work has the feature that when a business person deals with a customer, he / she can inquire a plurality of other business persons and acquire knowledge to learn new knowledge for dealing with the customer. In addition, the atypical business has a feature that the business knowledge of each business worker changes with time.

  FIG. 1A schematically shows a state in which the business staff 1 can ask other business staff 3 questions when the business staff 1 alone cannot respond to the request of the customer 1. FIG. 1 (b) schematically shows a situation in which, when only the employee 2 cannot respond to the request of the customer 1, the employee 3 who wants to ask a question is answering another customer 2 and cannot answer the question. Is shown in In the case of FIG. 1 (a), although the time required to respond to the customer request is 10 minutes, in the case of FIG. 1 (b), it is necessary to search for another worker and ask a question. The time (20 minutes) is about twice as long as that in FIG. As described above, in an atypical job, it takes time to search for a worker who has knowledge, and in some cases, a so-called fraud occurs.

  Further, in the non-standard work, as shown in FIG. 1C, a specific business person (for example, an experienced business person 4) is often asked questions and cannot deal with customers. As a result, there is a problem that the speed of knowledge improvement is slowed down in the entire organization, and productivity and customer satisfaction are lowered.

  The simulation apparatus according to the present embodiment has the characteristics of atypical work as shown in FIGS. 1 (a) to 1 (c), the number of employees corresponding to customers, the time required to solve customer requests for each customer, and other employees. It is possible to express the time required for the cooperation of employees, and an indicator of the amount of knowledge of employees, and simulate in advance the knowledge sharing measures (measures that should be executed to increase knowledge) required in atypical work, By outputting the change of the index, the effect of the measure is evaluated.

  FIG. 2 schematically shows an outline of the simulation apparatus 100 of the present embodiment. In this embodiment, the three measures that can be evaluated are “human resource allocation”, “human resource education”, and “information sharing”, which should be executed to increase knowledge. In addition, as an evaluation index for evaluating measures, four KPIs of “learning and growth KPI (key performance indicator / key performance indicator)”, “business process KPI”, “customer KPI”, and “financial KPI” are used. And

  Here, the “learning and growth KPI” is a KPI that evaluates the knowledge acquired by employees and the entire organization, and the “business process KPI” is a KPI that evaluates the satisfaction rate of customer requirements and the resolution speed of customer requirements. It is. The “customer KPI” is a KPI that evaluates customer satisfaction, and the “financial KPI” is a KPI that evaluates the service acceptance rate and the number of contracts.

  In the present embodiment, it is possible to comprehensively determine whether the measure is good or not by checking the change in the relationship between the KPIs when the knowledge sharing measure is implemented in the simulation.

  FIG. 3 shows a conceptual diagram of model operations executed in the simulation apparatus 100. The operation of the model will be described based on FIG. Note that this model applies an agent-based approach and models the three entities "customer", "business worker", and "manager" as agents. Each agent holds an attribute (parameter) that becomes its own action rule, and makes an autonomous decision based on this parameter. As a result of each entity making a decision autonomously, the performance of the entire organization is output as each KPI value.

  The “customer (model)” holds a request to the organization, for example, a request to receive various administrative services if the organization is a government office. The “business person (model)” has the knowledge to deal with customers and solve requests. The “manager (model)” determines the corresponding customer and implements various measures, and controls the behavior of employees. That is, the “manager (model)” has a role of controlling the simulation. As shown in FIG. 3, the “manager (model)” has policy scenarios such as a human resource allocation policy, a human resource education policy, and an information sharing policy.

  In the simulation apparatus 100, using each model of FIG. 3, based on the action of “service interaction” for request presentation and request resolution between the customer and the employee and the control guidelines (measures) of the manager, Express the behavior of “organizational learning” to share knowledge in the world. Here, since the manager's measures are different, the method of sharing knowledge among the employees is also different, so the method of changing the employee's knowledge is also different. Further, when the knowledge of the business staff changes, the service provision time and service content to the customer also change, so the customer satisfaction and the success or failure of service acceptance also change. Therefore, the simulation apparatus 100 analyzes the influence of the knowledge sharing measure in FIG. 2 from the relationship between the evaluation values of the plurality of KPIs.

  FIG. 4 is a diagram showing an outline of the operation process of the model.

  First, the customer inquires of his / her request to the organization ((1) in FIG. 4). In the real world, the customer may directly communicate the request to the employee, but in this model, the manager who controls the simulation accepts all inquiries (customer requests). After receiving the inquiry, the manager assigns a business worker to the customer ((2) in FIG. 4). The assigned business worker makes the customer present the request, thereby clarifying the request that the customer wants to solve ((3) in FIG. 4). Here, in the case where the knowledge about the customer request (referred to as “request knowledge”) is lacking in the employee, the employee makes an inquiry to the manager ((4) in FIG. 4). At this time, the employee tells the manager the employee who seems to have knowledge based on his / her Know-who knowledge. After that, the manager presents other cooperating workers to the inquiring employee (Fig. 4 (5)), or provides knowledge from shared knowledge such as databases and manuals existing in the organization. This is performed ((6) in FIG. 4). After acquiring knowledge related to the customer request, the employee resolves the request ((7) in FIG. 4).

  The above is the normal operation process. In the simulation apparatus 100, “assignment of business staff”, “inquiry”, “presentation of other business staff”, and “provide knowledge” are performed according to the current method used in the analysis target organization and the measure scenario verified by simulation. It is possible to set in detail.

  Next, details of the simulation apparatus 100 will be described with reference to FIG. FIG. 5A shows the hardware configuration of the simulation apparatus 100. As shown in FIG. 5A, the simulation apparatus 100 includes a CPU 90, a ROM 92, a RAM 94, a storage unit (HDD (Hard Disk Drive)) 96, a display unit 93, an input unit 95, and a portable storage medium drive. 99 etc. Each component of the simulation apparatus 100 is connected to the bus 98. The display unit 93 includes a liquid crystal display or the like, and the input unit 95 includes a keyboard or the like. In the simulation apparatus 100, the CPU 90 executes a program (including a simulation program) stored in the ROM 92 or the HDD 96 or a program (including a simulation program) read from the portable storage medium 91 by the portable storage medium drive 99. Thereby, the function of each part of FIG.5 (b) is implement | achieved.

  FIG. 5B shows a functional block diagram realized by the CPU 90 of the simulation apparatus 100. As illustrated in FIG. 5B, the CPU 90 executes the program, thereby causing the information input unit 30 as the reception unit, the specification unit, the reception unit, the simulation execution unit 32 as the calculation unit, and the simulation result as the display unit. The function as the output unit 34 is realized. FIG. 5B also shows the input information DB 40 and the result information DB 42 stored in the HDD 96.

  The information input unit 30 receives various types of information input by a user (hereinafter referred to as an operator) of the simulation apparatus 100 and stores it in the input information DB 40. The simulation execution unit 32 executes a simulation (model operation) based on the input information DB 40. Moreover, the simulation execution part 32 stores a simulation result in result information DB42. The simulation result output unit 34 displays the simulation result on the display unit 93.

  Next, details of the operation process of the model executed by the simulation execution unit 32 will be described along the flowchart of FIG. 6 with reference to other drawings as appropriate.

  As shown in FIG. 6, the simulation execution unit 32 generates a customer (S1), assigns a business worker (S2), dialogue between the customer and the business worker (S3), executes a policy scenario (S4), calculates a KPI value ( A process of repeating each process of S5) for a preset number of periods is executed. The term represents the unit time of business. Hereinafter, each process will be described in detail.

<Occurrence of customers (S1)>
First, the customer generation step (S1) will be described. In the customer generation step, the simulation execution unit 32 generates a customer for each unit time (period) of business according to a “customer generation pattern” preset by the operator and stored in the input information DB 40. It is assumed that the “customer generation pattern” is selected by the operator from “fixed” as shown in FIG. 7A or “busy” as shown in FIG. 7B. When a simulation other than those shown in FIGS. 7 (a) and 7 (b) is to be performed, the operator generates a customer generated by defining the number of customers generated per date and time as shown in FIG. 7 (c). A pattern is input from the input unit 95. In this case, the information input unit 30 stores the input information in the input information DB 40. Note that each customer 1, 2, 3,..., CN (CN is the number of customers) has a specific request (customer request) to be presented to the business staff when the customer is generated. The customer request will be described below.

(A) Overview of customer request The customer request has a tree structure for each of request categories 1, 2, 3,..., DN (DN is the number of request categories) indicating the contents of the inquiry as shown in FIG. It is represented by a structure composed of a plurality of requests whose resolution order is determined. Each node in the tree structure of FIG. 8A represents a customer request (sub-request). The tree structure shown in FIG. 8A represents the entire request that a customer can have, and each customer has a part of the request. When an employee processes one request, a request for a child node appears.

  Here, a specific example of the customer request tree structure will be described with reference to FIG.

  FIG. 8B shows an example of a tree structure of customer requests in an organization such as a customer support center. In an organization such as a customer support center, there are request categories such as “application for membership”, “charge inquiry”, “contract content change”, and “cancellation procedure”. Among these request categories, for example, in the “cancellation procedure”, as shown in FIG. 8B, the request is divided into two requests in the first branch. In response to a request to “cancel”, the next branch (second branch) is generated by performing the operation “confirmation of existence of penalty”. That is, when the employee indicates whether or not there is a penalty, the customer who agrees with it presents a request that “I want a document”, but the customer who cannot agree gives a request that “I want to postpone the cancellation”. Thereafter, if the employee performs the work for the customer request, the customer request is satisfied and the customer disappears.

  In this way, by expressing the customer request in a tree structure, it is possible to express a process specific to the service operation in which the request is gradually solved depending on the knowledge of the business staff.

  In the simulation, the operator of the simulation apparatus 100 does not input the meaning of each node as shown in FIG. 8B, but determines the number of branches (number of branches) and the number of requests for each branch. input. For example, if the information of the trees A to C as shown in FIG. 9A is input, the operator, as shown in FIG. 9B, the number of trees, the number of hierarchies of each tree, The number of requests for branching is input based on the customer service history and operator recognition. Thereby, a tree structure can be easily generated.

  Here, the larger the number of trees (required category number DN), the more customer request patterns that the organization should deal with. In addition, when there are many customer request patterns, it is required to improve the response performance by team differentiation. On the other hand, the number of leaf nodes in each tree (the number of nodes at the lowest end) expresses the diversity of requirements. As the number of leaf nodes increases, it means that there are various requests in one inquiry content, and a lot of knowledge is required for the business staff. Further, the distance (number of intermediate nodes) from the root node (uppermost node) to the leaf node represents the number of conversations for one customer. The longer this distance is, the more demands that a business person has to deal with a single customer, and thus a deeper knowledge of the specific requirements is required. This expresses the stickiness of the customer request.

  Thus, by using the tree structure, it is possible to express not only the amount of customer requests generated but also service service specific characteristics such as request diversity and stickiness.

  Note that the customer request does not have to have a tree structure, but may have a structure composed of a plurality of requests whose resolution order is determined.

(B) Customer requirements of each customer Next, customer requirements of each customer will be described. As shown in FIG. 10, each customer has one or more paths (paths indicated by bold lines) among the node paths in the tree. Hold as. Since the customer implicitly holds this path, the requests on the path are processed by the employee in order from the root node (topmost node) when interacting with the employee, and the customer request is gradually revealed. It will become. In other words, the customer cannot present a request for a leaf node (bottommost node) to the employee from the beginning.

  The path is uniquely determined by determining the leaf node held by the customer. The leaf nodes held by each customer are determined based on the “request determination probability”. FIG. 11A and FIG. 11B are input information necessary for determining the request determination probability, and are information input from the operator and stored in the input information DB 40 by the information input unit 30. First, as shown in FIG. 11A, the operator inputs an occurrence ratio for each request category (for each tree). As an example, in FIG. 11A, the generation rate of request category A (tree A) is 50%, and the generation rates of request categories B and C (tree B, C) are 30% and 20%, respectively. Yes. Further, as shown in FIG. 11B, the operator inputs the generation ratio of leaf nodes in each request category. The simulation execution unit 32 determines the occurrence probability of each leaf node from these two pieces of information. For example, in FIG. 11C, the occurrence probability of three leaf nodes (leaf node numbers 1 to 3) from the left of tree A is the occurrence rate of tree A (50%) × the occurrence rate of leaf nodes 1 to 3 ( (70%) × occurrence rate per leaf node (1/3) ≈0.117.

<Assignment of employee (S2)>
Next, the assignment (allocation) step (S2) of the employee will be described. The employee assignment step is executed by the simulation execution unit 32. As described above, the agents "customer", "worker", and "manager" are assumed to make autonomous decisions. .

  The manager who receives the inquiry from the customer assigns a business worker to the customer ((2) in FIG. 4). Each employee (operators 1, 2,..., SN) has (A) the team in charge and (B) the knowledge used for the work as attribute information, and the manager is based on this attribute information. Assign business personnel. The attribute information is input by the operator and stored in the input information DB 40 by the information input unit 30. Hereinafter, the attribute information will be described in detail.

(A) About the team in charge The team in charge is defined as shown in FIGS. 12 (a) and 12 (b). In FIG. 12A, it is defined which employee (1, 2,... SN) belongs to each team (1, 2,... TN). In FIG. 12B, one or more customer request categories that each team is in charge of are defined.

(B) Knowledge used for business “Knowledge used for business” includes (i) requirements processing knowledge and (ii) Know-who knowledge.

(i) Requirement processing knowledge Business staff understands customer requests and possesses knowledge to process those requests. This knowledge has the same structure as the customer request tree, and each employee has several paths in the tree as knowledge. For example, as shown in FIG. 13, a business employee holds a path (thick line) connecting nodes (sub-requests) indicated by black circles in the tree as knowledge. This means that the more passes the business staff holds, the more customer requests can be handled.

  There are the following two methods for determining the paths owned by the employees. First, for each customer request, how much of all employees in the organization understand the request (requirement understanding level) (see FIG. 14 (a)), and each employee requests the customer request. This is a method of estimating the path possessed by each employee, with the degree of knowledge possessed for each category (knowledge retention) (see FIG. 14B) as a constraint.

  As shown in FIG. 15, the second is a method in which the presence / absence of knowledge is directly input and defined for each pass of each employee. In the second method shown in FIG. 15, since it is necessary to actually interview each business person, it is preferable to adopt the first method when the interview time cannot be obtained.

  Note that the time required for request processing by each business employee is defined as shown in FIG. Note that the operator may individually set the time required for request processing by each worker.

(ii) Know-who knowledge When employees face customer requirements that they do not understand, they have knowledge of who to ask. At the start of the simulation, Know-who knowledge is determined based on the information “who to ask when you do not know everyday”. Note that this Know-who knowledge changes gradually according to the behavior of the employees.

