International Business Machines Corporation

Xiaobo Zheng

Arun Hampapur

Xuan Liu

Ajay Ashok Deshpande

Saurabh Gupta

Ali Koc

Brian Leo Quanz

Dahai Xing

Christopher Scott Milite

Yingjie Li

Chek Keong Tan

Alan Jonathan King

Dingding Lin

Shyh-Kwei Chen

Yue Tong

Ding Ding Lin

Pradyumnha Gopalkrishna Kowlani

Vadiraja S Ramamurthy

Steve Igrejas

Michael Yesudas

Sachin Sethiya

Sanjay E Cheeran

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 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 historical scenario and historical decisions made in the historical scenario are received. The historical decisions represent a set of decision variables of an objective function. A random set of decision variables having different values than the set of decision variables are generated. To determine a weight setting associated with multiple objectives of the objective function, a number of inequalities are built and solved with an assumption that, for an optimization that minimizes the objective function, the objective function having the set of random decision variables has a larger value than the objective function having the set of decision variables. The receiving, the generating and the building steps may be repeated to determine multiple sets of weight settings. The multiple sets of weight settings are searched to select a target weight setting for each of the multiple objectives. The target weight setting may be automatically and continuously learned.

Automatic learning of weight settings for multi-objective models

A method for enhancing on-premise order management systems (OMS) designed for fulfillment transactions with analytic and optimization technologies and services hosted in a shared multi-tenant software-as-a-service (SaaS) environment, such as a hybrid cloud. The computer-implemented method improves an order management system by leveraging a 'punch-out' approach based on user exits to integrate with and augment currently implemented order management processing and transaction flows. Using the hybrid cloud, an entity may retain data such as its accumulated business, sales, test and other data, and then run analytical queries, which can scale to support distributed computing tasks. A framework adaptor/connector is leveraged by the OMS to provide a web client for communicating with and integrating to the SaaS analytics runtime environment, encapsulating all necessary connection pooling, security, and data marshaling complexity away from the order management system to meet strict service response time windows.

Order optimization in hybrid cloud networks

A system, method and computer program product 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.

A system, method and computer program product for enhancing on-premise order management systems (OMS) designed for fulfillment transactions with analytic and optimization technologies and services hosted in a shared multi-tenant software-as-a-service (SaaS) environment, such as a hybrid cloud. The computer-implemented method improves an order management system by leveraging a 'punch-out' approach based on user exits to integrate with and augment currently implemented order management processing and transaction flows. Using the hybrid cloud, an entity may retain data such as its accumulated business, sales, test and other data, and then run analytical queries, which can scale to support distributed computing tasks. A framework adaptor/connector is leveraged by the OMS to provide a web client for communicating with and integrating to the SaaS analytics runtime environment, encapsulating all necessary connection pooling, security, and data marshaling complexity away from the order management system to meet strict service response time windows.

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.

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Shanghai, China

Xiaobo Zheng