Business Process Modelling of Autonomously Controlled Production Systems

Conventional production systems are characterised by central planning and control methods, which show a wide range of weaknesses regarding flexibility and adaptability of the production system to environmental influences. Centralised planning and control methods are based on simplified premises (predictable throughput times, fix processing times of production orders etc.), which lead to an inadequate and unrealistic description of the production system. The different centralised planning steps of the traditional ERP respectively MRP based PPC-Systems are executed sequentially, therefore adaptation to changing boundary conditions (e.g. planning data) is only possible within long time intervals. This means that changes to the job shop situation cannot be considered immediately, but during next planning run at the earliest. As a result, current planning is based on old data and the needed adaptation measures cannot be performed in time for a proper reaction of the discrepancy between the planned and the current situation (Scholz-Reiter et al. 2006). In case of disturbances or fluctuating demands, centralised planning and control methods are insufficient to deal with the complexity of the comprehensive planning tasks of centralised systems, which rises disproportionately to their size and heavily constrains fault tolerance and flexibility of the overall system (Kim and Duffie 2004; Prabhu and Duffie 1995).