Visual analysis of supply network risks: Insights from the electronics industry

Highlights

• A visual network analytic approach allows mapping of flow, information, and risk.

• Subtier risk is prevalent in electronics industry and distributions differ by tier.

• The study provides macroscopic view of supply network risk issues across multiple tiers.

• Multiple visual depictions reveal distribution of risk levels across supply network.

• Integrating depictions enables timely identification of dependencies and risks.

Abstract

In today's complex, global supply networks it has become increasingly challenging to identify, evaluate, and mitigate risks of disruption. Traditional supply chain practices have primarily focused on dyadic risk management, rarely considering risks in the sub-tier supply network. However, this approach severely limits a decision maker's ability to understand the highly interconnected nature of systemic risks and develop corresponding mitigation strategies. Grounded in theories of supply chains as complex systems, network analysis, and risk management, we demonstrate the importance of visual decision support for supply network risk assessment. We empirically illustrate our approach with supply network visualization examples from the electronics industry. We conclude the study with implications for the design and implementation of visual supply network decision support systems and future research opportunities.

Introduction

Managing supply chain risks in today's dynamic business environment is becoming increasingly challenging as traditionally linear supply chains are replaced by complex, global supply networks [12]. With growing interdependencies among firms in these networks, risks of all types – including operational failures, financial distress, labor issues, weak economic climates, cultural differences, and political instability – can rapidly cascade through the entire supply chain enterprise and cripple its performance [16]. Despite increasing digital interconnectedness, visibility into sub-tiers is often limited and not available. Yet, increased competition and customer demands require firms to design and manage their supply chain enterprise in such a fashion that sustains their capability to manufacture and deliver their products and services just as well or preferably better than their competitors [59].

Previous research has shown that many firms are not adequately prepared for assessing and addressing supply chain risks and disruptions [42]. One reason is that traditional practices rarely consider the complex interconnected and multi-dimensional nature of supply network risk beyond the first tier and there is a lack of literature on system analysis and decision support models for insight into systemic risk [93], [94]. Given the continuing shift of enterprises toward network-centric forms of organization and the multi-dimensional nature of supply network risk, novel tools and approaches for supply chain risk management are thus required.

One emerging field, visual network analytics, which fuses complex network analysis with information visualization, promises to provide important methods and interactive tools to comprehensively examine the structural complexities of supply networks and aid supply chain managers in the discovery, exploration, and resolution of potentially hidden sources of risk [7], [8], [6]. Grounded in theories of supply chains as complex systems, risk management, and network analysis, we demonstrate the importance of visual decision support for supply network risk assessment. We empirically illustrate our approach with supply network visualization examples from the electronics industry.

Theoretically, this study contributes to our broader understanding of visual decision support for complex risk management tasks. We show that a visual network analytic approach provides superior insight into supply network risk and can greatly facilitate decision making. More broadly, we contribute to research at the interface of information systems and operations management, in general, and decision support in a supply network context in particular. From a managerial perspective, we address an important practical need of designing and developing effective decision support capabilities that help identify and assess complex global supply risk in a systemic and interactive way. The visual approach proposed in our study thereby also addresses the call to expand decision makers' tool kit of decision support tools [29].

The remainder of the study is structured as follows. Section 2 provides the theoretical background. Section 3 describes our methodology, data, metrics, and visualization approach. Section 4 presents the analysis and discusses the results. Section 5 concludes the study and provides future research opportunities.