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1. 发明申请

US20160260109A1 SYSTEM AND METHOD FOR FORECASTING USING MONTE CARLO METHODS 审中-公开
标题翻译：使用蒙特卡罗方法预测的系统和方法
公开(公告)号：US20160260109A1
公开(公告)日：2016-09-08
申请号：US14638155
申请日：2015-03-04
申请人： Wal-Mart Stores, Inc.
发明人： Huijun Feng , Shubhankar Ray
IPC分类号： G06Q30/02
摘要： A system and method for calculating demand forecasts is presented. Sales data for a set of SKUs is received. The sales data is filtered to contain only data for low-selling SKUs. A set of clusters of SKUs is created. A generalized dynamic linear model for use with each cluster in the set of clusters is generated. A set of random data points is generated. The dynamic linear model is fitted at each data point in the set of random data points using a Monte Carlo method. This fitting can be performed using an unscented Kalman filter method. Calculating a forecast for sales based on the fitting at each data point. Using the forecast for sales, inventory is ordered. Other embodiments are also disclosed herein.
摘要翻译：提出了一种计算需求预测的系统和方法。收到一组SKU的销售数据。销售数据被过滤以仅包含低销售SKU的数据。创建一组SKU集群。生成用于集群中的每个集群的广义动态线性模型。生成一组随机数据点。动态线性模型使用蒙特卡罗方法拟合在随机数据点集合中的每个数据点。这个拟合可以使用无限卡尔曼滤波方法进行。根据每个数据点的拟合计算销售预测。使用销售预测，定货。本文还公开了其它实施例。

2. 发明申请

US20160260111A1 SYSTEM AND METHOD FOR GROUPING TIME SERIES DATA FOR FORECASTING PURPOSES 审中-公开
标题翻译：用于分类用于预测用途的时间序列数据的系统和方法
公开(公告)号：US20160260111A1
公开(公告)日：2016-09-08
申请号：US14638694
申请日：2015-03-04
申请人： Wal-Mart Stores, Inc.
发明人： Shubhankar Ray , Abhay Jha
IPC分类号： G06Q30/02 , G06Q10/08 , G06K9/62
CPC分类号： G06Q30/0202 , G06K9/6218 , G06Q10/087
摘要： A system and method for grouping units for forecasting purposes is presented. A plurality of stock keeping units (SKUs) is presented to an embodiment. Initial medoids are chosen based on a vertex within a set of vertices, each of which represent a SKU. Then, each vertex within the set of vertices is associated with its closest medoid to form initial clusters. There can be a cap on the number of vertices in each cluster. Thereafter, an iterative algorithm is performed wherein a probability is assigned to each vertex. One or more vertices are randomly chosen, with the weights of the vertices weighting the random choice. The chosen one or more vertices are moved to another cluster. The algorithm is performed until no further improvements result from moving one or more vertices to another cluster. Other embodiments are also disclosed herein.
摘要翻译：介绍了用于将单位分组以进行预测的系统和方法。多个库存单元（SKU）被呈现给实施例。基于一组顶点内的顶点来选择初始的类型，每个顶点代表SKU。然后，顶点集合中的每个顶点与其最近的中间体相关联，以形成初始聚类。每个群集中的顶点数可以有一个上限。此后，执行其中将概率分配给每个顶点的迭代算法。一个或多个顶点被随机选择，顶点的权重加权随机选择。所选的一个或多个顶点被移动到另一个集群。执行算法，直到将一个或多个顶点移动到另一个集群为止不再有改进。本文还公开了其它实施例。

3. 发明申请

US20160260110A1 SYSTEM AND METHOD FOR PREDICTING THE SALES BEHAVIOR OF A NEW ITEM 审中-公开
标题翻译：用于预测新项目的销售行为的系统和方法
公开(公告)号：US20160260110A1
公开(公告)日：2016-09-08
申请号：US14638637
申请日：2015-03-04
申请人： Wal-Mart Stores, Inc.
发明人： Shubhankar Ray , Abhay Jha
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0202
摘要： A system and method for forecasting the sales of a new item, i.e., one with no historical sales data, is presented. Two matrices are presented, a feature matrix and a sales matrix. The matrices are divided into training matrices and prediction matrices. The training matrices are decomposed, then regression analysis is performed to determine the weight of various columns of the training feature matrix. Thereafter, the weights could be used on the training prediction matrix to predict sales. The sales predictions can be used to order SKUs for a retailer or distributor. Other embodiments are also disclosed herein.
摘要翻译：呈现用于预测新项目的销售的系统和方法，即，没有历史销售数据的销售。呈现两个矩阵，一个特征矩阵和一个销售矩阵。矩阵分为训练矩阵和预测矩阵。训练矩阵被分解，然后进行回归分析以确定训练特征矩阵的各列的权重。此后，可以在训练预测矩阵中使用权重来预测销售。销售预测可用于为零售商或分销商订购SKU。本文还公开了其它实施例。

