Introduction
In today’s volatile, uncertain, complex, and ambiguous (VUCA) world, supply chains are not only influenced by these characteristics but are increasingly exhibiting VUCA features themselves. VUCA was defined by Bennett and Lemoine (2014) with the following traits:
Volatility: Supply chain events are unexpected or unstable, but their impacts can generally be predicted based on available information.
Uncertainty: The cause and effect of supply chain events are understood, yet other details about the events remain unknown.
Complexity: Both the supply chain and its environment have numerous interconnected parts and variables, making it challenging to establish clear cause-and-effect relationships.
Ambiguity: Supply chain events are unexpected, and the causal relationships behind them are unclear.
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As the global economy evolves and global enterprises cooperate deeply, modern supply chains involve many interdependent actors across different countries, resulting in substantial flows of materials, funds, and information. This complex network structure adds to the overall complexity of supply chains, making them even more susceptible to VUCA characteristics.
The increasing complexity of supply chains contributes to higher levels of volatility and uncertainty, leading to increased ambiguity in their operations. This situation is aggravated by the VUCA environment and the inherent VUCA features of supply chains, making disruptions more likely. Conventional risk management approaches, which follow steps of identification, evaluation, response, and monitoring, are inadequate to address these challenges, especially considering many risks are unforeseen. To effectively counteract these issues, it has become crucial to focus on constructing resilient supply chains, as a means to enhance the ability to respond to disruption risks.
Concept of Supply Chain Resilience
The concept of “supply chain resilience” was introduced by Rice and Caniato (2003) and formally defined by Christopher and Peck (2004) as the ability of a supply chain to return to its original state or move to a more desirable state after being disrupted. Scholars have reached a consensus that it involves the adaptive capability of a supply chain to prepare for unexpected events, respond to disruptions, and recover from them. Various metrics have been developed to evaluate supply chain resilience, including capabilities-based measurements, quantitative metrics, performance-based metrics, and topological network indicators.
The focus of existing literature largely centers around strategies for improving supply chain resilience, categorized into proactive and reactive approaches. Proactive strategies involve preparing for disruptions, while reactive strategies focus on recovering a supply chain after a disruption. Proactive strategies include network structure design, supplier selection, redundancy, flexibility, diversification, and building social capitals. Reactive strategies have received less attention, but emerging technologies like cloud computing and blockchain are increasingly recognized as tools that can enhance supply chain resilience by improving visibility, anticipation, and adaptability.
Research Methodology on supply chain resilience
Various theories have been applied to the study of supply chain resilience. Commonly used theories include the resource-based view (RBV), dynamic capability theory, relational view, and complexity theory/complex adaptive systems. RBV emphasizes that a firm’s competitive advantage comes from valuable and irreplaceable resources, suggesting that firms must continually integrate and reallocate resources to enhance supply chain resilience. Dynamic capability theory and relational view focus on understanding the capabilities and relationships firms need to develop to achieve resilience in a rapidly changing business environment. Complexity theory/complex adaptive systems, on the other hand, considers supply chain firms as self-organized and self-adaptive entities, adapting nonlinearly to their dynamic external environment.
Research on supply chain resilience employs various methodologies, categorized into three groups. Firstly, scholars often develop conceptual frameworks to build resilience in different contexts. Secondly, quantitative modeling approaches are widely adopted, including mathematical modeling, decision analysis, network modeling, and simulation. These methods address different aspects, such as optimizing supply chain structures, evaluating resilience, characterizing network interactions, and solving large-scale optimization problems. Lastly, empirical studies, such as case studies and surveys, are growing, focusing on examining the factors influencing supply chain resilience and developing resilience metrics.
Future Research on Supply Chain Resilience in Era of VUCA
The future directions for supply chain resilience research primarily emphasize responses to VUCA risks. However, there are other important areas for exploration. For instance, choosing among multiple strategies for building resilience and unifying conflicting evaluation metrics pose intriguing challenges. Additionally, investigating how supply chain resilience impacts integration and potentially reshapes firm boundaries in favor of resilience over efficiency is significant.
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A holistic approach is crucial when studying supply chain resilience. Rather than fragmented efforts, resilience should be developed within a coopetition context, recognizing the propagation of risks along the supply chain and the cost-intensive nature of resilience-building. Given the multidimensional challenges of the VUCA era, interdisciplinary research and diverse methodologies are essential.
Reference – Gao, Y., Feng, Z. and Zhang, S., 2021. Managing supply chain resilience in the era of VUCA. Frontiers of Engineering Management, 8(3), p.465.