Research On Classification Model Used Spare Parts Nventory Of Mold Manufacturing Enterprises

Cost and efficiency are the key for an enterprise to survive and thrive in market-oriented economy. As these manufacturing enterprises, it is very important to maintain the normal operation for spare parts inventory. Spare parts inventory can keep manufacturing enterprises operation, promote the continuity of the production, and reduce the losses caused by shortages of spare parts. Spare parts inventory management should minimize the losses, and simultaneously reduce stock-out cost to the reasonable lowest level.The dissertation, researched characteristics of production, spare parts and spare parts management in these mold manufacturing enterprises, combined with current situation of a mold manufacturing enterprises, explained the limitations of the traditional ABC classification in the spare parts inventory management, analyzed the reasons. For this purpose, it proposed a suitable ABCD classification of model spare parts inventory for the mold manufacturing enterprises, set the six important influence factors as classification index of model and took Class A, class B, class C, class D as the four spare parts category of model, provided corresponding inventory management strategy.Combining with the advantages of BP neural network, the dissertation solved the ABCD classification model of spare parts inventory, though the sample data which was extracted and MATLAB software, designed the simulation, and made lots of network training, preliminary built the spare parts inventory classification model based on BP neural network.Because of the slow convergence and learning efficiency of BP neural network, it is easy to fall into the local minimum and make BP neural network invalid, The dissertation, put forward to apply genetic algorithm to optimize and improve BP neural network, made up the insufficient of neural network, learned from each other, built the spare parts inventory classification model based on genetic algorithm and BP neural network, though testing and comparing the precision, proved the advantage of the spare parts inventory ABCD classification model that is based on GA-BP. Though computing and contrasting the actual data of the mold manufacturing enterprises, the dissertation, introduced the existing inventory as the judgment condition, and verified the rationality of the spare parts inventory classification model based on GA-BP.Finally, the dissertation made the summary and made the prospect for inventory management development of spare parts in the mold manufacturing enterprises.