Abstract: A machine-learning model (MLM) is trained to identify a given item identifier for an item and shelf dimensions of an empty space associated with the item from training images of a shelf. After training, real-time images of the shelf are provided as input to the MLM and the output provided by the MLM includes empty space identifiers, dimensions or pixel coordinates for each empty space identifier, and an item identifier for each empty space identifier. A quantity of each item identifier is determined based on known shelf dimensions that the corresponding item should occupy on a fully stocked shelf and based on the corresponding empty space dimensions for the empty space associated with the item. A real-time report is sent to store personnel and/or published on a website monitored by the store personnel. The report identifies the items, the shelves, and restocking item quantities that need restocked in the store.

Abstract: The present disclosure generally relates to profit optimization, and more particularly to methods and systems for profit optimization for an online/offline retail/wholesale category. Profit optimization includes demand forecasting, inventory planning, assortment planning, discount recommendation, price optimization, revenue optimization, and profit optimization in the online/offline retail/wholesale category. The method of profit optimization includes a hierarchical demand forecasting of products by using a stacked Long- and Short-Term Memory (LSTM) neural network architecture. Further, the method includes a quantification of price-demand causal effects and inter-product cross-causal effects using an eXtreme Gradient Boosting (XGBoost) technique. The method further includes a simulation of an effect of discount on a demand of products, and recommendation of an optimal discount using a non-linear optimization technique.

Abstract: Present disclosure generally relate to stock re-estimation, particularly relates to methods and systems for re-estimating stock and simulating demand, due to price drop in online/offline wholesale/retail products/appliances. System receives attribute data, business context data, price change data, historical sales data, store related data, inventory data, discount data, input plan data as input. System performs feature engineering on input data to extract data latent variables, calendar features, demographics data, derived variables, web extracted data. System performs operations such as price causal, sales forecast, Price Segment (PS) causal, and output data at DC level and determines delta change, multiplication factor, price segment distribution from output data at site level.

Abstract: The present disclosure generally relates to producing, transporting, distributing, and storing hydrogen fuel, more particularly to system and method for optimizing supply chain of hydrogen distribution network. A centralized server triggers production facility to produce gas/liquid Hydrogen. Centralized server stores at storage facility in hydrogen cylinders, produced gas/liquid Hydrogen, in Liquid Organic Hydrogen Carrier (LOHC) molecule, based on hydrogenation of chemicals. Centralized server transmits instructions for transporting hydrogenated LOHC molecule in tanker trucks, from production facility to depots, and dehydrogenates at depots, hydrogenated LOHC molecule to release hydrogen at low pressure. Centralized server compresses, at depots, released hydrogen, and fill compressed hydrogen in high-pressure tube trailers/flat-bed cylinder cascades.

Abstract: A system and method for prediction of failures and optimization, that can provide solution available for unsupervised learning models based on limited data that can predict different types of failure and pre-failure instances. The solution provides improvement upon previous methods of labelling by marking certain days data ahead of failure as belonging to failure data which will result in reduction of noisy data and improves good working condition data. The present invention helps with improved data quality due to labelling as the proposed method models complex distributions of feature vectors accurately and are better at finding deviations from normal data distribution which is used for detecting failures. The novel solution help to analyse and categorise the type of failures for PC Pumps currently deployed in CBM Fields for which failure days in advance can be predicted.

Abstract: The present disclosure provides a system and method for optimizing a team selection process during an auction. The method includes receiving a first input associated with a quality of a sports team, receiving a second input associated with a success probability of a bid associated with the sports team, and determining an optimized list of players for the sports team based on the first and the second inputs.

Abstract: A system and method for providing a robust and effective solution for forecasting inventory for a warehouse/fulfilment centre (FC) at a product batch level. The method includes calculating a demand forecast data based on a forecast algorithm, correcting the calculated demand forecast data based on one or more exogenous variable, categorizing the inventory into different buckets at a product batch level, forecasting a warehouse level inventory demand for a predefined time based on the categorization, and sending an alert to one or more users based on the categorization. The method further includes predicting a demand forecast data for one or more upcoming weeks.

Abstract: A network computer system operates to determine one or more transport service parameters for an upcoming scheduled event at a mass egress location. One or more activities associated with the scheduled event are monitored to determine whether to update the one or more transport service parameters. Based at least in part on the one or more transport parameters, a set of relocation parameters is determined, including (i) an intermediate location where the service provider is to be located to provide the user with the transport service, and (ii) an intermediate arrival time when the first service provider is to arrive at the intermediate location before the pickup time. Based at least in part on a current location of a selected service provider and the intermediate arrival time, a relocation time when the service provider is to initiate travel to the intermediate location is determined.