Claims:We claim:

1. A method implemented by a server for optimizing an organizational process, the method comprising:
identifying a number of steps in the organizational process;
identifying one or more assignees corresponding to the number of steps respectively, wherein the one or more assignees are selected from an organizational matrix; and
optimizing the number of steps based on a model, wherein the optimizing comprises:
eliminating at least one step in the number of steps; or
adding at least one step to the number of steps.

2. The method as claimed in claim 1, wherein optimizing the number of steps based on the model comprises:
retrieving historic data related to the organizational process, the historic data including one or more actions performed by the one or more assignees corresponding to one or more inputs provided by one or more users, wherein the one or more users are included in the organizational matrix;
analysing the historic data, for each step in the number of steps, to identify the number of successful instances based on the actions performed by the one or more assignees and the number of unsuccessful instances based on the actions performed by the one or more assignees; and
creating a correlation between the one or more inputs provided by the one or more users, the one or more actions performed by the one or more assignees, and identify the number of successful instances and the number of unsuccessful instances.

3. The method as claimed in claim 1, wherein the method comprises providing, for each step in the number of steps, a score to the one or more users based on the analysis of the historic data, wherein the number of steps is optimized based on the score, wherein:
at least one step in the number of steps is eliminated for a user where the score corresponding to the user is equal or greater than a predetermined score; and
the number of steps is unaltered or at least one step is added to the number of steps for a user where the score corresponding to the user is less than the predetermined score.

4. The method as claimed in claim 3, wherein the method comprises:
monitoring whether the eliminated step is reconsidered by the one or more assignees; and
when the eliminated step is reconsidered, storing the reconsideration instance in a performance matrix, wherein the performance matrix is stored in a repository and comprises a number of successful instances and a number of unsuccessful instances of the optimization performed based on the model.

5. The method as claimed in claim 4, wherein the method comprises:
monitoring whether the added step is removed by the one or more assignee; and
when the added step is removed, storing the removal instance in the performance matrix.

6. The method as claimed in claim 5, wherein the method comprises tuning the model based on the performance matrix.

7. The method as claimed in claim 6, wherein the method comprises adjusting the predetermined score based on the historical data and the performance matrix.

8. A server for optimizing an organizational process, the server comprising a processor communicating with a repository, wherein the processor is to:
identify a number of steps in the organizational process;
identify one or more assignees corresponding to the number of steps respectively, wherein the one or more assignees are selected from an organizational matrix; and
optimize the number of steps based on a model, wherein the optimizing comprises:
eliminate at least one step in the number of steps; or
add at least one step to the number of steps.

9. The server as claimed in claim 8, wherein to optimize the number of steps based on the model the processor is to:
retrieve historic data related to the organizational process, the historic data including one or more actions performed by the one or more assignees corresponding to one or more inputs provided by one or more users, wherein the one or more users are included in the organizational matrix;
analyse the historic data, for each step in the number of steps, to identify the number of successful instances based on the actions performed by the one or more assignees and the number of unsuccessful instances based on the actions performed by the one or more assignees; and
create a correlation between the one or more inputs provided by the one or more users, the one or more actions performed by the one or more assignees, and identify the number of successful instances and the number of unsuccessful instances.

10. The server as claimed in claim 8, wherein the processor is to provide, for each step in the number of steps, a score to the one or more users based on the analysis of the historic data, wherein the number of steps is optimized based on the score, wherein:
at least one step in the number of steps is eliminated for a user where the score corresponding to the user is equal or greater than a predetermined score; and
the number of steps is unaltered or at least one step is added to the number of steps for a user where the score corresponding to the user is less than the predetermined score.

11. The server as claimed in claim 10, wherein the processor is to:
monitor whether the eliminated step is reconsidered by the one or more assignees; and
when the eliminated step is reconsidered, store the reconsideration instance in a performance matrix, wherein the performance matrix is stored in the repository and comprises a number of successful instances and a number of unsuccessful instances of the optimization performed based on the model.

