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Supply chain risks and shock propagation
In global and fragmented supply chains, distant processes are interdependent, and unexpected issues may quickly propagate. The ‘Albuquerque accident’ involving Swedish company Ericsson is prototypical (Norrman and Jansson, 2004; Sheffi, 2005). In 2000, a lightning bolt hits an electric line in Albuquerque, New Mexico’s largest city, and induces a short power outage in a factory owned by the Dutch company Philipps. In the absence of alternative power generator, fire sparks; it is extinguished ten minutes later, but has affected some critical equipment. Two production lines are stopped for three weeks, and stay insufficiently productive for months. The disrupted process was very specific and Ericsson failed to promptly find an alternative supplier. In a booming market, the production of cellphone was disrupted. The losses incurred by Ericsson were about 50 times higher than the material damages6.
The quest for resilient supply chains
These phenomena are of high concern for businesses, insurers, and in general for organizations operating through such multi-tiered, complex web of intermediaries, such as humanitarian organizations. Managing supply chain risks faces specific challenges, some of which have been analyzed through the lens of resilience (Sec. 2.2.1). Although, competitiveness objectives may hamper the design of resilient supply chains, certain aspects of resilience, such as agility and permanent reorganization, are seen as being a way to better compete (Sec. 2.2.2).
The challenges of mitigating risks in complex supply chains
Supply chain disruptions are rather frequent — at least one per year for 80% of the respondents the BCI survey (2014). Their negative impact on the financial performance of companies has been empirically confirmed (Hendricks and Singhal, 2003, 2005). Mitigating the risk of supply chain disruptions widely differs from the management of other operational risks. Supply chains may indeed bring to your door the risks taken by another firm far away, both its operational risks — e.g., an accident on a production line — and its environmental risks — e.g., a climate or geopolitical event.
Managers therefore need to increase their monitoring capacity. However, they often lack visibility over their supply chain. While firms usually know their direct suppliers, they often struggle to keep track of their sub-suppliers, also called tier-2 suppliers, and of entities further away in the chain (BCI, 2014; Wang et al., 2015). Half of the disruptions seem however to originate from this deeper segment (BCI, 2014). In addition, supply chains are fluctuating systems — e.g., suppliers change their contractors, firms go bankrupt, others enter the market — and are therefore hard to map in real time. Inherent difficulties of interorganizational communication may also accentuate the propagation of supply disruptions. Jüttner et al. (2003) identify such network-related risks: unclear responsibility, lack of responsiveness or overreaction, distorted information and mistrust. A small fluctuation in demand at one point of the chain may be magnified as orders cascade up the chain, leading to excess inventory, production down time, and transportation peaks. This phenomenon, known as the bullwhip effect (Lee et al., 1997), is well known by supply chain managers, empirical documented (e.g. Thun and Hoenig, 2011) and was has been experimentally tested for decades through the so-called beer game (Sterman, 1989).
Systemic risks and the limits of risk mitigation
For some supply chain managers, even with mitigating efforts, a low-probability hazard will inevitably occur, unexpectedly propagate and generate losses. This share of risks is often called ‘systemic’. To many, systemic risk conveys the idea of a small event creating unexpectedly large consequences. It recalls the image that popularized the mathematical concept of chaos: a butterfly flapping its wings in one part of the world can create a hurricane in another. However, while chaos may emerge in very small systems, systemic risks arise precisely because systems are complex and highly connected (Helbing, 2009). In the context of supply chains, the World Economic Forum (2012) defines a risk as systemic when it has the three following property: it is triggered by an unexpected event, it propagates and induces ripple effects, and it gets amplified because of the inability of the network structure to absorb it. This definition emphasizes the role of the network structures and implies that some structures are more prone to risk propagation than others. From an economic standpoint, Lorenz et al. (2009, p. 441) defines systemic risk “as an undesired externality arising from the strategic interaction of the agents”. It is an externality, what individual firms cannot do about.
