Benfer Martin, M.Sc.

In recent years the importance of production network modeling has increased significantly, as companies face rising complexity and dynamics in their environment. Quantitative models help to gain understanding and support strategic decision making in production network management. Choosing the right abstraction level by determining the trade-off between model accuracy and effort for modeling is crucial for developing viable and applicable models. While experienced model builders can identify the right abstraction level intuitionally, a structured process would lead to results which are more consistent. This paper presents an approach to structure and streamline the determination of the abstraction level. A basic concept is presented, that enables systematic definition of the abstraction level for a given model framework. A process implementing the concept is proposed, that helps to identify the right abstraction level based on case-specific requirements and restrictions and tailor the model to solve the examined problem. The process is tested in a case study at a globally operating tooling machine company.

Die Nachverfolgbarkeit (engl. traceability) von Produkten entlang ihres Lebenszyklus ist eine Grundvoraussetzung für zahlreiche Anwendungsfelder: der Ermöglichung von Kreislaufwirtschaftsprozessen, dem Schutz vor Produktpiraterie und der zielgerichteten Qualitätssicherung. Jedoch existieren über die gesetzlich vorgeschriebenen Standards hinaus wenig umfassende Lösungen, um Produkte unternehmensübergreifend nachzuverfolgen. Dieser Beitrag zeigt ein Konzept zur strukturierten Konzeption, Implementierung und Bewertung solcher Traceability-Systeme.

Low information exchange in global production networks results in long response time to disruption and negative performance impact. Digitalization enables a more intensive information exchange. This paper analyses the performance of order management, quality problem resolution and engineering change management in production networks with respect to different disruptions and information flows. Causeeffect relationships are revealed based on a multi-method simulation model and statistical experiments. Using surrogate modelling and robust optimization, a target picture for information exchange is determined. The benefits of the approach are demonstrated using a case study for the production of metal-plastic parts for the automotive supplier industry.

Recently, traceability systems have become more common, but their prevalence and design vary significantly depending on the industry. Different law and customer-based requirements for traceability systems have led to diverse standards. This contribution offers a framework to compare the state of traceability systems in different industries. A comparison of industry characteristics, motivations for traceability system implementation, common data management, and identification systems are offered. Upon that analysis, the potential of cross-industry traceability systems and approaches is identified. This extended usage of traceability systems supports the quality assurance, process management and counterfeit protection and thus expands customer value.