377 results about "Product demand" patented technology

A data processing system-implemented method or data processing system readable medium can be used to model operating parameter(s) for a vendor. Detailed analysis of the impact of prices or other variables, on the demand of each item, is performed. These allow us to prune large numbers of prices or other variables which have little or no impact on a given item. After determining which prices and other variables are significantly related to an item, a more in-depth examination of that small list of variables may be performed. This in-depth examination will result in a set of final weighing factors, quantifying the effect of each on the item. The weighing factors for all other variables can be assigned a value of zero. By limiting the number of non-zero weighing factors, the time needed to generate all the weighing factors for a matrix (or matrices) is reduced.

A method and apparatus for predicting future data values based on past data values. Past data values such as past product demand values are received. Leading indicators are identified based on the past data values. The future data values are generated based on the leading indicators.

An apparatus is provided for an interface enabling a user to determine optimum prices of products for sale. The interface includes a scenario / results processor that enables the user to prescribe an optimization scenario, and that presents the optimum prices to the user. The optimum prices are determined by execution of the optimization scenario, where the optimum prices are determined based upon estimated product demand and calculated activity based costs. The scenario / results processor has an input / output processor and a scenario controller. The input / output processor acquires data corresponding to the optimization scenario from the user, and distributes optimization results to the user, where the data includes activity based cost data. The scenario controller is coupled to the input / output processor. The scenario controller controls acquisition of the data and the distribution of the optimization results in accordance with a price optimization procedure.

The current invention provides an enhanced user experience for consumers by minimizing the number of click-throughs, and optimizing time spent by providing customizable, interactive visual multimedia collages of information created from several sources of information in scrollable snapshots. Moreoyer, the same experience is made available for the users on-the-go on their mobile device where they can access the same collages and interact with them in a manner meaningful to the modalities provided on the mobile device, such as phone calls, text messaging, and Wireless Application Protocol (WAP) browser viewing. On the desktop Internet, interested consumers will be referred from the image collage result sets to content provider or retailers' web sites. Each image has all the information: the advertisement from the seller, customer review, product rating, and seller rating composited together. In one embodiment, the consumer can click on any of the regions of the collage (e.g. customer surveys) to click through to the WAP site or Internet site housing the information. In other embodiments, the consumer can just browse the information, consummate a buy transaction on-line: get it shipped or pick it up himself, place a bid for a used product, and / or call a number for more information. Through the system's analytics engine, the invention provides to the retailers competitive location-based information, consumer behavior data for advertising, and location based product demand. The consumer can save any of the interactive images and forward to others. For enterprises, retailers, and individuals interested in selling new / used products, they can create advertisements for their products using the Internet web based information collage authoring tool.

An apparatus is provided for determining optimum prices of products for sale. The apparatus includes a scenario / results processor through which a user prescribes an optimization scenario, and through which optimum prices are presented to the user. The optimum prices are determined by execution of the optimization scenario, where the optimum prices are determined based upon estimated product demand and calculated activity based costs. The scenario / results processor has an input / output processor and a scenario controller. The input / output processor acquires data corresponding to the optimization scenario from the user, and distributes optimization results to the user. The scenario controller is coupled to the input / output processor. The scenario controller controls acquisition of the data and the distribution of the optimization results in accordance with a price optimization procedure. The price optimization procedure enables the user to selectively prescribe the subset of the products for sale.

The invention provides an intelligent manufacturing method and system. The intelligent manufacturing method comprises the following steps: receiving product demand information and processing the received product demand information to generate a product order; decomposing the generated product order into a plurality of task plan tables for obtaining products in the order; executing corresponding tasks in the task plan tables to generate the products in the order in a controlled manner, and monitoring execution progress of each task in real time; and receiving the generated products, packaging the products and obtaining delivery information of the products to realize delivery of the packaged products. The method and system enable the order to be directly decomposed into production demands; and after production is finished and quality check passes, the products are delivered automatically according to the delivery information on the order, thereby realizing product customized production, processing and delivery, reducing stock to the maximum degree, saving cost for enterprises and realizing "smart factory", "smart production" and "smart logistics".

