Vulnerability and systemic risk of production systems 

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From ecology to interdisciplinary realms

From these ecological premises, the concept of resilience has been applied to a broader spectrum of systems. In particular, it was used to assess the management of natural resources, and was soon extended to socio-ecological systems (Sec. 1.2.1). This wider application has generated significant conceptual extensions, with contributions from social scientists and connections with the theory of complex adaptive systems (Sec. 1.2.2). These extensions have generated a wide range of resilience definitions, which we will summarize in three categories (Sec. 1.2.3).

From natural resources to socio-ecological systems

With the example of the collapse of fisheries, Holling (1973) pointed out the importance of identifying the processes underpinning the persistence of a resource, along with their interactions with larger systems. From this suggestion, some scholars proposed to study not only the dynamics of ecosystems but also the social dynamics that influence how resources are harvested. Social scientists studying the sustainable management of natural resources started cooperating with ecologists (Berkes and Folke, 2000; Adger, 2000; Folke, 2006). Berkes et al. (2003) proposed socio-ecological systems (SESs) as the adequate scope for studying the resilience of natural resources and local communities. This topic attracted considerable interests, and led to structuring of a new research community8 .
This stream of research highlights the constant interplay between social and eco-logical dynamics, which generates complex dynamical behaviors (Liu et al., 2007). A community adapts to the ecosystem it relies on, and thereby modifies some ecological processes. As with the example of fisheries, these modifications might lead to unexpected ecological responses, forcing the community to adapt to new conditions. The issue of finding new way of managing resources emerged. Novel management principles have been thus developed, such as community-based management (Berkes and Folke, 2000; Tompkins and Adger, 2004) or adaptive co-managment (Olsson et al., 2004). The contributions of social scientists are strongly connected to Ostrom’s work on the governance of commons (Ostrom and Janssen, 2004). They revolve around learning, trust, local knowledge, self-organization and multilevel governance (Lebel et al., 2006). Examples of SESs studied through this lens are: forestry (Fischer et al., 2006), agroecosystems (Cabell and Oelofse, 2012) or coastal systems (Adger et al., 2005).
The study of SESs pointed to dynamical behaviors that were going beyond the con-cept of ecological resilience originally proposed. SESs proved to sometimes exhibit rapid structural changes, such as the introduction of technologies or new institutions. These 8 Interdisciplinary research on resilience have long been undertaken at the Beijer Institute, Stockholm. Two research institutions have then been founded on the concept of SES: the Resilience Alliance, a network of scholars founded in 1999; the Stockholm Resilience Center, a research institute founded in 2007. Both institutions emphasize interdisciplinarity, a commitment to sustainability, and policy-oriented research. results pointed out the limits of the metaphor of a slowly changing stability landscape. Such observations stimulated the development of an integrated theory on the transforma-tion of SESs, called panarchy (Gunderson and Holling, 2002). The central element of this theory is the adaptive cycle; see Fig. 1.4. Originally developed to describe how productive ecosystems in temperate regions undergo changes, the adaptive cycle appeared to be a useful heuristic to qualitatively explore SES dynamics. The term ‘panarchy’ was used to stress that SES dynamics are not determined by a unidirectional, hierarchical causal-ity. They are rather shaped by cross-scale interactions between multiple adaptive cycles: small-scale dynamics influence large-scale dynamics and vice-versa. Practical guidelines based on the panarchy theory have been published to facilitate the empirical assessment of SESs (Gunderson et al., 2010).
One of the successes of the panarchy theory is the description of several development traps that seem to arise in many types of systems, such as the poverty trap or the rigidity trap (Carpenter and Brock, 2008). According to this theory, poverty traps (Bowles et al., 2006), which have been particularly documented for poor rural communities (Dasgupta, 2003), occur when a system fails to activate the minimal level of resources to put in place the positive feedbacks that drive growth. On the other hand, the rigidity trap, or lock-in, occurs when a system has developed so complex structures and processes, than it has no room left for innovation, just for maintenance. They are strong but brittle, leading to strong collapse following an unexpected crisis. It has been for instance described for agricultural regions in West Australia (Allison and Hobbs, 2004). From a management perspective, Fath et al. (2015) argue that recognizing the different stages and preparing for them facilitate the successful ‘navigation’ through the adaptive cycle (Fath et al., 2015). They provide examples of applications of such adaptive management principles to business and public organizations.

Resilience and adaptive system theory

These latter conceptual developments have been strongly influenced by the paradigm of complex adaptive systems (CASs) (Holland, 1992; Gell-Mann, 1994). The idea of CAS has been shaped by a multidisciplinary corpus of theories from physics, genetics, evolutionary biology, computer sciences, mathematics and social sciences. We list here the most influencing theories: the theory of neural networks and cybernetics (McCulloch and Pitts, 1943), cellular automata and their self-reproductive behavior (von Neumann, 1966), general system theory (von Bertalanffy, 1969), thermodynamics of phase transition and dissipative structures (Nicolis and Prigogine, 1977), coevolution of self-organization at ‘the edge of chaos’ (Kauffman, 1993) and self-organized criticality (Bak and Chen, 1991). All these theories describe open, out-of-equilibrium systems through an holistic lens, such as living organisms, ecosystems, economies, brains and immune systems.

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Supply chain risks and shock propagation

In global and fragmented supply chains, distant processes are interdependent, and un-expected 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 pro-duction 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 orga-nizations 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, compet-itiveness 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 re-spondents the BCI survey (2014). Their negative impact on the financial performance of companies has been empirically confirmed (Hendricks and Singhal, 2003, 2005). Miti-gating 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).
Managing the risk of supply disruption has become a prominent topic of supply chain management (SCM)8 . The main pillar of SCM is the coordination of processes across organizational boundaries, in which managers need to engage to avoid these issues.

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 
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
3 The modeling of production systems and their resilience 
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
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

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