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公开(公告)号：US20230196278A1
公开(公告)日：2023-06-22
申请号：US17553195
申请日：2021-12-16
发明人： Pavithra Harsha , Brian Leo Quanz , Ali Koc , Dhruv Shah , Shivaram Subramanian , Ajay Ashok Deshpande , Chandrasekhar Narayanaswami
CPC分类号： G06Q10/087 , G06Q10/06393 , G06Q30/0202
摘要： A processor in an omnichannel environment, over a specific network with transaction level operations, may receive one or more input configurations. The processor may identify, based on the one or more input configurations, one or more articles. The processor may identify one or more key performance indicators (KPIs) associated with the one or more articles. The processor may compute, based on an uncensored demand trajectory, an impact on the KPIs over a specified period in the omnichannel environment. The processor may provide the impact to a user.
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公开(公告)号：US20240135312A1
公开(公告)日：2024-04-25
申请号：US17965070
申请日：2022-10-13
发明人： Shivaram Subramanian , Pavithra Harsha , Ali Koc , Brian Leo Quanz , Mahesh Ramakrishna , Dhruv Shah
CPC分类号： G06Q10/087 , G06Q10/06315
摘要： Mechanisms are provided for generating a resource allocation in an omnichannel distribution network. Demand forecast data and current inventory data related to a resource and the omnichannel distribution network are obtained and an ally-adversary bimodal inventory optimization (BIO) computer model is instantiated that includes an adversary component that simulates, through a computer simulation, a worst-case scenario of resource demand and resource availability, and an ally component that limits the adversary component based on a simulation of a limited best-case scenario of resource demand and resource availability. The BIO computer model is applied to the demand forecast data and current inventory data, to generate a predicted consumption for the resource. A resource allocation recommendation is generated for allocating the resource to locations of the omnichannel distribution network based on the predicted consumption, which is output to a downstream computing system for further processing.
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公开(公告)号：US20230214764A1
公开(公告)日：2023-07-06
申请号：US17566739
申请日：2021-12-31
发明人： Brian Leo Quanz , Pavithra Harsha , Dhruv Shah , Mahesh Ramakrishna , Ali Koc
CPC分类号： G06Q10/087 , G06Q10/06315 , G06Q10/06375
摘要： A processor may estimate uncensored demand from historical supply chain data. The processor may ingest historical data. The processor may convert the historical data to a dataset of multiple time series corresponding to sales for different products and locations and channels across multiple time points that is usable by an uncensored demand estimation machine learning model. The processor may train the uncensored demand estimation machine learning model by applying optimization solver techniques for deep learning.
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公开(公告)号：US20220147669A1
公开(公告)日：2022-05-12
申请号：US17232099
申请日：2021-04-15
发明人： Brian Leo Quanz , Wesley M. Gifford , Stuart Siegel , Dhruv Shah , Jayant R. Kalagnanam , Chandrasekhar Narayanaswami , Vijay Ekambaram , Vivek Sharma
IPC分类号： G06F30/27
摘要： In various embodiments, a computing device, a non-transitory storage medium, and a computer implemented method of improving a computational efficiency of a computing platform in processing a time series data includes receiving the time series data and grouping it into a hierarchy of partitions of related time series. The hierarchy has different partition levels. A computation capability of a computing platform is determined. A partition level, from the different partition levels, is selected based on the determined computation capability. One or more modeling tasks are defined, each modeling task including a group of time series of the plurality of time series, based on the selected partition level. One or more modeling tasks are executed in parallel on the computing platform by, for each modeling task, training a model using all the time series in the group of time series of the corresponding modeling task.
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