DESC:FIELD
The present disclosure relates to the field of systems that predict career paths of employees within an organization.
DEFINITIONS
As used in the present disclosure, the following term are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicates otherwise.
The expression ‘user’ used hereinafter in this specification refers to, but is not limited to, a person(s) who applies for visa.
These definitions are in addition to those expressed in the art.
BACKGROUND
Career ascension is a very important factor in the professional life of an employee. Ascension in the employee’s career is only possible when every step forward is taken keeping in mind the strengths, weaknesses, and aptitude of the employee. The current trend in performance management, however, generally involves a discussion between the employee and a manager of the employee, subsequent to which the employee is given some immediate short term objective(s) based on the current needs of the organization. As such, the employee is at times forced to perform activities which are not helpful in his/her own career ascension.
Another path to career ascension generally involves the organization posting all the available positions on an Internal Job Portal (IJP) of the organization. Interested employees can then contact the respective talent acquisition executives for further discussions. However, this method is only helpful to those individuals who are actively looking to change roles. In doing so, the talent acquisition executives may miss out potential candidates who possess attributes that are better suited for the available position but have not thought of changing roles. This is also not desired.
Hence, in order to overcome the aforementioned drawbacks, there is need of a system and method for predicting probable career paths of employees, which can assist the employees to focus not only on the short term objectives of the organization but also discuss and plan for their long term performance and career development.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative.
An object of the present disclosure is to provide a system for predicting probable career paths of employees in an organization.
Another object of the present disclosure is to provide a system for predicting probable career paths of employees in an organization, which provides a ready reckoner, to an employee and his line manager, on a next possible position suitable for the employee.
Yet another object of the present disclosure is to provide a system for predicting probable career paths of employees in an organization, which facilitates robust career planning and development along with proactive and personalized approach in learning.
Still another object of the present disclosure is to provide a system for predicting probable career paths of employees in an organization, which enables short term as well as long term performance management and career development.
A further object of the present disclosure is to provide a system for predicting probable career paths of employees in an organization, which reduces ‘turn-around-time’ of recruiters for each critical position thereby resulting in substantial cost saving and ease in succession planning for the organization.
Furthermore, an object of the present disclosure is to provide a system for predicting probable career paths of employees in an organization, which enables employees to practice and sharpen their skills and competencies on the job.
Additionally, an object of the present disclosure is to provide an integrated system for predicting probable career paths of employees in an organization.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure discloses a computer implemented system for predicting probable career path of employees. The system includes a memory, a processor, a database, a query handler module, a model selection module, a career planning module, a succession planning module, an education planning module, and a presentation module. The query handler module further includes a query analyser, and an assignor module. The career planning module includes a first extractor module, and a career path generator. The succession planning module includes an employee selection module, a second extractor module, and a succession path generator.
The memory is configured to store a plurality of pre-defined system processing rules. The processor is configured to cooperate with the memory to receive the pre-defined system processing rules and further configured to generate system processing commands. The database is configured to store employee data corresponding to a plurality of employees and a plurality of predictive models. The query handler module is configured to receive a query from an employee and further configured to direct the query. The query analyser is configured to analyse the query to identify a query type. The assignor module is configured to direct the query based on the query type. The model selection module is configured to cooperate with the query analyser and the database, and further configured to select a predictive model from the plurality of predictive models based on the query type. The career planning module is configured to cooperate with the query handler module and the model selection module to receive the query type and the selected predictive model respectively. The first extractor module is configured to cooperate with the database to extract employee data corresponding to the respective employee. The career path generator is configured to generate career path based on the extracted employee data and the selected predictive model. The succession planning module is configured to cooperate with the query handler module to receive the query. The employee selection module is configured to cooperate with the database to select eligible employees based on the comparison of the employees data of the plurality of employees with a succession job details. The second extractor module is configured to cooperate with the database to extract employees data corresponding to the respective eligible employees. The succession path generator is configured to generate succession path for the eligible employees based on the extracted employee data and the selected predictive model. The education planning unit is configured to cooperate with the career path generator and the succession path generator to plan education path based on the career path and the succession path, respectively. The presentation module is configured to cooperate with the career path generator, the succession path generator and the education planning module and further configured to generate a graphical presentation of the career path, the succession path and the education path, respectively.
In an embodiment, the system includes a model generation module, which is configured to generate the plurality of predictive models by applying fuzzy logic and artificial intelligence on the employee data, organization data and historical data of the employees.
In one embodiment, the query type includes a career path query and a succession path query.
In another embodiment, the education path is based on adaptive learning approach.
The present disclosure also envisages a method predicting probable career path of employees comprising the following steps:
• storing a plurality of pre-defined system processing rules, in a memory;
• receiving the pre-defined system processing rules and generating system processing commands, by a processor;
• storing employee data corresponding to a plurality of employees and a plurality of predictive models, by a database;
• receiving a query from an employee, by a query handler module;
• analysing the query to identify a query type, by a query analyser;
• directing the query based on the query type, by an assignor module;
• selecting a predictive model from the plurality of predictive models based on the query type, by a model selection module;
• receiving the query type and the selected predictive model respectively, by a career planning module;
• extracting employee data corresponding to the respective employee, by a first extractor module;
• generating career path based on the extracted employee data and the selected predictive model, by a career path generator;
• receiving the query, by a succession planning module;
• selecting eligible employees based on the comparison of the employees data of the plurality of employees with a succession job details, by an employee selection module;
• extracting employees data corresponding to the respective eligible employees, by an second extractor module;
• generating succession path for the eligible employees based on the extracted employee data and the selected predictive model, by a succession path generator;
• planning education path based on the career path and the succession path, respectively, by an education planning module; and
• generating a graphical presentation of the career path, the succession path and the education path, respectively, by a presentation module.

