International Business Machines Corporation

Virage, Incorporated

Toshiba Global Commerce Solutions Holdings Corporation

Bloom Value Corporation

Globalfoundries Inc.

Wayfair LLC

Other

Doordash, Inc.

Utopus Insights, Inc.

Toshiba Global Commerce

Solutions Holdings Corporation

Arun Hampapur

Sharathchandra UmapathiRao Pankanti

Rogerio Schmidt Feris

Xuan Liu

Ying-Li Tian

Chiao-Fe Shu

Andrew William Senior

Yun Zhai

Christopher Scott Milite

Ajay Ashok Deshpande

Chek Keong Tan

Lisa Marie Brown

Brian Leo Quanz

Dahai Xing

Yingjie Li

Russell Patrick Bobbitt

Lisa Maire Brown

Ali Koc

Saurabh Gupta

Hongfei Li

Zuoxuan Lu

Jonathan Hudson Connell, Ii

Xiaobo Zheng

Quanfu Fan

Frederik C Kjeldsen

Akira Yanagawa

Qing He

Norman C Haas

Alan Jonathan King

Daniel Andre Vaquero

Songhua Xing

Stephen R Russo

Myron Dale Flickner

Amarnath Gupta

Michael J Desimone

Mojgan Monika Gorkani

Carl P Mercier

Zouxuan Lu

Dhaivat P Parikh

Wei Shan Dong

Yada Zhu

Ramesh Jain

Bradley Horowitz

Zhiguo Li

Longbin Chen

Frank D Fenhagen, Iv

Wei Sun

Rudolf Maarten Bolle

Hongliang Fei

Richard D Humphrey

Shilpa N Mahatma

Charles A Otto

Charles Fuller

Jeffrey R Bach

Xiao Mei Zheng

Zhong Bo Jiang

Michael J Portuesi

Pradyumnha Gopalkrishna Kowlani

Senem Velipasalar

Jayant R Kalagnanam

Ying Li

Tarun Kumar

Sharathchandra Umapathirao Pankanti

Jonathan H Connell

Lexing Xie

Ding Ding Lin

James R Culp

Hao-Wei Liu

Raymond A Cooke

Dingding Lin

Li Li

Jae-Eun Park

Shyh-Kwei Chen

Brenda Lynn Dietrich

Robert Stephen Olyha, Jr

Miao He

Sergio Borger

Chidanand V Apte

Kimberly D Hendrix

JoAnn Piersa Brereton

Debarun Bhattacharjya

Lei Ding

Frederik C M Kjeldsen

Andrew J Davenport

Ning Duan

Chun Ma

Emmanuel Yashchin

Naga A Ayachitula

Martin G Kienzle

Lalit Agarwalla

Ahmet Ekin

Sachiko Miyazawa

Shu-Ping Chang

Yue Tong

Mercan Topkara

Ke Hu

Yashodhara M Patnaik

Lei Cao

Bert Huang

Buyue Qian

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

Configurations of a system and a method for a dynamic trust-based authorization system (DTBAS), are described. In one aspect, the DTBAS may implement an execution of operations of multiple engines, models, framework, circuits, code, etc., for dynamically authorizing a request for a service or an operation. The DTBAS, when deployed in a managing entity, may receive a request from a managed entity to execute an operation or task. Based on parameters or attributes associated with the managed entity, the DTBAS may execute operations to evaluate a trust score. Based on the evaluated trust score, the DTBAS may compute a weight decision matrix that may include multiple weights associated with the managed entity. Based on the computed weight decision matrix and the evaluated trust score, the DTBAS may authorize or deny the execution of the requested operation.

Dynamic trust-based authorization system

A system and method to authorize requests for procedures or medical claim requests is described. In one aspect, datasets from multiple data sources, including historical authorizations associated with various medical claims, are processed. The machine learning engines and the machine learning models are trained based on the received data. Simultaneously, real-time authorization requests are received and analyzed from secondary data sources. Each request is split into service requests based on second attributes, which include identifiers linked to services. The services are encoded using fixed-length encoding, and unique numeric identifiers are assigned to categorical variables. For each encoded service request, contexts, actions, and decisions are determined by comparing the first attributes of historical authorization requests with the second attributes of real-time requests. Service requests are then categorized into cohorts based on similarity metrics between their second attributes and those of historical authorizations. This comparison enables efficient processing and informed decision-making.

Intelligent authorization system

A method for creating a spatio-temporal schedule includes receiving a set of available work crews and a set of workorders to be scheduled, sequencing the set of workorders, wherein each workorder of the set of workorders is associated with a geographic location, a start time and an end time, and iterating through the set of available work crews, assigning a plurality of workorders of the set of workorders fully allocating a current crew before selecting a next crew, until all work crews are fully allocated and/or all workorders are assigned, wherein the assignment of workorders for a given work crew generates a route comprising the plurality of workorders.

Spatio-temporal approach to scheduling field operations

A system and a method to optimize values delivered by an enterprise, are described. An integrated system may receive data from multiple data sources that may be processed by engines and/models. The engines and/or models may execute operations or functions, such as data processing, analysis based on rules, automated learning, machine learning and transforming the data to create value graphs associated with processes, tasks, and/or services in the enterprise. The value graphs may be generated that may provision continuous monitoring and insights to measure of KPIs and other influencing factors that may be associated with the processes, and services in the enterprise. The integrated system may generate visualizations that may be rendered via user interfaces. Further based on the value graphs, stakeholders may be able to identify opportunities associated with processes, tasks, and/or services that may be actioned to drive employee engagement, and value of the enterprise.

