International Business Machines Corporation

Steve Igrejas

Arun Hampapur

Xuan Liu

Ajay Ashok Deshpande

Christopher Scott Milite

Yingjie Li

Vadiraja S Ramamurthy

Jae-Eun Park

Hongliang Fei

Alan Jonathan King

Xiao Mei Zheng

Songhua Xing

Miao He

Kimberly D Hendrix

JoAnn Piersa Brereton

Joline Ann Villaranda Uichanco

Saurabh Gupta

Ali Koc

Brian Leo Quanz

Chek Keong Tan

Dahai Xing

Xiaobo Zheng

Ding Ding Lin

Pradyumnha Gopalkrishna Kowlani

Michael Yesudas

Sachin Sethiya

Sanjay E Cheeran

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 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

Growing community of inventors

Newton, NH, United States of America

Steve Igrejas