Accenture Global Solutions Limited

Amartya Ray

Arvind Maheswaran

Abhijit Kumar

Rajarajan Thangavel Ramalingam

Hitesh Bhagchandani

Basavaraj Chidanandappa

Zakir Hussain

Pratap Simha, C S

Ravikant Singh

Sriharsha Sravan Karavadi

Glanda Meera Nazareth

Jenoy Easow

Manish Kumar

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 material failure forecasting system accesses historical failure data to forecasts future failures. The failure data of a material is analyzed using text processing techniques to identify failures and suspensions. The text processing techniques provide for identifying failures when fault words are associated with negations. A fault ontology establishes different failure modes that include primary, secondary and tertiary levels which enable identifying a sequence of failures. The failures thus identified are fitted to a data distribution selected from a plurality of data distributions. The parameters from the data distribution are used for simulating a demand profile for the material which considers interchangeability. Similarly failure data of the materials in an equipment can be analyzed and the reliability of the equipment can be estimated.

Forecasting failures of interchangeable parts

Growing community of inventors

Mumbai, India

Amartya Ray