International Business Machines Corporation

Wayfair LLC

Yingjie Li

Xuan Liu

Ajay Ashok Deshpande

Ali Koc

Brian Leo Quanz

Arun Hampapur

Christopher Scott Milite

Dahai Xing

Saurabh Gupta

Chek Keong Tan

Xiaobo Zheng

Alan Jonathan King

Yada Zhu

Lei Cao

Xiao Mei Zheng

Jae-Eun Park

Bhavna Agrawal

Ding Ding Lin

Vadiraja S Ramamurthy

Steve Igrejas

Shyh-Kwei Chen

Elham Khabiri

Hongliang Fei

Miao He

Songhua Xing

Kimberly D Hendrix

Anuradha Bhamidipaty

JoAnn Piersa Brereton

Pradyumnha Gopalkrishna Kowlani

Shuxin Lin

Dingding Lin

Joline Ann Villaranda Uichanco

Chandrasekhar Narayanaswami

Jeffrey Owen Kephart

Zhengliang Xue

Michael Yesudas

Pranav Girish Sankhe

Sanjay E Cheeran

Sachin Sethiya

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

Temporal and spatially integrated forecast modeling includes generating a plurality of forecast models for a plurality of short-term to long-term time periods for a plurality of locations. Temporally integrating the plurality of forecast models sequentially over the plurality of time periods for the plurality of locations and spatially integrating the temporally integrated plurality of forecast models for each location hierarchically over the geographic areas. The forecast models are autoregressive distributed lag models with different explanatory variables for the short-term and long-term forecast models. The temporally integrating includes recursively integrating the plurality of forecast models over the time periods from the short-term to the long-term time periods and the spatially integrating includes recursively integrating the temporally integrated plurality of forecast models hierarchically from larger size geographic areas to smaller size geographic areas. The method includes optimizing the resultant spatially and temporally integrated forecast model based on a plurality of constraints.

Multi-locational forecast modeling in both temporal and spatial dimensions

A method is provided for creating a semantic model for submitting search queries thereto. The method includes an act of receiving data from one or more input sources in an entity and relationship capture service of a situational awareness engine. The method further includes an act of extracting entities and relationships between the entities in two or more extraction services, where the two or more extraction services include at least two of a table-to-graph service, an event-to-graph service, a sensor-to-graph service, a text-to-graph service, and an image-to-graph service. The method includes an act of generating a semantic model based on fusion and labeling the extracted data provided by the at least two extraction services, where the semantic model can receive a search query and respond to the search query based on the generated semantic model.

Situational awareness by fusing multi-modal data with semantic model

Tabular data is accessed that contains multiple entries of alphanumeric data. Multiple tokens are generated of the multiple entries of alphanumeric data using a tokenization process. The tokenization process maintains jargon-specific features of the alphanumeric data. Multiple embeddings of the multiple entries of alphanumeric data are generated using the tokens. The embeddings capture similarity of the multiple entries considering all of global features, column features, and row features in the tokens of the tabular data. A neural network is used to predict probabilities for pre-defined classes for the tabular data using the generated embeddings.

Generating unique word embeddings for jargon-specific tabular data for neural network training and usage

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

Techniques for facilitating estimation of node processing capacity values for order fulfillment are provided. In one example, a computer-implemented method can comprise: generating, by a system operatively coupled to a processor, a current processing capacity value for an entity; and determining, by the system, a future processing capacity value for the entity based on the current processing capacity value and by using a future capacity model that has been explicitly trained to infer respective processing capacity values for the entity. The computer-implemented method can also comprise fulfilling an order of an item, by the system, based on the future processing capacity value.

Estimation of node processing capacity for order fulfillment

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

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.

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Chappaqua, NY, United States of America

Yingjie Li