Abstract: A system and method are disclosed for generating intelligent multi-modal system actions based, at least in part, on predicting a user action and one or more stored user inputs. Embodiments include a database and a computer comprising a processor and memory, the computer is configured to monitor user inputs using one or more sensors and one or more tactile interface devices, detect at least two modes of user input and store the user inputs in the database. The computer is further configured to evaluate the stored user inputs in the database and the at least two modes of user input to generate a system action and generate a system action based, at least in part, on predicting a user action and one or more stored user inputs.

Abstract: A system and method are disclosed for generating an assortment plan by an assortment planner. The assortment planner stores value metrics for two or more products, each of the products including two or more of attributes. The assortment planner also analyzes the two or more attributes, by generating attribute values for each of the two or more attributes and communicates the attribute values for each of the two or more attributes to a planning engine. The assortment planner also further constructs a product attributes table and a value metrics table, duplicates the attribute values to generate a merged product attributes table including a number of duplicates of the attribute values and generates a binary value table for each of the attribute values, one or more itemsets, and an assortment of products based, at least in part, on the one or more itemsets. Other embodiments are also disclosed.

Abstract: A system and method is disclosed for incrementally adjusting a supply chain plan. The system includes a database operable to store data associated with one or more supply chain entities and a server system coupled with the database. The server system receives one or more perturbations in supply chain plan inputs from one or more of the supply chain entities, wherein the perturbations are received during a period of time separating a supply chain planning session from a subsequent supply chain planning session and accesses the data stored in the database associated with the one or more supply chain entities. The server system also incrementally and optimally adjusts the supply chain plan based on the one or more received perturbations and the data stored in the database and communicates the incrementally adjusted supply chain plan to the one or more supply chain entities.

Abstract: A system and method are disclosed for planning a product assortment based on a sales forecast without using a cross elasticity by receiving a percentage pricing change for at least two substitutable products of an inventory in a supply chain network having one or more supply chain entities, and at least two substitutable products are grouped in the same product category and at least one of at least two substitutable products is grouped in a product assortment, calculating an average percent pricing change for the product category including at least two substitutable products and a direct effect factor and cross-effect factor for each of at least two substitutable products, and identifying an item of at least two substitutable items to be removed from the product assortment based, at least in part, on a substitutable demand calculated by modeling a price increase of a substitutable item to infinity.

Abstract: A system and method are multi-level replenishment planning with independent channel demand prioritization that initiates production of a product by a first supply chain entity that receives orders for the product from a second supply chain entity and supplies the product to a third supply chain entity that receives orders for the product from a fourth supply chain entity. Embodiments further disclose calculating the supply of the product for the first supply chain entity for a supply allocation duration time period, receiving demand orders high priority demand orders from the fourth supply chain entity, allocating supplies within the supply allocation duration time period to firm plan arrivals based on a location priority and a demand date, and generating a shipment recommendation for each allocated supply that meets a firmed planned arrival.

Abstract: A system and method are disclosed for handling an available to promise inquiry. The method comprises requesting statistics for partitions of a distributed cache, determining whether a threshold for the partitions has been reached, in response to a determination that the threshold for the partitions has been reached, fetching inventory data for the available to promise inquiry from a local cache, and in response to a determination that the threshold for the partitions has not been reached, fetching inventory data for the available to promise inquiry from the distributed cache. The method further comprises setting an expiry time before the local cache is purged, and storing a status of the partitions based at least in part on the determining that the threshold for the partitions has been reached, the status indicating the partitions as being overloaded.

Abstract: A system and method for a risk management visualization system comprises a computer having a processor and memory and configured to model a supply chain network as a supply chain planning problem, one or more key process indicators (KPIs) of the supply chain planning problem is based, at least in part, on the one or more input variables, model an impact on the one or more KPIs from each of the one or more input variables at a selected confidence interval using a Bayesian optimization process, and display a visualization of a risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.

Abstract: A system and method for performing tactical segmentation including a supply chain network having a tactical segmentation planner, an inventory system, a transportation network, supply chain entities and a computer.

Abstract: A system and method are disclosed for extracting hindsights for assortment planning. The system provides for classifying each of one or more products in a product display area of a retail entity as high-performers or low-performers according to selected metrics. The system further provides for identifying a strength of each individual product attribute associated with each of the products, deriving a multi-combination strength of combinations of product attributes using the identified strength of each individual product attribute, and generating recommendations for product combinations based on the derived multi-combination strength of combinations of product attributes and the selected metrics.

Abstract: A system and method are disclosed including a production planner that receives a sales forecast for configurations of an automobile. The production planner also receives constraints associated with an automobile supply chain. The production planner further models configurations and constraints as a production planning problem, solves a production plan for automobiles, and displays an automobile production solution of the production planning problem.

Abstract: A system and method are disclosed including a computer and a processor and memory. The computer receives option constraint limiting values of a second option parameter based on a value for a first option parameter, wherein the first option parameter and the second option parameter comprise mix rates for options for a configuration. The computer further generates an interactive element for setting values of the first option parameter and an interactive element for setting the value for the second option parameter and displays the one or more interactive elements. The computer still further, in response to a value being set for the first option parameter, calculates limits for the values of the second option parameters and prevents the interactive element for setting the value for the second option parameter to be set to values outside of the calculated limits.