In an embodiment, the step of storing employee data corresponding to a plurality of employees and a plurality of predictive models include a step of generating the plurality of predictive models by applying fuzzy logic and artificial intelligence on the employee data, organization data and historical data of the employees, by a model generation module.

In one embodiment, the education path is based on adaptive learning approach.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
A system and method for predicting probable career paths of employees in an organization, of the present disclosure will now be described with the help of the accompanying drawing, in which:
Figure 1 illustrates a schematic block diagram of a system for predicting probable career paths of employees, in accordance with an embodiment of the present disclosure.
Figures 2A and 2B illustrate a flow diagram showing steps performed by the system for predicting probable career paths of employees of Figure 1, in accordance with an embodiment of the present disclosure.
LIST OF REFERENCE NUMERALS:
Reference Numeral Reference
100 system for predicting probable career paths of employees
10 memory
12 processor
20 database
30 query handler module
32 query analyser
34 assignor module
40 a model selection module
50 career planning module
52 first extractor module
54 career path generator
60 succession planning module
62 employee selection module
64 second extractor module
66 succession path generator
70 education planning module
80 presentation module

DETAILED DESCRIPTION
Figure 1 of the accompanying drawing illustrates a schematic diagram of a system (100) (herein after referred as system). The system (100) comprising a memory (),a processor (12), a database (20), a query handler module (30), a model selection module (40), a career planning module (50), a succession planning module (60) and an education planning module (70).
The memory (10) is configured to store a plurality of pre-defined system processing rules. The memory (10) may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or a non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the predefined system processing rules includes registration rules, login rules, visa application rules, authentication rules, and score assignment rules.
The processor (12) is configured to cooperate with the memory (10) to receive the pre-defined system processing rules. The processor (12) is further configured to generate system processing commands in order to control the modules of the system (100). The processor (12) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor (12) may be configured to fetch and execute computer-readable instructions that may be stored in the memory (10).
The database (20) is configured to store employee data corresponding to a plurality of employees and a plurality of predictive models. In an embodiment, the employee data includes employee id, job experience, education qualifications, certifications, interests, achievements, strengths, weakness, age, manager rating, self-rating, short-term goal of employee, long-term goal of employee and the like.
In an embodiment, the system includes a model generation module (14). The model generation module (14) is configured to generate the plurality of predictive models by applying the fuzzy logic and the artificial intelligence on employee data, organization data and historical data of the employees in the organization. In an embodiment, the organization data includes current turnover, future turnover, current technology on which organization is working, future technology on which the organization plans to work, short term goals of the organization, long term goals of the organization, future job requirement, and the like. In an embodiment, the historical data of the employees includes career path opted by all the previous and current employees with similar profile or role. In an embodiment, the predictive models are of dynamic nature, i.e. they are modified based on the query.
The query handler module (30) is configured to receive a query from an employee. The query handler module (30) includes a query analyser (32) and an assignor module (34). The query analyser (32) is configured to analyse the query to identify a query type. In an embodiment, the queries are of two types a career path query and a succession path query. The career path query is usually asked by an employee of the organization, who wants to know the career option/ career path, he/she can opt in the future. The succession path query is usually asked by the administrator/ HR, who want to identify a best-fit employee from the plurality of the employees, to replace the existing employee, in the future. The assignor module (34) is configured to direct the query based on the query type.
The model selection module (40) is configure to cooperate with the query analyser (32) to receive the query type and with the database (20) to select a predictive model from the plurality of predictive models. The model selection module (40) is further configured to select a predictive model from the plurality of predictive models based on the query type.
The career planning module (50) is configured to cooperate with the query handler module (30) and the model selection module (40) to receive the query type and the selected predictive model respectively. The career planning module (50) includes a first extractor module (52), and a career path generator (54). The first extractor module (52) is configured to cooperate with the database (20) to extract employee data corresponding to the respective employee. The career path generator (54) is configured to generate career path based on the extracted employee data and the selected predictive model. In an embodiment, the career path include a series of steps/ choices/ decision, the employee has to make to achieve his/her long term/ short term goals, which are in-lined with the organization’s short-term and long term goals. The career path may also include the skills and abilities, which are needs to be acquired, so that the employee will be eligible for his/her future role in the organization.
The succession planning module (60) is configured to cooperate with the query handler module (30) to receive the query. The succession planning module (60) includes an employee selection module (62), a second extractor module (64) and a succession path generator (66). The employee selection module (62) is configured to cooperate with the database (20) to select eligible employees based on the comparison of the employee data of the plurality of employees with a succession job details. In an embodiment, the succession job details includes