  The above (i) request processing knowledge and (ii) Know-who knowledge are held as shared knowledge in a virtual database existing in the organization. The “shared knowledge” of the organization is set as shown in FIG. 17 at the start of the simulation. It is assumed that the knowledge selected as “shared knowledge” is knowledge held by at least one business worker.

  In the present embodiment, the manager assigns a business worker to the customer from information related to “the team in charge” and “knowledge used for business”. Hereinafter, a specific method of assignment will be described.

  First, the manager selects a “responsible team” corresponding to the request category of the customer who has inquired. Next, in the team in charge, among the vacant business staff, the business staff to be assigned is determined in consideration of the degree of “knowledge to be used for business” and the past customer service number. Specifically, the assignment rules of FIG. 18 are referred to as “person with a lot of required knowledge”, “person with a small amount of knowledge of request”, “person with a large number of customer services”, “person with a small number of customer services”, and “random”. Using the five patterns of customer assignment ratios, the operator to be assigned is determined. In addition to the above, a more detailed assignment rule may be determined by a measure scenario described later.

<Dialog between customer and employee (S3)>
Next, the dialogue step (S3) between the customer and the employee will be described. The employee assigned to the customer in step S2 solves the customer request using his / her knowledge. FIG. 19 is a flowchart showing the specific process of step S3. Note that the processing of step S3 is also executed by the simulation execution unit 32, but it is assumed that the agents "customer", "business worker", and "manager" autonomously make decisions as described above. To do.

  In the process of FIG. 19, first, the business worker understands the customer request using the request knowledge and performs the request response (step S31). In the present embodiment, the request response (S31) is performed in order from the request of the first branch of the customer request (tree) and repeated until the final branch (leaf node) (S33: No). Then, when the processing up to the final branch is completed (S33: Yes), the processing in FIG. 19 ends.

  For customer service, the maximum time “maximum response time” that can be spent on one customer is determined. If the processing up to the final branch does not end even when the maximum response time is reached, the customer service ends. At this time, if the ratio of the number of requests resolved among the number of requests (number of nodes) that the customer has exceeds a predetermined “acceptance threshold (0 to 1)”, it is assumed that the request is satisfied, The customer disappears. On the other hand, if the request is not satisfied, the customer disappears or revisits in accordance with a predetermined “revisit probability”. In the case of revisiting, an inquiry is randomly generated after the next term, and the worker processes a request that has not been resolved. It is assumed that the acceptance threshold can be changed according to the business content.

  Hereinafter, the request response in step S31 will be described in detail with reference to FIG.

  FIG. 20A shows the request process in step S31 in a flowchart. In the process of FIG. 20A, first, in step S311, the employee confirms the success / failure of matching between the request knowledge held by the employee and the customer request. In this case, as shown in FIG. 20 (b), if the request knowledge and the customer request match, the customer request can be processed. As shown in FIG. 20 (c), if the request knowledge and the customer request do not match, Means that the request cannot be processed. If the employee can process the request, in step S312, the request processing is performed using his / her request knowledge. The time required for request processing in this case (processing time t) is as shown in FIG. On the other hand, when the request cannot be processed, that is, when the knowledge required by the business staff is insufficient, an inquiry is made to the manager in step S313 to acquire the requested knowledge.

  Here, details of acquisition of required knowledge in step S313 will be described.

  In order to acquire the required knowledge, the employee performs the work of (i) getting other employees to help, or (ii) browsing the shared knowledge in the manual or database. The operator determines in advance the priorities of the knowledge acquisition methods shown in FIG. 21 from the daily activity status and stores them in the input information DB 40. Which method is to be prioritized is determined based on the priorities in FIG. Note that the priority order shown in FIG. 21 can be set for each employee.

  FIG. 22 is a flowchart showing a specific process flow of step S313.

  In the process shown in FIG. 22, first, in step S500, the employee makes an inquiry to the manager together with Know-who information corresponding to the corresponding request. Next, in step S501, the manager extracts a method with the highest priority among the knowledge acquisition methods shown in FIG. Next, in step S502, the manager determines whether the extracted method is browsing shared knowledge. If the determination here is negative, that is, if the method extracted in step S501 is “Get help from another employee” in FIG. 21, the process proceeds to step S504.

  In step S504, in the case of the extracted knowledge acquisition method (for example, “listen to the employee based on Know-who knowledge”), the manager corresponds to the corresponding employee based on the Know-who knowledge of the employee. Request. Next, in step S506, the manager determines whether the requested worker is free. That is, it is determined whether the requested employee is dealing with the customer or is cooperating with another employee. If the determination in step S506 is affirmative (if the hand is free), the process proceeds to step S508, and the manager determines whether the requested employee has the required knowledge. If the determination in step S508 is affirmative (if the requested knowledge is held), the process proceeds to step S510, a predetermined question time is added to the processing time t, and the process proceeds to step S512.

