Research On Inventory Management Of N Port Based On Demand Forecasting

Driven by the construction of Guangdong,Hong Kong and Macao Bay area,the port faces both opportunities and challenges.As a traditional industry,port operation requires a variety of large-scale equipment,involving a variety of spare parts,but in the internal inventory management still retains the traditional thinking,lack of efficient and appropriate inventory management system,according to the current situation of port n,there are three problems,namely,out of stock,overstock and inventory management confusion.The main reasons are as follows: the classification of spare parts is simple,the actual effect is still confused;the lack of demand forecast,can not accurately determine the number of spare parts;the lack of effective management strategy,inventory personnel responsibility is not clear.Combined with the actual production situation,the importance of equipment is brought into the inventory management system.This paper will optimize the inventory management of port n from the following three parts:First,the demand forecast of spare parts in port n is compared.Three methods of quantitative analysis are used to forecast,namely Holt winter algorithm,seasonal change forecast method and ARIMA model.According to the forecast results,the appropriate method is selected to forecast the demand of spare parts,so as to ensure its applicability and accuracy.Secondly,the spare parts of port company are professional,which can not be distinguished by the traditional capital occupancy rate and demand rate.There are many factors that affect the classification.Using fuzzy evaluation method to classify can consider more factors and experts’ weight score of spare parts.Increasing the weight score of port experts can make the classification more targeted.Thirdly,based on the results of demand forecasting and spare parts classification,reasonable management strategies are formulated for all kinds of spare parts to help warehouse managers better manage inventory,save inventory cost and improve inventory management efficiency.