The second extractor module (64) is configured to cooperate with the database (20) to extract employee data corresponding to the respective eligible employees. The succession path generator (66) is configured to generate succession path for the eligible employees based on the extracted employee data and the selected predictive model. In an embodiment, the succession path includes a series of steps/ choices/ decision, the eligible employees has to take to meet the requirement of the succession job. The succession path may also include the skills and abilities, which are needs to be acquired by the eligible employees. In an embodiment, the succession planning module (60) is also configured to identify employees who have all the required skills and competencies, with respect to a current job opening, available within the organization. This enables the recruiters to view a list of possible candidates in real-time and thus reduce the ‘turn-around-time’ for each critical position within the organization. This may result in substantial cost saving for the organization.
The education planning module (70) is configured to cooperate with the career path generator (54) and the succession path generator (66) to receive the career path and the succession path. The education planning module (70) is further configured to plan education path based on the career path and the succession path, respectively. In an embodiment, the education planning module (70) uses an adaptive learning approach to provide recommendations for learning programs especially suited to fill the gaps in an employee’s skills & competencies. In an embodiment, the education planning module (70) also assigns short term assignments posted in the ‘internal market place’ portal based on the employee’s skills, to enable the employee to practice and sharpen his/her skills & competencies on the job.
The presentation module (80) is configured to cooperate with the career path generator (54), succession path generator (66) and the education planning module (70). The presentation module (80) is further configured to generate a graphical presentation of the career path, the succession path and the education path, respectively.
At block 202, storing a plurality of pre-defined system processing rules. In an embodiment, the memory (10) stores a plurality of pre-defined system processing rules.
At block 204, receiving the pre-defined system processing rules and generating system processing commands. In an embodiment, the processor (12) receives the pre-defined system processing rules and generates system processing commands.
At block 206, storing employee data corresponding to a plurality of employees and a plurality of predictive models. In an embodiment, the database (20) stores employee data corresponding to a plurality of employees and a plurality of predictive models.
At block 208, receiving a query from an employee. In an embodiment, the query handler module (30) receives a query from an employee.
At block 210, analysing the query to identify a query type. In an embodiment, the query analyser (32) analyses the query to identify a query type.
At block 212, directing the query based on the query type. In an embodiment, the assignor module (34) directs the query based on the query type.
At block 214, selecting a predictive model from the plurality of predictive models based on the query type. In an embodiment, the model selection module (40) selects a predictive model from the plurality of predictive models based on the query type.
At block 216, receiving the query type and the selected predictive model respectively. In an embodiment, the career planning module (50) receives the query type and the selected predictive model respectively.
At block 218, extracting employee data corresponding to the respective employee. In an embodiment, the first extractor module (52) extracts employee data corresponding to the respective employee.
At block 220, generating career path based on the extracted employee data and the selected predictive model. In an embodiment, the career path generator (54) generates career path based on the extracted employee data and the selected predictive model.
At block 222, receiving the query. In an embodiment, the succession planning module (60) receives the query.
At block 224, selecting eligible employees based on the comparison of the employees data of the plurality of employees with a succession job details. In an embodiment, the employee selection module (62) selects eligible employees based on the comparison of the employee data of the plurality of employees with a succession job details.
At block 226, extracting employee data corresponding to the respective eligible employees. In an embodiment, the second extractor module (64) extracts employee data corresponding to the respective eligible employees.
At block 228, generating succession path for the eligible employees based on the extracted employee data and the selected predictive model. In an embodiment, the succession path generator (66) generates succession path for the eligible employees based on the extracted employee data and the selected predictive model.
At block 230, planning education path based on the career path and the succession path, respectively. In an embodiment, the education planning module (70) plans education path based on the career path and the succession path, respectively.
At block 232, generating a graphical presentation of the career path, the succession path and the education path, respectively. In an embodiment, a presentation module (80) generates a graphical presentation of the career path, the succession path and the education path, respectively.
TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a system for predicting probable career paths of employees in an organization, which:
• provides a ready reckoner, to an employee and his line manager, on a next possible position suitable for the employee;
• facilitates robust career planning and development along with proactive and personalized approach in learning;
• enables short term as well as long term performance management and career development;
• reduces ‘turn-around-time’ of recruiters for each critical position thereby resulting in substantial cost saving and ease in succession planning for the organization;
• enables employees to practice and sharpen their skills and competencies on the job; and
• facilitates improved internal mobility and cross functional collaboration.
The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein
The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