An improved process for forecasting product demand to be used within inventory management and / or production planning systems. This improved process overcomes the limitations of the prior art by providing an optimal combination of automated statistical data processing and human intelligence, in one interactive system. The new method provides an efficient means for an operator to incorporate much information, known to the operator, that would otherwise be missed by the systems of the prior art. It allows the operator to analyze information very quickly by viewing graphs drawn on a graphical user interface and to make changes very quickly using that same graphical user interface. The method relies on the use of edit markings / symbols drawn on the display portion of the graphical user interface. These edit markings / symbols are used to make changes to the parameters forming the basis for a product demand forecast. The markings / symbols are recognized by the forecast software algorithm and a new forecast is generated based on the revised parameters.

A method and system for forecasting product order quantities required to meet future product demands for a retail distribution center or warehouse. The method includes the steps of determining for each one of a plurality of retail stores, a long range order forecast for a product sold by said retail store; accumulating said long range order forecasts for said plurality of retail stores to generate a distribution center demand forecast for said retail distribution center; comparing said distribution center demand forecast with current and projected future inventory levels at said distribution center of said product; and determining from distribution center demand forecast and said current and projected future inventory levels suggested order quantities necessary for maintaining a minimum inventory level sufficient to meet said distribution center demand forecast for said product.

A combinatorial heterogeneous-homogeneous reactor configuration in which an array or groups of homogeneous fuel assemblies are interlinked together in a heterogeneous lattice. The present invention removes the limitation of a homogeneous reactor by providing a reactor concept that utilizes the inherent advantages of homogeneous fuel elements but in a heterogeneous fuel lattice arrangement that limits the power density of any one homogeneous fuel element and yet forms a reactor arrangement that is capable of producing any product demand of interest. The present invention provides a method for producing medical isotopes by the use of a modular reactor core comprised of homogeneous fuel assemblies arranged in a regular rectangular or triangular pitch lattice. The aqueous fuel solution is contained within individual fuel assemblies that are right circular cylinders clad in corrosion-resistant alloys such as stainless steel, zircalloy, zircalloy alloys, or other metal alloys that are resistant to corrosive fissile environments but preserve neutron economy. The fuel assemblies are supported below by a core plate that is tied directly to the lower reactor support structure. The bottom of each assembly opens into a common plenum area which provides a hydrodynamic communication / coupling path between the individual assemblies in the lattice. The fuel assemblies are supported above by an upper plate that is welded to each assembly tube. The top of each assembly opens to a common upper plenum which provides a means of thermodynamic pressure equalization among the four assemblies in the reactor core lattice.

An improved method for forecasting and modeling product demand for a product. The forecasting methodology blends information about the future price of a product with historical sales data to better forecast the future product demand. This forecasting methodoloy takes into account three main parameters that may affect the future demand for a product: seasonality (using seasonal factors), recent sales trends (through average rate of sale analysis) and the product price (by estimating the price driven demand).

A method, system and computer program for accumulating information relating to product demand and consumption for each of a plurality of end users by a buying group representing a single purchase in bulk to disintermediate the supply chain, accumulating the received product demand into a predictive demand schedule by an expert service provider, and transmitting the accumulated predictive product demand schedule to a manufacturer / original source of the product by the expert service provider. Product availability information based upon the accumulated predictive product demand is received from the manufacturer / original source of the product, and is provided to the plurality of end users. One or more product orders are received, the product is ordered from the manufacturer / original source of the product, all aspects of the supply chain being managed by the expert service provider, and the product is shipped to one or more of the plurality of end users.

Projection factors are used to project product sales data from sample retail outlets to the universe of outlets. Weekly forecasts of market conditions and product demand are generated based on the projected product sales data. The projection factors and the weekly forecasts are updated during the course of the forecasted week, for example, daily, as data on actual product sales during the forecasted week is received.

