806 results about "Demand forecasting" patented technology

A computer-based method and system accomplish automatic optimization of a preferred merchandising figure of merit like revenue, profit, etc. by concurrent optimization of prices and in-store promotion schedules for groups of interrelated products in a supermarket or in a chain of supermarkets. The basis of the system is a statistical model that combines effects of prices, promotion schedules and other factors that could influence demands, and produces a flexible nonparametric predictive demand function that can be optimized simultaneously in prices and promotion schedules. The system generates demand predictions for groups of interrelated products by applying data mining procedures to historical database that contains sales data along with various sales conditions, adjusting statistical predictive demand models, and then optimizing them in prices and promotion schedules. The user can interact with the system by selecting and customizing optimization and prediction scenarios provided by the system, and by requesting customized reports with results of particular optimization and prediction schemes.

Disclosed herein are systems and methods for demand forecasting that enable multiple-scenario comparisons and analyses by letting users create forecasts from multiple history streams (for example, shipments data, point-of-sale data, customer order data, return data, etc.) with various alternative forecast algorithm theories. The multiple model framework of the present invention enables users to compare statistical algorithms paired with various history streams (collectively referred to as “models”) so as to run various simulations and evaluate which model will provide the best forecast for a particular product in a given market. Once the user has decided upon which model it will use, it can publish forecast information provided by that model for use by its organization (such as by a downstream supply planning program). Embodiments of the present invention provide a system and method whereby appropriate demand responses can be dynamically forecasted whenever given events occur, such as when a competitor lowers the price on a particular product (such as for a promotion), or when the user's company is launching new sales and marketing campaigns. Preferred embodiments of the present invention use an automatic tuning feature to assist users in determining optimal parameter settings for a given forecasting algorithm to produce the best possible forecasting model.

Aspects of the present invention include improvements to a management decision support system. Before improvement, the system includes a computer system having memory and resources, a retail demand forecasting program applying one or more forecasting approaches, running on the computer system and utilizing the output, said analysis programs generating at least one of (a) order of goods from a supplier-related data, (b) allocation of the goods to be shipped by the supplier-related data, or (c) distribution of goods to selling locations-related data. The improvements, according to one aspect of the invention, include a causal calendar utilized by the forecasting program to generate the output, the calendar including certain attributes for a plurality of events, and an analysis program to generate open to buy reports. Other aspects of the present invention are described in the specification, drawing and claims.

The invention discloses a traveler home-work location identification and trip chain depicting method based on mobile phone signaling data, and belongs to the field of analysis of transportation planning data. The method comprises the steps of: triggering an information database by communication network records provided by a mobile network operator; after cleaning, integrating and calculating by using a position conversion algorithm, converting the data of the operator into a reference dataset; aiming at each user, converting the dataset into a mobile time-space trajectory, identifying stopover points, high frequency points and long time points in the mobile time-space trajectory, and on the basis, identifying home-work locations, activity target locations and insignificant stopover points; and finally depicting a closed trip chain of the user by being combined with the mobile time-space trajectory. The traveler home-work location identification and trip chain depicting method based on mobile phone signaling data has the advantages of being conductive to reducing trip survey costs of residents, obtaining more precision trip characteristics of crowds with different identities, and thereby allowing the traffic demand forecasting to be more accurate; and 2, being conductive to improving scientificity of planning layout construction, such as transport corridors, public transit networks and traffic facilities, in urban traffic planning.

A method for managing inventory within a supply chain. The method is performed by providing forecasts of demand for items distributed within the supply chain, using the forecasts to establish base stocking levels and reorder points within the supply chain, and using the established base stocking levels and reorder points to position items within the supply chain so as to maximize efficiency and profitability when responding to an order for an item.

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.

The invention provides a method of judging a mode of transportation of a train chain based on a GPS (Global Positioning System) positioning and fuzzy theory. The method comprises the steps of carrying out GPS positioning to a traveler, and collecting personal attribute data; preprocessing the collected GPS data, and dividing a travelling section to a behavior of the traveler; carrying out fuzzy identification on the travelling section, and judging the travelling way of the traveling section; and finally, judging a travelling way which is judged to the travelling of a motor vehicle again, and judging whether the travelling way is either a car travelling way or a bus travelling way. According to the method provided by the invention, a smart mobile phone, a GPS positioning technology and other technologies can be combined to be convenient and comprehensive to obtain the travelling data of the traveler and the personal attribute data, dividing the travelling of the traveler practically, and analyzing and processing the aforesaid information based on the fuzzy theory, the personal attribute data of the traveler and the like; furthermore, compared with the prior art, the mode of transportation of a resident can be more accurately obtained, and the effectively data references can be provided to traffic rules, traffic demand forecasting and the like.

The invention relates to a spare part assembling demand forecasting information processing method applied to inventory management, comprising the following steps:1) take one part of historical sales data as method learning data and take the other part of the historical sales data as weight building data; 2) use the method learning data to practice an ARMA (Autoregression and Moving Average) method, a multiple linear regression method and a BP (Back Propagation) neural network approach respectively for each spare part; 3) apply the above three methods respectively by using the weight building data, so as to obtain forecasting relative error values for different methods; 4) calculate the weights of the above methods; 5)construct an assembling demand forecasting model; 6) forecast the future sales demands of all spare parts. Compared with the prior art, the method is suitable for the spare parts with different demand features, and has the advantages of strong objectivity, high forecasting precision, and can greatly reduce the inventory cost on the premise of having a certain service level and improves the supply chain management efficiency and customer satisfaction.