,CLAIMS:WE CLAIM:
1. A computer implemented system (100) for predicting probable career path of employees, said system (100) comprising:
a memory (10) configured to store a plurality of pre-defined system processing rules;
a processor (12) configured to cooperate with the memory (10) to receive the pre-defined system processing rules and further configured to generate system processing commands;
a database (20) configured to store employee data corresponding to a plurality of employees and a plurality of predictive models;
a query handler module (30) configured to receive a query from an employee, said query handler module (30) includes:
a query analyser (32) configured to analyse the query to identify a query type; and
an assignor module (34) configured to direct said query based on the query type;
a model selection module (40) configured to cooperate with the query analyser (32) and the database (20), and further configured to select a predictive model from the plurality of predictive models based on the query type;
a career planning module (50) configured to cooperate with the query handler module (30) and the model selection module (40) to receive said query type and said selected predictive model respectively, said career planning module (50) includes:
a first extractor module (52) configured to cooperate with the database (20) to extract employee data corresponding to the respective employee; and
a career path generator (54) configured to generate career path based on the extracted employee data and the selected predictive model;
a succession planning module (60) configured to cooperate with the query handler module (30) to receive said query, said succession planning module (60) includes:
an employee selection module (62) configured to cooperate with the database (20) to select eligible employees based on the comparison of the employees data of the plurality of employees with a succession job details;
a second extractor module (64) configured to cooperate with the database (20) to extract employees data corresponding to the respective eligible employees; and
a succession path generator (66) configured to generate succession path for the eligible employees based on the extracted employee data and the selected predictive model.
an education planning module (70) configured to cooperate with the career path generator (54) and the succession path generator (66) to plan education path based on the career path and the succession path, respectively;
a presentation module (80) configured to cooperate with the career path generator (54), succession path generator (66) and the education planning module (70) and further configured to generate a graphical presentation of the career path, the succession path and the education path, respectively.

2. The system as claimed in claim 1, wherein the system (100) includes a model generation module (14) configured to generate the plurality of predictive models by applying fuzzy logic and artificial intelligence on the employee data, organization data and historical data of the employees.
3. The system as claimed in claim 1, wherein the query type includes a career path query and a succession path query.

4. The system as claimed in claim 1, wherein the education path is based on an adaptive learning approach.

5. A computer implemented method for predicting probable career path of employees, said method comprising the following steps:
storing a plurality of pre-defined system processing rules, in a memory (10);
receiving the pre-defined system processing rules and generating system processing commands, by a processor (12);
storing employee data corresponding to a plurality of employees and a plurality of predictive models, by a database (20);
receiving a query from an employee, by a query handler module (30);
analysing the query to identify a query type, by a query analyser (32);
directing said query based on the query type, by an assignor module (34);
selecting a predictive model from the plurality of predictive models based on the query type, by a model selection module (40);
receiving said query type and said selected predictive model respectively, by a career planning module (50);
extracting employee data corresponding to the respective employee, by a first extractor module (52);
generating career path based on the extracted employee data and the selected predictive model, by a career path generator (54);
receiving said query, by a succession planning module (60);
selecting eligible employees based on the comparison of the employees data of the plurality of employees with succession job details, by an employee selection module (62);
extracting employees data corresponding to the respective eligible employees, by a second extractor module (64);
generating succession path for the eligible employees based on the extracted employee data and the selected predictive model, by a succession path generator (66); and
planning education path based on the career path and the succession path, respectively, by an education planning module (70); and
generating a graphical presentation of the career path, the succession path and the education path, respectively, by a presentation module (80).

6. The method as claimed in claim 5, wherein the step of storing employee data corresponding to a plurality of employees and a plurality of predictive models include a step of generating the plurality of predictive models by applying fuzzy logic and artificial intelligence on the employee data, organization data and historical data of the employees, by a model generation module (14).

7. The method as claimed in claim 5, wherein the education path is based on adaptive learning approach.