12. The server as claimed in claim 11, wherein the processor is to:
monitor whether the added step is removed by the one or more assignee; and
when the added step is removed, store the removal instance in the performance matrix.

13. The server as claimed in claim 12, wherein the processor is to tune the model based on the performance matrix.

14. The server as claimed in claim 13, wherein the processor is to adjust the predetermined score based on the historical data and the performance matrix.

Dated this 29th day of January 2021

, Description:Field of Invention
[0001] The present disclosure relates generally to the organizational processes. More specifically, the present disclosure relates to optimizing an organizational process.

Background
[0002] Organizations implement different workflow processes to streamline various set of tasks/actions and to organize the resources for maximized and timely output. The workflow processes are generally based on the policies adopted by the organizations. For example, tools such as leave management system, reimbursement system, payroll system, travel request system, exit management system, order/purchase system, etc. are the kinds which implement different workflow processes. These workflow processes may alternatively be referred to as organizational processes.
[0003] In the organizations, specifically the large organizations, employee movement due to restructuring, employee exit, promotions, change of roles, change in department, addition of new employee, acquisitions of an entity, or merger of two or more entities may affect the workflow processes. For example, promotions in an organization may result in change of hierarchy and may result in change of roles and responsibilities of various employees and which may introduce redundancy in the workflow process. Further, such change of hierarchy may also require introduction of additional steps to the workflow processes.
[0004] In another example, certain steps in a workflow process may become redundant or non-specified due to change in policies or hierarchal structure of the organization.
[0005] In another example, the workflow processes implemented originally may include one or more steps that may be redundant. However, such redundancy may be invisible due lack of information. Further, one or more steps may become irrelevant due to change in working conditions or due to advancement in technology. Such redundant steps may consume resources in terms of man hours as well as processing time/cycles and may reduce the efficiency of the computing system(s) implementing the above discussed tools. Considering large organization where the employee strength is high, the consumption of resources due to redundancy or non-specified steps may be exponential. Accordingly, the adverse effect of such exponential consumption of resources will be massive.

Summary of Invention
[0006] The present subject matter relates to systems and methods for optimizing an organizational process. In accordance with an example implementation, a server in communication with repository optimizes the organization process where a number of steps in the organizational process are identified. One or more assignees corresponding to the number of steps are identified respectively, wherein the one or more assignees are selected from an organizational matrix. Thereafter, the server utilizes a model to optimize the number of steps by eliminating at least one step in the number of steps or by adding at least one step to the number of steps.
[0007] In accordance with another example implementation, the server retrieves historic data related to the organizational process. The historic data includes one or more actions performed by the one or more assignees corresponding to one or more inputs provided by one or more users, wherein the one or more users are included in the organizational matrix. Thereafter, the server analyses the historic data, for each step in the number of steps, to identify the number of successful instances and the number of unsuccessful instances based on the actions performed by the one or more assignees. A correlation is generated between the one or more inputs provided by the one or more users, the one or more actions performed by the one or more assignees, and identify the number of successful instances and the number of unsuccessful instances.
[0008] In accordance with another example implementation, for each step in the number of steps, the server provides a score to the one or more users based on the analysis of the historic data, wherein the number of steps is optimized based on the score. At least one step in the number of steps is eliminated for a user where the score corresponding to the user is equal or greater than a predetermined score. At least one step is added to the number of steps is eliminated for a user where the score corresponding to the user is less than the predetermined score.
[0009] In accordance with another example implementation, the server maintains a performance matrix in the repository. The performance matrix includes a number of successful instances and a number of unsuccessful instances of the optimization performed based on the model. The server monitors whether the eliminated step is reconsidered by the one or more assignees and stores the reconsideration instance in the performance matrix. The server further monitors whether the added step is removed by the one or more assignees and stores the removal instance in the performance matrix. The performance matrix is used to tune the model. The server adjusts the predetermined score based on the historical data and the performance matrix.