4. 发明授权

US11093954B2 System and method for predicting the sales behavior of a new item 有权
公开(公告)号：US11093954B2
公开(公告)日：2021-08-17
申请号：US14638637
申请日：2015-03-04
申请人： Wal-Mart Stores, Inc.
发明人： Shubhankar Ray , Abhay Jha
IPC分类号： G06Q30/02 , G06Q10/06 , G06Q10/08
摘要： A system and method for forecasting the sales of a new item, i.e., one with no historical sales data, is presented. Two matrices are presented, a feature matrix and a sales matrix. The matrices are divided into training matrices and prediction matrices. The training matrices are decomposed, then regression analysis is performed to determine the weight of various columns of the training feature matrix. Thereafter, the weights could be used on the training prediction matrix to predict sales. The sales predictions can be used to order SKUs for a retailer or distributor. Other embodiments are also disclosed herein.

5. 发明授权

US10373105B2 System and method for item-level demand forecasts using linear mixed-effects models 有权
公开(公告)号：US10373105B2
公开(公告)日：2019-08-06
申请号：US14622252
申请日：2015-02-13
申请人： Wal-Mart Stores, Inc.
发明人： Huijun Feng , Shubhankar Ray , Abhay Jha
IPC分类号： G06Q10/00 , G06Q10/08 , G06Q10/06 , G06Q30/02
摘要： A system and method for forecasting sales is presented. A method might begin by receiving a request to produce a demand forecast for a stock keeping unit (SKU). Then, the SKU is placed in one or more clusters. A cluster seasonality profile is calculated for each of the one or more clusters. An item seasonality profile is calculated for the SKU. Then the demand forecast for the SKU is generated. The demand forecast is adjusted using the cluster seasonality profile for each of the one or more clusters and the item seasonality profile for the SKU. Then inventory can be ordered based on the adjusted demand forecast. Other embodiments are also disclosed herein.

6. 发明授权

US10521809B2 System and method for grouping time series data for forecasting purposes 有权
公开(公告)号：US10521809B2
公开(公告)日：2019-12-31
申请号：US14638694
申请日：2015-03-04
申请人： Wal-Mart Stores, Inc.
发明人： Shubhankar Ray , Abhay Jha
IPC分类号： G06Q10/04 , G06Q30/02 , G06K9/62 , G06Q10/08
摘要： A system and method for grouping units for forecasting purposes is presented. A plurality of stock keeping units (SKUs) is presented to an embodiment. Initial medoids are chosen based on a vertex within a set of vertices, each of which represent a SKU. Then, each vertex within the set of vertices is associated with its closest medoid to form initial clusters. There can be a cap on the number of vertices in each cluster. Thereafter, an iterative algorithm is performed wherein a probability is assigned to each vertex. One or more vertices are randomly chosen, with the weights of the vertices weighting the random choice. The chosen one or more vertices are moved to another cluster. The algorithm is performed until no further improvements result from moving one or more vertices to another cluster. Other embodiments are also disclosed herein.

7. 发明授权

US10445689B2 System and method for forecasting using Monte Carlo methods 有权
公开(公告)号：US10445689B2
公开(公告)日：2019-10-15
申请号：US14638155
申请日：2015-03-04
申请人： Wal-Mart Stores, Inc.
发明人： Huijun Feng , Shubhankar Ray
IPC分类号： G06Q10/00 , G06Q10/08 , G06Q30/02 , G06Q10/06
摘要： A system and method for calculating demand forecasts is presented. Sales data for a set of SKUs is received. The sales data is filtered to contain only data for low-selling SKUs. A set of clusters of SKUs is created. A generalized dynamic linear model for use with each cluster in the set of clusters is generated. A set of random data points is generated. The dynamic linear model is fitted at each data point in the set of random data points using a Monte Carlo method. This fitting can be performed using an unscented Kalman filter method. Calculating a forecast for sales based on the fitting at each data point. Using the forecast for sales, inventory is ordered. Other embodiments are also disclosed herein.

