Abstract
The very complexity and the extended reach of today’s globe-spanning supply chain networks expose businesses to a huge range of unexpected events. This means that the new operating environment calls for a supply network structure and strategy that is robust and resilient enough to hedge supply chain vulnerability. Resilience must be intended not just as the ability to recover from mishaps, but should be considered a proactive, structured and integrated exploration of capabilities within the supply chain to cope with unforeseen events. In this sense the concept of resilience is tied up with the concept of network robustness, that is the ability of the supply chain to maintain its function unchanged, or nearly unchanged, when exposed to perturbations. Literature on supply chain risk management is informative in effective strategies able to increase robustness and resilience of the supply chain. A detailed dynamic analysis of behaviour of the supply chain to understand the suitability of different robustness and resilience strategies over time and under different scenarios is not carried out. The thesis addresses this gap by studying and providing methods for improving the assessment and management of supply chain vulnerability and thereby improving robustness and resilience in real-life supply chains.
The robustness of the supply chain is studied using network optimization models. A robust stochastic approach for designing supply chains under uncertainty is proposed, taking into account the trade-off between efficiency and effectiveness of the network. The efficiency is usually related to the total logistics cost, while the effectiveness is a measure of supply chain robustness, defined as the extent to which the supply chain is able to carry its functions for a variety of possible future scenarios. Since the efficiency and the effectiveness are in conflict with each other, it is proposed to set up a timedependent mixed integer linear programming model which includes stochastic variables. The model aims at figuring out the most robust supply chain configuration, i.e. the configuration that allows for minimising the impact of unexpected events, which can occur according to a probability function, on the overall logistics cost. The resilience of the supply chain is studied using a normative quantitative research founded on a simulation-based framework, with particular reference to a specific supply chain process, i.e. global sourcing. The simulation, using the Monte Carlo method, allows comparing the impact that different supply chain risk management strategies have on the process, in particular on its variability and its related total logistics cost. To fill the gap of practical examples, besides an illustrative case study, two specific reallife supply chains are considered. Moreover a sensitivity analysis on the key parameters affecting the optimal risk reduction strategy is performed to generalise the results.
The important contribution of this research is to study and provide methods for improving the management of vulnerability and thereby improving structural robustness and operational resilience of complex supply chains. Another significant contribution is to produce a conceptual framework for supply chain risk management, through a critical and dynamic review of the extant literature. By using quantitative methods, such as network optimization models and discrete event simulation described in the thesis, outcomes of the supply chain under a significant range of possible untoward events can be explored, and improved levels of robustness and resilience can be found. Building such models as a means to understand and improve robustness and resilience of supply networks is a significant contribution for research going forward.