Kamil J. Mizgier

Managing operational disruptions through capital adequacy and process improvement

Firms maintain a capital charge to manage the risk of low-frequency, high-impact operational disruptions. The loss distribution approach (LDA) measures the capital charge using two inputs: the frequency and severity of operational disruptions. In this study, we investigate whether or not capital charge could be combined with process improvement, an approach predominantly employed for managing high-frequency, low-impact operational disruptions. Using the categorization of events defined by the Basel Accord for different types of operational risk events, we verify three propositions. First, we test whether classification of operational disruptions is warranted to manage the risk. Second, we posit that classification of operational disruptions will display different statistical properties in manufacturing and in the financial services sector. Finally, we test whether risk of operational disruptions can be managed through a combination of process improvement and capital adequacy. We obtained data on 5442 operational disruptions and ran Monte Carlo simulations spanning both these sectors and seven event types. The results reveal that process improvement can be a first line of defense to manage certain types of operational risk events.

Disruptions in tightly coupled supply chain networks: the case of the US offshore oil industry

Managers have paid increasing attention to the exposure of their supply chains to disruptions and seek ways to mitigate supply chain vulnerability. The interconnectedness of tightly coupled supply chain networks makes this a challenging task, because interconnectedness and tight coupling of nodes in the network lead to an amplification of the actual risk exposure. This phenomenon can be attributed to the propagation of losses through the network, which exhibits certain dynamics. In order to investigate this mechanism, we studied the complex supply chain network of the oil industry in the Gulf of Mexico. Our results provide an estimate of the economic impact of eventual random and hurricane-related disruptions and can be used as a decision support tool for risk management of supply disruptions in interconnected supply chain networks.

Global sensitivity analysis and aggregation of risk in multi-product supply chain networks

Manufacturing firms manage complex supply chain networks which are exposed to a plethora of hazard events. An essential part of the risk management process is the calculation of the stand-alone risk exposures of the product-specific supply chains, but also of the entire multi-product system. In this paper, first, a global sensitivity analysis of the statistical supply chain risk model is conducted. This method helps the decision-makers to understand the risk of the model they are using. Second, a methodology for risk aggregation in multi-product supply chain networks is proposed. The real-world data is used to analyse and validate the model. Supply chain managers equipped with the proposed method will better cope with the risk in supply chains for different product configurations.

Multiobjective capital allocation for supplier development under risk

Abstract Supplier development is increasingly important due to the complexity of today’s supply chains and the globalisation of businesses. Since manufacturers have only limited resources, they need to make an informed decision about which suppliers to develop. Moreover, the returns from investment in supplier development are uncertain, so manufacturers have to take this risk into account when choosing their suppliers for development programmes. In this paper, we propose a multi-objective model for capital allocation for supplier development under risk. We apply it to an example of a global car manufacturer and support the decision-making process with data downloaded from the Bloomberg database. We use stock market returns and cost of capital of suppliers to assess their performance. Our model supports an informed decision, which is that tradeoffs exist between risk and cost of supplier development programme. Depending on the risk aversion of the manufacturer, we demonstrate different allocation schemes for supplier development.

Multiobjective optimization of credit capital allocation in financial institutions

The evolution of international regulation leads to new capital requirements imposed on globally active companies. Financial services firms allocate capital to business lines in order to withstand the materializing credit losses and to measure the performance of various business lines. In this study, we introduce a methodology for optimal credit capital allocation based on operations research approach. In particular, we focus on the efficient allocation of capital to business lines characterized by credit risk losses and cost of capital. We compare different allocation methods and provide a rationale behind using the OR approach. Finally, we formulate a multiobjective optimization model to capital allocation problem and apply it to a real-world case of two financial conglomerates.

Integrating the customers’ perceived risks and benefits into the triple-channel retailing

Multiple channel retailing and channel selection strategy have become key issues for many corporations to place themselves at the heart of a new era of retailing. This paper studies a triple-channel system in which a manufacturer operates a conventional channel, a direct online channel, as well as increases its online presence with an online shopping platform. It also takes the heterogeneity of consumers’ price sensitivity and channel preferences into account. It highlights that the perceived risks and perceived benefits are aggregated into an inhibitor or activator entering the customers’ acceptance of triple channels and further affecting their purchasing decisions. Three cases are discussed to investigate the demand distributions, the profit behaviours, the optimal pricing strategies and the channel selection strategies. Theoretical analysis is further validated by a case study with the aid of agent-based modelling. Sensitivity analysis demonstrates that two acceptance indices – respectively, regarded as the consumers’ willingness to tolerate the perceived risks of the online channel and approve the perceived benefits from the third-party online platform – significantly govern the total profit in two stylised scenarios. This study schematically characterises the structure of demand distributions, helps the corporation to discern consumers’ channel choices and then focus its marketing efforts towards more profitable customers and strategic channel structures. Furthermore, some implications are outlined for the optimal supply chain designation decision.

Incorporating single and multiple losses in operational risk: a multi-period perspective

Operational disruptions can have serious repercussions for firms over extended periods of time. In this work, we develop a multi-period model of operational risk. We define the loss process of operational disruptions as a sum of events triggering single and multiple losses. We empirically validate our approach using an extensive dataset of operational disruptions experienced by firms from the financial services and manufacturing industry sectors. The results of our simulations point out that operational risk is significantly underestimated if the events leading to multiple losses are not accounted for in the firms’ long-term capital planning.

Zurich Insurance Uses Data Analytics to Leverage the BI Insurance Proposition

As the interdependencies due to global trade and interconnected value chains have grown, firms and their value chains have become more prone to disruptions. Consequently, many firms resort to business interruption (BI) insurance to transfer the disruption risk. Given the limited amount of literature available about BI loss and claims characteristics, insurance companies and their customers will benefit from the insights that resulted from the project underlying this study. The project involved a collaboration between Zurich Insurance and the Swiss Federal Institute of Technology Zurich, in which we extracted a large amount of data pertaining to BI claims from various data sources and analyzed these data. We found, for example, that the average share of BI losses has increased significantly over the past 15 years. Moreover, the BI risk exposure, measured as BI share of the total insurance claims, the average recovery time, and the increased costs of working, differs significantly based on the industry affi...

On the Association Between Economic Cycles and Operational Disruptions

In this research we empirically verify the relationship between operational disruptions and economic cycles in the manufacturing industry of the United States. Contemporary and lagged correlation estimates are measured to demonstrate the degree of co-movement between the severity and the number of operational disruptions and several macroeconomic variables. Our findings suggest that the severity of operational disruptions follows the economic cycles with a lag of two years.

Understanding risk exposure and operational disruptions in supply chain networks

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 identifica-