International Business Machines Corporation

Alan Jonathan King

Arun Hampapur

Xuan Liu

Ajay Ashok Deshpande

Christopher Scott Milite

Yingjie Li

Saurabh Gupta

Ali Koc

Brian Leo Quanz

Chek Keong Tan

Dahai Xing

Xiaobo Zheng

Xiao Mei Zheng

Amanda Peters

Jae-Eun Park

Patrick Joseph McCarthy

Hongliang Fei

James C Sexton

Songhua Xing

Miao He

Kimberly D Hendrix

JoAnn Piersa Brereton

Vadiraja S Ramamurthy

Steve Igrejas

Joline Ann Villaranda Uichanco

Dharmashankar Subramanian

Pu Huang

Michael B Mundy

William Grey

Gordon G Stewart

Ding Ding Lin

Pradyumnha Gopalkrishna Kowlani

Thomas A Budnik

Stuart Irwin Feldman

Dailun Shi

Richard Perret

Todd A Inglet

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 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 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

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 one or more of a plurality of costs and capacity utilization 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.

A simulator is configured to simulate the fulfillment of orders by nodes. Each node has an inventory of products and is capable of shipping the products to destinations in response to receipt of a corresponding order. The simulator divides the nodes into groups and assigns a different priority to each group based on input provided by a user to the simulator to generate an ordered sequence of priorities. The simulator maintains safety stock data corresponding to each node that indicates minimum quantities of the products required to be present at the corresponding node. The simulator selects a current priority of the sequence and next simulates a first group among the groups having the current priority fulfilling the orders for a given product among the products while a quantity of the given product at each of the nodes in the first group is below the minimum quantity in the corresponding safety stock data.

Big data sourcing simulator

A method and system for evaluating node fulfillment capacity in node order assignment. The method includes receiving a current order for node order assignment. The method also includes retrieving data of each node from a plurality of nodes, the retrieved data comprising current capacity utilization, capacity of a current day and capacity of a future day. The method then includes 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. Further, the method includes automatically converting the probability of backlog, backlog cost, and labor cost of each node into a capacity utilization cost of the each node using a capacity utilization cost model defining a set of predetermined capacity utilization threshold values. Then, the method includes automatically calculating a fulfillment cost of each node of the current order by adding a plurality of costs.

A method, system and computer program product for managing an order in an Omni-channel order fulfillment system is disclosed. A stock-keeping unit (SKU) node level cost is predicted for a plurality of SKUs. The order is received containing one or more SKUs. A candidate list of fulfillment nodes is determined for fulfilling each of the one or more SKUs in the order using the predicted SKU node level cost and a fulfillment node-destination shipping distance. One or more fulfillment nodes are selected from the candidate list, and the order is fulfilled using the selected one or more fulfillment nodes.

Two phase predictive approach for supply network optimization

A computer program product stored on machine readable media including machine readable instructions for selecting a project portfolio from available projects, the instructions for implementing a method include inputting an investment cost and a return for each available project and user-specified constraints; creating a formulation, the formulation comprising the investment cost and the return for each available project, the constraints and variables, the variables comprising for each available project a threshold probability of success and a selection decision; solving the formulation to select the project portfolio and to determine the threshold probability of success for each available project; and outputting the project portfolio and the threshold probability of success for each available project.

Method and system for risk-hedging in project management

Executing Multiple Instructions Multiple Data ('MIMD') programs on a Single Instruction Multiple Data ('SIMD') machine, the SIMD machine including a plurality of compute nodes, each compute node capable of executing only a single thread of execution, the compute nodes initially configured exclusively for SIMD operations, the SIMD machine further comprising a data communications network, the network comprising synchronous data communications links among the compute nodes, including establishing a SIMD partition comprising a plurality of the compute nodes; booting the SIMD partition in MIMD mode; executing by launcher programs a plurality of MIMD programs on compute nodes in the SIMD partition; and re-executing a launcher program by an operating system on a compute node in the SIMD partition upon termination of the MIMD program executed by the launcher program.

Executing Multiple Instructions Multiple Data ('MIMD') programs on a Single Instruction Multiple Data ('SIMD') machine

Executing MIMD programs on a SIMD machine, including establishing on the SIMD machine a plurality of SIMD partitions; booting a first SIMD partition in MIMD mode; executing, on a compute node of the first SIMD partition booted in MIMD mode, a MIMD accelerator program; executing a SIMD program in a second SIMD partition, one instance of the SIMD program executing on each compute node of the second SIMD partition, each instance of the SIMD program carrying out a portion of the data processing effected by the SIMD program; and accelerating, by an instance of the SIMD program through the MIMD accelerator program, a portion of the data processing of the instance of the SIMD program.

Executing multiple instructions multiple date ('MIMD') programs on a single instruction multiple data ('SIMD') machine

An apparatus, system and method for managing supply chain risk are provided. The apparatus, system and method identify which components are most critical to the assembly of the final product, in terms of placing the largest amount of revenue or profit at risk. The impact on profit and revenue of the failure to effectively deliver one or more of these critical products along one or more of the dimensions of quantity, time, space, or quality, is then quantified. The revenue and profit distribution from the supply chain is characterized given a projected distribution supply uncertainty, taking into consideration that input products are only useful if all of the BOM components are present. The revenue at risk is then determined. From the set of possible final products that can be produced, the portfolio of final products with the best risk-return characteristics are determined. Efficient hedges may then be developed.

Apparatus, system and method for measuring and monitoring supply chain risk

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South Salem, NY, United States of America

Alan Jonathan King