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1.发明申请METHODS AND SYSTEMS FOR FORECASTING PRODUCT DEMAND FOR SLOW MOVING PRODUCTS 审中-公开用于预测缓慢移动产品的产品需求的方法和系统公开(公告)号：US20080133310A1
公开(公告)日：2008-06-05
申请号：US11565685
申请日：2006-12-01
IPC分类号： G06F17/30
CPC分类号： G06Q30/06 , G06Q30/02 , G06Q30/0202
摘要： An improved method for forecasting and modeling product demand for a slow moving product. The method includes the steps of maintaining a database of historical product demand information, calculating the average rate of sales (ARS) for a product from the historical demand information corresponding to the product, determining if the product is a slow moving product (SMP), and if the product is a SMP modifying the ARS using a mean reverting forecast method called GARCH (Generalized Autoregressive Conditional Heteroscedasticity) to accurately model the expected demand and variability of the slow moving product.
摘要翻译： 一种改进的缓慢移动产品的产品需求预测和建模方法。 该方法包括以下步骤：维护历史产品需求信息的数据库，根据对应于产品的历史需求信息计算产品的平均销售率（ARS），确定产品是否是慢动产品（SMP） 并且如果产品是使用称为GARCH（广义自回归有条件异方差）的平均回复预测方法来修改ARS的SMP，以精确地建模缓慢移动产品的预期需求和变异性。
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公开(公告)号：US20080162270A1
公开(公告)日：2008-07-03
申请号：US11951364
申请日：2007-12-06
IPC分类号： G06Q10/00
CPC分类号： G06Q10/087 , G06Q30/0202
摘要： A method and system for forecasting distribution center (DC) or warehouse product suggested order quantities required to meet future product demands for a retailer. In determining DC/warehouse order quantities, a bias factor and Adaptive Forecast Error (AFE) are calculated from prior product demand and sales data and applied to DC/warehouse effective inventory calculations to account for forecast errors in DC/warehouse suggested order quantities. If the bias indicates a forecast that is too high, the method and system will attempt to compensate by increasing the suggested order quantity. If the bias indicates a forecast that is too low, the method and system will attempt to compensate by decreasing the suggested order quantity.
摘要翻译： 用于预测配送中心（DC）或仓库产品的方法和系统，建议满足零售商未来产品需求所需的订单数量。 在确定DC /仓库订单数量时，根据先前的产品需求和销售数据计算偏差因子和自适应预测误差（AFE），并应用于DC /仓库有效库存计算，以解决DC /仓库建议订单数量中的预测误差。 如果偏差指示太高的预测，则方法和系统将尝试通过增加建议的订单数量进行补偿。 如果偏差表示太低的预测，则方法和系统将通过减少建议的订单数量来尝试进行补偿。
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公开(公告)号：US20090177520A1
公开(公告)日：2009-07-09
申请号：US11967645
申请日：2007-12-31
申请人： Arash Bateni , Edward Kim , Jean-Philippe Vorsanger , Rong Zong
发明人： Arash Bateni , Edward Kim , Jean-Philippe Vorsanger , Rong Zong
IPC分类号： G06F17/30
CPC分类号： G06Q30/0202 , G06F16/26
摘要： Techniques for casual demand forecasting are provided. Information is extracted from a database and is preprocessed to produce adjusted input regression variables. The adjusted input regression variables are fed to a regression service to produce regression coefficients. The regression coefficients are then post processed to produce uplifts and adjustments to the uplifts for the regression coefficients.
摘要翻译： 提供了休闲需求预测技术。 从数据库中提取信息，并进行预处理以产生调整后的输入回归变量。 将调整后的输入回归变量输入回归服务以产生回归系数。 然后对回归系数进行后处理，以产生对提取回归系数的提升和调整。
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4.发明申请METHODS AND SYSTEMS FOR FORECASTING PRODUCT DEMAND USING A CAUSAL METHODOLOGY 审中-公开使用原理方法预测产品需求的方法和系统公开(公告)号：US20080154693A1
公开(公告)日：2008-06-26
申请号：US11613404
申请日：2006-12-20
申请人： Arash Bateni , Edward Kim , Philip Liew , Jean-Philippe Vorsanger
发明人： Arash Bateni , Edward Kim , Philip Liew , Jean-Philippe Vorsanger
IPC分类号： G06Q10/00
CPC分类号： G06Q30/02 , G06Q30/0202 , G06Q30/0206
摘要： An improved method for forecasting and modeling product demand for a product. The forecasting methodology employs a causal methodology, based on multiple regression techniques, to model the effects of various factors on product demand, and hence better forecast future patterns and trends, improving the efficiency and reliability of the inventory management systems. The demand forecasting technique seeks to establish a cause-effect relationship between product demand and factors influencing product demand in a market environment. Such factors may include current and recent product sales rates, seasonality of demand, product price changes, promotional activities, weather forecasts, competitive information are examples of the other primary factors which can be modeled. A product demand forecast is generated by blending the various influencing factors in accordance with corresponding regression coefficients determined through the analysis of historical product demand and factor information.
