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: An approach for generating a coding schema for identifying a spatial location of an event within video image data is provided. In one embodiment, there is a spatial representation tool, including a compression component configured to receive trajectory data of a trajectory of an object for an event within video image data; generate a lossless compressed contour-coded blob to encode the trajectory data of the trajectory of the object for the event within video image data; generate a lossy searchable code of the trajectory of the object for the event within the video image data; convert a region of interest within the video image data to a lossy query code, the region of interest corresponding to a sub-section of a visual display output of the video image data; and compare the lossy query code to the lossy searchable code within a relational database to identify a corresponding lossless trajectory data of the trajectory of the object for the event within the video image data.

Abstract: The present disclosure relates generally to the field of product supply networks (e.g., for order fulfillment and inventory control). In one specific example, mechanisms are provided for presenting visualizations to aid in co-locating two or more products in the same location (e.g., at a common order fulfillment facility) based upon associations between the products. In various embodiments, systems, methods and computer program products are provided.

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: Causal performance analysis for store merchandising may be provided. A clustering technique may be performed based on target store location data and existing store data. Based on the clustering technique, a peer selection group is determined comprising a group of stores determined to have similar attributes to the target store location. Sales distortions for a plurality of divisions associated with the group of stores in the peer selection group may be determined. A distortion matrix may be generated comprising a ranked list of the plurality of divisions. A merchandise mix recommendation for the target store location may be presented via a user interface device.

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 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 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: 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: An aspect of the disclosure includes a method, a system and a computer program product for determining a personalized bundle offer for a consumer. The method includes determining an interest in an initial product by a consumer. A demand group is identified based on the initial product. A purchase probability is determined for the consumer to purchase the product. An inventory expected profit-to-go is determined for the product. At least one additional product from the demand group is determined based at least in part on the purchase probability and the inventory expected profit-to-go, the expected profit to go being based on a current inventory state of the product and the at least one additional product. A signal is transmitted to the consumer, the signal including at least one additional product and a price for a bundle containing both the product of interest and the at least one additional product.

Abstract: Geo-defect repair modeling is provided. A method includes logically dividing a railroad network according to spatial and temporal dimensions with respect to historical data collected. The spatial dimensions include line segments of a specified length and the temporal dimensions include inspection run data for inspections performed for each of the line segments over a period of time. The method also includes creating a track deterioration model from the historical data, identifying geo-defects occurring at each inspection run from the track deterioration model, calculating a track deterioration condition from the track deterioration model by analyzing quantified changes in the geo-defects measured at each inspection run, and calculating a derailment risk based on track conditions determined from the inspection run data and the track deterioration condition.

Abstract: Geo-defect repair modeling is provided. A method includes logically dividing a railroad network according to spatial and temporal dimensions with respect to historical data collected. The spatial dimensions include line segments of a specified length and the temporal dimensions include inspection run data for inspections performed for each of the line segments over a period of time. The method also includes creating a track deterioration model from the historical data, identifying geo-defects occurring at each inspection run from the track deterioration model, calculating a track deterioration condition from the track deterioration model by analyzing quantified changes in the geo-defects measured at each inspection run, and calculating a derailment risk based on track conditions determined from the inspection run data and the track deterioration condition.

Abstract: A method and system optimizing source selection of an online order with the lowest fulfillment cost by considering multiple types of parameters, including shipping costs, backlog costs and markdown savings of the order. The method includes obtaining an order from the order retrieval subsystem of the OMS, selecting the candidate sources, and retrieving data from retailers or shipping companies of each selected candidate sources. The system then calculates the costs and savings parameters of the candidate sources from the retrieved data. The system identifies all possible candidate sourcing selections of the order and calculates the total fulfillment cost of each sourcing selection of the order by adding the shipping costs with the backlog costs, and subtracting the markdown savings of all candidate sources in each sourcing selection. The system identifies the optimized sourcing selection of the order with the lowest fulfillment cost and renders the selection to the OMS.

Abstract: An invention for identifying a spatial location of an event within video image data is provided. Disclosed are embodiments for detecting an object and obtaining trajectory data of a trajectory of the object within the video image data from a sensor device; converting the trajectory data into a contour-coded compressed image; generating, based on the trajectory data, a searchable code that contains a set of locations traversed by the trajectory of the object within the video image; associating the searchable code with the contour-coded compressed image in a database; and returning, in response to a query having a selected location that corresponds a location of the set of locations in the searchable code, an image of the trajectory data corresponding to the object based on the contour-coded compressed image in the database.

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 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 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: The methods, systems, and computer program products described herein provide optimized order fulfillment in an omni-channel order fulfillment system. In an aspect of the present disclosure, dimensional multipliers for a plurality of dimensions are optimized by iteratively solving a multi criteria decision analysis framework (MCDA) sequentially for each dimension. The optimized dimensional multipliers may be used by the MCDA as inputs along with current data to generate a solution for each dimension. The solution may be integrated by the order fulfillment system to optimize order fulfillment based on the current data.

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 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.