Abstract
Essential characteristics of products are set during early phases of product development as well as manufacturing. During these processes, decisions are made without awareness of their final impacts on long term key success factors. Industrial businesses lack concepts that enable decision-makers within knowledge based engineering processes, to anticipate possible impacts of their decisions. Due to this, many industrial companies employ so called knowledge engineers to manually gather and analyze information of product lifecycles. In order to improve the decision support within knowledge based engineering, a concept was developed, which contains the extension of business intelligence environments with product-orientated data warehouses. It is thus possible to combine technical information of product features with the traditional dimensions of managerial analysis in order to identify impacts of decisions on the product lifecycle and hence support knowledge engineers in their daily work.
Recommended Citation
Lasi, Heiner, "DECISION SUPPORT WITHIN KNOWLEDGE-BASED ENGINEERING – A BUSINESS INTELLIGENCE-BASED CONCEPT" (2012). AMCIS 2012 Proceedings. 12.
https://aisel.aisnet.org/amcis2012/proceedings/DecisionSupport/12
DECISION SUPPORT WITHIN KNOWLEDGE-BASED ENGINEERING – A BUSINESS INTELLIGENCE-BASED CONCEPT
Essential characteristics of products are set during early phases of product development as well as manufacturing. During these processes, decisions are made without awareness of their final impacts on long term key success factors. Industrial businesses lack concepts that enable decision-makers within knowledge based engineering processes, to anticipate possible impacts of their decisions. Due to this, many industrial companies employ so called knowledge engineers to manually gather and analyze information of product lifecycles. In order to improve the decision support within knowledge based engineering, a concept was developed, which contains the extension of business intelligence environments with product-orientated data warehouses. It is thus possible to combine technical information of product features with the traditional dimensions of managerial analysis in order to identify impacts of decisions on the product lifecycle and hence support knowledge engineers in their daily work.