  In step S <b> 512, the manager determines whether or not to leave the response to the requested employee. Here, when the manager changes the customer response based on the predetermined customer response change probability shown in FIG. 23 (a), the manager who makes the inquiry also acts together, or The business person who made the inquiry decides whether to move to another customer service. If the determination in step S512 is negative, the process proceeds to step S514, and the manager changes the customer support person to the employee who has made an inquiry with the requested employee. As described above, when the process proceeds to step S514, the inquired employee can see the response of the requested employee, so that the inquired employee can acquire the required knowledge. On the other hand, if the determination in step S512 is affirmed, the process proceeds to step S516, and the manager changes the customer service person to the requested business employee. After step S514 or S516 is executed, the entire process of FIG. 22 is terminated, and the process proceeds to step S312 of FIG.

  In step S508, if the requested employee does not have the required knowledge, the process proceeds to step S523, and the manager makes a request to all applicable employees regarding the extracted method. Judging. If the determination in step S523 is negative, the process returns to step S504. If the determination is positive, the process proceeds to step S524. In step S524, the manager determines whether all the methods in FIG. 21 have been applied. If the determination in step S524 is affirmed, that is, if all the methods of FIG. 21 are applied, the employee develops the required knowledge in step S526. The requirement knowledge development in this case means knowledge generation activities such as making inquiries to other departments, listening to knowledge from employees in the organization in the past, and creating new correspondence methods with the organization manager. Since a predetermined time (required knowledge development time) is required for the employee to generate knowledge, a predetermined required knowledge development time is added to the processing time t in step S528. After step S528, all the processes in FIG. 22 are terminated, and the process proceeds to step S312 in FIG. On the other hand, if the determination in step S524 is negative, the process returns to step S501, the method having the next highest priority is extracted, and the processes in and after step S502 are executed.

  In the above-described step S506, when the requested worker is not available, the process proceeds to step S518. In step S518, the employee enters a waiting state, and in step S520, a predetermined waiting time is added to the processing time t. In step S522, it is determined whether the waiting time is within the range of the maximum value of the waiting time. If the waiting time is within the range of the maximum value (S522: Yes), the process returns to step S506. On the other hand, when the waiting time exceeds the maximum waiting time (S522: No), the process proceeds to step S523. In step S523, it is determined whether the manager has made a request to all the other business workers. If the determination is negative, the process returns to step S504. If the determination is positive, the process proceeds to step S524. The processing after step S524 is as described above. If the requested business person is not available (S508: No), the next business person may be requested without waiting (without executing step S518).

  By the way, if the determination in step S502 described above is affirmative, that is, if the method extracted in step S501 is “browsing shared knowledge”, the process proceeds to step S530. In the case of the priorities shown in FIG. 21, if the "worker based on knowledge-who knowledge" is not available, the next priority "view shared knowledge" is extracted. The determination in S502 is affirmed, and the process proceeds to step S530.

  When browsing shared knowledge, an employee searches for a predetermined time based on a “shared knowledge search rule” as shown in FIG. 23B, and the presence or absence of a discovery is determined. When a “measure scenario” exists separately, the discovery probability of required knowledge and the time required for discovery vary based on the information. After step S530, the process proceeds to step S532, the shared knowledge browsing time (see FIG. 23B) is added to the processing time t, and the process proceeds to step S534.

  In step S534, it is determined whether or not the employee has acquired the required knowledge. If acquired, the processing in FIG. 22 is terminated, and the process proceeds to step S312 in FIG. On the other hand, if the determination in step S534 is negative, the process returns to step S501, and the processes after step S501 are executed.

<Execution of measure scenario (S4)>
Returning to FIG. 6, in step S <b> 4, the simulation execution unit 32 executes the measure scenario. In this embodiment, three policy scenarios are prepared: (i) human resource allocation, (ii) human resource development, and (iii) information sharing. In this step S4, an event occurs according to the measure scenario to be implemented. It is assumed that information on the measure scenario is input in advance by the operator via the input unit 95 and stored in the input information DB 40 from the information input unit 30.

(i) Personnel resource allocation This measure is an event for changing the employees belonging to each team shown in FIG. FIG. 24A is a table showing an example of a human resource resource allocation scenario. In the event time set in FIG. 24A, the number of employees and the replacement of business personnel between teams occur.

(ii) Human resource development In human resource development measures, two methods are prepared: (a) review measures and (b) customer assignment measures. The “review measure” is also called a pairing measure, in which a veteran employee reviews the operation behavior of a new employee. The “customer assignment measure” is a method for assigning a customer request with no experience to an employee.

(a) Review measure FIG. 24B shows an input example of a review measure scenario. In the review measure scenario, as shown in FIG. 24B, a reviewing worker (reviewer), a worker to be reviewed (reviewee), a knowledge transfer probability, and a reviewer's action are input. The review event is started when the review subject is assigned to the customer, and the reviewer acts together until the review subject finishes dealing with the customer. When the reviewer is not available, the customer is not assigned to the review subject. At the time of review, request processing is performed based on the knowledge of the reviewer, and knowledge of the request processing used there is transmitted to the review subject according to the knowledge transfer probability.

