Blue Yonder Group, Inc.

Jda Software Group, Inc.

Tushar Shekhar

Narayan Nandeda

Devanand R

Narasimha Kamath

Mayank Tiwari

Brenda Christy

Vidhi Chugh

Rashid Puthiyapurayil

Phani Bulusu

Deb Mohanty

Manish Kumar

Vishal Shinde

Ankit Shankdhar

Devender Pal Singh Chauhan

Chaitanya Pai Kalasa Gopinath

Arijit Deb

A system and method are disclosed for solving a multi-objective linear programming supply chain problem. Embodiments include defining a hierarchy of objectives of a supply chain problem, executing a first thread as a mainline solve of a first objective and executing secondary threads as auxiliary solves of additional objectives and determining if a next objective has been solved by the auxiliary solves in response to the first objective being solved. Embodiments further include using the auxiliary solve of a next objective as a starting solution for a mainline solve of the next objective, using a solution from a previous solved mainline objective as a starting solution for a mainline solve of the next objective in response to the next objective of the hierarchy not being solved by the auxiliary solves, and repeating the determining and using steps to solve each objective in the hierarchy.

Systems and methods for solving multi-objective hierarchical linear programming problems in parallel

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.

System and method of cognitive risk management

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.

Mutual information resolution recommendations and graphical visualizations using probabilistic graphical models

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.

System and method of segmented modelling for campaign planning in a very large-scale supply chain network

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.

Synchronized analysis of mixed-reality OLAP and supply chain network visualizations

A system and method for efficiently determining the fair-share bands of a supply chain planning problem modeled as a multi-objective hierarchical linear programming problem include a processor and memory and are configured to model a supply chain planning problem as a multi-objective hierarchal linear programming problem, assign weights at each band of a fixed number of at least two bands, determine a direction of improved band values from a value of a Key Process Indicator (KPI) calculated from an expected demand and short quantities, wherein the expected demand and short quantities are calculated from the multi-objective hierarchical linear programming problem using a sample generated by Gibbs sampling of a conditional Gaussian Bayesian Network, and generate a supply chain plan.

Fair share band optimization using Gaussian Bayesian network

A system and method are disclosed to generate mixed-reality visualizations and objects to identify exceptions within large volumes of data and provide resolution options. 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 first mixed-reality object comprising a first cuboid comprising one or more adjustable display dimensions and one or more cubelets divided along at least one of the one or more adjustable display dimensions. Embodiments display the first mixed-reality object on the display and transform one or more cubelets of the first cuboid in response to a first sensor input. Embodiments identify one or more exceptions, generate one or more resolution options to resolve at least one of the one or more exceptions, and display the transformed one or more cubelets of the first cuboid and the one or more resolution options.

Mixed-reality assisted exception analysis and resolution

A system and method for a risk management visualization system having a computer comprising 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 comprising an exploration phase and a learning phase, and display a visualization of the risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.

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.

Mixed-reality assisted dimensionality expansion and manipulation analytics

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.

Domain-aware decomposition for supply chain master planning using linear programming

A system and method are disclosed for solving a supply chain planning problem modeled as a linear programming (LP) problem. Embodiments include receiving a matrix formulation of at least a portion of the LP 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 LP subproblem from each of the at least two partitions, and solving the LP subproblems to generate a global solution to the supply chain planning problem.

Image-based decomposition for fast iterative solve of complex linear problems

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.

Immersive supply chain analytics using mixed reality

Fair share band optimization using Gaussian Bayesian Network

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.

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Bengaluru, India

Tushar Shekhar