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The Research of Forecasting Model of Automobile Parts Recycling Cost Based on Data Intelligence

In view of the problem of reducing the cost control of the used parts in the after-sales service cost analysis process of the automobile enterprises, the multi-dimensional intelligent analysis of the used business data is carried out by extracting and cleaning the relevant business data resources of the used parts. This paper proposes a method based on data intelligence for cost forecasting, which assists enterprises in mining valuable information and knowledge contained in used parts data, making the used parts business data intelligent and realizing the value-added of used parts data. The management of used parts recycling is an important value link for automotive enterprises to reduce costs and increase efficiency, and is the key to building a reverse supply chain coordination architecture and achieving circular economy. There are many factors influencing the cost of recycling used parts, and too much interference will affect the forecasting effect. Therefore, before establishing the corresponding used parts recovery cost prediction model, the data should be decomposed into a stable time series, so that the prediction results are more accurate. This paper compares several typical prediction algorithms, uses the theory of empirical mode decomposition to preprocess the business data of used parts data, then builds the ARMA model, selects the ARMA model parameters through the AIC information criterion, and designs the supplier's used parts recycling cost prediction model. And the model is validated using the used piece business data piece data to predict the cost of the supplier's used parts recovery and provide support for the actual business system.