摘要翻译： 一种改进的产品需求预测和建模方法。 预测方法采用基于多元回归技术的因果方法来模拟各种因素对产品需求的影响，从而更好地预测未来模式和趋势，提高库存管理系统的效率和可靠性。 需求预测技术旨在在市场环境中建立产品需求与影响产品需求的因素之间的因果关系。 这些因素可能包括当前和最近的产品销售率，需求季节性，产品价格变化，促销活动，天气预报，竞争信息是可以建模的其他主要因素的例子。 产品需求预测是通过根据历史产品需求和因素信息分析确定的相应回归系数混合各种影响因素产生的。
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5.发明申请METHODS AND SYSTEMS FOR FORECASTING PRODUCT DEMAND DURING PROMOTIONAL EVENTS USING A CAUSAL METHODOLOGY 有权使用原理方法在促销活动中预测产品需求的方法和系统公开(公告)号：US20090125375A1
公开(公告)日：2009-05-14
申请号：US11938812
申请日：2007-11-13
CPC分类号： G06Q30/02 , G06Q10/04 , G06Q30/0202 , G06Q30/0206
摘要： An improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs a multivariable regression model to model the causal relationship between product demand and the attributes of past promotional activities. The model is utilized to calculate the promotional uplift from the coefficients of the regression equation. The methodology utilizes a mathematical formulation that transforms regression coefficients, a combination of additive and multiplicative coefficients, into a single promotional uplift coefficient that can be used directly in promotional demand forecasting calculations.
摘要翻译： 一种在促销期间预测和建模产品需求的改进方法。 预测方法采用多变量回归模型来模拟产品需求与过去促销活动的属性之间的因果关系。 该模型用于从回归方程的系数计算促销隆起。 该方法利用将回归系数（加法和乘法系数的组合）转换成可以在促销需求预测计算中直接使用的单个促销隆起系数的数学公式。
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6.发明授权Methods and systems for forecasting product demand during promotional events using statistical confidence filters 有权使用统计置信滤波器在促销活动期间预测产品需求的方法和系统公开(公告)号：US08359229B2
公开(公告)日：2013-01-22
申请号：US11863958
申请日：2007-09-28
申请人： Arash Bateni , Edward Kim , Philip Liew , Jean-Philippe Vorsanger
发明人： Arash Bateni , Edward Kim , Philip Liew , Jean-Philippe Vorsanger
IPC分类号： G06Q40/00
CPC分类号： G06Q30/0202 , G06Q10/04 , G06Q30/02
摘要： An improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs information about prior promotional demand forecasts, prior product sales, and the data dispersion and the number of data samples in a product class hierarchy to dynamically determine the optimal level at which to compute promotional uplift coefficients. The methodology calculates confidence values for promotional uplift coefficients for products at each level in a merchandise product hierarchy, and uses the confidence values as a filter to determine the optimal level for promotional uplift aggregation.
摘要翻译： 一种在促销期间预测和建模产品需求的改进方法。 预测方法采用关于先前的促销需求预测，先前的产品销售以及产品类层次结构中的数据分散和数据样本的数量的信息来动态地确定计算促销隆起系数的最佳级别。 该方法计算商品产品层级中每个级别的产品的促销提升系数的置信度值，并使用置信度值作为过滤器来确定促销隆起聚合的最佳级别。
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7.发明授权Methods and systems for forecasting product demand during promotional events using a causal methodology 有权使用因果方法在促销活动期间预测产品需求的方法和系统公开(公告)号：US07996254B2
公开(公告)日：2011-08-09
申请号：US11938812
申请日：2007-11-13
IPC分类号： G06Q99/00
CPC分类号： G06Q30/02 , G06Q10/04 , G06Q30/0202 , G06Q30/0206
摘要： An improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs a multivariable regression model to model the causal relationship between product demand and the attributes of past promotional activities. The model is utilized to calculate the promotional uplift from the coefficients of the regression equation. The methodology utilizes a mathematical formulation that transforms regression coefficients, a combination of additive and multiplicative coefficients, into a single promotional uplift coefficient that can be used directly in promotional demand forecasting calculations.
摘要翻译： 一种在促销期间预测和建模产品需求的改进方法。 预测方法采用多变量回归模型来模拟产品需求与过去促销活动的属性之间的因果关系。 该模型用于从回归方程的系数计算促销隆起。 该方法利用将回归系数（加法和乘法系数的组合）转换成可以在促销需求预测计算中直接使用的单个促销隆起系数的数学公式。
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8.发明申请METHODS AND SYSTEMS FOR FORECASTING PRODUCT DEMAND DURING PROMOTIONAL EVENTS USING STATISTICAL CONFIDENCE FILTERS 有权使用统计信心滤波器在促销活动中预测产品需求的方法和系统公开(公告)号：US20090089143A1
公开(公告)日：2009-04-02
申请号：US11863958
申请日：2007-09-28
申请人： Arash Bateni , Edward Kim , Philip Liew , Jean-Philippe Vorsanger
发明人： Arash Bateni , Edward Kim , Philip Liew , Jean-Philippe Vorsanger
IPC分类号： G06Q10/00
CPC分类号： G06Q30/0202 , G06Q10/04 , G06Q30/02
摘要： An improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs information about prior promotional demand forecasts, prior product sales, and the data dispersion and the number of data samples in a product class hierarchy to dynamically determine the optimal level at which to compute promotional uplift coefficients. The methodology calculates confidence values for promotional uplift coefficients for products at each level in a merchandise product hierarchy, and uses the confidence values as a filter to determine the optimal level for promotional uplift aggregation.