(b) Customer Assignment Policy FIG. 24 (c) shows an input example of a customer assignment policy scenario. In the customer assignment measure scenario, as shown in FIG. 24C, a customer who has a request that the subject has never experienced is assigned to the assignee at the time of event occurrence.

(iii) Information sharing There are two methods for information sharing: (a) Know-how sharing and (b) Know-who search.

(a) Know-how sharing FIG. 25A shows an input example of a Know-how sharing scenario. An employee registers Know-how when a specific event occurrence condition is satisfied. Information registration time is required for Know-how registration. The employee cannot take any other action during that time. In addition, information retrieval takes a longer time as the number of registered Know-how for each request category increases. This information search time is added when browsing the shared knowledge in step S3.

(b) Know-who search FIG. 25B shows an input example of a Know-who search measure scenario. The Know-who search allows you to search for who you should ask based on the processing status of past employees. When there is no knowledge for request processing, each employee takes an action of asking a question of “the person who listens with the highest priority” in FIG. 25B regardless of FIG.

<KPI value calculation (S5)>
Returning to FIG. 6, in the next step S5, the simulation executing unit 32 calculates a KPI value. In the simulation, as a simulation result, an evaluation index (KPI value) of an organization in which the operator is interested is calculated in time series. Evaluation indicators include, for example, the satisfaction rate of the needs (the ratio that can be satisfied among all the requests held by the customer), the response time per customer (the time required to solve one customer's request), the knowledge retention rate (the customer Possession of knowledge necessary to meet the requirements of The needs fulfillment rate is included in the above-mentioned “customer KPI”, the response time per customer is included in the above-mentioned “business process KPI”, and the knowledge retention rate is included in the above-mentioned “learning and growth KPI”. included. In the simulation, depending on the probabilistic factors, the simulation results are different even for the same measure. Therefore, the simulation is repeatedly performed several tens to several hundreds of times to calculate all the simulation results (KPI values). The KPI value calculated in step S5 is output via the display unit 93 and the like by the simulation result output unit 34 in FIG. 5B.

  FIG. 26 shows an example of the simulation setting. In this example, as shown in FIG. 26, as an actual atypical business organization setting, as shown in FIG. 26, organization structure, customer category, occurrence probability of each request, processing time of each request, demand threshold of request processing, revisit probability, assignment rule Suppose that knowledge acquisition rules and customer service change rules are set.

  When the simulation execution unit 32 performs the simulation based on the simulation setting, it is possible to reproduce the situation occurring in the atypical business organization. For example, it can be reproduced that it takes a long time to search for a worker who has knowledge in an atypical task. FIG. 27A shows the “customer reception time” and “time to search for a question partner” of all the workers for 12 months as simulation results output by the simulation result output unit 34. From this table, it can be seen that a little less than 10% of the customer service time is spent searching for questions. In addition, FIG. 27 (b) shows the number of customers after the number of service workers. From this table, it is necessary that more than 20% of all customers need to be serviced by multiple business workers. I understand.

  In addition, in the simulation, it is possible to reproduce that in a non-standard business organization, a specific employee is asked only questions and cannot respond to customers. In FIG. 27C, the customer assigned to each employee at the time when each employee receives a question (that is, the time for enjoying knowledge) as the simulation result output by the simulation result output unit 34. It is a graph which shows the sum total for several minutes, and the sum total for the number of customers allocated to each business worker of the time (namely, time which solves a customer's request | requirement) of customer reception. From this figure, it can be seen that the businessmen with high knowledge amount in the team (businessmen 4, 5, 9, 10) receive more questions than other businessmen on the same team. You can see that there is little time.

  Further, in the simulation, under the simulation setting of FIG. 26, a measure scenario such as moving the business workers 1 and 2 to the team 2 can be executed in order to reduce the load on the team 2 having a large number of customers. Note that the simulation result output unit 34 may output data other than that shown in FIG. 27C as the simulation result.

  FIGS. 28 and 29 show the need satisfaction rate, knowledge retention rate, and per customer before and after the execution of the measure scenario output by the simulation result output unit 34 (after movement). A graph of the change in response time is shown. The operator can comprehensively determine whether or not the measure to be executed is appropriate from a plurality of viewpoints by referring to the graphs of FIGS.

  As described above in detail, according to the present embodiment, the information input unit 30 is owned by the customer in the simulation apparatus that simulates the change of knowledge held by each of the plurality of business workers corresponding to the customer's request. The request (FIG. 8) to be received, the knowledge for solving the request (FIG. 13) held by each of a plurality of business workers, and the rules (FIG. 21) for acquiring knowledge among the plurality of business workers are received ( S1, S2), the simulation execution unit 32 compares the request held by the customer with the knowledge held by any of the employees assigned to the customer among a plurality of employees, and lacks in solving the request In addition to specifying the knowledge that is being performed (S311), based on the rules for acquiring knowledge, knowledge that is insufficient for solving the request is received from any other worker (S313). Thereby, it becomes possible to calculate the change in the knowledge amount of the worker in the non-standard work by simulation.

