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Production planning is a critical step for the implementation of sustainable production. It is necessary to consider energy and resource efficiency in all planning phases to promote sustainable production. In this paper, an approach for environmental impact assessment in all phases of process chain planning supported by process models is presented. The level of detail of the assessment is determined based on the level of detail of the planning phase. During the assessment, consumption of energy and resources is considered. This approach aims to align planning phases with the objective of sustainable production. In rough planning, the approach allows the selection of an ecologically favorable process chain. In detailed planning, process parameters can be selected based on their ecological sustainability. The approach can be integrated into the planning of process chains in order to consider ecological factors throughout all planning phases. The approach is evaluated by using an exemplary use case. The results indicate that rough planning under the consideration of uncertainties can form a reasonable prediction about resource efficiency for possible manufacturing routes. By systematically selecting a resource-efficient process chain, energy savings of up to 21% can be achieved for the presented use case.

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Die Planung von fertigungstechnischen Prozessketten über Unternehmensgrenzen hinweg ist aufgrund technologischer Wirkbeziehungen zwischen Prozessschritten eine große Herausforderung. In diesem Beitrag wird eine Planungsmethode vorgestellt, die mit dezentralen Modulen eine unternehmensübergreifende Optimierung der Fertigungskosten ermöglicht. Zusätzlich werden Unsicherheiten durch Modellfehler sowie externe Einflüsse in die Methode eingebunden. Dadurch wird eine kollaborative Planung umgesetzt, die zu robusten Ergebnissen führt.   Planning of manufacturing process chains across companies is a great challenge due to technological interdependencies. This paper presents a planning method enabling cross-company manufacturing cost optimization with decentralized modules. In addition, uncertainties due to model errors as well as external factors made are integrated into the method. This allows for collaborative planning and leads to robust results.