Accenture Global Solutions Limited

Santosh Kumar Soni

Manu Khanna

Vinay Avinash Dorle

Vikash Choudhary

Lokesh Dharmane

Ravi Shankar Nori

Siva Rama Sarma Theerthala

Theerthala Siva Rama Sarma

Sumeet Pushpam

Theerthala Sarma Siva Rama Sarma

Anirudh Shrivastav

Shivam Mathur

A system and method for facilitating predictive maintenance of an equipment is disclosed. The system may include a data capturer, a plurality of edge computing nodes and a cloud computing device. Each edge computing node may include a first processor. The cloud computing device may include a second processor. The first processor may receive the raw input data from the data capturer and may process the raw input data to obtain a representative data. The representative data may include an insight pertaining to a deviation in the at least one variable and a corresponding remedial action to be taken to correct the deviation. The deviation may be related to a deterioration in the condition of the equipment. The respective edge computing node may facilitate a regulation of the deviation by performing an automated actuation based on the corresponding remedial action.

Predictive maintenance of equipment

Examples for a prospect assessment system are provided. The system may receive a query from a user and obtain prospect data from a plurality of data sources. The system may create a plurality of prospect clusters. The system may determine a cluster significance value. The system may determine a prospect churn value. The system may determine a set of significant prospect clusters from among the plurality of prospect clusters having the cluster significance value and the prospect churn value above a threshold value. The system may determine a prospect pattern matrix including a plurality of patterns from the set of significant prospect clusters. The system may identify a set of products for each of the plurality of patterns. The system may determine a prospect profile for each of the plurality of patterns. The system may generate a prospect assessment result based on the prospect profile for the resolution of the query.

Intelligent prospect assessment

Examples of prospect recommendation including a prospect recommendation system are provided. The system may receive a prospect assessment query. The system may receive and sort prospect data, sales attribute data, and product data by applying an artificial intelligence component. The system may determine a prospect matrix by correlating the prospect data with the product data. The system may determine a prospect profile by collating historical prospect data and the product data. The system may determine a prospect assessment matrix by correlating the prospect matrix and the prospect profile. The system may determine a decisional pathway based on a comparison between the prospect profile and the prospect assessment matrix. The decisional pathway may include a plurality of assessment interpretations. The system may determine a prospect recommendation index based on the decisional pathway. The prospect recommendation index may facilitate the generation of a prospect assessment result for resolution of the prospect assessment query.

Prospect recommendation

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Bengaluru, India

Santosh Kumar Soni