Brief Description of Drawings
[0010] The following detailed description references the drawings, wherein:
[0011] Fig. 1 illustrates an environment for optimizing an organization process, according to an example implementation of the present subject matter.
[0012] Fig. 2 illustrates a server for optimizing the organization process, according to an example implementation of the present subject matter.
[0013] Fig. 3 illustrates a flow diagram showing an exemplary process for optimizing the organization process based on a model, according to an example implementation of the present subject matter.
[0014] Fig. 4 illustrates a flow diagram showing an exemplary process for tuning the model implemented for optimizing the organization process, according to an example implementation of the present subject matter.
[0015] Fig. 5 illustrates a flow diagram showing an exemplary process for optimizing a presence management system, according to an example implementation of the present subject matter.

Detailed Description
[0016] The present subject matter describes systems and methods for optimizing an organization process. The systems and the methods of the present subject matter may eliminate one or more steps in the organization process to reduce redundancy. Further, the systems and the methods of the present subject matter may add one or more steps in the organization process to improve the resource management.
[0017] In accordance with an example implementation of the present subject matter, data related to an organizational process is obtained from various sources. The data is processed to identify the number of steps in the process and to obtain an organization matrix. The organization matrix provides details about all the users of the process and the assignees responsible for the steps implemented in the process. A trained machine learning model is implemented for the optimization.
[0018] The historical records with respect to the process is provided to the model as training data. Specific machine learning techniques may be implemented to correlate the inputs provided by the users, the actions performed by the one or more assignees for the one or more steps, and the number of successful and unsuccessful instances based on the actions performed by the one or more assignees. The correlation is used by the model to determines whether to eliminate or to add one or more steps in the process.
[0019] In another example implementation, the model provides a score to each of the users based on the correlation. Alternatively, the model may also rank the users based on the score. Further, the model generates a predetermined threshold score to evaluate the users in real time.
[0020] When a user provides an input to the process, the model optimizes the process for said user input by determining whether to eliminate one or more steps in the process or to add an additional step in the process based on the scores.
[0021] The systems and methods of the present subject matter optimizes the organizational process. Therefore, any step which is redundant or non-specific is removed. Thus, the mean-time to complete the process and the resources utilized by such avoidable steps are reduced and the efficiency of the system implementing the process is improved. Further, the introduction of an additional step prevents any misapplication of the process.
[0022] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several examples are described in the description, modifications, adaptations, and other implementations are possible. Accordingly, the following detailed description does not limit the disclosed examples. Instead, the proper scope of the disclosed examples may be defined by the appended claims.
[0023] Fig. 1 illustrates an environment 100 for optimizing an organization process, according to an example implementation of the present subject matter. The environment includes a server 102 implementing an organizational process which may alternatively be referred to as ‘workflow process’ or ‘process’. The server 102 communicates with plurality of terminal devices 104-1, 104-2, …,104-N operated by the users. The organizational processes may be implemented in form of web-based applications hosted by the server.
[0024] For representational purpose, a single server has been shown in fig. 1. Each of the organizational processes may be implemented by independent servers or by a different set of servers. In an embodiment, the server may be a virtual server. Each of the processes may be implemented by a separate virtual server configured on a single virtual machine or on separate virtual machines.
[0025] The terminal devices 104 communicate with the server 102 over a network 108. The network 108 may be an internet or an intranet or a combination of both. The terminal devices 104 may communicate with the server 102 through the network 108 over wireless interface or wired interface. In an example implementation, a terminal device may be a laptop, a desktop, a mobile phone, a smartphone, a computing system with operating system such as Windows, Linux, Android, iOS, or MacOS. The terminal devices 104 are operated by the user to access the process implemented by the server 102.
