International Business Machines Corporation

Wayfair LLC

Doordash, Inc.

Ajay Ashok Deshpande

Xuan Liu

Ali Koc

Brian Leo Quanz

Arun Hampapur

Yingjie Li

Dahai Xing

Christopher Scott Milite

Saurabh Gupta

Chek Keong Tan

Xiaobo Zheng

Kimberly D Hendrix

Alan Jonathan King

Yada Zhu

Herbert Scott McFaddin

Ashish Jagmohan

Pavithra Harsha

Chandrasekhar Narayanaswami

Krishna Chaitanya Ratakonda

Ramesh Ambat Gopinath

Elisabeth Claire Paulson

Lei Cao

Jae-Eun Park

Hongliang Fei

Xiao Mei Zheng

Chandra Narayanaswami

Pradyumnha Gopalkrishna Kowlani

Ding Ding Lin

Dingding Lin

Vadiraja S Ramamurthy

Steve Igrejas

Raman Srinivasan

Shyh-Kwei Chen

Songhua Xing

Miao He

Simone Bianco

JoAnn Piersa Brereton

Vishrawas Gopalakrishnan

James H Kaufman

Joline Ann Villaranda Uichanco

Sayali Navalekar

Kun Hu

Pan Ding

Sambit Sahu

Younghun Kim

Anna Wanda Topol

Rakesh Mohan

Pin-Yu Chen

Hongfei Li

Ming Li

Pavankumar Murali

Yue Tong

Jacob O Miller

Jing Dai

Yun Zhang

Michael Yesudas

George Sirbu

Sanjay E Cheeran

Sachin Sethiya

Yam Huo Ow

Sarah Kefayati

Ujwal Reddy Moramganti

A computer implemented method and system of evaluating a fulfillment strategy in an omnichannel distribution system is provided. Input parameters are received from a computing device of a user. Historical data related to a network of nodes is received from a data repository. A synthetic demand status is determined based on the historical data and the input parameters. A synthetic network status based on the historical data and the input parameters are determined. A fulfillment strategy is identified based on the synthetic demand status and the synthetic network status. Key performance indicators (KPIs) for the fulfillment strategy are determined based on the synthetic demand status and the synthetic network status.

System for modeling the performance of fulfilment machines

An example operation may include one or more of receiving, by a retailer node, an encrypted inventory of goods data from a plurality of supplier nodes over a blockchain network, computing, by the retailer node, an ordering proportion based on the encrypted inventory of goods data, generating, by the retailer node, an ordering policy based on the ordering proportion, and executing a smart contract to order goods from the plurality of the supplier nodes based on the ordering policy.

Upstream visibility in supply-chain

Mechanisms are provided to hypothetical scenario evaluations with regard to infectious disease dynamics based on similar regions. A user definition of a hypothetical scenario for a target region is received which specifies scenario features affecting an infectious disease spread amongst a population within the target region. Other predefined regions, in the set of predefined regions, having similar region characteristics to the target region are identified and attributes of the other predefined regions corresponding to the scenario features are identified. Modified model parameter(s) for an infectious disease computer model are derived based on the identified attributes. An instance of the infectious disease computer model is configured with the modified model parameter(s) and the instance is executed on case report data for the target region to generate a prediction for an infectious disease spread in the target region according to the hypothetical scenario, which is then output.

Hypothetical scenario evaluation in infectious disease dynamics based on similar regions

A medical episode analysis engine is provided. The engine generates a first matrix data structure having an entry for each concept pairing and storing a value representing relatedness weighted according to a temporal weighting function. The engine generates a second matrix data structure by calculating, for each entry in the first matrix, a relatedness measure of the concepts in the concept pairing based on a frequency of occurrence together. The engine generates, for each first concept, a concept embedding, based on the second matrix, that specifies, for each other second concept, a temporally weighted relatedness measure. The engine generates, for each anchor concepts, a corresponding episode definition comprising a plurality of related concepts corresponding to a same episode, based on the concept embedding. The engine processes new input data based on the episode definition data structures to identify instances of corresponding episodes in the new input data.

