Identifying dynamical instabilities in supply networks using generalized modeling

Abstract

Supply networks need to exhibit stability in order to remain functional. Here, we apply a generalized modeling (GM) approach, which has a strong pedigree in the analysis of dynamical systems, to study the stability of real‐world supply networks. It goes beyond purely structural network analysis approaches by incorporating material flows, which are defining characteristics of supply networks. The analysis focuses on the network of interactions between material flows, providing new conceptualizations to capture key aspects of production and inventory policies. We provide stability analyses of two contrasting real‐world networks—that of an industrial engine manufacturer and an industry‐level network in the luxury goods sector. We highlight the criticality of links with suppliers that involve the dispatch, processing, and return of parts or sub‐assemblies, cyclic motifs that involve separate paths from a common supplier to a common firm downstream, and competing demands of different end products at specific nodes. Based on a critical discussion of our findings in the context of the supply chain management literature, we generate five propositions to advance knowledge and understanding of supply network stability. We discuss the implications of the propositions for the effective management, control, and development of supply networks. The GM approach enables fast screening to identify hidden vulnerabilities in extensive supply networks.

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

“…• Stability -ability to return to a pre-disturbance state and ensure a continuity (Ivanov and Sokolov 2013;Demirel et al 2019) • Robustness -ability to withstand a disruption (or a series of disruptions) to maintain the planned performance (Nair and Vidal 2011; Simchi-Levi, Wang, and Wei 2018) • Resilience -ability to withstand a disruption (or a series of disruptions) and recover the performance (Spiegler, Naim, and Wikner 2012;Hosseini, Ivanov, and Dolgui 2019). Demirel et al (2019) point to stability as a 'basic desirable property of a supply network without an explicit consideration of performance' while the robustness and resilience explicitly include the performance in the analysis of disruption impacts.…”

“…• Stability -ability to return to a pre-disturbance state and ensure a continuity (Ivanov and Sokolov 2013;Demirel et al 2019) • Robustness -ability to withstand a disruption (or a series of disruptions) to maintain the planned performance (Nair and Vidal 2011; Simchi-Levi, Wang, and Wei 2018) • Resilience -ability to withstand a disruption (or a series of disruptions) and recover the performance (Spiegler, Naim, and Wikner 2012;Hosseini, Ivanov, and Dolgui 2019). Demirel et al (2019) point to stability as a 'basic desirable property of a supply network without an explicit consideration of performance' while the robustness and resilience explicitly include the performance in the analysis of disruption impacts. Ivanov and Sokolov (2013) show that robustness allows to analyse the system ability to withstand a disruption (or a series of disruptions) without any structural and parametrical changes/adaptations, while resilience analysis explicitly allows the system to employ some recovery/adaptation in order to restore the disrupted operations and performance (Craighead et al 2007;Zhao, Zuo, and Blackhurst 2019).…”

“…Ecological modelling is a research area concerned with the analysis of ecosystems in dynamics (Gross, Ebenhöh, andFeudel 2004, 2009). Recent literature point to resemblance of the SCs to ecosystems (Byrne et al 2018;Gross, MacCarthy, and Wildgoose 2018;Demirel et al 2019;Nair and Reed-Tsochas 2019). The applications of ecological modelling to SC uncertainty have been mostly focused on the stability analysis and multi-echelon SC synchronisation in terms of balancing the demand and inventory levels (Anne, Chedjou, and Kyamakya 2009;Demirel et al 2019;Mondal 2019).…”

The relationship between nested patterns and the ripple effect in complex supply networks

“…A key feature of material flow SNs, that set them apart from other types of networks is their directed, tiered structure aimed at facilitating an output toward a defined consumer base. In particular, review of the literature reveals that little effort has been made to quantify the robustness and resilience of material flow SNs, which we explore in this paper, with the exception of recent work from Demirel et al (2019), who used generalised modelling to explore how motifs in a complex material flow network influence stability.…”

Dynamic generic and brand advertising decisions under supply disruption

“…In February, global exports decreased by about $50 billion, breaking many global supply chains. 4 The consequences of supply disruptions are substantial and lasting, with rippling effects felt throughout the supply chain downstream, often cause serious damages to the wealth and reputation of enterprises (Demirel et al 2019). In contrast to most of the research on supply chain disruption contingency schemes, this paper will discuss how the manufacturer and the retailer can ensure the effective execution of advertising strategies and subsidy scheme under supply disruption.…”