公开(公告)号：US20160335586A1

公开(公告)日：2016-11-17

申请号：US14709702

申请日：2015-05-12

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Kiran V. PANCHAMGAM

IPC: G06Q10/08 ,  A47F5/00 ,  A47F5/08

CPC classification number: G06Q10/087

Abstract: Systems, methods, and other embodiments associated with assortment and display space optimization are described. In one embodiment, a method creates an optimal planogram. The example method includes receiving data describing i) a set of items described by item dimensions, ii) display space dimensions; iii) business rules, and iv) a key performance indicator. A set of possible shelf positions is identified for each item. An expected sales volume is calculated for each item and shelf position pair based, at least in part, on a selected demand model. The method includes providing i) the expected sales volume for the item and shelf position pairs, ii) a set of constraints that embody the business rules, and iii) an objective function to an optimization problem solver that computes a solution. Based on the solution, a planogram is output that specifies the assortment of items and respective optimal shelf positions of the items.

Abstract translation: 描述了与分类和显示空间优化相关联的系统，方法和其他实施例。 在一个实施例中，一种方法创建最佳平面图。 示例性方法包括接收描述的数据，i）由项目维度描述的一组项目，ii）显示空间维度; iii）业务规则，以及iv）关键绩效指标。 为每个项目识别一组可能的货架位置。 至少部分地基于所选择的需求模型来计算每个物品和货架位置对的预期销售量。 该方法包括提供i）项目和货架位置对的预期销售量，ii）体现业务规则的一组约束，以及iii）计算解决方案的优化问题解决者的目标函数。 基于该解决方案，输出一个指定物品的分类和物品的各个最佳货架位置的平面图。

公开(公告)号：US20230096633A1

公开(公告)日：2023-03-30

申请号：US17449112

申请日：2021-09-28

Applicant: Oracle International Corporation ,  MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Inventor： Leann THAYAPARAN ,  Kiran V. PANCHAMGAM ,  Setareh BORJIAN ,  Georgia PERAKIS

IPC: G06Q10/06 ,  G06N20/20 ,  G06K9/62 ,  G06Q10/08

Abstract: Embodiments generate an optimized demand model for a retail item. Embodiments train a tree ensemble machine learning model comprising a plurality of trees, the training comprising storing upper bounds for each of the trees, the trees comprising levels and branches that correspond to the demand features that influence demand for the item. Embodiments generate an objective function for the demand model. At a top split of each tree, embodiments determine optimal child nodes using the stored upper bounds and calculate a new feasible region for each tree. Using bounds on the new feasible region, embodiments move down each tree to a next level of splits and generate the optimized demand model when a leaf node of every tree has been reached.

公开(公告)号：US20170068962A1

公开(公告)日：2017-03-09

申请号：US14845792

申请日：2015-09-04

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Kiran V. PANCHAMGAM ,  Su-Ming WU

IPC: G06Q30/00 ,  G06Q30/02

CPC classification number: G06Q30/0251 ,  G06Q30/01 ,  G06Q30/02 ,  G06Q30/0204

Abstract: Systems, methods, and other embodiments associated with forecasting customer channel choice using cross-channel loyalty are described. In one embodiment, a method includes accessing historical values for each of one or more loyalty variables for respective customers. The method also includes determining respective loyalty variable predictors for each of the one or more loyalty variables for each customer based on the historical values. In response to a trigger event associated with a given customer, the loyalty variable predictors for the customer are used to estimate a present value of each of the one or more loyalty variables for the customer. The present value of each of the loyalty variables is input to a forecast model that calculates, for each channel, a probability that the customer will make a purchase using the channel. The purchase probabilities are provided for use in selecting a marketing message for the customer.

