Supply network resilience learning: An exploratory data analytics study

Abstract

When a supplier experiences a disruption, it learns how to better prevent and recover from future disruptions. As suppliers learn to become more resilient, the overall supply network also learns to become more resilient. This research draws on the organizational learning literature to introduce the concept of supply network resilience learning, which we define as the improvement of supply network resilience when suppliers learn from their own disruptions. The analysis integrates agent-based modeling, experimental design, data analytics, and analytical modeling to investigate how supplier learning improves supply network learning. We examine how two types of supplier learning, namely, learning-to-prevent and learning-to-recover, affect supply network learning. The results show that suppliers' learning-to-prevent results in a disruption-free supply network when time approaches infinity. However, the results differ across a more realistic finite time horizon. In this setting, learning-to-recover improves network learning when suppliers face a lower chance of disruption. The analysis also shows that centrally located suppliers enhance network learning, except when the risk of a disruption is high and the chance of diffusing a disruption to another supplier is high. In this setting, noncentral suppliers become more critical to supply network learning. This research provides a framework that will help practitioners understand the contingencies that influence the effect of supplier learning on the overall supply network resilience learning.

Supply chain risk management in the COVID-19 pandemic: strategies and empirical lessons for improving global logistics service providers’ performance

“…LSPs with unique, inimitable capabilities (Esper et al, 2007) are likely to respond dynamically to disruption, benefitting their customers (Gligor and Holcomb, 2014;Dubey et al, 2018). It is therefore vital for LSPs to learn from disruptive events how to adapt their strategies for disruption and their SCRM processes continuously in order to respond effectively to similar future threats (Zollo and Winter, 2002;Esper et al, 2007;Chen et al, 2021) and remain competitive in dynamic conditions (Defee and Fugate, 2010;Ivanov and Dolgui, 2021).…”

“…The SCRM and DCV literature argue that proactive practices, such as maintaining an excess capacity to increase flexibility (Govindan and Chaudhuri, 2016;Chen et al, 2021), and reactive practices, such as using digital means to collaborate with partners and simplifying various processes (Queiroz et al, 2020;Dubey et al, 2021), allow companies to reconfigure their SCRM systems more rapidly and more rigorously to diminish the ripple effects of disruptive events (e.g. Dolgui and Ivanov, 2021;Ivanov and Dolgui, 2021).…”

“…Improves detection, assessment and treatment of risk. Training and past experience are key to knowing when to take action (Blackhurst et al, 2011;Hohenstein et al, 2015;Chen et al, 2021) • •…”

“…performance (Zollo and Winter, 2002;Esper et al, 2007). Firstly, companies that foster learning over time and apply the knowledge they gather to adapt their SCRM routines tend to be more robust and more agile in their responses to unexpected disruptions (Norrman and Wieland, 2020;Chen et al, 2021). Secondly, disseminating that knowledge both internally and among their SC partners enables collaborative learning (Fawcett et al, 2012).…”

Building Organizational Resilience Through Organizational Learning: A Systematic Review

“…Several authors highlight the value of organizations learning from the practices and experiences of other organizations (Gressgard and Hansen, 2015;Johannesen et al, 2020;Bhaskara and Filimonau, 2021;Fasey et al, 2021;Friday et al, 2021;Habiyaremye, 2021 as central to OL (Khan et al, 2017;Herbane, 2019). This may also spread further, to a whole network, as suppliers interact with each other, thereby also facilitating network resilience (Chen K. D. et al, 2021). Effective learning involves critical reflection at several levels, with effective communication and information sharing among the involved actors throughout the system (Johnsen and Habrekke, 2009;Dutra et al, 2015;Nicolletti et al, 2019).…”