Research on production logistics decision manageme

Research on production logistics decision management system based on data warehouse

Abstract: the traditional operational database has some shortcomings, such as scattered data, inconsistent data specifications, and poor data analyzability. Therefore, this paper introduces data warehouse technology, and puts forward a production logistics decision management system based on data warehouse on the basis of analyzing its system architecture, At the same time, build the data warehouse system structure of the production logistics decision management system, and form effective data through multidimensional analysis and data mining of the data warehouse, which is conducive to the production logistics decision managers to make scientific decisions and improve their position in the market competition. Photoelectric encoder will have pulse signal output

Keywords: data warehouse; Production logistics; Decision management; Data mining

introduction

decision managers collect and store a large amount of data in daily management business, but it is difficult to grasp the desired information, because on the one hand, there is a lack of enough information to support scientific decision-making, on the other hand, the accumulated rich data does not play its due role. This is because of the large amount of data involved and the wide range of sources, the traditional operational database has been unable to support the analysis function of the production logistics management system. It is mainly manifested in the following aspects: (1) the information sources of production logistics decision management are different databases, including production, distribution, warehousing and other data. These data are lack of organization, and there are many repetitions and inconsistencies. At the same time, they also contain different business processing logic. The traditional operational database is difficult to integrate these data; (2) The key of production logistics decision management is to analyze a large number of historical data to facilitate decision-making, while the traditional operational database is oriented to daily business processing, which requires that the data can be updated quickly; (3) The traditional database language has poor numerical calculation ability and weak integrated data processing ability, which is difficult to meet the needs of production logistics decision management analysis

data warehouse technology is produced to solve these problems. It provides a data environment for analysis and processing: a new data organization method is adopted to centralize, process, temper and reorganize a large number of original data and convert them into useful information. Through the analysis of these data, the informatization construction of production logistics rises from supporting daily business operations to supporting the analysis and decision-making of management. Therefore, this paper proposes to use data warehouse technology to analyze all kinds of data in the process of production logistics, and establish a production logistics decision management system based on data warehouse technology, so that production logistics decision managers can make in-depth analysis of their own business conditions and the development trend of the whole market related industries, and then make scientific decisions, Improve your position in the market and our company. Please rest assured of your position in the competition

1 data warehouse technology

1.1 basic concept of data warehouse

data warehouse is a subject oriented, integrated, non-volatile and time-varying data set, which is used to support the decision-making of managers [1]. It can be seen that data warehouse is an analytical database, which is separated from the operating system, integrated based on the standard enterprise model, with time attribute, subject oriented and non updatable data set. It is essentially different from the traditional transactional operation database, which mainly supports queries. As the basis of analysis and processing services, it provides decision-makers with the required information

the data in the data warehouse has the following four basic characteristics:

(1) subject oriented: the operational database is application-oriented for data organization, while the data in the data warehouse is subject oriented. Topic is an abstract concept, which integrates, classifies, analyzes and utilizes the data in the enterprise information system at a higher level

(2) integrated: operational databases are usually related to some specific applications. Databases are independent of each other and often heterogeneous. The data in the data warehouse is obtained through systematic processing, summarization and sorting on the basis of extracting and cleaning the original scattered database data. This integration includes consistency processing in coding, naming, measurement and other aspects

(3) nonvolatile: the data warehouse is different from the public investigation and the criminal investigation of the Metropolitan Police on the cause of the fire. The data in the database is mainly used to support the analysis and decision-making of enterprises. Therefore, these data are mainly used for query operations. Generally, the data will not be modified and updated, that is to say, the data in the data warehouse is not the latest, but from other data sources, It reflects historical information, that is, the data in the data warehouse is nonvolatile (rarely updated). The data in the operational database is usually updated in real time, and the data changes in time according to needs

(4) time variant: operational databases are mainly concerned with the data in a current time period, so they do not emphasize the need for time information. The data warehouse is different. For the needs of decision-making, the data in the data warehouse should be marked with time attribute, which changes over time, mainly in the following three points: the data warehouse will continue to add new data content over time; The data warehouse will continuously export and delete expired data content over time; A large amount of comprehensive data in the data warehouse will be re integrated over time

