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公开(公告)号：US20210312488A1
公开(公告)日：2021-10-07
申请号：US17340335
申请日：2021-06-07
摘要： A system and method are disclosed to identify one or more price-demand elasticity causal factors and to forecast demand using price-demand elasticity causal factors and a corrected demand target. Embodiments include a computer comprising a processor and memory. Embodiments train a first machine learning model to identify one or more external causal factors that influence demand for one or more products. Embodiments train the first machine learning model to generate one or more price-demand elasticity causal factors to predict a target outcome for a given product demand. Embodiments determine, using a second machine learning model, a corrected demand target based on total sales and markdown sales. Embodiments predict, with the first machine learning model, a demand for the one or more products based, at least in part, on the identified one or more external causal factors, the generated one or more price-demand elasticity causal factors, and the corrected demand target.
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公开(公告)号：US20210295251A1
公开(公告)日：2021-09-23
申请号：US17341014
申请日：2021-06-07
摘要： A system and method are including a computer and a processor and memory. The computer receives a product class representing a product in a supply chain network including one or more supply chain entities and generates one or more new products for the product class using one or more automatically generated templates including a graphical representation of an exemplary product using a first smart product attribute value, the first smart product attribute value defined by a quantifiable measurement of a product attribute. The computer further causes items to be transported among the one or more supply chain entities to restock the inventory of the one or more items of the product class according to the current state of items in the supply chain network and the one or more new products.
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公开(公告)号：US20210224732A1
公开(公告)日：2021-07-22
申请号：US17224461
申请日：2021-04-07
摘要： A system and method are disclosed for an inventory planner that generates an inventory policy using any form of demand distributions, non-linear cost functions and/or multiple target measures of service levels, while taking into account a supply order lead time, such as, for example, a static or stochastic supply order lead time. The inventory policy generated by the inventory planner comprises an optimal and reproducible solution to one or more supply chain planning problems.
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公开(公告)号：US10762455B1
公开(公告)日：2020-09-01
申请号：US15823298
申请日：2017-11-27
发明人： Jon Edward Sager , Dan Morris , Alun Jones
摘要： A system and method are disclosed for determining long-range staff planning. Embodiments include determining a baseline measurement of labor needs over a time period of one or more employees at one or more entities and modifying the baseline measurement of the labor needs over the time period based on one or more constraints that allow the one or more employees to work additional types of labor needs at the one or more entities. Embodiments further include determining working times and job assignments of the one or more employees based on one or more simulated employees that represent potential employees to the modified baseline measurement of the labor needs over the time period and storing the determined working times and job assignments in the database for the one or more employees at the one or more entities.
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公开(公告)号：US12131282B1
公开(公告)日：2024-10-29
申请号：US17679871
申请日：2022-02-24
发明人： Vishal Shinde
IPC分类号： G06Q10/00 , G06Q10/04 , G06Q10/083
CPC分类号： G06Q10/083 , G06Q10/04
摘要： 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.
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公开(公告)号：US20240354696A1
公开(公告)日：2024-10-24
申请号：US18517755
申请日：2023-11-22
发明人： Timothy Reed Hayes
IPC分类号： G06Q10/087
CPC分类号： G06Q10/087
摘要： A system and method are disclosed for layered scheduling. The method includes partitioning a scheduling problem into ordered subsets based on a prioritization scheme, applying a scheduling algorithm to optimize a first subset of the ordered subsets and freeze a corresponding schedule, determining whether there are any remaining subsets that have not been optimized, in response to determining that there are remaining subsets that have not been optimized, loading a next subset ordered according to the prioritization scheme, optimizing the loaded subset without disturbing the frozen schedule, and in response to determining that there are no remaining subsets to optimize, running a final pass of the scheduling algorithm to improve the global schedule metrics. The method further includes where the prioritization scheme is based on a relative priority of tasks to be performed, a value of finished goods that are to be produced or requirements regarding a use of resources.
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公开(公告)号：US20240311856A1
公开(公告)日：2024-09-19
申请号：US18671595
申请日：2024-05-22
IPC分类号： G06Q30/0204 , G06Q30/0201
CPC分类号： G06Q30/0204 , G06Q30/0201
摘要： 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. The computer performs multi-dimension segmentation on input data by computing feature importance to generate multi-dimensional segments, assigns policy parameters to the supply chain network based on the generated multi-dimensional segments, trains a machine learning model by applying a cyclic boosting process to the standardized features data, where the cyclic boosting process iteratively learns relationships associated with the generated multi-dimensional segments, stores the machine learning model in a database, performs multi-dimension segmentation based on the stored machine learning model, determines whether data drift has occurred in the input data and in response to determining that data drift has occurred, repeats the perform, assign, trains steps, and stores an updated machine learning model in the database.
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公开(公告)号：US12056639B2
公开(公告)日：2024-08-06
申请号：US18510015
申请日：2023-11-15
发明人： Piyush Shah
IPC分类号： G06Q10/00 , G06Q10/04 , G06Q10/0631 , G06Q10/08
CPC分类号： G06Q10/06315 , G06Q10/04 , G06Q10/06312 , G06Q10/08
摘要： 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.
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公开(公告)号：US20240193528A1
公开(公告)日：2024-06-13
申请号：US18443957
申请日：2024-02-16
发明人： Anand Iyer
IPC分类号： G06Q10/08 , G06Q10/06 , G06Q10/063 , G06Q10/0631 , G06Q30/0202
CPC分类号： G06Q10/08 , G06Q10/06 , G06Q10/063 , G06Q10/06315 , G06Q30/0202
摘要： 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.
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公开(公告)号：US20240193140A1
公开(公告)日：2024-06-13
申请号：US18585478
申请日：2024-02-23
发明人： Rubesh Mehta
IPC分类号： G06F16/215 , G06F16/2455 , G06N20/00
CPC分类号： G06F16/215 , G06F16/24556 , G06F16/2456 , G06N20/00
摘要： 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.
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