摘要翻译： 一种在促销期间预测和建模产品需求的改进方法。 预测方法采用关于先前的促销需求预测，先前的产品销售以及产品类层次结构中的数据分散和数据样本的数量的信息来动态地确定计算促销隆起系数的最佳级别。 该方法计算商品产品层级中每个级别的产品的促销提升系数的置信度值，并使用置信度值作为过滤器来确定促销隆起聚合的最佳级别。
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9.发明申请METHODS AND SYSTEMS FOR DETERMINING RELIABILITY OF PRODUCT DEMAND FORECASTS 审中-公开用于确定产品需求预测的可靠性的方法和系统公开(公告)号：US20070156510A1
公开(公告)日：2007-07-05
申请号：US11614258
申请日：2006-12-21
申请人： Edward Kim , Jean-Philippe Vorsanger , Jinsheng Gao , Mazhar Khan
发明人： Edward Kim , Jean-Philippe Vorsanger , Jinsheng Gao , Mazhar Khan
IPC分类号： G06F17/30
CPC分类号： G06Q10/04 , G06Q30/02 , G06Q30/0202
摘要： A method has been devised to produce a Confidence Prediction metric which gives the business user some indication as to the future reliability of the current week's forecast. The forecasting method analyzes historical demand data and prior product demand forecasts to calculate forecast errors for the prior product demand forecasts, and determine a confidence level for current and future product demand forecasts, the confidence level providing an indication of whether a given product forecast is unreliable or not. Reliable product demand forecasts can be automatically passed to a purchase order system, while unreliable forecasts may need to be reviewed and adjusted manually. A method for assessing, before-hand, whether a given product's forecast is reliable has been devised.
摘要翻译： 已经设计了一种产生可信度预测度量的方法，其给予业务用户关于当前本周预测的未来可靠性的一些指示。 预测方法分析历史需求数据和先前产品需求预测，以计算先前产品需求预测的预测误差，并确定当前和未来产品需求预测的置信水平，置信水平提供给定产品预测是否不可靠 或不。 可靠的产品需求预测可以自动传递到采购订单系统，而不可靠的预测可能需要手动审核和调整。 已经设计了一种用于评估给定产品的预测是否可靠的方法。
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10.发明授权Methods and systems for determining daily weighting factors for use in forecasting daily product sales 有权确定日常加权因子的方法和系统，用于预测日常产品销售公开(公告)号：US07457766B1
公开(公告)日：2008-11-25
申请号：US10702963
申请日：2003-11-06
IPC分类号： G06F17/30
CPC分类号： G06Q10/087 , G06F2216/03 , G06Q30/0202
摘要： A method for determining daily weight values and store closure coefficients for use in forecasting daily sales patterns for retail products. The method uses historical daily demand data for a product to calculate a daily weight value for the product for each day of the week, each daily weight value representing the ratio of the historical daily demand for a corresponding day of the week to a total of the historical daily demands for the entire week. A daily demand forecast for each day of a forthcoming week is determined by applying the daily weight values to a predetermined weekly demand forecast for the forthcoming week. Historical demand data for weeks including holidays or store closures is used to calculate store closure coefficients, representing the ratio of the historical daily demand for days immediately preceding and following a store closure, to the historical demand for a corresponding day during a regular, non-holiday, week. The store closure coefficients are applied to the daily demand forecasts for days immediately preceding and following store closures or holidays to adjust the daily forecasts to accommodate changes in customer buying patterns resulting from the store closures.
摘要翻译： 一种确定每日重量值和存储关闭系数的方法，用于预测零售产品的日常销售模式。 该方法使用产品的历史每日需求数据来计算一周中每天的产品的每日重量值，每个每日重量值代表一周中相应日期的历史每日需求与总计 每周的历史日常需求。 通过将每日重量值应用于即将到来的一周的预定每周需求预测来确定下一周的每一天的每日需求预测。 用于包括假日或商店关闭在内的数周的历史需求数据用于计算商店关闭系数，表示紧接在商店关闭之前和之后的几天的历史每日需求的比率与常规非商品期间相应日期的历史需求， 假期，周。 商店关闭系数适用于紧随商店关闭或假期之前和之后的几天的每日需求预测，以调整每日预测，以适应商店关闭导致的客户购买模式的变化。
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