Study On Raw Materials Inventory For Manufactures With Order Cancellation

Order-oriented production refers to a mode in which manufacturers purchase raw materials for production after receiving orders from customers.However,the cancellation of orders by customers for various reasons will lead to the surplus of raw materials and the increase of various costs for manufacturers,which will bring great difficulties to the purchasing and inventory management activities of manufacturers.The existing research on raw material inventory optimization based on order cancellation prediction mainly focuses on establishing the optimal order quantity and order point,and seldom considers the economic results of modeling and solving with the goal of minimizing the total cost on the basis of order cancellation prediction.In this paper,considering the customer orders can be cancelled situation,combined with the order cancellation forecast and inventory decisions,research bases on the quantity of order cancellation to predict manufacturers of raw materials inventory optimization problem,and establishes the raw material inventory optimization model with the target of minimizing the total cost.Finally determined its inventory optimization program by solving the model,it is of great significance for manufacturers of raw materials inventory optimization research.The main innovative results are as follows.Raw material inventory optimization model and its solution based on linear regression prediction of order cancellation.On the basis of customer order cancellation forecast,established the raw material inventory optimization model of manufacturer with target of minimizing the total cost,and work out the optimal inventory plan by designing approximate algorithm called CA,the time complexity of CA is O(np),n represents the species number of raw materials,p represents the period,then analyzed the approximate ratio of CA.When the difference between the maximum value and the minimum value of the predicted order cancellation quantity is not large,the approximate ratio is close to 1.Finally,the model and algorithm are used to analyze the raw material inventory of XAHL company.Raw material inventory optimization model and its solution based on Bayesian combination forecast of order cancellation.In third chapter using linear regression model to predicting the order cancellation,the single prediction model is generally difficult to get accurate prediction results,so,in this chapter choose Bayesian combination model which is composed of multiple linear regression model and grey system model to predict the amount of customer order cancellation,and then establish inventory optimization model and solution with target of minimizing the total cost based on Bayesian combination forecast,for the actual situation,using the approximate algorithm A*to solving the model,the algorithm’s time complexity is O(np2),n represents the species number of raw materials,p represents the period,then analyzed the approximate ratio of A*.When the difference between the maximum value and the minimum value of the predicted order cancellation quantity is not large,the approximate ratio is close to 1.Finally,an example is given to verify the effectiveness of the model and algorithm.