An Adaptable Model for the Factory Planning Process: Analyzing Data Based Interdependencies

Various models of the factory planning process have been developed in the past decades. For these process models, we conducted a literature review with focus on dealing with unexpected changes in planning premises due to the turbulent corporate environment. The results were compared with the best-practice approach, which we identified in numerous interviews with industry experts. It can be concluded that a process model which takes into account data based interdependencies and at the same time allows adaptation to individual planning cases, is lacking. With the aim of defining a reference process for factory planning, we adopted the modular approach of Condition Based Factory Planning in which the planning data is regarded as input and output of each planning task. In order to minimize planning effort, a tool named aranea to individually adapt this reference process is introduced. Within this tool we implemented an algorithm to automatically convert the interdependencies between the planning tasks into a Design Structure Matrix. This step enables the factory planner to apply methods from structural complexity management to identify planning data for which fuzziness is especially critical and which could lead to delays and iterations in further planning tasks.