Mitigating supply chain disruptions – a normal accident perspective

Abstract

Purpose – Existing supply chain literature provides examples of countermeasures that firms can adopt to mitigate abnormal or catastrophic supply chain disruptions. However, none address reducing interactive complexity prior to adopting countermeasures to mitigate everyday or normal supply chain disruptions. Most mitigation strategies focus on adding capabilities or resources to protect an organization. Here, the authors aim to consider an alternative strategy of examining current processes to determine whether processes can be simplified by using the normal accident theory and its constructs of interactive complexity and coupling as a theoretical basis. Design/methodology/approach – The authors develop a model based on the normal accident theory and use logistic regression to test their propositions in the context of a steel processing plant and its customers. Findings – The findings show the importance of reducing interactive complexity to mitigate supply chain disruptions. However, high inventory is not considered a significant countermeasure, and high inventory levels may increase the likelihood of causing a disruption downstream. These findings support the lean management approach of operating under low inventory levels while eliminating complexity to make problems more visible, causing fewer disruptions. Originality/value – While others have examined the impact of mitigation strategies conceptually, no study has captured information from actual supply chain disruptions to assess how interactive complexity and inventory levels affect disruption potential at downstream customers' facilities. Capturing information from supply chain disruptions enables managers to assess the situation as the disruption is occurring. The authors suggest a strategy in which countermeasures that increase slack in the system should be considered only after the system is sufficiently simplified to mitigate disruptions.

Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions

“…This is an important question, because firms seek stability in their operations (Katz and Kahn, 1978; Thompson, 1967), and therefore managers need to know how the organization of their supply chains affects the occurrence of supply chain disruptions. To the best of our knowledge, only the recent study by Marley et al (2014) has investigated this relationship using a normal accident theory perspective to predict the occurrence of “every‐day” downstream supply chain (demand‐side) disruptions.…”

Supply chain disruption propagation: a systemic risk and normal accident theory perspective

“…Hence, we see ties to supply chain disruption propagation. Tight coupling between supply chain firms is a form of dependence (Wolf 2001), and because of the interactive complexity of the system, failures will occur in unexpected ways (Marley et al 2014). Accidents stem from the interaction of this dependence (tight coupling) and the complexity of the system (Wolf 2001).…”

“…While it may be possible to manage disruptions affecting a firm by dispersing purchases across different suppliers (Gupta et al 2015;He et al 2016), that management practice may actually increase the propagation of the disruption at an aggregated level. Therefore, disruptions must be contained before they can affect a larger portion of the supply chain (Chopra and Sodhi 2004;Blackhurst et al 2011;Marley et al 2014).…”

Supply chain risk management: creating an agenda for future research

“…Disruptions are unpredictable and in order to safeguard supply chain from the adverse effects of these disruptions, managers need to have complete visibility across entire network (Colicchia and Strozzi, 2012;Ghadge et al, 2012). Recently, researchers have started studying impact of disruptions on supply chains (Durowoju et al, 2012;Hopp et al, 2012;Tsiakkouri, 2010;Marley et al, 2014;Son and Orchard, 2013). We propose treating supply network as an engineering system network and applying the techniques of system reliability in assessing reliability of supply networks.…”