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 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 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: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure relates generally to computational solution algorithms (and associated systems and methods) applied to a stochastic unit commitment problem. In one example, the computational solution algorithms (and associated systems and methods) may be applied to the energy industry.

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 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: Technology for reducing buffer overflow at a Third Generation Partnership Project (3GPP) Serving Gateway (S-GW) is described. A buffer overflow message may be received, at an evolved node B (eNB) from the S-GW, indicating potential overflow of downlink information at an S-GW buffer. The downlink information may be stored at the S-GW buffer until a plurality of user equipments (UEs) awake from a low power mode during a discontinuous reception (DRX) sleep cycle. One or more UEs may be selected from the plurality of UEs according to predefined criteria, wherein the one or more UEs are in a connected mode. The DRX configurations of the one or more UEs may be modified in order to reduce the downlink information that is stored at the S-GW buffer, thereby reducing the potential for overflow at the S-GW buffer.

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: 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.

Abstract: 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.

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: Technology for communicating a small data set between a user equipment (UE) and an evolved node B (eNB). A channel condition of a downlink channel with the eNB may be determined at the UE. A channel condition indication based on the channel condition may be communicated to the eNB. The small data may be transmitted to the eNB using a signaling radio bearer (SRB) when the channel condition is greater than a selected threshold.

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 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: 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.