System for enterprise value optimization

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

Evaluating node fulfillment capacity in node order assignment by receiving a current order for node order assignment, retrieving data of each node, the retrieved data of each node including current capacity utilization, determining a probability of backlog on an expected ship date of each node, the probability of backlog being based on the retrieved current capacity utilization, determining a capacity utilization cost of each node based on the probability of backlog on the expected ship date, automatically calculating a fulfillment cost of each node of the current order based on the capacity utilization cost, identifying one or more nodes for the current order with the lowest fulfillment cost and automatically generating a node order assignment assigning the current order to one of the one or more nodes with the lowest fulfillment cost.

System and method to incorporate node fulfillment capacity and capacity utilization in balancing fulfillment load across retail supply networks

Causal performance analysis for store merchandising may be provided. A clustering technique may be performed based on target store location data and existing store data. Based on the clustering technique, a peer selection group is determined comprising a group of stores determined to have similar attributes to the target store location. Sales distortions for a plurality of divisions associated with the group of stores in the peer selection group may be determined. A distortion matrix may be generated comprising a ranked list of the plurality of divisions. A merchandise mix recommendation for the target store location may be presented via a user interface device.

Causal performance analysis approach for store merchandizing analysis

A method for continuously tracking business performance impact of order sourcing systems and algorithms that decide how ecommerce orders should be fulfilled by assigning the items of the order to nodes in a fulfillment network such as stores, distribution centers, and third party logistics&#x2014;to provide automatic root cause analysis and solution recommendations to pre-defined business problems arising from KPI monitoring. A Business Intelligence (BI) dashboard architecture operates with: 1) a monitoring module that continuously monitors business KPIs and creates abnormality alerts; and 2) a root cause analysis module that is designed specifically for each business problem to give real time diagnosis and solution recommendation. The root cause analysis module receives the created alert, and triggers conducting a root cause analysis at an analytics engine. The BI dashboard and user interface enables visualization of the KPI performance and root cause analysis results.

Tracking business performance impact of optimized sourcing algorithms

A method and system for considering customized capacity utilization cost in node order fulfillment. The method includes receiving by a customized capacity utilization cost module an electronic record of a current order. The method includes retrieving data of a plurality of nodes and calculating an actual capacity utilization. The method includes automatically converting the actual capacity utilization of each node of the plurality of nodes and a predetermined maximum amount of cost to balance capacity utilization across the plurality of nodes into a customized capacity utilization cost, and transmitting the customized capacity utilization cost to an order fulfillment engine. The method includes receiving by the order fulfillment engine the current order, the processing cost data, and the customized capacity utilization cost. The method includes automatically calculating a fulfillment cost and identifying a node-order assignment with the lowest fulfillment cost.

System and method to incorporate customized capacity utilization cost in balancing fulfillment load across retail supply networks

The present disclosure relates generally to the field of retail supply networks. In one specific example, mechanisms are provided to model markdown-avoidance savings for omni-channel fulfillment in retail supply networks. In various embodiments, systems, methods and computer program products are provided.

System, method and computer program product for order fulfillment in retail supply networks

A predictive engine on a computer environment comprising a shared pool of configurable computing resources is executed to perform a predictive analysis on data pipelined into the computer environment, the data received from a plurality of sources and in a plurality of different formats, the predictive engine generating a network level cost information based on the predictive analysis on a dynamic and continuous basis. Asynchronous communication comprising the network level cost information from the predictive engine is received and a set of candidate nodes for order fulfillment is generated based on the network level cost information and a defined distance between the set of candidate nodes and a target destination. An optimization engine on the computer environment is invoked that filters the set of candidate nodes. A number of fulfillment nodes that meet one or more of a constraint and preconfigured rule is output.

Order sourcing with asynchronous communication and using optimization for large sourcing networks

A method and system determining node order fulfillment performance considering cancelation costs. The method includes receiving a current order for fulfillment node assignment and calculating a cancelation ratio of a node of a plurality of nodes by dividing orders canceled due to back order from the node by orders scheduled from the node collected from a pre-assigned time period. The method also includes determining a cancelation cost of the node based on the cancelation ratio of said node. The method then includes automatically generating a node order assignment based on the determined cancelation cost for fulfillment of a current order.

System and method for determining node order fulfillment performance

A method and system determining an inventory threshold for offering for online sale or an inventory threshold for sourcing in node order assignment. The method includes receiving by a computer processor of a probabilistic cancellation module an electronic record of a current order or item. The program instructions executed by the processor of the probabilistic cancellation module allows the module to retrieve historical and current data of each node from a plurality of nodes. The method then includes automatically converting the retrieved historical data into a probability of cancellation of an item comprising the one or more items from the plurality of items. Further, the method includes identifying an inventory threshold for offering of an item or an inventory threshold for sourcing of one or more items of the current order, where the probability of item cancellation is lower than a predetermined order cancelation rate of the one or more items from the plurality of items.

System and method for setting inventory thresholds for offering and fulfillment across retail supply networks

A method and system for evaluating node fulfillment capacity in node order assignment. The method includes receiving by a network average capacity utilization cost module an electronic record of a current order. The method includes retrieving data of a plurality of nodes, calculating an actual capacity utilization on an expected date, and determining a probability of backlog on the expected date. The method includes generating a network average capacity utilization cost model, automatically converting a regular labor cost or a overtime labor cost into a capacity utilization cost, and transmitting the capacity utilization cost of each node to an order fulfillment engine. The method includes receiving by the engine the current order, the processing cost data, and the capacity utilization cost. The method includes automatically calculating a fulfillment cost and identifying a node with the lowest fulfillment cost for assignment.

System and method to incorporate node fulfillment capacity and network average capacity utilization in balancing fulfillment load across retail supply networks

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Norwalk, CT, United States of America

Arun Hampapur