Das Angebot konfigurierbarer Produkte mit hoher Varianz stellt eine zentrale Herausforderung für die Produktionsplanung dar. Bisher stützt sich die Planung stark auf bestehendes Prozesswissen und Prognosen zu einzelnen Produktvarianten, was in der Praxis oft zu unzureichenden Planungsergebnissen führt. Dieser Beitrag beschreibt die Nutzung von synthetischen Fertigungsaufträgen auf Basis von Merkmalsspezifikationen zum Aufbau eines statistischen Kollektivs. Neben der Fertigungszeit lassen sich Erwartungswerte von Zielgrößen bestimmen und frühzeitig optimieren.

The essential properties of a product are defined at the beginning of the development process [1]. Consequently, dependencies of the product and the context must be considered at an early stage with regard to the effects on the impact areas of sustainability, such as climate change or social inequalities. In addition to the implementation of assessment methods, product requirements are derived from sustainability models [2]. Previous work has shown a heterogeneous problem picture with complexity-related causes in interdisciplinary cooperation [3]. Ambiguities can have a detrimental effect on the interaction of partial models used in product development. Differences in the semantics of models can result from the purpose of a model and can be represented, for example, by different levels of abstraction or depth of detail. These limitations in the interaction of the models lead to the need to continuously analyze dependencies between the requirements and their effects on the product context to avoid problem shifts within the effective areas of sustainability. Therefore, the objective must be to support the analysis of problem shifts in the early phase of product development by optimizing the interaction of partial models at the interface to product development. With the help of the interdisciplinary approach of semantic interoperability [4], a systematic analysis of the interaction of partial models should be supported. In addition to technical aspects of standardization and the mapping of information models, research should also focus on human and organizational influences on interoperability. Initial contributions address a context-sensitive extension of Model-based Systems Engineering to systematically support the inclusion of the continuously changing context of a product [5]. The use of knowledge graphs enables further analysis of the interactions of requirements as well as support for communication in the localization of information in interdisciplinary collaboration. Future work will build on this to address further questions of interdisciplinarity in the context of autonomous open source communities to explore practical knowledge, methods and ways of working in the transition of sustainability models in product development.

Gaining knowledge about the sustainable context is fundamental for the success of a product development, as a lack of knowledge limits the possibilities of innovation. The contextual boundaries of a system often do not adequately describe the influence on sustainability, so that product developers are unaware of problem shifts within and between the areas of impact on sustainability. For reasons of complexity, a therefore needed holistic captured system context is insufficiently taken into account in existing approaches. Within the scope of this article, a framework for Sustainable Context Engineering is developed, that provides a formalization of a holistic sustainable system context for the analysis and synthesis of relations in product development. Furthermore, a requirements methodology based on SPES 2020 is proposed, which supports the decomposition of requirements by model-based templates via a subtractive structure. This promotes communication and situational awareness among developers in order to influence the sustainability goals in the early phase of the product development process. Further research will be made in order to implement the approach in a model-based manner and to define a finite number of measurement parameters based on interdisciplinary sustainability models.