Abstract: A system and method are disclosed for solving a supply chain planning problem modeled as a linear programming (LP) problem. Embodiments further include receiving a multi-period matrix formulation of a least a portion of an LP supply chain master planning problem representing a supply chain planning problem for a supply chain network and having a planning horizon divided into time buckets separated by time-bucket boundaries, mapping constraints of the LP supply chain master planning problem and variables of the LP supply chain master planning problem to the time buckets, calculating a quantity of cross-over variables for the constraints and the time buckets, selecting one or more decomposition boundaries from the time-bucket boundaries, and formulating at least two time-based decomposed subproblems by decomposing the LP supply chain master planning problem at the one or more decomposition boundaries.

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 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 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 prediction 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 in order to resolve one or more underlying causes of the at least one alert supply chain event.

Abstract: A system and method are disclosed including a computer and a processor and memory. The computer receives historical sales data comprising aggregated sales data for one or more items from one or more store for at least one past time period. The computer further trains a cyclic boosting model to learn model parameters by iteratively calculating for each feature and each bin factors for at least one full feature cycle. The computer further predicts one or more demand quantities during a prediction period by applying a prediction model to historical supply chain data, wherein a training period is earlier than the prediction period, and each of the one or more demand quantities is associated with at least one item of the one or more items and at least one stocking location of the one or more stocking locations during the prediction period and rendering a demand prediction feature explanation visualization.

Abstract: A system and method are disclosed for an application server having one or more web application archives and one or more endpoints, determining whether a functionality of one or more endpoints is to be changed, and responsive to the determination of whether an endpoint of the one or more endpoints is to be changed, determining a link to a functionality component located external to the one or more web application archive that is to replace the functionality of the endpoint, and causing the external functionality component to link to the endpoint.

Abstract: A system and method is disclosed for providing data staging and loading through a master data management (MDM) system coupled with one or more business entities. The system is operable to invoke batch data loading of data associated with one or more source systems associated with the one or more business entities, into an input staging area. The system is further operable to load the data from the input staging area into a master repository and subsequently load the data from the master repository into an output staging area.

Abstract: A system and method are disclosed for augmented visualization of a planogram of one or more supply chain entities. Embodiments include creating a planogram comprising one or more products, the products associated with a location, and retrieving an image of the planogram and one or more customer segments. Embodiments further include mapping one or more coordinates in an image of a planogram that correspond to the one or more customer segments and rendering an augmented visualization comprising altering the image of a planogram at one or more coordinates to illustrate the one or more customer segments.

Abstract: A system and method is disclosed for allocating products to one or more sellers. The system includes a database operable to store data associated with one or more enterprises. The system further includes an order promising system coupled with the database and operable to model the flow of the products through the one or more enterprises and allocate resources to the one or more sellers based on the modeled flow of the products.

Abstract: A system and method are disclosed for a supply chain entity having an inventory of one or more items and a warehouse management system receiving orders for one or more items, determining a container for the one or more items, and generating a packing plan for the items. Aspects of the embodiments may include one or more instruction rendering devices having a sensor and a displaying augmented reality instructions and feedback.

Abstract: A system and method are disclosed to generate mixed-reality visualizations and objects to facilitate efficient interaction with large volumes of data. Embodiments include a computer comprising a processor and memory and configured to select data stored in a data table coupled to the computer. Embodiments select one or more key process indicators (KPIs), dimensions by which to display the selected data, and color schemes by which to display the selected data. Embodiments generate a first mixed-reality object displaying the selected data according to the selected one or more KPIs, dimensions, and color schemes, and a second mixed-reality object displaying the selected data as a three-dimensional model of nodes and connections. Embodiments display the first and second mixed-reality objects on a display device, and manipulate one of the first and second mixed-reality objects in response to input received by one or more sensors.

Abstract: A system and method are disclosed for solving a supply chain planning problem modeled as a linear programming (LP) problem. Embodiments include receiving an LP problem representing a supply chain planning problem for a supply chain network comprising material buffers and resource buffers, partitioning the supply chain network at a complicating node into at least two supply chains sharing the complicating node, formulating a decomposed subproblem for each of the supply chains, calculating an effective dual based, at least in part, on a mathematical difference of at least two dual values calculated by solving the functional-based decomposed subproblems, and generating a globally-optimal LP solution to the LP problem using subgradient descent with the effective dual.

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 solving a supply chain planning problem modeled as a linear programming problem. Embodiments include receiving a matrix formulation of at least a portion of the linear programming problem representing a supply chain planning problem for a supply chain network, generating an image based on the matrix formulation to identify connected components, partitioning the matrix formulation based, at least in part, on the connected components constraint into at least two partitions, formulating an linear programming subproblem from each of the at least two partitions, and solving the linear programming subproblems to generate a global solution to the supply chain planning problem.

Abstract: A system and method are disclosed for solving subsequent runs of a supply chain planning problem modeled as a multi-objective hierarchical linear programming problem. Embodiments further include receiving supply chain input data for a supply chain planning problem, modeling the supply chain planning problem as a multi-objective hierarchal linear programming problem having a first objective and at least one additional objective, solving a base run of the multi-objective hierarchical linear programming problem for the first objective and the at least one additional objective, generating a base plan by converting a solution of the base run of the multi-objective hierarchical linear programming problem, receiving one or more changes to the supply chain input data, identifying primal feasibility change of the one or more changes that affect only primal feasibility, and identifying dual feasibility changes of the one or more changes that affect only dual feasibility.

Abstract: A system and method are disclosed to locate one or more data anomalies in a supply chain network comprising two or more supply chain entities. Embodiments receive supply chain data comprising a plurality of data points. Embodiments select categories and measures by which to cluster the supply chain data points. Embodiments cluster the data points into intersection clusters as measured by the selected categories and measures. Embodiments generate time interval clusters that divide the intersection clusters into one or more time intervals. Embodiments generate K-values for the plurality of data points in relation to the number of time interval clusters. Embodiments generate local outlier factors for the plurality of data points using the generated K-values.

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 generate one or more mixed-reality visualizations and objects to facilitate efficient interaction with large volumes of data. Embodiments include a computer comprising a processor and memory and a mixed-reality display device comprising a display and one or more sensors. Embodiments generate a mixed-reality object comprising a first cuboid, the first cuboid comprising one or more adjustable display dimensions corresponding to one or more key process indicators (KPIs) and one or more cubelets divided along at least one of the one or more adjustable display dimensions. Embodiments display the mixed-reality object on the display, and adjust at least one of the one or more adjustable display dimensions in response to receiving a first input to the one or more sensors. In response to a second input, embodiments transform the one or more cubelets of the first cuboid.

Abstract: A system and method of a multi-level tank-based production system. Embodiments include planning data for one or more finished goods, the one or more finished goods produced from one or more semi-finished goods stored in one or more tanks, identifying, from the planning data, planned production orders for the one or more finished goods in each time bucket of a planning period, modifying the planned production orders to satisfy lot-size requirements of production operations of the one or more finished goods and time and tank capacity constraints of the one or more semi-finished goods, generating a tank-based production plan based, at least in part, on the modified planned production orders, and producing the one or more finished goods according to the tank-based production plan.

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.