Navigating through STORMS: A review of supply chain resilience practices
Priyabrata Chowdhury et al.
Abstract
Supply chain actors are continually navigating unfolding exogenous disruptions. Advances in technology, connectivity and network visibility have provided practitioners with new ways to prepare for, cope with, and manage unknown unknowns. Scholars have also made significant advances in capturing and disseminating supply chain resilience management techniques, with multiple review papers producing conceptual frameworks and recommending further research directions. Yet, none of the review papers has drawn specifically from case-study-based research to tap into the rich insights provided by study participants. This provides an opportunity for an in-depth synthesis of case-based supply chain risk management and resilience literature. Treating each paper as a case study, we use abductive reasoning to examine how supply chain actors anticipate and prepare for disruptions, respond and react to disruptions, and restore and recover from disruptions to develop resilient supply chain practices. By focusing on the linkages between the risk management activities across the different phases—before, during, and after disruption—we contribute to the literature by offering an integrated supply chain resilience process framework and presenting an agenda for future research using the case study approach. • This paper reviews case-based supply chain risk management and resilience studies. • It sheds light on how disruptions are managed before, during, and after they occur. • It theorizes an enactment concept of inter-phase disruption management activities. • An integrated supply chain resilience process framework is proposed. • An agenda for further research on supply chain resilience is presented.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.