[0026] Referring to Fig. 1, the environment 100 includes a repository 106. The server 102 stores the data related to the organizational processes in the repository 106. The repository 106 may be provided as memory in the server or may be provided as a separate database. In an example implementation, the repository 106 may be coupled with the sever 102 over wireless LAN or wired LAN or though internet connectivity. In another example implementation, the repository 106 may be in form of clustered pieces distributed over a could network. Data of all the applications hosted by the server 102 for the organizational processes are whether processed or unprocessed are stored in the repository 106. The repository 106 may be, for example, implements SQL database, Oracle, Teradata, IBM DB2, SAP Sybase ASE, etc.
[0027] Fig. 2 illustrates the server 102 for optimizing the organization process, according to an example implementation of the present subject matter. The server 102 comprises a processor 202, a memory 204, an optimization module 206, a network interface 208, and an input/output interface 210.
[0028] The processor 202 may be implemented as 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 202 may fetch and execute computer-readable instructions stored in the memory 204 coupled to the processor 202 of the sever 102. The memory 204 may include any non-transitory computer-readable storage medium including, for example, volatile memory (e.g., RAM), and/or non-volatile memory (e.g., EPROM, flash memory, etc.). The memory 204 may be internal or external to the server 102. The functions of the server 102 may be provided through the use of dedicated hardware as well as hardware capable of executing computer-readable instructions.
[0029] The server 102 access the repository 106 to obtain the data for a process. The server analyze the data to identify a number of steps in the process and the organization matrix. Based on the organization matrix, the server 102 identifies one or more assignees corresponding to the identified number of steps, respectively. The optimization module 206 determines whether one or more steps are to be eliminated from the identified number of steps. Further, the optimization module 206 determines whether one or more steps are to be added to the identified number of steps.
[0030] The optimization module 206 may utilize a modal 206A trained using machine learning techniques. The machine learning techniques may be unsupervised learning techniques or supervised learning techniques. The modal 206A may be created using a training data set. For the present subject matter, the training dataset may be the historical data for the process. The historical data may include the inputs provided by the users to the process and the actions performed by the assignees, for each of the steps, corresponding to inputs provided by the users.
[0031] The processor 202 analyses the historic data for each step in the process and identify the number of successful instances based on the actions performed by the one or more assignees and the number of unsuccessful instances based on the actions performed by the one or more assignees. The modal 206A is created based on a correlation established between the inputs provided by the users, the actions performed by the one or more assignees for the one or more steps, and the number of successful and unsuccessful instances based on the actions performed by the one or more assignees. The correlation may be presented in form of a correlation matrix for each user. For each input received from the user for the organizational process, the model 206A utilize the correlation matrix to optimize the process for that user.
[0032] The processor 202 may tune the modal 206A to predict more accurately whether the one or more steps needs to be eliminated from the process or to be added in the process. A performance matrix is stored in the repository 106 for each of the organizational processes. The performance matrix stores details of all the instances where the optimization performed for the process by the optimization module 206, based on the model 206A, has been successful or has been unsuccessful.
[0033] The processor 202 monitors whether the eliminated one or more steps in the process is reconsidered by the one or more assignees, i.e., whether the one or more steps has been erroneously eliminated by the optimization module 206 from the process. When the eliminated one or more steps is reconsidered, the processor 202 stores the reconsideration instance in the performance matrix. Further, the processor 202 monitors whether the added one or more step is removed by the one or more assignee, i.e., whether the added one or more steps has been incorrectly included in the process. When the added step is removed, the processor 202 stores the removal instance in the performance matrix.
[0034] The performance matrix is utilized to adjust the correlation established between the actions performed by the one or more assignees for the one or more steps and the number of successful and unsuccessful instances based on the actions performed by the one or more assignees.
[0035] Further, the processor 202 may provide a score to each of the users based on the correlation and may generates a predetermined threshold score to evaluate the users in real time. The optimization module may eliminate one or more steps in the identified number of steps for a user where the score corresponding to the user is equal or greater than a predetermined score. Further, the optimization module may add one or more steps to the identified number of steps for a user where the score corresponding to the user is less than the predetermined score. Further, the performance matrix may be utilized to adjust the predetermined score.
[0036] Further, the historical data may include time stamps associated with each of the steps in the process and may be utilized to determine the efficiency of the system.