The invention provides a product demand preference characteristic digging and quality evaluation method based on comment information. The method comprises the following steps: 1, crawling data, namely by using a network crawling technique, crawling product comment appointed information of an E-commerce platform and storing in a databank; 2, performing data preprocessing and product characteristic word extraction, namely performing data cleansing and preprocessing on acquired data, and further performing product characteristic extraction on preprocessed data by using a BiLSTM-CRF (Bidirectional Long Short-Term Memory-Conditional Random Field) model; and 3, performing product demand preference characteristic digging and quality evaluation. By adopting the method, quality problems of products can be rapidly understood according to feedback information of customers, demand preference characteristics of the customers can be understood, and thus companies can make relatively good decisions to meet the customers.

Estimating price elasticity of product demand assists an understanding of total demand for a product and a forecast of how the level of demand for that product will change based on a change in the price of the product. A multi-level hierarchical regression methodology can be used to forecast product demand, and thereby calculate price elasticity. The methodology accepts historical product sales, product prices, and revenue management controls of a company and predicts the estimated demand at the product level. This methodology can also accept the historical product sales, product prices for the company and its competitors, revenue management controls of a company, and an estimate of the revenue management controls of its competitors to predict the estimated demand at the product level.

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.

The invention provides a method for controlling the content of biuret in compound fertilizers for melt granulation in a prilling tower. A low melting point mixture is formed by urea and compounds in a melting tank, and is melted completely at the temperature ranging from 104 to 114 DEG C; nitrogen fertilizers, phosphate fertilizers, potash fertilizers, fillers and trace elements are used for melting urine and production compound fertilizers and are added in a mixer according to product demand ratios, are premixed rapidly under the temperature condition ranging from 90 to 110 DEG C, then are mixed through a slurry emulsifying machine at a high speed so as to form emulsified slurry, and then are processed through the procedures such as granulation, cooling sieving and the like in the prilling tower so as to produce the granular compound fertilizers with low content of biuret; in addition, the content of biuret in the compound fertilizers that are produced by adopting the method is less than 0.6 percent, the quality is safe, and fertilizer damages cannot be caused. The method belongs to a novel environment-friendly production method with low consumption, which can produce the granular compound fertilizers on the basis of urea-based prilling tower melt granulation technology.

The embodiment of the invention discloses a big data production planning method. The method is used for solving the technical problems that an existing plan demand analysis system cannot calculate the product production demand in advance and cannot conduct production in advance before a customer places an order. The method comprises the steps of obtaining historical product sales data from a database; working out the product sales forecasting amount according to the historical sales data; obtaining the non-delivery amount, the preparation amount and the to-be-delivered amount of ordered products; obtaining the current inventory and the purchase number of the products; obtaining the product demand amount according to the sales forecasting amount and the non-delivery amount of the ordered products; obtaining the available inventory quantity of the products according to the current inventory, the purchase number, the preparation amount and the to-be-delivered amount; finally, obtaining the production forecasting amount by adding the minimum safety inventory quantity to the difference between the demand amount and the available inventory quantity. The embodiment of the invention further provides a big data production planning system.

The system distributes workload amongst a plurality of pharmacy resources that are connected by a computer network. Work orders are queued at each pharmacy resource and redistributed based on existing workload distribution, capacity of pharmacy resources, and / or product demand.

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.

The invention provides an automatic production scheduling optimization management information model based on a big data analysis and processing technology. The model based on the construction method comprises a customer requirement information collection module, a customer information processing module, a production plan scheduling module and an inventory classification module. The concrete steps include that customer requirement information is established; data are summarized to a data exchanger center to perform requirement classification processing on the customer information; current inventory rate is intelligently calculated, and corresponding customer order tasks are established; a production plan is issued; production is organized; product offline inventory classification (to be delivered) is performed; and the customer order production and processing tasks and product offline inventory classification are completed and the detailed data are returned to the data exchanger center to be stored, and the corresponding tasks are eliminated. The standby inventory coefficient is intelligently calculated, product demanding requirements of users are predicted and reasonable standby inventory is performed according to the product demanding habit of the users and various other factors so that the production and processing plan meeting the product demanding requirements of the users can be automatically arranged under the premise of guaranteeing supply.

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.

The system distributes workload amongst a plurality of pharmacy resources that are connected by a computer network. Work orders are queued at each pharmacy resource and redistributed based on existing workload distribution, capacity of pharmacy resources, and / or product demand.