  Further, according to the present embodiment, the information input unit 30 has a request (FIG. 8) held by each of a plurality of customers and a knowledge for solving the request held by each of a plurality of business workers (FIG. 13). And a rule (FIG. 21) for acquiring knowledge among the plurality of business workers and a rule for time for solving the request (FIG. 16), and the simulation executing unit 32 selects any one of the plurality of customers. Compare the request held by the customer with the knowledge held by any of the employees assigned to one of the employees, and identify the knowledge that is insufficient to resolve the request ( Along with S311), based on the rules for acquiring knowledge, the time required for any employee to acquire knowledge that is insufficient to solve the request from other employees is calculated and held by any customer. Resolve the request, the knowledge held by any employee, and the request Based on the time rules, either business personnel to calculate the time to solve the requirements of any of the customer (S313). Then, the simulation result output unit 34, for each of a plurality of business workers, totals the number of allocated customers for the time to solve the request and the allocated number of customers for the time to enjoy the knowledge. And the information indicating the percentage of the total are displayed on the same screen (FIG. 27C). As a result, it becomes possible to grasp the work status of each worker in the atypical work.

  Further, according to the present embodiment, the simulation execution unit 32 generates a customer in the organization (S1), assigns a predetermined worker to the generated customer (S2), and assigns a response to the customer's request. (S3) A simulation is executed and a measure for the operator to acquire knowledge necessary for responding to the customer's request is executed (S4), and the simulation result output unit 34 displays the simulation result. Output (S5). In this case, the customer's request is expressed as a structure composed of a plurality of sub-requests whose resolution order is determined (FIG. 8), and the simulation execution unit 32 corresponds to a request held by a predetermined business worker. If there is a lack of knowledge for the purpose, the action based on the rule for requesting cooperation from other employees (Fig. 21) is executed, and in response to the request from other employees for cooperation, Knowledge transfer from the employee to the predetermined employee is caused (S514). In this way, it is possible to express the difficulty of responding to individual customer requests by expressing the customer requests as a structure composed of a plurality of sub-requests whose resolution order is determined. In addition, when the simulation execution unit 32 lacks the knowledge of a predetermined employee, the simulation execution part 32 executes an action based on a rule that requests other employees to cooperate and requests other employees to cooperate. In response to the above, by giving and receiving knowledge from other employees to a predetermined operator, it is possible to express knowledge exchange between employees in atypical operations. Therefore, in this embodiment, the number of employees who deal with the characteristics of atypical work (characteristics such as that it takes a long time to search for knowledgeable workers, questions that cannot be handled by customers, etc.), customers for each customer It can be expressed by the time required to solve the request, the time required for cooperation with other workers, and an indicator of the amount of knowledge of the workers. It becomes possible to evaluate from the change of the KPI value.

  Further, in this embodiment, the simulation execution unit 32 (manager) assigns a worker based on a rule for assigning a worker regardless of the content of the customer's request, so that the request content can be solved in advance in an atypical task. It is possible to express that it is not clear who the employee is. As a result, it is possible to accurately perform a simulation when a measure is executed in an atypical task.

  In the present embodiment, the simulation execution unit 32 (manager) changes the behavior of the predetermined employee when the predetermined employee assigned to the customer requests cooperation from another employee ( Based on FIG. 23), the response of a predetermined employee to the customer's request is changed. As a result, it is possible to express a quick response at a customer contact point in a customer-facing business, and therefore it is possible to accurately perform a simulation when a measure is executed in an atypical business.

  In the above-described embodiment, a case has been described in which a business worker is assigned based on a rule for assigning a business worker regardless of the content of a customer request. However, the present invention is not limited to this. For example, it is good also as assigning a business person in consideration of a customer's request contents. In the above-described embodiment, the case has been described in which the employee's response to the customer's request is changed based on the rule of FIG. 23. However, the present invention is not limited to this, and rules other than FIG. The response of the employee may be determined based on the rule of acting with the employee).

  In the above-described embodiment, a probability that a business worker acquires knowledge from another business worker, a probability that a customer request can be processed, and the like may be set and used in the simulation. Thereby, simulation accuracy can be improved.

  The above processing functions can be realized by a computer. In that case, a program describing the processing contents of the functions that the processing apparatus should have is provided. By executing the program on a computer, the above processing functions are realized on the computer. The program describing the processing contents can be recorded on a computer-readable recording medium (except for a carrier wave).

  When the program is distributed, for example, it is sold in the form of a portable recording medium such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory) on which the program is recorded. It is also possible to store the program in a storage device of a server computer and transfer the program from the server computer to another computer via a network.

  The computer that executes the program stores, for example, the program recorded on the portable recording medium or the program transferred from the server computer in its own storage device. Then, the computer reads the program from its own storage device and executes processing according to the program. The computer can also read the program directly from the portable recording medium and execute processing according to the program. Further, each time the program is transferred from the server computer, the computer can sequentially execute processing according to the received program.

  The above-described embodiment is an example of a preferred embodiment of the present invention. However, the present invention is not limited to this, and various modifications can be made without departing from the scope of the present invention.

