Spare part assembling demand forecasting information processing method applied to inventory management

Abstract

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.

Description

technical field [0001] The invention relates to a method for processing combined demand forecast information, in particular to a method for processing spare parts combined demand forecast information applied to inventory management. Background technique [0002] In 1954, Schmitt used a combination forecasting method to predict the population of 37 cities in the United States, which improved the forecasting accuracy. In 1959, J.M.Bates and C.W.J.Granger conducted systematic research on combined forecasting methods, and first proposed the idea of ​​"combined forecasting" in 1969, that is, to combine different single forecasting methods by comprehensively considering the characteristics of each single forecasting method . After the 1990s, there was an unprecedented upsurge in research on combined forecasting, and scholars also began to learn and use the "combined forecasting" method, and achieved a series of forecasting results. [0003] Xia Jingming (2004) used the combined ...

AI Technical Summary

Problems solved by technology

However, for the forecasting of spare parts demand in the manufacturing industry, due to the large variety of spare parts and the different characteristics of the demand for various spare parts, a method that has a good prediction effect in one type of spare parts may have a poor predictive effect in another type of spare parts

Therefore, if only one of the aforementioned methods is used to realize the demand forecast of different types of spare parts, it will lead to low demand forecast accuracy and poor stability

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