Life and Death of Industrial Ecosystems

Self-organized industrial ecosystems (SOIEs) refer to communities of firms in diverse industries that spontaneously engage in Industrial Symbiosis (IS); that is, firms independently develop bi-lateral and multi-lateral... [ view full abstract ]

Self-organized industrial ecosystems (SOIEs) refer to communities of firms in diverse industries that spontaneously engage in Industrial Symbiosis (IS); that is, firms independently develop bi-lateral and multi-lateral interactions involving material, energy, and knowledge sharing for individual and collective benefit. Like biological ecosystems, self-organized industrial ecosystems have to constantly respond to external perturbations. Resilience of SOIEs, that is, the ability of the system to maintain its structure and function in response to perturbations, has been the focus of a few recent studies. However, these studies have only examined the network characteristics for resilience of IS in a static manner. The current study contributes to this emerging literature by examining the dynamics associated with growth (life) and demise (death) of self-organized industrial ecosystems in light of changing network dynamics and external perturbations, with emphasis on bio-physical and socio-economic aspects of connectivity between firms. This research is grounded in real world cases, but expands beyond these through hypothetical network models in order to ascertain the characteristics of the networks that lead to more resilient structures and outcomes. A key distinction is made between SOIEs that include an anchor firm versus scavenger firms. The former typically involve a scale-free network structure where new member firms preferentially connect to actors with the most connections, while the latter involve more random, fully-connected networks as new member firms connect with multiple existing actors.

To understand the tradeoffs between network properties responsible for resilience of SOIEs, we propose a novel stochastic data-driven framework that focuses on the statistical mechanics of the topology and dynamics by simulating the evolution of hypothetical SOIEs. To this avail, we begin with a 5-node model of SOIE, which evolves through the addition and deletion of nodes (firms) and edges (synergies) based on their individual attributes. The network evolution is modeled under two schemes: 1) preferential attachment of new nodes to well-connected nodes and 2) random attachment of new nodes to existing nodes. Graph-level measures such as size, average degree, centralization, global efficiency, assortativity etc. are computed at every snapshot to evaluate tradeoffs among structural properties that enable resilient SOIEs. Distributions of network metrics are compared for SOIEs that live (survive perturbations) with those that are pronounced dead (disintegrate). We find that that once SOIEs cross a particular size threshold, they seem to be less likely to collapse as a dense network of connections increases their resistance to disturbances affecting one or more actors. Also, the presence of strong social ties in addition to individual economic benefits embed the value of IS among the participants, and make full scale collapse harder to occur, as the actors continuously seek collaborative resource management arrangements after individual synergies or firms die. This model provides insights for establishing a resilience driven framework for development of circular economy practices, which has been advocated in the literature. It reinforces the view that resilience of SOIEs do not arise from its intrinsic properties of a system alone, but from the interplay of network topology with social and ecological attributes of nodes.