[0037] The network interface 208 enables the sever 102 to communicate with the terminal devices 104, repository 106, and other computing devices over the network 108. The network interface 208 includes a wired interface and a wireless interface. The input/output interface 210 enables a user of the server to provide inputs and receive output. The input/output interface 210 may be coupled to various input device, for example, a keyboard, a mouse, etc. and various output devices, for example, a display.
[0038] Fig. 3 illustrates a flow diagram 300 showing an exemplary process for optimizing the organization process based on the model 206A, according to an example implementation of the present subject matter. At step 302, a number of steps in the organizational process is identified by the processor 202. The data for the organization process is obtained by the processor 202 from a data source such as the repository 106.
[0039] At step 304, the one or more assignees corresponding to the number of steps are identified by the processor 202. The one or more assignees are selected from the organizational matrix. The organizational matrix is obtained by the processor 202 based on the data of the organizational process. At step 306, the number of steps is optimized by the optimization module 206. The optimization module 206 utilize the model 206A to determine one or more steps to be eliminated from the identified number of steps or one or more steps to be added to the identified number of steps.
[0040] Fig. 4 illustrates a flow diagram 400 showing an exemplary process for tuning the model 206A implemented for optimizing the organization process, according to an example implementation of the present subject matter. At step 402, the optimized process is monitored by the processor 202 to determine whether the eliminated one or more steps in the process is reconsidered by the one or more assignees. At step 404, when the eliminated one or more steps is reconsidered, the reconsideration instance is stored in the performance matrix by the processor 202. At step 406, the optimized process is further monitored by the processor 202 to determine whether the added one or more step is removed by the one or more assignees. At step 408, when the added step is removed, the removal instance is stored in the performance matrix by the processor 202. The performance matrix is utilized to adjust the correlation established between the inputs received from the users, the actions performed by the assignees, and the successful and unsuccessful instances based on the actions performed by the assignees.
[0041] Fig. 5 illustrates a method 500 for optimizing a presence management system, according to an example implementation of the present subject matter.
[0042] The presence management system is an organizational process which keeps track of attendance of the employees in an organization. An employee may remain absent from office for one or more days. The absence may be categorized as casual leave, paid leave, paternity leave, maternity leave, earned leave, sick leave, leave without pay, offsite, meeting, official travel, etc. An absence request submitted by an employee may be approved by one or more approvers. The decision to approve the absence request may be based on the criticality of the leave, roles and job responsibilities of the employee, current status of work pending with employee, etc.
[0043] At step 502, a number of steps for approval of absence request in the presence management system is identified by the processor 202. The data for the presence management system is obtained by the processor 202 from a data source such as the repository 106. At step 504, the one or more approvers corresponding to the number of steps are identified. The one or more approvers are selected from the organizational matrix. The organizational matrix is obtained by the processor 202 based on the data of the presence management system. At step 506, the number of steps is optimized by the optimization module 206. The optimization module 206 utilizes the model 206A to determine one or more steps to be eliminated from the identified number of steps or one or more steps to be added to the identified number of steps. For example, based on the correlation matrix, the modal predicts whether to send the absence request for approval to the reporting manager of the employee or to approve the absence request and notify the reporting manager and the employee about the status of the absence request or to escalate the absence request beyond the reporting manager based on the organizational matrix. With present subject matter, the presence management system is optimized by addressing the redundancy in approval of absence request.
[0044] With the systems and methods of the present subject matter various workflow processes in an organization may be optimized. With optimization process, the efficiency of the system improves as well as resource utilization is reduced.
[0045] While aspects of the present disclosure have been particularly shown, and described with reference to the embodiments above, it will be understood by those skilled in the art that various additional embodiments may be contemplated by the modification of the disclosed machines, systems, and methods without departing from the spirit and scope of what is disclosed. Such embodiments should be understood to fall within the scope of the present disclosure as determined based upon the claims and any equivalents thereof.