توجه به برخی از جوانب مدل های شبیه سازی مدیریت لجستیک

The main purpose of the paper is to develop a neural network application that could predict supplier problems in terms of stock management, lead time and production. Stocks of goods that manufacturers would classify as raw materials stocks are, in a special sense, goods in early stages of the production process. In a logic-based economic competitiveness, the company which holds the largest stock is underperforming! Technically, the stock is inevitable and in some cases even desirable; economically it is associate with an asset value stock, therefore, it should be minimized. Minimizing the value, the costs will also be minimized.

The companies perform in an unstable environment, where the tastes of the consumers are in a continuous changing Anastasiu, 2009. For small and medium sized manufactures, there are five emerging trends in supply chain management: green supply chains, supply chain risk management, supply chain agility, moving supply

In this paper the cost model for the lack of stock or storage costs have been successfully carried out. We used for this cost function also the neural networks. Compared with the classical methods, the proposed methodology has the advantage of a faster time required to obtain the cost function with its two components: the lack of stock or the storage costs with the same precision as the classical model. Also, the model takes into account the lead time of supply. In this way, organizations can know in advance the cost. The costs were determined taking into account the factors that influence the stock level.