Abstract translation: 描述了使用跨渠道忠诚度预测客户渠道选择相关联的系统，方法和其他实施例。 在一个实施例中，一种方法包括访问各个客户的一个或多个忠诚度变量中的每一个的历史值。 该方法还包括基于历史值确定针对每个客户的一个或多个忠诚度变量中的每一个的相应忠诚度变量预测器。 响应于与给定客户相关联的触发事件，客户的忠诚度变量预测器用于估计客户的一个或多个忠诚度变量中的每一个的现值。 每个忠诚度变量的现值被输入到预测模型，该预测模型针对每个通道计算客户使用该频道进行购买的概率。 提供购买概率用于为客户选择营销信息。

公开(公告)号：US20240303577A1

公开(公告)日：2024-09-12

申请号：US18666037

申请日：2024-05-16

Applicant: Oracle International Corporation ,  MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Inventor： Leann THAYAPARAN ,  Kiran V. PANCHAMGAM ,  Setareh BORJIAN ,  Georgia PERAKIS

IPC: G06Q10/067 ,  G06F18/214 ,  G06N20/20 ,  G06Q10/08 ,  G06Q10/087

CPC classification number: G06Q10/067 ,  G06F18/2148 ,  G06N20/20 ,  G06Q10/08 ,  G06Q10/087

Abstract: Embodiments generate and train an optimized demand model for predicting a demand of an item. Embodiments receive a plurality of trees, each of the plurality of trees including one or more levels of splits and a plurality of nodes, each of the plurality of nodes corresponding to a demand feature that influences demand for the item. Embodiments store a first bound as a current bound for each of the plurality of trees. Starting at a top split of each of the plurality of trees, embodiments select a first demand feature that a greatest number of the plurality of trees split on. Embodiments optimize the first demand feature using the stored current bound to generate a second bound. Embodiments store the second bound as the current bound for each of the plurality of trees and move down each of the plurality of trees to a next level of splits.

公开(公告)号：US20200380452A1

公开(公告)日：2020-12-03

申请号：US16426360

申请日：2019-05-30

Applicant: Oracle International Corporation

Inventor： Andrew VAKHUTINSKY ,  Kiran V. PANCHAMGAM ,  Su-Ming WU

IPC: G06Q10/08 ,  G06Q30/02 ,  G06Q10/04

Abstract: Embodiments optimize the inventory allocation of a retail item that is provided from a plurality of warehouses to a plurality of price zones, each of the warehouses adapted to allocate inventory of the retail item to at least two of the price zones via links. Embodiments generate an initial inventory allocation for each warehouse to price zone link to generate a plurality of warehouse to price zone allocations. For each of the warehouse to price zone allocations, embodiments determine a marginal profit as a function of inventory allocated. Embodiments construct a bi-partite graph corresponding to each warehouse to price zone allocation, each bi-partite graph having a link weight equal to the marginal profit. Embodiments determine when there is a positive weight path between any two price zones and then reallocate the initial inventory allocation and repeat the functionality.

公开(公告)号：US20200342475A1

公开(公告)日：2020-10-29

申请号：US16380185

申请日：2019-04-10

Applicant: Oracle International Corporation

Inventor： Su-Ming WU ,  Andrew VAKHUTINSKY ,  Setareh Borjian BOROUJENI ,  Santosh Bai REDDY ,  Kiran V. PANCHAMGAM ,  Sajith VIJAYAN ,  Mengzhenyu ZHANG

IPC: G06Q30/02 ,  G06Q10/06 ,  G06F17/17

Abstract: Embodiments determine a price schedule for an item by, for each item, receiving a set of prices for the item, an inventory quantity for the item, a per-segment demand model for the item, and an objective function that is a function of the per-segment demand model and maximizes revenue based at least on a probability of a return of the item and a cost of the return. Embodiments allocate the inventory quantity among a plurality of customer segments based at least on a predicted contribution of each customer segment to the objective function. Embodiments determine a markdown portion of the price schedule for the item that maximizes the objective function, where the markdown portion assigns a series of prices selected from the set of prices for respective time periods during a clearance season for the item.