Entering a demand prediction for a discrete component product manufactured in a supply chain into a model predictive controller, entering inventory information for an inventory in the supply chain that is associated with the discrete component product into the model predictive controller, and controlling the inventory with the model predictive controller based on the demand prediction is disclosed.

The invention discloses a public bicycle peak time demand prediction method considering user reservation data. The public bicycle peak time demand prediction method is characterized in that 1) user peak time reservation data can be acquired and extracted; 2) the use data of the public bicycle historical peak time can be acquired; 3) the reservation data can be processed, and the reservation demand quantities of different rental stations at different times in the next day can be acquired; 4) the historical data can be processed, and the demand prediction values of different rental stations at different times in the next day can be acquired; 5) the prediction values can be integrated according to the reservation data and the historical data, and the final borrowing and returning demand quantities of different rental stations at different times can be determined; 6) the scheduling demand table having the time window can be drawn. The public bicycle peak time demand prediction method is advantageous in that the accuracy of predicting the bicycle and locking pile demands of different public bicycle rental stations at peak times can be effectively improved, and the data having the actual reference value can be provided for the peak time scheduling.

Method and apparatus suitable for demand forecasting. The invention can enable sales forecasting “by item, by size, by location”. The invention features combining a demand profile and a demand model into a single encompassing model which is capable of projecting demand for an identified set of merchandise.

Disclosed are a demand forecasting method, a demand forecasting program, and a demand forecasting system that can adequately forecast a demand for a commodity. A management computer calculates a trend function with respect to demand values. The management computer then calculates the ratio of the demand values to a trend curve, and calculates a logarithm of the ratio. Then, the management computer approximates the calculated natural logarithm curve with a periodic model, and converts the logarithmic scale of the acquired periodic model to a normal scale. Demand forecasting is performed using a demand forecasting model acquired by combining a periodic change model of the normal scale with a trend curve (trend function).

The invention discloses P2P (peer-to-peer) content delivery network based on the social attribute of users. The P2P content delivery network comprises a central server, an edge server and a P2P sub-network, wherein the edge server and the central server are mutually connected through a special network or a high-speed network; the edge server and the P2P sub-network are mutually connected through the Internet; the P2P sub-network consists of a plurality of user terminals; the central server is used for acquiring the interaction between all users in the P2P sub-network and the content delivery network, tracking the users and computing the document demand and the demand degree of the users; and the demand similarity and demand forecast among the users are computed according to the document demand and the demand degree of the users, and demand evolution treatment is carried out according to the change of the social attribute and the unceasingly acquired user demand. The invention also discloses a P2P content delivery method based on the social attribute of the users. By adopting the invention, the content delivery efficiency is improved.

The invention proposes a double-layer-optimization-based optimized operation management method for an electrical-thermal-storage boiler by a microgrid. The method comprises: a photovoltaic output prediction model, a wind power output prediction model, a micro gas turbine model, a diesel engine model, and a storage battery model are constructed, and a thermal load demand model is constructed by a method of improving a similar day so as to improve the precision of a thermal load demand prediction model; a double-layer optimization model is established, so that the wind curtailment and light curtailment values of the system can be reduced under the circumstance that the integrated cost of the system is minimized, wherein an energy management system is introduced into the lower layer and a centralized energy management system is introduced into the upper layer; and an objective function is solved by using an adaptive quantum chaos genetic algorithm to obtain outputs of all units in a microgrid system precisely, so that the high economic benefits and the high renewable energy source utilization rate can be realized under the circumstance that the electrical load and thermal load demands in the system are satisfied. Therefore, energy dispatching values of all units in the system can be calculated scientifically.

The invention discloses a traffic demand forecasting method and system for a dynamic road network. According to the invention, multi-source traffic data is collected and integrated all the time; dynamic grid data is formed through overlapping traffic flow data, geographic information data and traffic management information on the road network; and a data forecasting model is established on the basis of dynamic road network data. The method has characteristics of good real time performance, high accuracy and the like, can improve jamming study and judge precision effectively, so as to provide reasonable traffic demand data (queue, flow, speed, occupancy rate and the like) for signal optimization. The system can improve an optimization model and relieves traffic jams.

The present invention discloses an in-service life evaluation based spare part demand forecasting method. The present invention uses the historical record of changing parts and components during equipment running process to establish a statistic model and evaluate the life of a spare part under the current in-service condition; spare part demand function is defined according to the evaluation value of in-service life and practical service life, and the spare parts demand total quantity of a plurality of sets of equipment within certain rang of time is forecasted further. The method combines the spare part life evaluation with practical service condition closely, improves the accuracy for forecasting the spare part demand, is applicable to the maintenance instruction of frequently changed parts and components at the equipment and has important meaning for the enterprise to ensure the supply of the spare part and reduce the spare part stock.

Conventionally, for a product accompanied by supplies such as a copy machine or a printer, the demand figures relating to the supply which such a main machine consumes have been determined empirically from the transition of the past sales records of the supply, the trends of the market, and a sales scheduled figures of the main machine. Therefore, the forecasted values of the demand figures differ in accordance with the skills or experiences of individuals carrying out the forecasting, and necessarily, the accuracy of the forecasted values deteriorates. Further, a demand forecast method relating to the supplies of a product has not been realized due to the large number of parameters, the complexity of calculation, and the difficulty of a systematic approach for solving problems. In order to overcome the above-described problems, the invention of the present application forecasts consumption figures of a supply for outputting an outputted matter in the future, on the basis of inputted output figures of an outputted matter outputted by a product and sales figures of the supply.