System and method for effort estimation

A system (200) for configuring one or more components among a plurality of components of a process plant to optimize an industrial process deployed in the process plant, the system (200) comprising:

• a data acquisition unit (212) communicatively coupled to a client station (202, 204) and configured to receive a change request (226), wherein the change request (226) comprises a specification of modification required in a component among the plurality of components of the process plant;
• a database unit (218) communicatively coupled to the data acquisition unit and configured to store historical data generated by the process plant and a plurality of configuration files corresponding to the plurality of components;
• a graph builder unit (220) communicatively coupled to the database unit and configured to build a system dependency graph (228) corresponding to the process plant based on a plurality of configuration files corresponding to the plurality of components, wherein the system dependency graph (228) comprises a plurality of nodes representative of the plurality of components of the process plant coupled with one another via a plurality of links representative of dependency weights;
• and
• a machine learning unit (214) communicatively coupled to the graph builder and configured to:
• select a subset of components (230) among the plurality of components related to the component specified by the change request based on configuration of the process plant;
• identify a subset of nodes (232) among the plurality of nodes corresponding to the subset of components (230) and the component specified by the change request by traversing the system dependent graph (228), wherein the subset of nodes (232) corresponds to a plurality of categories of components;
• compute an impact parameter (234) value based on a traversed path in the system dependent graph (228), wherein the impact parameter (234) is representative of degree of complexity of changes in the system due to the component;
• compute a plurality of change parameter values (236) based on the traversed path, wherein the plurality of change parameter values (236) are representative of degree of complexity of changes in the system due to the plurality of categories of components;
• determine an effort estimate based on the impact parameter (234) value and the plurality of change parameter values (236) using a machine learning technique, wherein the effort estimate comprises a schedule for implementing the change request and a cost of implementing the change request;
• and
• implementing the change request based on the effort estimate, wherein the implementing the change request comprises configuring the one or more components of the process plant.