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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. Cause-effect 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.

Kurzfassung 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.

Kurzfassung Vielen kleinen und mittleren Unternehmen bedarf es eines effizienten Managements des Produktionsanlaufs, um ihre globale Wettbewerbsfähigkeit zu erhöhen. Dieser Beitrag stellt einen Ansatz vor, mithilfe einer Standortanalyse und eines Planungs- und Steuerungsmodells den Produktionsanlauf an verschiedenen Standorten effizient durchzuführen. Hierzu werden gegebene Rahmenbedingungen eines Produktionsanlaufs an global verteilten Standorten analysiert und mit Hilfe einer Methodik einem Referenzprozess zugeordnet, um eine Anpassung an wechselnde globale Einflussfaktoren, wie z. B. Faktorkos-ten oder Marktentwicklungen, zu gewährleisten.

An increasingly uncertain and dynamic competitive environment is challenging industrial companies nowadays. Against this backdrop, companies are focusing on their core competences. They organize their production in global production networks. While the competitiveness of production networks could be maintained for a long time by optimizing individual production sites, the overall network is increasingly becoming the focus of attention. In particular, the elimination of redundant production technologies offers the potential to exploit economies of scale, to bundle technology-specific competences and to achieve an increase in efficiency. The purely mathematical optimization models disseminated in research are unable to consider all the sub tasks of planning. For this reason, this article proposes a practice oriented methodology for reallocating production technologies to production locations in global production networks. The procedure consists of three phases: the investigation of current production technology-to-site allocation in the production network, the generation and planning of alternative reallocations as well as the evaluation of reallocations. For testing its practical suitability, the procedure is exemplary applied to the global production network for forging processes of a medical device manufacturer.

Operational management of global production networks is challenged by disruptions which result e.g. in order changes, quality problems and engineering changes. Their negative impact on performance may be reduced by a more intensive exchange of information between the network partners. Digitalization offers many new measures for such an increase of transparency. However, the interactions, the optimal level and the realisation of a broader information exchange define a challenging decision problem. This paper improves operational performance of production networks by applying multimethod simulation, statistical experiments and metamodeling techniques to identify a state of information exchange which is robust to disruptions.

Modern globalization is characterized by a production in networks. With an increasingly dynamic business environment, the focus of production planning shifts to network management. This paper presents a method for managing production networks. The emphasis is on capacity planning and the use of instruments that render the staff more flexible. The methodology also integrates an approach to hedge against the risk of systematic errors in forecasting the market demand.