Optimalizace a automatizace procesů ve výrobním podniku

The master’s thesis focused on the optimization and automation of selected business processes in a medium-sized manufacturing company using SAP ECC. The aim was to analyze, optimize and automate selected processes in order to improve data quality, reduce routine workload and improve management reporting. The research was grounded in the Design Science Research paradigm and carried out through three case studies following the DSRM methodology: supplier declarations and material origin data, the HR process with user authorization management, and production capacity planning. Data collection was based on internal documentation, system data, observation, interviews, a supplementary questionnaire survey and comparison of AS-IS and TO-BE states. The results included supporting automation of selected activities, process standardization, clarification of responsibilities, implementation of control mechanisms and reduction of personal dependency in the processes. The measurable impacts were demonstrated by an approximately 86% reduction in the processing time of material origin data, a reduction of a standard JML request from hours of searching to approximately 12 minutes of net work, a reduction of directly assigned single roles in pilot profiles by 97.6% and 98.8%, a reduction of executable transactions by 71% and 91%, and a reduction of capacity forecast preparation from approximately eight hours to less than twenty minutes, i.e. by more than 95%. It was found that governance in an organization affected by historical debt only works when it is translated into specific responsibilities and control mechanisms embedded directly in technical artifacts. It was also shown that the optimization and automation of processes burdened by workarounds and shadow IT should start with the stabilization of data and process flow, rather than with the implementation of a tool itself. A complementary output is a generalized framework for process optimization and automation in an ERP environment. It was inductively and iteratively derived from the three case studies and represents a best-fit design artifact for a defined class of business problems.