Research And Application Of Logistics Warehousing Decision Support System Based On Data Mining

As an advanced organization mode and management technique, modern logistics has become an important part of national economic development. For the circulation enterprises adopting modern logistics management mode, warehousing and inventory generally occupy more than40%of the enterprise’s liquidity. Therefore inventory decisions and optimization have crucial significance for enterprises. In this study, data warehouse and data mining techniques are used to analyze the vast amounts of business data produced by warehouse management system in circulation enterprises. Through this way, the study is intended to discover the hidden information that are valuable for enterprises. And these information is able to directly provide guidance for the enterprise decision makers to do correct decisions. Thus logistics enterprises can respond to market changes more quickly, and inventory, backlog of funds and logistics warehousing system cost can also be reduced, then the efficiency of logistics management can be improved.After fully studying the development of modern logistics and computer data mining techniques, this paper discusses the combination of data mining techniques with logistics warehouse management system. Aiming at the inventory management business activities and relationships among data in warehouse management, this paper created a data warehouse of inventory decision support system through data extraction and data conversion. After studying the concept and structure of decision-support system, the paper provides a system framework and an overall design scheme based on B/S mode. It includes functional modules, structural design and workflow. In the end, this paper gives the operating visual interface for front-end system.In the study of the Logistics Warehousing Decision Support System, the paper uses the popular data mining techniques that is C4.5decision tree algorithm. Firstly, the study makes an in-depth study of the logistics inventory warehousing. Then considering the storage costs, production costs, order costs, shortage costs, replenishment time and some other factors, the paper generates an inventory warehousing decision model, according to which the system sets up relatively accurate, convenient and fast logistics decisions that provide basis for decision makers. The algorithm is simple, easy to understand, and the amount of calculation is small, and fast. And the results can be easily converted into comprehensible classification rules. It can eventually provide appropriate decision-making knowledge for senior managers as well. A series of test results on data sets show that C4.5 decision tree algorithm is indeed a good solution to inventory warehousing decision problems.