Understanding Risk Exposure and Operational Disruptions in Supply Chain Networks: Methodologies and Empirical Evidence

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1 Diss. ETH No Understanding Risk Exposure and Operational Disruptions in Supply Chain Networks: Methodologies and Empirical Evidence A dissertation submitted to the ETH ZURICH for the degree of Doctor of Sciences presented by KAMIL J. MIZGIER Master of Science in Engineering, Warsaw University of Technology born the 18th of May, 1983 citizen of Poland accepted on the recommendation of Prof. Dr. Stephan M. Wagner, examiner Prof. Dr. Juliana Sutanto, co-examiner 2012

2 Abstract Driven by the increasing complexity of global supply chain networks and the recent economic downturn, supply chain risk management has become an important field of study for researchers from different scientific domains. The interdisciplinary nature of risk management requires a holistic approach to correctly assess the risk profile of a firm and of the entire supply chain network. According to recent studies there is a growing need for a new, integrative framework to capture quantitatively the uncertainties stemming from the interconnectedness of the global supply chains. Additionally, the number of potential supply chain risks is evolving, predominantly driven by the operations activities being outsourced to emerging markets, but also to the regions exposed to natural catastrophes. This doctoral thesis is a contribution to the field of supply chain risk management. As it is a cumulative dissertation, it consists of the introductory chapter, followed by five scientific papers. The choice of methodologies is motivated by the multifaceted nature of risk management in supply chain networks. First, we examine how companies in the extended supply chain network default. We use an agent-based modeling approach to describe the interaction among heterogeneous agents. We show that companies should extend the level of analysis to the entire supply chain network in order to correctly assess the risk of supplier defaults. Based on the numerical results, we provide recommendations on which strategies companies should employ to improve their performance in the uncertain economic environment. We continue the discussion by investigating more closely the propagation of supply chain disruptions across the network. We incorporate stochastic point processes to model disruptions at the firm level. We aggregate losses and provide distributional risk measures, which allow for quantification of supply chain risk for a given supply chain network topology. We study the correlation effects and draw conclusions about diversification of supplier portfolios. In the next step, we focus on a methodology for bottleneck identification in supply chain networks. We compare some established network theory-based measures for identificaiii

3 tion of the critical nodes in a supply chain network. We present our new method, which includes the risk profiles of suppliers. An essential part of the model development process is validation and calibration. For this purpose, we conduct an empirical analysis of data, both from publically available sources and from commercial data providers. We show empirical evidence of two fundamental assumptions, namely the network topology and the occurrence and severity of operational disruptions. We collect and analyze data on the existing supply chain structures in the offshore US oil industry in the Gulf of Mexico. Moreover, we investigate the secondary operational loss data. We perform a quantitative analysis at an industry level to find out which management actions companies should consider to deal with different types of operational disruptions. The results obtained in this part of the thesis validate our modeling approach. Each of these research projects provides managerial implications, which are of practical relevance for supply chain and operations managers. iv

4 Kurzfassung Aufgrund der wachsenden Komplexität von globalen Netzwerken von Lieferketten und der neuesten Konjunkturabschwünge ist Supply Chain Risikomanagement für die Forscher aus unterschiedlichen Wissenschaftsbereichen zu einem wichtigen Forschungsgebiet geworden. Die fachübergreifende Natur des Risikomanagements erfordert einen holistischen Ansatz, damit das Risikoprofil der Firmen und der gesamten Lieferkette korrekt ausgewertet werden kann. Den neuesten Studien zufolge besteht ein grosser Bedarf nach einem integrativen Rahmen, der die Unsicherheiten, die aus der Querverbindung der globalen Lieferketten stammen, quantitativ erfassen würde. Des Weiteren entwickelt sich stets eine Anzahl von potenziellen Supply Chain Risiken, vor allem aufgrund der Verschiebung der operationellen Aktivitäten, wie etwa Outsourcing in den Entwicklungsländern oder in den Katastrophengebieten. Diese Dissertation soll einen Beitrag auf dem Gebiet Supply Chain Risikomanagement leisten. Diese Abhandlung ist eine kumulative Dissertation, die aus einem Einführungskapitel und fünf wissenschaftlichen Artikeln besteht. Die Methodologieauswahl wurde durch die vielschichtige Natur des Risikomanagements motiviert. Als Erstes wird der Insolvenzprozess der Firmen in einem erweiterten Supply Chain Netzwerk untersucht. Es wird eine agentenbasierte Modellierung angewendet, damit die Wechselwirkung zwischen den heterogenen Agenten dargestellt werden kann. Wir zeigen auf, dass die Unternehmen das Analyseniveau auf die gesamte Struktur des Supply Chain Netzwerks erweitern sollten, um das Risiko des Unternehmens korrekt beurteilen zu können. Wir setzen unsere Diskussion fort, indem wir die Propagierung von Supply Chain Störungen im Netzwerk erforschen. Wir wenden die stochastischen Prozesse an, um die Störungen auf dem Firmenniveau zu modellieren. Wir aggregieren die Verluste und schlagen die aus der Verteilung abgeleiteten Risikomasse vor, die uns eine Quantifizierung von Supply Chain Risiken erlauben. Wir untersuchen die Korrelationseffekte und ziehen v

5 Schlussfolgerungen über Diversifikation von Lieferantenportfolio. Im weiteren Teil steht die Methodologie zur Identifikation von Engpässen in Supply Chain Netzwerken im Mittelpunkt. Es werden einige etablierte und auf Netzwerktheorie basierende Masse zur Identifikation von kritischen Knoten verglichen. Wir präsentieren unsere eigene Methode, die die Risikoprofile der Firmen berücksichtigt. Den wesentlichen Teil des Modellentwicklungsprozesses bilden die Validierung und die Kalibrierung. Zu diesem Zwecke wird eine empirische Datenanalyse aus verschiedenen Quellen verwendet. Die Daten stammen sowohl aus öffentlich zugänglichen Quellen als auch von den kommerziellen Datenanbietern. Wir zeigen empirische Beweise von zwei grundlegenden Annahmen auf: der Netzwerk-Topologie sowie dem Vorkommen und der Grösse der operationellen Störungen. Wir führen eine quantitative Analyse auf dem Industrieniveau durch, um aufzuzeigen, welche Managementstrategien Firmen gegen bestimmte Störungstypen anwenden sollten. Die aus diesem Dissertationsteil stammenden Ergebnisse sind ein Beweis für die Plausibilität von unserem Modellierungsansatz. Jedes Forschungsprojekt schliesst mit Implikationen fürs Management ab, die von einer hohen praktischen Relevanz sind. vi

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