8. 发明申请

US20170091790A1 DATA PROCESSING SYSTEM FOR OPTIMIZING INVENTORY PURCHASING AND METHOD THEREFOR 审中-公开
公开(公告)号：US20170091790A1
公开(公告)日：2017-03-30
申请号：US14869564
申请日：2015-09-29
申请人： WAL-MART STORES, INC.
发明人： Shubhankar Ray , Brian Seaman , Ashin Mukherjee
IPC分类号： G06Q30/02 , G06Q10/08
CPC分类号： G06Q30/0202 , G06Q10/087
摘要： A system and method for optimizing inventory purchasing is presented. A system can include one or more processing modules and one more non-transitory storage modules. The storage modules can contain instructions that perform acts corresponding to optimizing inventory purchasing, particularly for seasonal items. The demand for related items can be rolled-up to create a more complete base from which to make calculations. Price can be used to create a more accurate measure of demand. One can also forecast the effect of a future price reduction on demand. This more accurate measure of demand can then be used to order goods. Other embodiments are also disclosed herein.

9. 发明申请

US20160239776A1 SYSTEM AND METHOD FOR ITEM-LEVEL DEMAND FORECASTS USING LINEAR MIXED-EFFECTS MODELS 审中-公开
标题翻译：使用线性混合效应模型的项目级需求预测的系统和方法
公开(公告)号：US20160239776A1
公开(公告)日：2016-08-18
申请号：US14622252
申请日：2015-02-13
申请人： Wal-Mart Stores, Inc.
发明人： Huijun Feng , Shubhankar Ray , Abhay Jha
IPC分类号： G06Q10/06 , G06Q10/08 , G06Q30/02
CPC分类号： G06Q10/087 , G06Q10/06315 , G06Q30/0201 , G06Q30/0202 , Y04S10/54
摘要： A system and method for forecasting sales is presented. A method might begin by receiving a request to produce a demand forecast for a stock keeping unit (SKU). Then, the SKU is placed in one or more clusters. A cluster seasonality profile is calculated for each of the one or more clusters. An item seasonality profile is calculated for the SKU. Then the demand forecast for the SKU is generated. The demand forecast is adjusted using the cluster seasonality profile for each of the one or more clusters and the item seasonality profile for the SKU. Then inventory can be ordered based on the adjusted demand forecast. Other embodiments are also disclosed herein.
摘要翻译：介绍了销售预测的系统和方法。一种方法可以从接收到为库存单位（SKU）生成需求预测的请求开始。然后，将SKU置于一个或多个簇中。针对一个或多个群集中的每个群集计算群集季节特征。为SKU计算项目季节性概况。然后生成SKU的需求预测。对于一个或多个集群中的每个集群和SKU的项目季节性配置文件，使用集群季节性配置文件来调整需求预测。然后可以根据调整后的需求预测订购库存。本文还公开了其它实施例。

10. 发明授权

US10453026B2 System and method for forecasting high-sellers using multivariate bayesian time series 有权
公开(公告)号：US10453026B2
公开(公告)日：2019-10-22
申请号：US14641075
申请日：2015-03-06
申请人： Wal-Mart Stores, Inc.
发明人： Shubhankar Ray , Abhay Jha
IPC分类号： G06Q10/00 , G06Q30/00 , G06Q10/08 , G06Q30/02
摘要： A system and method for grouping units for forecasting purposes is presented. A sales forecast for a set of stock keeping units (SKUs) is desired. The SKUs are separated into clusters based on the similarity of the SKUs. Then a set of Bayesian multivariate dynamic linear models is chosen to be used to calculate a sales forecast for each of the clusters of SKUs. The accuracy of each dynamic linear model is determined in a training procedure and a set of weights for each dynamic linear model is calculated. Thereafter, the weights can be used with the dynamic linear models to create a weighted average forecast model. The training procedure can be run periodically to maintain the accuracy of the weights. Each procedure can operate on a sliding window of data. Other embodiments are also disclosed herein.

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