Key Technologies Of Production Management For Intermittent Manufacturing Enterprises

With the development of manufacturing informatization and informationtechnology, a variety of manufacturing information systems has been widely applied inintermittent manufacturing enterprises. The successful implementation of theseheterogeneous manufacturing systems has made important contributions to businessneeds for discrete manufacturing enterprises. However, the integration of heterogeneousmanufacturing systems is a difficult problem, because in the design and modelingprocess, every manufacturing system only considers its own function and the businessneeds, so the integration function is ignored. For this reason, the information islandsappear. On the other hand, the irrational problems of production planning in productionmanagement also becomes increasingly serious, the reason is that the determination ofthe output and the makespan is inaccurate, so it is necessary to realize the prediction ofoutput and makespan accurately based on the solution of information islands problem.Due to the existence of information islands, enterprise-level managers can not feel thecomprehensive achievement of the informatization, the reason is that the decisioninformation of production management comes from the integration framework ofmultiple manufacturing systems but not a manufacturing system, and the data from theintegration framework is abstract, complicated and difficult to understand, so the datavisualization operation is needed. In order to overcome these problems above, theresearch contents of this paper include the following sections:The principle workflow of production management for discrete manufacturingenterprises is analyzed, and every node of the principle workflow is optimized based onthe requirement of an aviation manufacturing enterprise. The application example ofworkflow in this aviation manufacturing enterprise is obtained based on the principleworkflow optimization. The optimiazed production management workfolw can supplysupport for the research of integrated framework modeling, output prediction, makespanprediction and information visualization of production management.To overcome the flexible integration issue of heterogeneous manufacturingsystems, the integrated framework modeling technology is studied based on theproduction management workflow analysis. An integration framework modeling systemof heterogeneous manufacturing systems is presented. The integration framework modelof heterogeneous manufacturing systems is established via the method of IDEF0Functional modeling, GRAI Functional modeling, and the network mapping methodbetween IDEF0Functional modeling and GRAI functional modeling is studied. The information model of part level and assembly level are studied, and then a unifiedinformation model of production management is proposed. The flexible integrationproblem and information mining problem are solved by unified information model.To overcome the unreasonable setting of production output, a new approach topredict the output of production planning is presented. In this new approach, an initialmultiple linear regression model is established based on the related factors which canaffect the output of production planning, these factors are extracted from the integrationframework of PDM, ERP,CAPP and MES. Then the low significance factors areremoved by backstepping method, and a dynamic-improved multiple linear regressionmodel for output prediction in the next season is obtained. This new approach proposedtakes the factors in production lifecycle into account so that prediction results can bemore accurately. Finally, we demonstrate the performance of our model by comparingthe prediction results with actual output of production planning.A novel AIGA-DBP(Adaptive Immune Genetic Algorithm-Dynamic BackPropagation) model is developed for solving the problem of maximum completion timeprediction (makespan prediction). By analyzing the history data of makespan and itsrelated factors on enterprise level, the prediction model based on BP neural network isestablished, then the weight values and threshold values of the BP neural network modelimproved dynamically, and the dynamic BP neural network model is further optimizedvia AIGA, so the AIGA-DBP model is obtained. The proposed AIGA-DBP algorithm isapplied to an aviation enterprise’s makespan prediction. Computational experimentssuggest that the algorithm yields accurate results and minimum error. The usefulness ofaccurate makespan prediction on enterprise level as a tool for improving productionefficiency is highlighted.Due to the difficulty of decision support information access and the problem ofinformation’s abstract, complicated and difficult to understand, decision informationvisualization architecture is presented based on the integration framework of productionmanagement. In this architecture, the data mart of heterogeneous manufacturing systemsin production management is established, then the API of information access isdeveloped. The information visualization technologies of Abstract data Visualization,Tree visualization and dynamic workflow visualization are studied. Based on thevisualization technologies, the histogram, Gantt chart, line chart and dynamic flowchartof decision information in production management are realized. The application case inan aviation manufacturing enterprise shows suggests that this architecture can improvethe efficiency of information transmission and the degree of information visualization, and it can provide support for the decisions in production management.