Machine learning augmented system for medical episode identification and reporting

Mechanisms are provided for compartmental epidemiological computer modeling based on mobility data. Machine learning training of an isolation rate prediction computer model is performed to generate a trained isolation rate prediction model that predicts an isolation rate of a biological population. Isolation data is received which comprises data indicating a measure of mobility of the biological population. The trained isolation rate prediction model is executed on input features extracted from the isolation data to generate a predicted isolation rate. A compartmental epidemiological computer model, comprising a plurality of compartments representing states of a population with regard to an infectious disease, is executed to simulate a progression of the infectious disease and flows of portions of the population from between compartments in the compartmental epidemiological computer model are controlled based on the predicted isolation rate.

Controlling compartmental flows in epidemiological modeling based on mobility data

An example operation may include one or more of acquiring, by a retailer node, an inventory data from a supplier node over a blockchain network, receiving, by the retailer node, outstanding orders data of the supplier node, generating, by the retailer node, an order distribution policy based on the inventory data and the outstanding orders data, and executing a smart contract to order goods from the supplier node based on the ordering policy.

An example operation may include one or more of receiving, by a first node, a freshness of goods data from a second node over a blockchain, and executing, by the first node, a smart contract to: calculate an initial order quantity based on a pre-set critical order number and a non-expiring goods order quantity and alter a final order quantity based on the initial order quantity and the freshness of the goods data.

Freshness visibility in supply-chain

Data content delivery and validation in a computer environment may provide a file system in the computer environment, the file system subdivided into unique folder locations per content type, each of the unique folder locations representing a content type folder. The file system is monitored for changes to a content type folder. An occurrence of a manifest file in the content type folder may be detected, the occurrence of the manifest file ensuring that all files in a package of files associated with the manifest file have arrived. Content of the manifest file may be analyzed to check validity of the files. A content package registry may be queried to determine a base job for processing a given content type associated with the package of files, and the base job may be run to process the package of files.

Data delivery and validation in hybrid cloud environments

An example operation may include one or more of collecting, by a first node, a plurality of permissioned data inputs from a plurality of second nodes of a supply-chain, performing, by the first node, a granular simulation based on the permissioned data inputs to generate a plurality of key performance indicators (KPIs), and executing a smart contract to adjust order processes of the supply-chain based on the KPIs.

Supply-chain simulation

Embodiments herein describe a return network simulation system that can simulate changes in a retailer's return network to determine the impact of those changes. Advantageously, being able to accurately simulate the retailer's return network means changes can be evaluated without first making those adjustments in the physical return network. Doing so avoids the cost of implementing the changes on the return network without first being able to predict whether the changes will have a net positive result (e.g., a positive result that offsets any negative results). A retailer can first simulate the change on the return network, review how the change affects one or more KPIs, and then decide whether to implement the change in the actual return network. As a result, the retailer has a reliable indicator whether the changes will result in a desired effect.

Simulating a return network

A computer implemented method and system of setting values of parameters of nodes in an omnichannel distribution system, the method comprising is provided. Input parameters are received from a computing device. Historical data related to the network of nodes is received from a data repository. A synthetic scenario is determined based on the received input parameters and the historical data. Each node is clustered into a corresponding category. For each category of nodes, key parameters are identified. A range of each key parameter is determined based on the synthetic scenario. A number of simulations N to perform with data sampled from the synthetic scenario within the determined range of each key parameter is determined. For each of the N simulations, a multi-objective optimization is performed to determine a cost factor of the parameter settings. The parameter settings with a lowest cost factor are selected.

Fulfilment machine for optimizing shipping

A computer implemented method and system of calculating labor resources for a network of nodes in an omnichannel distribution system. Input parameters are received from a computing device of a user. Historical data related to a network of nodes is received, from a data repository. A synthetic scenario is determined based on the received input parameters and the historical data. For each node, key parameters are identified and set based on a multi-objective optimization, wherein the multi-objective optimization includes a synthetic inventory allocation to the node based on the synthetic scenario. A synthetic labor efficiency is determined for the node from the synthetic scenario. Labor resources are calculated based on the synthetic inventory allocation for the synthetic scenario. The labor resources of at least one node are displayed on a user interface of a user device.

Machine for labor optimization for efficient shipping

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White Plains, NY, United States of America

Ajay Ashok Deshpande