Accenture Global Solutions Limited

Arvind Maheswaran

Abhijit Kumar

Amartya Ray

Madhura Shivaram

Deepak Kumar

Guruprasad Dasappa

Abhishek Datta Sharma

Suraj Govind Jadhav

Rajarajan Thangavel Ramalingam

Kaushal Mody

Hitesh Bhagchandani

Ravikant Singh

Pratap Simha, C S

Glanda Meera Nazareth

Jenoy Easow

Siddhartha Chakravarty

Neha Kagwad

Renuka R Jogade

Mahadevaswamy Basavanna

Sunny Balani

An Artificial Intelligence (AI) based delivery time estimation system employs a plurality of delivery time estimation models or actual time of arrival (ATA) estimation models to provide ATA estimates for shipments. Each of the ATA estimation models is trained on historical data from a plurality of external data sources to generate an ATA estimate for a given delivery route via a specific delivery mode. The ATA estimates are generated based on current data retrieved for a shipment from the external data sources. The time series data sets retrieved from the external data sources for the historical data and the current data are transformed into corresponding categorical data. The categorical data is converted into binary data to be provided to the plurality of ATA estimation models for training purposes or to a trained, selected ATA estimation model for generating an ATA estimate.

Artificial intelligence (AI) driven estimated shipment delivery time advisor

Systems and methods for estimating freight costs are disclosed herein. The system receives, from a user interacting with the system, a first document and a second document, where an actual value is associated with the second document. Further, the system determines a set of variables for each of the first document and the second document based on a statistical analysis of historical data. Furthermore, the system estimates, using a trained artificial neural network model, a cost associated with each of the first document and the second document based on the determined set of variables. The cost associated with the first document includes an estimated freight cost for the first document, and the cost associated with the second document includes an estimated true value for the second document.

Systems and methods for providing a self-learning estimation advisor

A device may receive information associated with an entity. The information may include a first resource and a second resource. The first resource may be associated with a first file type, and the second resource may be associated with a second file type that is different than the first file type. The first resource may be associated with a first source, and the second resource may be associated with a second source that is different than the first source. The device may extract a plurality of attributes associated with the entity based on the information. The device may implement a natural language processing technique to extract the plurality of attributes. The device may associate the plurality of attributes with a plurality of elements based on extracting the plurality of attributes. The device may provide information that identifies the plurality of elements and the plurality of attributes to permit and/or cause an action to be performed.

Identifying attributes associated with an entity using natural language processing

A machine learning (ML) based intermittent data processing system accesses a collection of intermittent data points, determines a data distribution associated with the collection and generates one or more calculated values based on the data distribution. A simulation can be employed to determine the accuracy of the calculated values based on which, the calculated values can be employed for further processing. The collection of intermittent data points is initially processed to determine if one or more of the data distribution identification, bootstrapping or variability capping techniques are to be applied in order to obtain the calculated values. The calculated values are used to generate visualizations and recommendations.

Distribution selection and simulation of intermittent data using machine learning (ML)

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Bengaluru, India

Arvind Maheswaran