Abstract: A system and method of solving supply chain planning problems modeled as multi-objective hierarchical linear programming problems receive supply chain input data for a supply chain planning problem, solve a first multi-objective hierarchical linear programming problem, store a cumulative list of bound changes, receive changes to the supply chain input data, model a second supply chain planning problem as a second multi-objective hierarchal linear programming problem based, at least in part, on the one or more changes to the supply chain input data, derive an intermediate objective based, at least in part, on the cumulative list of bound change, and solve the second multi-objective hierarchical linear programming problem, using the basis of the solved intermediate objective.

Abstract: A system and method are disclosed for propagating changes to a schema model. Embodiments include detecting a change to a system represented by the schema model, generating a change event based on the change to the schema model, determining microservice actors that execute within a container-based computation service, where the microservice actors are configured to be triggered by the change event, dispatching the change event to the microservice actors, and updating, by the microservice actors, the schema model. The system further comprises setting up and controlling one or more virtual machines to execute the microservice actors. The behavior of each of the microservice actors is specified by a configuration file, which is associated with a particular microservice actor and specifies the name of a particular microservice actor, what objects a microservice actor may update, actions a microservice actor may take and change events that trigger a microservice actor.

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 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 evaluating a forecast. The method includes aggregating a set of predictions and a set of actuals into prediction buckets, rating prediction data of the set of predictions using forecast metrics, computing an ideal benchmark for the forecast metrics, determining a scaling adjustment to calculate one or more non-ideal benchmarks to create a forecast rating scale, determining a score for each of the prediction buckets, the score corresponding to the forecast rating scale; and aggregating the determined scores of the prediction buckets to generate an overall score. The method further includes generating a calibration diagram comprising observed frequency and predicted probability of the set of predictions and the set of actuals.

Abstract: A system and method are disclosed including a distributed computer network having one or more application servers and one or more data nodes. The one or more data nodes each include a shard of data representing a measure. The one or more application servers receive a measure model and an edit to the measure. The one or more application servers append the edit to a register and generate a calculation sequence. The one or more application servers further disaggregate one or more values to a shard of data on the one or more data nodes based, at least in part, on the edited measure and calculate one or more unedited measures based, at least in part, on the disaggregation.

Abstract: A system and method are disclosed for performing supply chain execution.

Abstract: A system and method are disclosed for quality assurance and performance testing of supply chain applications and systems. Embodiments include providing a user interface for receiving a test case file that describes one or more actions to be tested and a properties file that maps one or more elements to one or more values, translating a received test case file and a corresponding properties file into a test case, and executing the test case by identifying the one or more actions identified in the test case and automatically invoking one or more testing components configured to execute a test using the one or more actions, wherein at least one of the one or more testing components is configured execute performance testing, and at least one of the one or more testing components is configured to execute regression testing.

Abstract: A system and method are disclosed to train machine learning models, generate predictions, and evaluate the predictions as individual probability density functions. Embodiments include a computer comprising a processor and memory and configured to train a first machine learning model to predict a mean demand of one or more items. Embodiments train a second machine learning model to predict a variance associated with the predicted mean demand. Embodiments use the first and second machine learning models and received current sales data to predict a negative binomial variance of demand of the one or more items, comprising a confidence interval specifying a stocking level for the one or more items that will satisfy a defined number of estimated outcomes. Embodiments generate an individual probability density function using the predicted mean demand of one or more items and the predicted negative binomial variance of demand, and evaluate the individual probability density function.

Abstract: A system and method are disclosed for a warehouse management system having a task database and a server comprising a processor and memory. The system groups one or more tasks from the task database into one or more groups, estimate the latest feasible start time for each group of the one or more groups and assigns a priority for the one or more tasks in each group based, at least in part, on the estimated latest feasible start time for each group.

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 dynamic switching in hierarchical LPOPT solving for supply chain entities. The system provides an architecture of a supply chain planner including a server including a user action processor, a visualization engine, a dynamic solver engine and a similarity comparison engine. The system architecture and method enables the solving of hierarchical supply chain problems with reduced computational requirements and in a shorter period of time.