DESCRIPTION OF SYMBOLS 30 Information input part 32 Simulation execution part 34 Simulation result output part 100 Simulation apparatus

Claims (7)

In a simulation device that simulates changes in knowledge held by each of a plurality of employees corresponding to customer requirements,
A reception unit that accepts the request held by the customer, the knowledge that each of the plurality of business employees holds, the knowledge that solves the request, and the rules that acquire knowledge among the plurality of business workers;
Compare the request held by the customer with the knowledge held by any of the employees assigned to the customer among the plurality of operators, and identify knowledge that is insufficient to resolve the request Specific part to do,
Based on the rules for acquiring the knowledge, a receiving unit that allows any one of the employees to enjoy the knowledge that is insufficient to solve the request;
Equipped with a,
The request held by the customer is expressed as a structure including a plurality of sub-requests whose resolution order is determined.
A simulation apparatus characterized by that.   The simulation apparatus according to claim 1, wherein the request held by the customer is expressed as a tree structure including a plurality of sub-requests whose resolution order is determined. A request held by each of a plurality of customers, a knowledge held by each of a plurality of business workers, a rule for acquiring knowledge among the plurality of business workers, and a solution of the request A reception unit that accepts time rules,
The request held by any one of the plurality of customers is compared with the knowledge held by any of the employees assigned to any of the plurality of employees, A specific part for identifying knowledge lacking in solving the request;
Based on the rules for acquiring the knowledge, the time required for the operator to acquire from the other operator the knowledge that is insufficient to solve the request is calculated and held by the customer. Based on the request, the knowledge held by any one of the employees, and the rules for the time to solve the request, the calculation for calculating the time at which the any employee solves the request of any one of the customers And
For each of the plurality of business workers, the ratio of the total number of allocated customers for the time to solve the request and the total number of allocated customers for the time to enjoy the knowledge A display unit for displaying information to be displayed on the same screen;
A simulation apparatus comprising: In a simulation method in which a computer simulates a change in knowledge held by each of a plurality of employees corresponding to customer requests,
Accepting the request held by the customer, the knowledge of each of the plurality of business workers, the knowledge for solving the request, and the rules for acquiring knowledge among the plurality of business workers,
Compare the request held by the customer with the knowledge held by any of the employees assigned to the customer among the plurality of operators, and identify knowledge that is insufficient to resolve the request And
Based on the rules for acquiring the knowledge, let any one of the employees enjoy the knowledge that is insufficient to solve the request,
The computer executes the process ;
The request held by the customer is expressed as a structure including a plurality of sub-requests whose resolution order is determined.
A simulation method characterized by that . Computer
A request held by each of a plurality of customers, a knowledge held by each of a plurality of business workers, a rule for acquiring knowledge among the plurality of business workers, and a solution of the request Accept time rules,
The request held by any one of the plurality of customers is compared with the knowledge held by any of the employees assigned to any of the plurality of employees, Identify knowledge that is insufficient to resolve the request;
Based on the rules for acquiring the knowledge, the time required for the operator to acquire from the other operator the knowledge that is insufficient to solve the request is calculated and held by the customer. Based on the request, the knowledge held by any one of the employees, and the rule of time for solving the request, the time for which the any employee solves the request of any one of the customers is calculated,
For each of the plurality of business workers, the ratio of the total number of allocated customers for the time to solve the request and the total number of allocated customers for the time to enjoy the knowledge Display the information shown on the same screen,
A simulation method for executing processing. In a simulation program that causes a computer to simulate changes in knowledge held by each of a plurality of employees corresponding to customer requirements,
Accepting the request held by the customer, the knowledge of each of the plurality of business workers, the knowledge for solving the request, and the rules for acquiring knowledge among the plurality of business workers,
Compare the request held by the customer with the knowledge held by any of the employees assigned to the customer among the plurality of operators, and identify knowledge that is insufficient to resolve the request And
Based on the rules for acquiring the knowledge, let any one of the employees enjoy the knowledge that is insufficient to solve the request,
Causing the computer to perform processing ;
The request held by the customer is expressed as a structure including a plurality of sub-requests whose resolution order is determined.
Simulation program that is characterized in that. On the computer,
A request held by each of a plurality of customers, a knowledge held by each of a plurality of business workers, a rule for acquiring knowledge among the plurality of business workers, and a solution of the request Accept time rules,
The request held by any one of the plurality of customers is compared with the knowledge held by any of the employees assigned to any of the plurality of employees, Identify knowledge that is insufficient to resolve the request;
Based on the rules for acquiring the knowledge, the time required for the operator to acquire from the other operator the knowledge that is insufficient to solve the request is calculated and held by the customer. Based on the request, the knowledge held by any one of the employees, and the rule of time for solving the request, the time for which the any employee solves the request of any one of the customers is calculated,
For each of the plurality of business workers, the ratio of the total number of allocated customers for the time to solve the request and the total number of allocated customers for the time to enjoy the knowledge Display the information shown on the same screen,
A simulation program that executes processing.

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