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Research On Inventory Control Strategy Of X Material In Z Enterprise

Posted on:2020-03-31Degree:MasterType:Thesis
Country:ChinaCandidate:Z Z LiuFull Text:PDF
GTID:2439330590452278Subject:Logistics engineering
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Z is the backbone enterprise engaged in the development and manufacture of professional equipment for wood-based panel machinery in China.As the market competition becomes more and more fierce,the profit of the enterprise is continuously compressed.At the same time,reducing the internal management cost of Z is an important issue that Z enterprise is facing at present.Based on the analysis of the actual problems of enterprise,this paper analyzes the current situation of inventory management of Z,and finds that it has the problems of inaccurate demand forecasting,stock backlog and low inventory turnover rate,and the shortage of goods will occur at the same time as the production of stagnant materials.Therefore,it is important for Z enterprise to choose reasonable demand forecasting mode and inventory control strategy to reduce costs.First of all,in the actual production management process,many materials are affected by uncertain factors,and the demand is characterized by unstable and random changes,and the fluctuation of demand is the key to affect the enterprise inventory decision.In this paper,the typical material X material is selected,and its historical demand data is statistically analyzed.It is found that its demand fluctuation is large and does not have obvious change rules.According to the characteristics of its small sample and multi-factor,the prediction method based on support vector machine is selected to forecast its demand.The specific programming operation is carried out by using Matlab software.The results prove the effectiveness of the method and provide data support for the subsequent formulation of corresponding inventory control strategies.Secondly,based on the demand characteristics of X materials,this paper aims at minimizing the total cost such as order cost,purchase cost and inventory holding cost.Considering the problem of lead time arrival and the last lead time order,two inventory control strategies of non-stationary stochastic demand under multi-cycle are constructed.The static strategy and the static-dynamic strategy of non-stationary stochastic demand are respectively adopted.After that,an example is given to verify the advantages of the two strategies and to analyze their sensitivity factors.The research results show that:(1)When fixed order cost,variable order cost,inventory holding cost,coefficient of variation and customer service level are small,the effect of two inventory control strategies is better;(2)The unit variable order cost is the most direct factor of the total cost change;(3)When other conditions remain unchanged,with the increase of variable ordering cost,inventory holding cost,coefficient of variation and customer service level,the advantages of static-dynamic combination strategy become more and more obvious.When the fixed order cost is small,the total cost reduction obtained by the static-dynamic combination strategy is the largest.Therefore,no matter how the parameters change,the static-dynamic combination strategy has a greater advantage for the management of raw materials with non-stationary stochastic demand characteristics,and the order quantity and inventory holding level can be effectively reduced,which leads to the reduction of the total cost of the enterprise.Through the research in this paper,the problems of inaccurate demand forecasting and unreasonable inventory control strategy of Z can be solved.It also provides some theoretical guidance for general manufacturing enterprises to solve similar problems.But at the same time,there are still some shortcomings in this paper.In the process of demand forecasting,the influencing factors of demand change still need to be further considered.Moreover,the lead time of order is only considered by the fixed condition in inventory control strategy,without considering the impact of lead time changes.
Keywords/Search Tags:demand forecasting, support vector machine, non-stationary stochastic demand, static-dynamic combination strategy
PDF Full Text Request
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