Journal article
Modelling supply chain adaptation for disruptions: An empirically grounded complex adaptive systems approach
Journal of operations management, Vol.65(2), pp.190-212
03/2019
DOI: 10.1002/joom.1009
Abstract
Through the development and usage of an agent‐based model, this article investigates firms' adaptive strategies against disruptions in a supply chain network. Viewing supply chain networks as complex adaptive systems, we first construct and analyze a real‐world supply chain network among 2,971 firms spanning 90 industry sectors. We then develop an agent‐based simulation to show how the model of firms' adaptive behaviors can leverage competition relationships within a supply chain network. The simulation also models how disruptions propagate in the supply chain network through cascading failures. With the simulation, we seek to understand if a firm's adaptive behaviors can reduce the impact of disruptions in supply chain networks. Therefore, we propose, evaluate, and analyze two types of adaptive strategies a firm can leverage to reduce the negative effects of supply chain network disruptions. First, we deploy in our model a reactive strategy, which restructures the network in response to a disruption event among first‐tier suppliers. Next, we develop and propose proactive strategies, which are used when a distant disruption is observed but has not yet hit the focal firm. We discuss the implications related to how and when firms can improve their resilience against supply disruptions by leveraging adaptive strategies.
Details
- Title: Subtitle
- Modelling supply chain adaptation for disruptions: An empirically grounded complex adaptive systems approach
- Creators
- Kang Zhao - University of IowaZhiya Zuo - City University of Hong KongJennifer V Blackhurst - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of operations management, Vol.65(2), pp.190-212
- Publisher
- Wiley Periodicals, Inc
- DOI
- 10.1002/joom.1009
- ISSN
- 0272-6963
- eISSN
- 1873-1317
- Number of pages
- 23
- Language
- English
- Date published
- 03/2019
- Academic Unit
- Business Analytics; Bus Admin Graduate Programs
- Record Identifier
- 9984083864202771
Metrics
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