Raw Material Procurement Optimization Of Q Power Equipment Manufacturing Enterprise With Multi-value Chain Coordination

With the continuous development of artificial intelligence technology,the transformation of power manufacturing industry is deepening.There are problems of poor internal business synergy and external synergy failure in the development of power equipment manufacturing enterprises,which lead to unreasonable equipment production plan and raw material procurement plan.Power equipment demand forecasting is an important part of the enterprise’s production and procurement plan.Due to the many factors affecting demand,it exists between various business links of the enterprise,and it is difficult to make full use of it efficiently.In this context,how to comprehensively utilize multi-value chain information and mine the correlation between multi-value chain data features is of great significance for accurately predicting power equipment demand and formulating reasonable raw material procurement plans.Based on the above content,this paper focuses on the demand forecasting and raw material procurement optimization of power equipment,integrates the data of supply value chain,production value chain,marketing value chain and service value chain,and constructs the multivalue chain data set of power equipment manufacturing enterprises.On the basis of multi-chain data sets,combined with external macro data sets,using machine learning and deep learning algorithms,monthly,weekly and daily power equipment demand forecasting models are constructed and applied to different business scenarios of Q enterprises.Firstly,statistical methods are used for data cleaning such as outlier processing and missing value processing.Secondly,the correlation analysis method is used to explore the correlation between each influencing factor and the number of power equipment requirements,and the characteristics of strong correlation are extracted.Finally,the demand forecasting model is constructed by using machine learning and deep learning algorithms,and the variation rules between features and demand under different time series are fully explored,which effectively improves the demand forecasting effect of power equipment.Based on the weekly power equipment demand forecast results,combined with the equipment bill of materials,the demand for raw materials is summarized,and the raw material procurement cost and inventory holding cost are fully considered.This paper constructs an optimization model with the objective function of minimizing the total cost.Taking the actual raw material procurement data of Q power equipment manufacturing enterprise as an example,the genetic algorithm is used to quickly solve and verify the optimization model.The research shows that in the monthly demand forecasting of power equipment,the prediction performance of eXtreme Gradient Boosting(XGBoost)is better than the other three models,and the error evaluation index is the lowest,indicating that XGBoost has the best prediction effect and is helpful to grasp the development trend of future demand.In the weekly power equipment demand forecasting link,the Informer model has the highest prediction accuracy;in the daily power equipment demand forecasting link,Long Short Term Memory(LSTM)has the best prediction effect.Accurate weekly and daily demand forecasting is of great help to the actual production management of enterprises and the fine management of power equipment demand.Under the guarantee of the above prediction accuracy,the optimization model of raw material procurement constructed in this paper can effectively reduce the cost of raw material procurement and inventory,assist enterprises to formulate appropriate raw material procurement strategies,improve the rationality and scientificity of raw material procurement strategy formulation,meet the economic requirements in the process of enterprise production and operation,and play a guiding role in reducing enterprise costs and improving enterprise economic benefits.The study are helpful for Q enterprises to grasp the future market demand of power equipment in the actual raw material procurement management process,formulate a refined raw material procurement plan in advance,ensure the accurate and timely delivery of equipment and reduce the cost of enterprises,and also help to improve the economic benefits and core competitiveness of enterprises.