Knowledge framework for production planning and controlling considering sustainability aspects in smart factories

As the human population increases, the demand for products rises. Mass production and the use of these products cause different kinds of sustainable impacts, such as consumption of local and global non-renewable resources or different types of emissions during each stage of the product life-cycle (PLC). Through a systematic overview of these sustainable impacts during the production planning and controlling (PPC) process, sustainability impacts can be identified and possibly avoided. Identifying sustainability impacts is enabled through current developments for manufacturing and product systems digitalization. Data, information, and knowledge are available from each phase from the PLC and can be used for operational, as well as tactical and strategic production management processes. Companies exist, which are unprepared for the impending changes caused by digitalization and related applications. They do not know what and when to measure, as well as how measured data can be connected and analyzed to create new information and knowledge to meet management requirements. To contribute to this problem, the paper proposes a knowledge framework for the collection of data from different PLC phases, analysis of such data to create information, and generation and collection of knowledge for PPC processes. The knowledge framework seeks to answer the research question, which data, information, and knowledge from different PLC phases can be used in PPC processes to identify and avoid sustainability impacts. For this approach, existing concepts for digitalization (RAMI4.0) and knowledge management (business intelligence) are combined. Moreover, the paper presents a qualitative example using the knowledge framework and considering the problem statement about the environmental effects of greenhouse gas (GHG) emissions. Other sustainability problem statements can be considered too, such as employees’ working conditions.