The invention relates to a method for automatically generating test cases aiming at document converters. The method includes step 1, acquiring formalized test needs according to product needs of test objects, and dividing test function points based on the formalized test needs; step 2, performing dynamic instantiation processing on class objects in the formalized test needs, writing test data in the class objects, acquiring class object sets containing the test data, serializing the class object sets, and generating XML (extensive makeup language) segments based on corresponding test function points; step 3, writing the XML segments based on the test function points in corresponding bottom-layer XML instance document sets, and generating test case documents. The method is independent of an office software interface, the problem of test blind points caused by the fact that office software is insufficiently supportive of standards is avoided, and test case generation is short in time and high in efficiency.

The invention discloses a configuration platform and configuration method of a high speed train product. The configuration platform comprises a demand analysis module, a configuration model module, a configuration solving module and a version management module. A hierarchical design method is adopted, on the basis of demand analysis, configuration is divided into the configuration of a high speed train component module and the customization of parts, a product configuration model based on ontology is constructed, a configuration technology based on ECA (Event-Condition-Action) and a tabular layout of article characteristics is constructed, a small quantity of standardization modules and module transformation can be used for finishing the design of the high speed train, and development efficiency is improved. The technical problem that a mapping relationship between a product demand variable and a design parameter is undefined since demand information lacks a structured mapping relationship in the prior art can be solved, and the invention provides the configuration platform and method of the high speed train product, wherein the configuration platform and method is characterized in that the demand information has the good mapping relationship and the mapping relationship between the product demand variable and the design parameter is clear.

The invention provides a product customization method and device, which are applied to a server side for providing a mobile social platform. The method comprises the following steps: receiving customization demand information issued by a client on the mobile social platform; obtaining identification information included in the customization demand information and searching information of at least one merchant matched with the identification information, and outputting the information to the client; obtaining a target merchant selected by the client from the at least one merchant; generating an order according to the customization demand information, and sending the order to the target merchant and the client; and in response to a first confirmation request issued jointly by the target merchant and the client for the order, establishing an association relationship among the target merchant, the client and the order to enable the target merchant to customize a product demanded by the client according to the order. Therefore, it can be ensured that the client can purchase the products that fully meet demand.

Collected trial data is weighted to, for example, reflect the relevance of the data. A weighted confidence interval determination and application process described herein determines a confidence interval used to interpret the data. The confidence interval is derived by adjusting the sample size N to account for the impact of weighting when determining confidence intervals. The sample size N is adjusted in a downward trend to avoid overestimating the confidence interval. Lower and upper bounds of the confidence interval are determined using weight influenced variables. Thus, interpretation of weighted trial data, such as product demand data, can be achieved and acted upon with an accurate estimation of risk.

Embodiments generate a model of demand of a product that includes an optimized feature set. Embodiments receive sales history for the product and receive a set of relevant features for the product and designate a subset of the relevant features as mandatory features. From the sales history, embodiments form a training dataset and a validation dataset and randomly select from the set of relevant features one or more optional features. Embodiments include the selected optional features with the mandatory features to create a feature test set. Embodiments train an algorithm using the training dataset and the feature test set to generate a trained algorithm and calculate an early stopping metric using the trained algorithm and the validation dataset. When the early stopping metric is below a predefined threshold, the feature test set is the optimized feature set.

A method and system for forecasting product demand using a causal methodology, based on multiple regression techniques. In order to better predict product demand changes associated with causal variables having seasonal patterns, such as temperature, the method and system include a technique for removing the seasonal variation of causal variables, i.e., to de-seasonalize the causal factors. The de-seasonalized causal variables are utilized within the causal methodology to generate product demand forecasts.

A product demand forecasting methodology is presented that applies daily weight values to a weekly forecast to determine daily forecasts for a product or service. The method determines daily weight values for use in forecasting current product sales by blending daily weight values calculated from historical demand data for both recent weeks and year-prior weeks. Recent weeks are used to account for recent correlations and alternation effects, and year-prior weeks are used to account for seasonality effects. The method automatically calculates a measure of significance for the daily weights calculated from the recent weeks and year-prior weeks. The significance of each week is applied as a weighting factor during the blending of recent weeks and year-prior daily weight values.