TY  - CHAP
A1  - Grum, Marcus
A1  - Thim, Christof
A1  - Roling, Wiebke
A1  - Schüffler, Arnulf
A1  - Kluge, Annette
A1  - Gronau, Norbert
ED  - Masrour, Tawfik
ED  - El Hassani, Ibtissam
ED  - Barka, Noureddine
T1  - AI case-based reasoning for artificial neural networks
T2  - Artificial intelligence and industrial applications
N2  - Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level.
KW  - case-based reasoning
KW  - neural networks
KW  - industry 4.0
Y1  - 2023
SN  - 978-3-031-43523-2
SN  - 978-3-031-43524-9
U6  - https://doi.org/10.1007/978-3-031-43524-9_2
VL  - 771
SP  - 17
EP  - 35
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Grum, Marcus
A1  - Rapp, Simon
A1  - Gronau, Norbert
A1  - Albers, Albert
ED  - Shishkov, Boris
T1  - Accelerating knowledge
BT  - the speed optimization of knowledge transfers
T2  - Business modeling and software design
N2  - As knowledge-intensive processes are often carried out in teams and demand for knowledge transfers among various knowledge carriers, any optimization in regard to the acceleration of knowledge transfers obtains a great economic potential. Exemplified with product development projects, knowledge transfers focus on knowledge acquired in former situations and product generations. An adjustment in the manifestation of knowledge transfers in its concrete situation, here called intervention, therefore can directly be connected to the adequate speed optimization of knowledge-intensive process steps. This contribution presents the specification of seven concrete interventions following an intervention template. Further, it describes the design and results of a workshop with experts as a descriptive study. The workshop was used to assess the practical relevance of interventions designed as well as the identification of practical success factors and barriers of their implementation.
KW  - knowledge transfers
KW  - business process optimization
KW  - interventions
KW  - product development
KW  - product generation engineering
KW  - empirical evaluation
Y1  - 2019
SN  - 978-3-030-24853-6
SN  - 978-3-030-24854-3
U6  - https://doi.org/10.1007/978-3-030-24854-3_7
VL  - 356
SP  - 95
EP  - 113
PB  - Springer
CY  - Cham
ER  -