Research On The Demand Forecast Of D Company’s Spare Parts

Spare parts are actually a kind of inventory,the main role of which is to ensure the normal and effective operation of production equipment.Under normal circumstances,an excessive stock of spare parts will,in a sense,occupy too much working capital,making the company’s operating costs increase significantly;insufficient stock of spare parts may lead to a passive situation where no spare parts can be replaced,and may lead to serious production accidents,such as work stoppages and production shutdowns.Therefore,the purpose of effective spare parts management is to keep the spare parts inventory as low as possible while maintaining the normal and effective operation of the equipment.In this dissertation,based on the current situation of spare parts management in Company D,through statistical analysis of spare parts management data in the process of enterprise operation and maintenance,many problems of current spare parts management in the company are explored,such as poor level of spare parts classification management and low accuracy of spare parts consumption prediction.Subsequently,we analyzed the characteristics of spare parts by using the indicators of demand volatility,trend and periodicity as the benchmark,and classified them into four spare parts modules according to continuous and discrete demands.Based on the four spare parts classification models and their continuous and discrete characteristics,the paper constructs time series and gray forecast spare parts demand forecasting models respectively,and selects five spare parts for model fitting test to verify the validity and operability of the models.Finally,based on the demand prediction and model validation results,this paper proposes countermeasures and safeguards for the improvement of spare parts demand prediction management in Company D,such as improving the spare parts outbound management process at a deeper level,strengthening outbound inspection,and standardizing the management of warehouse and used spare parts,in order to maximize the application value of enterprise spare parts demand management.The results show that the method used in this dissertation is better than the original demand forecasting method of company D.For example,the forecasting based on the forecaster’s experience can largely reduce the human errors caused by the lack of forecaster’s experience,and improve the accuracy and operability of the forecasting,and the sorting of spare parts in Company D will be more scientific and feasible,and the safety stock quota will be significantly reduced.Moreover,the adjusted and optimized spare parts demand control mode and control mechanism will largely suppress the new low of spare parts inventory,and the company’s spare parts inventory will show signs of decline and realize a virtuous cycle,which will suppress the cost of spare parts management in Company D at a deeper level and largely improve the economic efficiency of the company.