1.2 data warehouse system architecture

the system architecture of a complete data warehouse solution includes: data source layer, data collection layer, data storage and management layer, application service layer, portal management layer and end-user layer [2]. Figure 1 shows the hierarchical structure of data warehouse

among them, the data storage and management layer is the core of the whole data warehouse, which is used to store and manage data from various source data systems, and provide data services for accessing users. The data collection layer is mainly used to complete the extraction, transmission, conversion and loading of data to the data warehouse. This process is also called ECTL processing. It needs to be equipped with an ECTL server to complete the extraction, transmission, conversion and loading of data. The application service layer includes data mart module and various front-end tools. Data mart mainly reorganizes the data to be analyzed through multidimensional data model, so as to meet the needs of users for multi angle and multi-level analysis. The front-end tools include various data analysis tools (such as report tools, query tools, data mining tools) and various application technologies developed based on data warehouse, mainly dealing with data Analysis to provide decision support for managers

2 production logistics decision management system based on data warehouse

2.1 production logistics decision management system

production logistics of an enterprise refers to the logistics activities involved in the production of the whole product with the warehousing of raw materials required by the production of the enterprise as the starting point and the warehousing of finished products processed and manufactured by the enterprise as the end point [3]. Production logistics is a unique logistics management link of manufacturing enterprises. It is closely combined with the production process of enterprises, which is inseparable and synchronous. The modern production logistics system is mainly composed of management layer, control layer and executive layer [4]. According to the different division of labor at all levels, the logistics system requires high intelligence for the management, and can analyze, mine, convert and integrate from a large amount of data, so as to facilitate the production logistics decision-making managers to conduct in-depth analysis of their own business conditions and the development trend of the whole market related industries

for production logistics decision management, the data of production logistics decision management system may come from various departments, such as warehouse, sales, production and finance. These data are independent of each other, which is not conducive to the decision-makers' query and analysis. Production logistics decision management is based on management science, operations research, cybernetics and behavioral science, making full use of computer technology, artificial intelligence technology, simulation technology and information technology, integrating and modeling data by using data warehouse technology, providing decision-makers with data, information and background materials needed for decision-making, helping to clarify decision-making objectives and identify problems, and providing a variety of reference schemes for decision-making, And evaluate and select them, so as to facilitate each functional management department to make thematic analysis and assist the leadership in making decisions

2.2 system architecture based on data warehouse

in the design of system architecture, it is mainly through bottom-up information exploration to analyze the data in each business of production logistics, including inventory management data, distribution data and production data; At the same time, through top-down business exploration, we can find the most urgent analysis application in business analysis, and combine the logical structure of data warehouse manufacturers to form a solution of production logistics decision management system [5]. Its system architecture is shown in Figure 2

the working process of the production logistics decision management system based on data warehouse is divided into three parts from top to bottom: (1) from source system data to data warehouse: the source system includes inventory, distribution, production, sales and finance systems. Through the data extraction module, different kinds of data are converted into Unified Data Warehouse Metadata and stored in the data warehouse by using data extraction, mining, conversion, synthesis and other technologies. (2) Data processing: use data mining tools and multidimensional analysis tools to establish data models, knowledge bases and domain knowledge bases for a large number of Data Warehouse Metadata, so as to classify and model a large amount of data information and form information data with guiding value. (3) Decision makers conduct decision-making management through human-computer interaction platform: build a human-computer interaction platform based on the existing data model, knowledge base and domain knowledge base. Through the platform, decision managers can easily and accurately query, obtain relevant data information, and then make correct decisions

2.3 business application

in business application, after repeated communication with business personnel, and according to different objectives, the production logistics decision-making system is divided into three application parts:

(1) statistical reports are mainly oriented to analysts, reflecting some details of production logistics business, so that decision-making analysts can follow the original analysis mode, so as to maintain the consistency of use

(2) multidimensional analysis is mainly aimed at grass-roots analysts to establish some analysis topics to analyze the issues that decision-makers are more concerned about. For example:

customer development analysis: for production logistics, customers are an important factor affecting profits, so analyze customers from different levels and provide faster and comprehensive services to increase new customers to provide production efficiency and profits

market competition analysis: provide scientific information for decision-makers through the analysis of market share and competitors, so as to improve their position in market competition

department performance analysis: transform the management strategy of promoting industrial progress flow by products into the executive power of each department within the enterprise, and form the performance of each department on the basis of statistical analysis by formulating the evaluation indicators of each department, including finance, warehousing, distribution, production, etc