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Diversity is increasingly being addressed as an innovation-promoting factor. For this reason, companies and institutions tackle the integration of a diversity management approach that enables a heterogenic perspective on innovation development. However, system-theoretical frameworks state that the implementation of diversity measures that are not tailored to the needs of the organization often leads to a rejection or reactivity with regard to the management approach. In this context, especially organizations, which are characterized by a specific hierarchical structure, a dominant habitus or specialist culture, must face the challenge of realizing a sustainable change of the corporate culture that sets the basis for implementing diversity management approaches. The presented research project focuses on analyzing the situation in a huge scientific collaborative project - so called Cluster of Excellence (CoE) - with the aim to implement a diversity - and innovation management strategy. Considering the influencing determinants, the CoE is characterized by its embeddedness in the scientific system, a complex organizational structure, and a high fluctuation rate. The paper presents a systemic approach of reflecting these factors in order to develop a diversity- and innovation management strategy. In this frame, the results of a quantitative survey of CoE employees and derived mindset-types are presented. The results show a need for taking different mindset-types into account, to be able to develop a tailored management strategy. The aim of the project is to give recommendations for developing a sustainable management concept that promotes both diversity and innovation by drawing on the persisting mindsets of organization members while reflecting top down as well as bottom up factors of implementation processes as well as the psychology of change. This paper addresses all who are concerned with the management of human resources in innovation processes and are striving for a cultural change within the framework of complex organizations.
In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead.