Analysis of processes with the stochastic networks in logistical management and an industrial practice

With management of more complex production activities, firms need new approaches specially in result of logistics Firstly, meaning and importance of the logistics for any company is emphasized. Business Process Reengineering methodologies parallel with logistics researches are also mentioned. In addition to this, specifications and elements of the business processes are explained so, similarities between network form and business processes models are introduced. After that, business process modeling techniques? general characteristics are mentioned and their sufficiency are tested to compare with this study?s objective. Later, all modeling techniques are compared and the importance of the proposed analysis techniques among those previously mentioned methods is determined. In the process projection section, after a general explanation is made about the current project scheduling technique, the usage of GERT in process analyzing is mentioned so, stochastic state is provided. Expectations from the general modeling techniques are tested on the GERT models. An industrial practice is made to test the applicability of GERT in process modeling and analyzing. Then the results from GERT is verified with PERT-path and Monte Carlo simulation package. Finally, two important aspects are found in the applicability of GERT as a modeling technique. Methods that have the ability to perform stochastic structure like GERT, find a new application area. This is important because it is improved to be used and a new structure and method can also be found. Also by bringing forward the stochastic structure in process analysis and using analytical calculations, realistic analysis can be made so, process management becomes more efficient with new methods that can handle processes not only as improvement of interrelation between activities but also capable of an analytically analysis AS-IS and TO-BE models of the process. In this way it is shown how processes and companies goals coincide. Keywords: Business processes modelling and analysis, GERT, stochastic networks.