Abstract: A method is provided for selecting eligible nodes and item assignments to those nodes for fulfillment based on satisfaction of a reward constraint and fulfillment cost. This method includes several steps, including determining by a computer processor of the fulfillment engine whether each of the plurality of assignments to the plurality of nodes satisfies a reward constraint based on a first customer loyalty reward level of the plurality of customer loyalty reward levels, identifying by a computer processor of the fulfillment engine one or more satisfactory assignments to nodes by identifying which of the one or more assignments is both associated with an order fulfillment cost that is less than the maximum fulfillment cost and satisfies the reward constraint and automatically generating by a computer processor of the fulfillment engine a node order assignment assigning the current order to the one or more satisfactory nodes.

Abstract: 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.

Abstract: 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.

Abstract: 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—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.

Abstract: 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.

Abstract: A method is provided for selecting eligible nodes and item assignments to those nodes for fulfillment based on satisfaction of a reward constraint and fulfillment cost. This method includes several steps, including determining by a computer processor of the fulfillment engine whether each of the plurality of assignments to the plurality of nodes satisfies a reward constraint based on a first customer loyalty reward level of the plurality of customer loyalty reward levels, identifying by a computer processor of the fulfillment engine one or more satisfactory assignments to nodes by identifying which of the one or more assignments is both associated with an order fulfillment cost that is less than the maximum fulfillment cost and satisfies the reward constraint and automatically generating by a computer processor of the fulfillment engine a node order assignment assigning the current order to the one or more satisfactory nodes.

Abstract: A method is provided for determining an order fulfillment by a simplified fulfillment deciding engine or a full fulfillment deciding engine. This method includes several steps, including determining whether the number of nodes considered in making the fulfillment order decision of the current order can be a second number of node decisions, automatically transmitting the current order to one of a simplified fulfillment deciding engine and the full fulfillment deciding engine, the simplified fulfillment deciding engine considering the second number of node decisions in making the fulfillment order decision, wherein the current order is transmitted to the simplified fulfillment deciding engine when the number of node decisions is equal to the second number of node decisions.

Abstract: A method and computer readable medium for selecting order fulfillment nodes within an order fulfillment system. The method determines one or more product-node pairs of an omni-channel order fulfillment system responsive to a received customer order. There is received for each determined product-node pair, a first input including an associated price and a price markdown rate (r) for the product. There is further received for each product-node pair, input representing a current amount of supply availability of the product (w) for a period of time in an inventory; and a first threshold value and a second threshold values defining a supply availability window of the product. The method computes an inventory performance value based on the current w, the defined supply availability window, and one or more the p and r values; and selects a node for customer order fulfillment based on the computed cost of inventory performance value.

Abstract: Examples of techniques for generating an inter-store inventory transfer are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method may include defining a subset of stores of a plurality of stores to fulfill inter-store inventory transfer request for a product category of a plurality of product categories. The method may further include, responsive to determining that an order for a product of the product category cannot be fulfilled by one of the stores of the subset of stores of the plurality of stores, determining, by a processing device, an alternate store of the subset of stores to fulfill the order.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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—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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.