Abstract: A system and method are disclosed for a mixed-reality visualization system having a computer configured to render a mixed-reality three-dimensional surface, identify one or more current demand regions, identify one or more potential demand regions; map the one or more current demand regions and the one or more potential demand regions on the rendered three-dimensional surface, model a free body coupled with demand centers of each of the one or more demand regions and each of the one or more potential demand regions using a spring model, calculate a location on the rendered three-dimensional surface representing an equilibrium position between the demand centers of the one or more demand regions and the one or more potential demand regions using the spring model, and render, for display on a rendering device, a visual indicator within the area corresponding to the location of the calculated equilibrium position.

Abstract: A system and method of autonomous data hub processing that uses semantic metadata, machine learning models, and a permissioned blockchain to autonomously standardize, identify and correct errors in supply chain data is disclosed. Embodiments input supply chain data stored in a supply chain database, train with the machine learning model trainer, one or more machine learning models to identify one or more data errors in the supply chain data, clean the one or more identified data errors from the supply chain data, and store cleaned supply chain data. Embodiments also update one or more machine learning models to identify one or more data errors in cleaned supply chain data, and join and aggregate one or more sets of cleaned supply chain data.

Abstract: A system and method are disclosed for detecting and reacting to unseen events in demand forecasting, comprising preparing, by a server, data from a supply chain domain and entity to predict a demand, selecting features in the prepared data and training a machine learning model, generating a demand prediction with residual time series corrections, using the machine learning model, monitoring the demand prediction and data from the supply chain domain and entity to detect an occurrence of an unseen event; and in response to detecting the occurrence of the unseen event, revising the demand prediction by updating the machine learning model. The system and method further comprises detecting that a greater than threshold increase has occurred in a prediction of a target variable, and revising the demand prediction further comprises performing a second residual time series corrections that incorporates time information associated with the unseen event.

Abstract: A system and method are disclosed for determining inventory to stock at a supply chain site. The method includes determining a set of inventory clusters identifying inventory items that have been purchased together, ranking the set of inventory clusters based on a number of times each cluster of inventory items was purchased together, determining an amount of shelf space needed for each of the set of inventory clusters, ranking any inventory clusters that were purchased an equal number of times based on the amount of shelf space needed to generate a cluster ranking, and determining inventory to stock at the supply chain site based on the cluster ranking. The method further includes generating an inventory stocking plan based on the determined inventory to stock, and implementing the inventory stocking plan at the supply chain site based on the inventory stocking plan using one or more pieces of automated stocking machinery.

Abstract: A system and method are disclosed for campaign planning and include modeling the use of campaign operations and campaignable resources of a supply chain network including a production line to produce products using campaign operations and campaignable resources as campaign planning problems, defining an evaluation function comprising a weighted sum of features evaluated from the campaign planning problem, initializing weights to build a consumption profile and evaluation function, determining fitness values that indicate a level of variability, evaluating reward values based on the fitness values, selecting a sub-sample of the top fitness values having the best associated objective function, repeating the generating, the evaluating and the selecting steps to adjust the weights until a stopping criterion is met indicating an optimal solution has been reached, and determining a campaign plan for the use of the campaign operations and campaignable resource.

Abstract: A system and method are disclosed for sharing cart data. The method includes detecting a request to share cart data between a primary device and a non-primary device, retrieving cart data from the primary device based at least in part on a sharing method, filtering private user data from the cart data, displaying the filtered cart data on the non-primary device, and updating the cart data based on communications between the primary device and the non-primary device. The method further comprises monitoring non-text communications between the primary device and the non-primary device, converting the non-text communications into text in order to perform text classification, generating temporary access credentials to allow access to the cart data by the non-primary device, and purging the temporary access credentials upon ending sharing of the cart data.

Abstract: A system and method are disclosed for displaying a visualization of tasks to be performed across one or more time intervals. Embodiments include receiving two or more tasks from a task database comprising one or more attributes in a table format, the table having two or more rows and two or more columns, adding a column to each of the two or more tasks comprising the volume of tasks to be cumulatively performed during a time interval and adding a column to each of the two or more tasks comprising the cumulative volume due during the time interval. Embodiments also include displaying, on a graphical user interface, a visualization of the cumulative volume due and the cumulative planned curve representing the volume of tasks to be performed across one or more time intervals.

Abstract: A computer-implemented method is provided for aggregating state information associated with a composite business object representing at least one collaboration between business entities. The method includes retrieving the state information for the composite business object from a computer-readable medium and determining a state for the composite business object based on an assigned priority level for a state in a hierarchy of states associated with the composite business object, each state in the hierarchy of states having a corresponding assigned priority level.