Abstract: A system and method efficiently solve subsequent runs of a supply chain planning problem modeled as a multi-objective hierarchical linear programming problem. Embodiments include modeling a supply chain planning problem as a multi-objective hierarchal linear programming problem having first Run1 objectives and based, at least in part, on supply chain input data, receiving one or more changes to the supply chain input data, modeling a second supply chain planning problem based, at least in part, on the one or more changes to the supply chain input data, and modeled as a second multi-objective hierarchal linear programming problem having Run2 objectives, generating a superset matrix, and generating a supply chain plan comprising the one or more changes to the supply chain input data by converting a solution of the second supply chain planning problem.

Abstract: A system and method are disclosed for identifying supply chain segmentations from an initially non-segmented supply chain is disclosed. Embodiments of identifying supply chain segmentations includes receiving a current state of items in a supply chain network, wherein an inventory of the one or more supply chain entities is used to store one or more items, organizing one or more supply chain entities into one or more customer clusters, associating supply chain models for customer clusters, and transporting items among one or more supply chain entities.

Abstract: A system and method are disclosed for predicting recommendations for a user interface. The method includes generating a graphical user interface that receives an input from a user of a client portal, crawling tasks associated with the user in the client portal, ranking the tasks according to an intent associated with the input from the user, fetching at least one task from the ranked tasks, calculating a quantity of steps to complete the task from the ranked tasks; identifying one or more slots used by the task in at least one step from the quantity of steps, and generating one or more recommendations comprising a subsequent action for the user to complete the task.

Abstract: A system and method are disclosed for computer vision in-store planogram analysis. The system includes a retail entity comprising a product display area defined by a planogram, and a planogram analyzer comprising a server which trains a machine learning model to generate recommendations to optimize one or more planogram metrics using historical data and computer vision data. The system generates, by the trained machine learning model, recommendations to optimize the one or more planogram metrics, and alters the planogram based on the one or more recommendations. The system further uses the trained machine learning model with a probabilistic optimization technique approximating a global optimum to generate the one or more recommendations to optimize the one or more planogram metrics. The computer vision includes one or more of eyeball movement, eyeball engagement and eyeball lingering time.

Abstract: A system and method are disclosed for a low-touch centralized system to predict service level failure in a supply chain using machine learning. Embodiments include receiving only historical supply chain data from an archiving system for one or more supply chain entities storing items at stocking locations, predicting one or more supply chain events during a prediction period by applying a predictive model to a sample of historical supply chain data, calculating an occurrence risk score for at least one of the one or more supply chain events and indicating a possibility that the at least one of the one or more supply chain events will occur, generating one or more alerts identifying at least one item and at least one alert stocking location, rendering an alert heatmap visualization comprising one or more selectable user interface elements, and provide one or more tools for initiating corrective actions to be undertaken.

Abstract: A system and method are disclosed for training a probabilistic graphical model based on historical attributes of a supply chain to represent supply chain performance, selecting supply chain entity target variables, collating with the use of machine learning models, a list of features and classes pertaining to selected supply chain entity target variables, calculating first and second level features associated with the list of features and classes, generating supply chain predictions based on the trained probabilistic graphical model, where the supply chain predictions are based on test data, comparing the supply chain predictions to desired supply chain outputs to determine a delta distance, and generating resolution actions, to decrease the delta distance between the supply chain output predictions and the desired supply chain outputs.

Abstract: A system and method are disclosed for aggregating product trends. Embodiments include receiving an initial set of images from one or more data feeds by a trend aggregation system comprising a server, identifying one or more attribute values from the initial set of images, and quantifying a social affinity score of one or more items of an inventory of a supply chain entity based on recentness, relevance, and similarities of the identified one or more attribute values to an attribute value of a potential product for a product assortment. Embodiments may further include receiving an image of at least one additional item, identifying a product attribute from the image, and assigning an attribute value to the at least one additional item based, at least in part, on the identified attribute value from the image of the least one additional item and the attribute values of the at least two items.

Abstract: A system and method are disclosed including a planner having a processor and memory. The planner models a supply chain network over a planning horizon having one or more time buckets and a production line to produce one or more products using one or more campaign operations and one or more campaignable resources. The planner further formulates a supply chain master planning problem comprising a hierarchy of objective functions and one or more constraints and segments a campaign planning problem into three stages. The planner solves a first stage to determine prioritized production demands on campaignable buffers, solves a second stage to determine a timing and a sequence for allocating campaignable resources to campaignable buffers, and solves a third stage to determine a quantity of products to produce on campaignable resources.

Abstract: A system and method are disclosed for searching alternate inventory policies from an initial inventory policy of an inventory of one or more supply chain entities to a target inventory policy by receiving a current state of items in a supply chain network determining an initial inventory policy comprising a reorder point and a target quantity, identifying one or more provisional inventory policies calculating a cost, a fill rate, and a no-stockout probability, and transporting items among the one or more supply chain entities to restock the inventory.