Modeling and Analyzing Dynamic Cycle Networks for Manufacturing Planning

Plehn, Christian; Koch, Jonas; Diepold, Klaus; Ståhl, Benjamin; Lohmann, Boris; Reinhart, Gunther; Zäh, Michael F.

doi:10.1016/j.procir.2015.04.025

Cited by 8 publications

(5 citation statements)

References 24 publications

(17 reference statements)

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“…A promising approach is the use S-shaped logistic functions as in [5,7,16]. A fully quantified model allows simulation based testing and optimization of strategic decisions of PSS companies.…”

Section: Discussion and Outlookmentioning

confidence: 99%

“…At some point a manufacturing change cycle is required to adopt the manufacturing structure. [7] Fig. 5.…”

Section: Manufacturing Structure Cyclementioning

confidence: 99%

“…Suitability of the manufacturing structure over time To reacquire productivity, during the manufacturing change cycle all depleted resources are reset back to a high level of productivity. Resources can be replaced by newer instances, if a new production technology is available [7]. Production technology evolves continuously following a logistic function [16], but can only be incorporated into the manufacturing structure during a manufacturing change cycle.…”

Section: Manufacturing Structure Cyclementioning

confidence: 99%

“…However, changes also influence other disciplines, such as the requirement and planning cycle and team process cycle. For example, different means of production change the way a product is designed and create new tasks for the involved teams [7].…”

Section: Manufacturing Structure Cyclementioning

confidence: 99%

“…[7][8][9]. With this perspective, System Dynamics promises to be a plain and adequate modeling environment for both qualitative and quantitative approaches to the innovation process of PSSs.…”

Section: Introductionmentioning

confidence: 99%

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Qualitative System Dynamics Cycle Network of the Innovation Process of Product Service Systems

et al. 2015

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The innovation process of Product Service Systems (PSS) is affected by a vast number of internal and external influences. Especially the management of timely or structurally repeating influences, so called cycles, shows a great potential for improving the innovation process. Thereby two types of cycles can be differentiated. Firstly, internal cycles during the innovation process itself going from engineering change cycles, to manufacturing resource cycles or even team building processes. Secondly, the external, environmental cycles that have an impact on the innovation process, i.e. government-and customer-related demands and dependencies. A lot of these dependencies, most likely external ones, contain uncertainties, that have to be handled for successful innovation of PSS.To cope with these struggles, methods and tools have to be developed, to allow analysis and forecast of the cycles in the innovation process. Especially the high degree of cross-linking between the different cycles indicates the need of integrated modeling and analysis. An interdisciplinary cycle network of 30 relevant cycles and external influences as well as 51 interconnections so far, was set up in a System Dynamics environment. Though not all influences can be determined yet, the created causal loop diagram can already serve as a framework for analyzing the innovation process of PSSs and support deeper understanding of the interdisciplinary interdependencies.

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Section: Discussion and Outlookmentioning

confidence: 99%

“…At some point a manufacturing change cycle is required to adopt the manufacturing structure. [7] Fig. 5.…”

Section: Manufacturing Structure Cyclementioning

confidence: 99%

Section: Manufacturing Structure Cyclementioning

confidence: 99%

Section: Manufacturing Structure Cyclementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Qualitative System Dynamics Cycle Network of the Innovation Process of Product Service Systems

et al. 2015

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Concept for modeling and quantitative evaluation of life cycle dynamics in factory systems

Dér

Hingst

Nyhuis

et al. 2023

Prod. Eng. Res. Devel.

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Against the background of the climate crisis, fast innovation space in emerging technologies and the global competitive environment for manufacturing companies, a sound understanding of the life cycle behavior of factory systems becomes more and more important. The decision context of the factory life cycle conveys a high level of complexity, e.g. by the heterogeneous nature of factory element life cycles, manifold interactions between them as well as external change drivers. A model-based understanding as well as methods and tools are required that support factory planners and operators in this regard. This paper presents an approach for the modeling and quantitative evaluation of life cycle dynamics in factory systems while respecting the dynamic behavior of factory operation, as well. The purpose of the modeling is to deepen the knowledge of the prevailing life cycle mechanisms and their implications for factory planning and operation. The application of the approach is demonstrated in an exemplary case study.

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Anticipative strategic production technology planning considering cyclic interactions

Schönmann

Dengler

Reinhart

et al. 2018

CIRP Journal of Manufacturing Science and Technology

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Modeling and Analyzing Dynamic Cycle Networks for Manufacturing Planning

Cited by 8 publications

References 24 publications

Qualitative System Dynamics Cycle Network of the Innovation Process of Product Service Systems

Qualitative System Dynamics Cycle Network of the Innovation Process of Product Service Systems

Concept for modeling and quantitative evaluation of life cycle dynamics in factory systems

Anticipative strategic production technology planning considering cyclic interactions

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