Rule engine for real time restructuring of enterprise application

1 . A system for restructuring of an enterprise or supply chain management (SCM) application, the system comprises:

an entity machine configured to initiate at least one task to be performed for restructuring the enterprise or supply chain application;

an application server configured to receive input from the entity machine, the application server having a micro-frontend support architecture and a rule engine for restructuring the enterprise or supply chain application, the rule engine comprises:

a compiler configured for processing a task received from a user through at least one configurable user interface (UI) component embedded on a micro-front end of enterprise and SCM application; and

a code module configured for generating a plurality of protocols based on the at least one task, a plurality of metadata and data models associated with the at least one UI component of the application wherein the protocols are generated for executing the task based on an AI based processing logic wherein the protocols are backend scripts created by a bot based on the at least one task, the plurality of metadata, data models and artificial intelligence (AI) processing for enabling execution of the at least one task thereby restructuring the application, wherein a controller coupled to the rule engine triggers a dynamic module injector for conditionally loading at least one module on an application UI based on the protocols by interacting with the at least one UI component, the data layer components, the platform layer components and the data repository for executing the task and restructure the application.

2 . The system of claim 1 , wherein the at least one task includes embedding templates for restructuring the application wherein the templates are related to sourcing, procurement or supply chain functions.

3 . The system of claim 2 , wherein the at least one task includes modification of an existing UI component on the application for accommodating transformations required based on new operational requirement for the application.

4 . The system of claim 3 , wherein the at least one task includes configuring labels in multi languages of form elements and publish the labels on the application to start leveraging operational forms including Purchase Order, Contract and Inventory.

5 . The system of claim 4 , wherein the data layer component and platform layer component enable the user to change the labels and add additional strings to support localization.

6 . The system of claim 1 , wherein the AI based processing logic integrates deep learning, predictive analysis, information extraction and robotics for triggering the dynamic module injector to conditionally load at least one module on an application UI thereby processing the at least one task.

7 . The system of claim 1 , wherein the rule engine is configured to switch the plurality of data models for different applications based on application functions and efficiency of data models tested for those functions wherein the switching occurs in real time using AI based analysis of a performance data of the data models, the at least one task, and an impact of the at least one task on the functions related to the enterprise and SCM applications.

8 . The system as claimed in claim 1 , wherein the data model is a conceptual, logical and physical structure relating data objects associated with an identifier of the dynamic module injector and the plurality of meta data.

9 . The system of claim 8 , wherein the identifier is an element of the injector associated to a code of the protocol and configured for uniquely identifying the at least one module to be loaded on the application UI.

10 . The system of claim 1 , wherein the dynamic module injector is configured for initialization of outlet, initialization of injection context, injection of queuing the at least one task, initialization of a loader, manifesting of a parser and module cache mapping for conditionally loading the at least one module.

11 . A method of restructuring of an enterprise or supply chain management (SCM) application, the method comprises:

processing by a compiler, a task received from a user through at least one configurable user interface (UI) component embedded on a micro-front end of enterprise and SCM application; and

generating by a code module, a plurality of protocols based on the at least one task, a plurality of metadata and data models associated with the at least one UI component of the application wherein the protocols are generated for executing the task based on an artificial intelligence (AI) based processing logic wherein the protocols are backend scripts created by a bot based on the at least one task, the plurality of metadata, data models and AI processing for enabling execution of the at least one task thereby restructuring the application, wherein a controller coupled to the rule engine triggers a dynamic module injector for conditionally loading at least one module on an application UI based on the protocols by interacting with the at least one UI component, the data layer components, the platform layer components and the data repository for executing the task and restructure the application.

12 . The method of claim 11 , wherein the dynamic module injector is configured for initialization of outlet, initialization of injection context, injection of queuing the at least one task, initialization of a loader, manifesting of a parser and module cache mapping for conditionally loading the at least one module.

13 . The method of claim 11 , wherein the at least one task includes embedding templates for restructuring the application wherein the templates are related to sourcing, procurement or supply chain functions.

14 . The method of claim 13 , wherein the at least one task includes modification of an existing UI component on the application for accommodating transformations required based on new operational requirement for the application.

15 . The method of claim 14 , wherein the at least one task includes configuring labels in multi languages of form elements and publish the labels on the application to start leveraging operational forms including Purchase Order, Contract and Inventory.

16 . The method of claim 15 , wherein the data layer component and platform layer component enable the user to change the labels and add additional strings to support localization.

17 . The method of claim 11 , wherein the AI based processing logic integrates deep learning, predictive analysis, information extraction and robotics for triggering the dynamic module injector to conditionally load at least one module on an application UI thereby processing the at least one task.

18 . The method of claim 11 , wherein the rule engine is configured to switch the plurality of data models for different applications based on application functions and efficiency of data models tested for those functions wherein the switching occurs in real time using AI based analysis of a performance data of the data models, the at least one task, and an impact of the at least one task on the functions related to the enterprise and supply chain management (SCM) applications.

19 . The method of claim 11 , wherein the data model is a conceptual, logical and physical structure relating data objects associated with an identifier of the dynamic module injector and the plurality of meta data.

20 . The method of claim 19 , wherein the identifier is an element of the injector associated to a code of the protocol and configured for uniquely identifying the at least one module to be loaded on the application UI.

21 . A computer program product for restructuring of an enterprise or supply chain management (SCM) application of a computing device with memory, the product comprising a computer readable storage medium readable by a processor and storing instructions for execution by the processor for performing a method of claim 11 .