张家口煤机公司库存分类优化研究

发布时间：2018-05-15 04:18

本文选题：煤矿装备 + 库存分类　；参考：《辽宁工程技术大学》2014年硕士论文

【摘要】：随着经济的发展,库存在企业中的地位不断提升。作为供应链的重要环节,它发挥着平衡物流采购和生产消耗的重要作用。然而经济发展的背后企业库存问题却日趋尖锐,逐渐的成为阻碍企业可持续发展的主要障碍。因此,在保证产品质量的前提下优化库存结构,降低库存成本已成为节约整个企业成本的重要环节和保持行业竞争力的有效手段。张家口煤机公司是煤矿机械装备制造领域的典型企业,长期的库存积压和居高不下的库存成本已成为阻碍企业发展的最大障碍。因此,研究该企业的库存优化对于该行业的可持续发展具有重要的意义。本文以张煤机公司原辅材料库为研究对象,针对该企业库存的实际情况,首先建立了基于ABC管理的灰色聚类评价模型。选取采购提前期、供应商数量、月需求量、客户指定、功能性、物料的单位价值和库存成本作为评价指标对原辅材料库中的物料进行分类。通过综合打分的方式,确定本文所选取的该公司的20种物料的A、B、C类别。其次,运用神经网络对建立的基于ABC管理的灰色聚类评价模型进行评价,将上文中参与评价的20种物料的前10种物料作为训练样本,后10种作为测试样本,并把通过ABC管理的灰色聚类评价所得出的综合评分作为期望值,对评价的结果进行测试,证明本文所建立的上述模型的有效性,以及通过该方式进行物资分类的合理性。最后,针对A类和B类库存物资,进行库存分类优化,针对A类和B类物料建立了以订货量为参数的库存模型,运用遗传算法求解最优订货量和安全库存。
[Abstract]:With the development of economy, the status of inventory in enterprises is increasing. As an important link of supply chain, it plays an important role in balancing logistics procurement and production consumption. However, the problem of enterprise inventory is becoming more and more acute behind the economic development, which gradually becomes the main obstacle to the sustainable development of enterprises. Therefore, optimizing inventory structure and reducing inventory cost under the premise of ensuring product quality have become an important link to save the cost of the whole enterprise and an effective means to maintain the competitiveness of the industry. Zhangjiakou Coal Machine Company is a typical enterprise in the field of coal mine machinery and equipment manufacturing. The long-term inventory backlog and high inventory cost have become the biggest obstacle to the development of enterprises. Therefore, it is of great significance to study the inventory optimization of the enterprise for the sustainable development of the industry. In this paper, taking the raw and auxiliary material library of Zhangshan Coal Machine Company as the research object, according to the actual situation of the enterprise inventory, a grey clustering evaluation model based on ABC management is established. Purchasing lead time, number of suppliers, monthly demand, customer designation, functionality, unit value of materials and inventory cost are selected as evaluation indicators to classify the materials in the raw and auxiliary materials library. In this paper, 20 kinds of materials of this company are selected by the way of comprehensive scoring. Secondly, using neural network to evaluate the grey cluster evaluation model based on ABC management, the first 10 kinds of materials of the 20 kinds of materials involved in the evaluation above are used as training samples, the last 10 kinds of materials as test samples. The comprehensive score obtained by grey clustering evaluation managed by ABC is taken as the expected value and the evaluation results are tested to prove the validity of the above model established in this paper and the rationality of the material classification by this method. Finally, the inventory classification optimization is carried out for class A and B inventory materials, and the inventory model based on order quantity is established for class A and B materials. The genetic algorithm is used to solve the optimal order quantity and safety inventory.
【学位授予单位】：辽宁工程技术大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F274;F426.4

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