A Framework for Digital Twins for Production Network Management

The dynamic and highly complex task of production network management requires decision support through quantitative models. In the industrial praxis, these models are specifically designed and implemented for particular management decisions, requiring significant one-time effort for model creation. This contribution utilizes the digital twin concept to facilitate production network models that are continuously synchronized with the examined production network to support several different management decisions. The approach structures data from existing information systems as a synchronized generic base model, which is used to create problem-specific executable models, thereby saving costs through repeated model use and quicker decision making

Robustheitssteigerung in Produktionsnetzwerken mithilfe eines integrierten Störungsmanagements

Manufacturing companies operating in global production networks face increasing susceptibilities to disruptions that may have far-reaching consequences for the entire network. To cope with disruptions and to maintain the network\u27s performance even if disruptions occur, companies are in need of a holistic, systematic disruption management, which includes all network actors in the identification of advantageous reaction measures and thus ensures the network\u27s robustness against disruptions. However, current implementations of operational disruption management are mostly exclusively based on experience or intuition and are limited to individual, production or logistics-related partners or areas, hence not forcing a holistically advantageous reaction. Therefore, the objective of the present thesis lies in the development of a methodology for increasing robustness in production networks by means of an integrated disruption management, taking both production and logistics perspectives into account. Based on the analysis and modelling of significant, production- and logistics-related disruptions, a simulation-based approach is used to identify (combinations of) countermeasures that are suitable both for the elimination of disruptions as well as the minimization of their consequences. The simulation thereby combines design of experiments with methods of metamodeling in order to obtain comprehensive statements about the interactions between disruptions, countermeasures and system performance and thus about the suitability of certain measures. Based on the knowledge about the suitability of certain measures, proactive strategies are derived, which promote the implementation of advantageous measures from a planning point of view by appropriately adjusting the respective capacities in the production network. This combined approach, which optimally coordinates the planning and control components of disruption management, allows to increase robustness in production networks. Within the scope of the research project FlexPLN, the developed methodology has been discussed and applied to a use case from the aviation industry. The results thereby do not only unveil that a joint consideration of production and logistics measures provides a promising means for a comprehensive understanding of disruptions and their consequences for production networks, but also indicate that a metamodeling-based approach might be meaningful to predict suitable countermeasures for the reaction to disruptions

Towards increasing robustness in global production networks by means of an integrated disruption management

Manufacturing companies operating in global production networks face rising complexities and increasing susceptibilities to disruptions. For coping with disruptions, companies are in need of a holistic, comprehensive disruption management, involving all network actors to find optimal measures. However, today’s disruption management approaches are characterized by intuitive, experienced-based reactions, limiting themselves to solely the production or the logistics perspective and hence not permitting an overarching reaction. Therefore, this paper presents an integrated approach to disruption management, combining the production and logistics perspectives. It incorporates DoE and metamodelling methods in a simulation model to enable efficient, robust decision-making in highly complex environments

Adaptive and Dynamic Feedback Loops between Production System and Production Network based on the Asset Administration Shell

In production networks, production must run efficiently across company boundaries. Companies must be able to react quickly as a single unit. Two trends are influencing this situation: On the one hand, the progressing servitization leads to the increased offering of digital services in the field of manufacturing. From the literature, it is known that digital services let manufacturers, suppliers, and industrial customers interact more closely and frequently in a production network. On the other hand, the concept of the digital twin is trending. It promises the real-time prognosis and control of production systems. Although the concept of the digital twin itself can be vague there are some technologies trying to implement the digital twin of production. The asset administration shell (AAS) is an example of such a technology that draws growing attention. Picking up the initial situation these two trends could be used to create a feedback loop between the production system and network and thus improve the overall efficiency in production networks. Based on this idea, the paper first presents an approach to model systematically a possibility for a feedback loop orienting to the business model concept. Second, a reference architecture is derived from the RAMI 4.0 standard. The specified reference architecture is the basis for the specific implementation. Third, a procedure is developed to implement a specific architecture. For implementing an architecture, the usage of the asset administration shell is assumed. Finally, the approach is validated in a use case from the high precision weight industry

The Influence of Intangible Factors in the Strategic Network Configuration on Operations Performance

The configuration of global production networks is influenced by numerous tangibles (e.g. wage and transportation costs) as well as intangibles (e.g. employee qualification and political stability). However, due to the difficulty of quantifying intangibles, they are often neglected in decision-making resulting in not optimal configuration decisions. For this reason, this paper examines the role of intangibles in strategic network configuration in relation to corporate performance. 13 influencing factors are analyzed regarding their perceived relevance and consideration as well as the corporate network and competitive capabilities. The results show statistically significant relationships between intangibles and performance