artificial intelligence (AI)<\/a> offer potential for overcoming these limits. Established calculation methods in product engineering, such as finite element analysis or fluid dynamics simulations, combine mechanical domain knowledge with the fundamentals of mathematics.<\/p>\n\n\n\nDS processes and methods combine the skills of computer science and mathematics (especially stochastics) with the specialist knowledge of the respective domains (see left in Figure 2, based on [13]).<\/p>\n\n\n\n
DS forms the basis for methods such as machine learning or reasoning with ontologies. Such processes can be used to provide machines with (artificial) intelligence. According to the European Commission’s Expert Council [14], AI “<\/em>refers to systems that display intelligent behaviour by analysing their environment and taking actions \u2013 with some degree of autonomy \u2013 to achieve specific goals\u201c.<\/p>\n\n\n\nFigure 2: Understanding DS (cf. [13]) and categories of DS\/AI (cf. [15]).<\/em><\/figcaption><\/figure>\n\n\n\nDS\/AI can be divided into data-based approaches and knowledge-based approaches [15] (right in Figure 2). The crucial factor for decision support is not the categorization into DS and AI but the arising capabilities for engineers in product creation. The focus is on processes and methods that represent a fundamentally new approach compared to established approaches in product creation. The aim is to support humans in their cognitive processes in the sense of weak AI but not to map cognitive processes as an alternative to human intelligence in the sense of strong intelligence (cf. hybrid intelligence [16]). <\/p>\n\n\n\n
Opportunity and challenge: extreme data<\/h2>\n\n\n\n The main requirement regarding data and information is to support the handling of extreme data (see [17]). Extreme data is understood here as data within which various characteristics interact simultaneously. Large volume, high speed and great variety, for example, often only become “extreme” when data is scattered and shows different extreme deviations in values. In many cases, this data can even contradict previously established approaches in product creation: Using machine-generated information in the decision-making process, which, according to previous scientific consensus, requires verified and validated information (Figure 3). The focus here is not on general decision support systems [18] but on the domain-specific challenges in product creation.<\/p>\n\n\n\n
Product engineering relies on heuristics, methods, models and simulations that have been researched and developed according to established standards (see above). Based on this, data must be condensed into information and knowledge along the Knowledge Pyramid before it is integrated into decisions.<\/p>\n\n\n\n
This compression can be understood as the “qualification” of data [19]. Unlike machine learning, sorting, calculation, optimization and evaluation processes are based on this understanding. Guidelines and standards are the result of such qualification, which in this case takes place through consensus building among specialist personnel and which engineers can refer to in their actions.<\/p>\n\n\n\nFigure 3: Contradiction in the handling of data that is systematically secured as before or directly available from processing by DS\/AI algorithms<\/em>.<\/figcaption><\/figure>\n\n\n\nTo be able to follow machine-generated decision support, a clear association of responsibility and liability issues in the interaction between humans and AI systems is required. When deciding which technical concept and which specific technical realization is to be pursued, various influencing elements are to be equally combined in the future: mathematical interpretation based on formulas, technical simulation and heuristic rules on one side, and data availability on usage situations in operational mode, as well as additional recorded conditions such as weather, environmental influences, measured vibrations etc. on the other side.<\/p>\n\n\n\n
The aviation industry provides an example: Aircraft turbines contain turbine blades that wear out during operation and require regular maintenance. During operation, it was recognized that wear is significantly higher on flight routes over India than in other airspaces (see, for instance, [20]). It is the responsibility of product creation to make decisions on adjusted maintenance intervals, a flight ban over India or design changes to the components based on such observations – and thus to help decide on the safety of the aircraft passengers.\u00a0<\/p>\n\n\n\n
New approach: Hybrid Decision Support<\/h2>\n\n\n\n Hybrid Decision Support describes the synergetic combination of previously used methods, heuristics, models and simulations on the one hand and DS and AI processes and methods on the other. In order to responsibly deploy this type of Hybrid Decision Support in product creation, individual key activities and their dependencies must be highlighted. These include understanding a problem or innovation potential and designing and validating against requirements and customer needs. On the one hand, engineers must be supported in narrowing down solution spaces and thus optimally focusing engineering activities. On the other hand, they should be empowered to creatively develop and implement new solutions. Six promising approaches for increasing the quality of results in product creation can be identified:<\/p>\n\n\n\n
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