This perspective connects with two other networked systems where systemic risks are a prominent issue: power grids and financial systems. In these networks, systemic risks are also what the individual node — electricity consumers and power stations in the first case, banks and financial firms in the second — cannot manage alone. In power grids, a systemic risk is the potential occurrence of a sizeable and unexpected blackout resulting from a small perturbation or a minor reconfiguration of the grid. The concept of systemic risks is most used in finance, and is a central concern for central banks (National Research Council of the National Academies, 2007)9. As experienced in many financial crises, strong interlinkages between banks and other players make the system prone todomino effects and bankruptcy chains. Contagion risks are well illustrated by the 2007 subprime mortgage crisis, which paved the way to the largest financial crisis since the 1930s. Information of systemic risks in power grids, financial systems and supply chains are summarized in Tab. 2.1.
Economic resilience and the challenge of interdependence
Mapping production interdependencies (Sec. 3.1.1) is the first necessary step towards the analysis of the ripple impacts of natural disasters (Sec. 3.1.2). We highlight that finer models are still lacking to capture the propagation of disruptions and systemic risks (Sec. 3.1.3). The differentiated responses of regions to disasters, but also to macroeconomic shocks, have motivated some economists to investigate economic resilience (Sec. 3.1.4).
Mapping the structure of production
As discussed in Chapter 2, what makes a production system particularly vulnerable to a localized disruption is that one produces what another needs. Mapping such interdependencies at the national scale was Leontief’s key motivation when developing the theory of input–output tables (1936). Instead of describing the interactions between specific firms, this theory views the whole production system as a network of sectors1. Each sector is connected to others through financial flows, which correspond to exchanges of goods and services. Each flow is the output of one sector and the input of another. To balance the total amount of inputs consumed and of outputs produced, input–output tables also account for imports, exports, and household consumption.
Input–output tables meaningfully aggregate business census data collected by national administrations, and may help guide public policies. Leontief published the firsttables of the U.S. economy for the year 1919 and 1929 with data from the U.S. National Bureau of Economic Research (Polenske, 2004)2. Since then, the methodology has been refined, standardized, and national statistics services of many countries now provide tables with 15 to 70 sectors3.
Table of contents :
Introduction
1 Persistence through resilience
Introduction
1.1 Resilience: reviving the dynamic nature of systems
1.1.1 Resilience versus equilibrium in the study of ecosystems
1.1.2 Alternative stable states, thresholds and tipping points
1.1.3 Resilience in the theory of dynamical systems
1.2 From ecology to interdisciplinary realms
1.2.1 From natural resources to socio-ecological systems
1.2.2 Resilience and adaptive system theory
1.2.3 The three dimensions of resilience
1.3 Spectacular diffusion and debated applications
1.3.1 Resilience of people, communities and cities to disaster
1.3.2 Boundary object, buzzword or political agenda?
Conclusion
References
2 Vulnerability and systemic risk of production systems
Introduction
2.1 Interconnectedness and the propagation of disruptions
2.1.1 More globalized production systems
2.1.2 Supply chain risks and shock propagation
2.2 The quest for resilient supply chains
2.2.1 The challenges of mitigating risks in complex supply chains
2.2.2 Is resilience competitive?
2.3 Systemic risks and the limits of risk mitigation
Conclusion
References
3 The modeling of production systems and their resilience
Introduction
3.1 Economic resilience and the challenge of interdependence
3.1.1 Mapping the structure of production
3.1.2 Evaluating the economic impacts of a disaster
3.1.3 Analyzing systemic risks through networks
3.1.4 Economic resilience of regions
3.2 Where does resilience fit in economic models?
3.2.1 Is resilience heterodox?
3.2.2 Economic dynamics
3.2.3 Resilience from the bottom up
3.2.4 Rational versus ‘zero intelligence’ agents in structured systems
3.3 Conclusion: A new conceptual framework for economic resilience
3.3.1 Evaluating resilience and systemic risks
3.3.2 Networks: a meso-level between agents and economic resilience
3.3.3 An introduction to the three papers
References
4 Bifurcation analysis of an agent-based model for predator–prey interactions
5 Economic networks: Heterogeneity-induced vulnerability and loss of synchronization
6 The fragmentation of production amplifies systemic risk in supply chains
7 Research outlook
Conclusion
Résumé long en français
