The Structure of the Toyota Supply Network: An Empirical Analysis

Kito, Tomomi; Brintrup, Alexandra; New, Steve; Reed‐Tsochas, Felix

doi:10.2139/ssrn.2412512

Cited by 48 publications

(48 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While these topologies have been observed in various complex social and physical networks, their applicability for supply chain networks are not clear in the literature. For example, empirical studies on supply chain network structures have found that some supply chain networks have truncated power‐law degree distributions (Saavedra, Reed‐Tsochas, & Uzzi, ), while some do not (Atalay, Hortaçsu, Roberts, & Syverson, ; Kito, Brintrup, New, & Reed‐Tsochas, ). Another disadvantage of using synthetically created supply chain networks is that the connection between firm/node attributes (such as industry and size) and their network positions is often ignored, even though both firm attributes and network positions are important when measuring resilience against disruptions.…”

Section: Literaturementioning

confidence: 99%

Modelling supply chain adaptation for disruptions: An empirically grounded complex adaptive systems approach

Zhao

Zuo

Blackhurst

2019

J of Ops Management

217

114

View full text Add to dashboard Cite

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.

show abstract

Section: Literaturementioning

confidence: 99%

Modelling supply chain adaptation for disruptions: An empirically grounded complex adaptive systems approach

Zhao

Zuo

Blackhurst

2019

J of Ops Management

217

114

View full text Add to dashboard Cite

show abstract

“…Change in the automotive industry has been intensively investigated in past studies [9,14,15,16,17], focusing on changing geographies and challenges of the technological transformation, and on technical, environmental, and management implications. Moreover, network analysis has been applied in the field, mainly to understand Supply-Chain systems in the automotive industry [18,19].…”

Section: Related Workmentioning

confidence: 99%

Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends

Stoehr

Braesemann

Frommelt

et al. 2020

Springer Proceedings in Complexity

View full text Add to dashboard Cite

The digital transformation is driving revolutionary innovations and new market entrants threaten established sectors of the economy such as the automotive industry. Following the need for monitoring shifting industries, we present a network-centred analysis of car manufacturer web pages. Solely exploiting publicly-available information, we construct large networks from web pages and hyperlinks. The network properties disclose the internal corporate positioning of the three largest automotive manufacturers, Toyota, Volkswagen and Hyundai with respect to innovative trends and their international outlook. We tag web pages concerned with topics like e-mobility & environment or autonomous driving, and investigate their relevance in the network. Sentiment analysis on individual web pages uncovers a relationship between page linking and use of positive language, particularly with respect to innovative trends. Web pages of the same country domain form clusters of different size in the network that reveal strong correlations with sales market orientation. Our approach maintains the web content's hierarchical structure imposed by the web page networks. It, thus, presents a method to reveal hierarchical structures of unstructured text content obtained from web scraping. It is highly transparent, reproducible and data driven, and could be used to gain complementary insights into innovative strategies of firms and competitive landscapes, which would not be detectable by the analysis of web content alone.

show abstract

“…The studies on the impact of supply network structure on performance (e.g., Kim et al, ; Nair & Vidal, ; Thadakamalla et al, ) use synthetic networks, while structures of real‐world supply networks tend to be different from those of idealized models (Brintrup et al, ; Kito et al, ). Therefore, the impact of network structure on performance in real‐world supply networks remains an open research question.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, the second case provides a contrasting example to the first case, that is, a theoretical replication (Yin, ). Industry‐level networks have been mapped in the literature, for instance, for the automotive industry by Kito et al () and Brintrup et al (). In particular, we are interested in industries where all firms involved are exclusively supplying within that sector where the overall network provides a self‐contained unit of analysis.…”

Section: Research Design: Real‐world Supply Network Case Studiesmentioning

confidence: 99%

Identifying dynamical instabilities in supply networks using generalized modeling

Demirel

MacCarthy

Ritterskamp

et al. 2019

J of Ops Management

View full text Add to dashboard Cite

Supply networks need to exhibit stability in order to remain functional. Here, we apply a generalized modeling (GM) approach, which has a strong pedigree in the analysis of dynamical systems, to study the stability of real‐world supply networks. It goes beyond purely structural network analysis approaches by incorporating material flows, which are defining characteristics of supply networks. The analysis focuses on the network of interactions between material flows, providing new conceptualizations to capture key aspects of production and inventory policies. We provide stability analyses of two contrasting real‐world networks—that of an industrial engine manufacturer and an industry‐level network in the luxury goods sector. We highlight the criticality of links with suppliers that involve the dispatch, processing, and return of parts or sub‐assemblies, cyclic motifs that involve separate paths from a common supplier to a common firm downstream, and competing demands of different end products at specific nodes. Based on a critical discussion of our findings in the context of the supply chain management literature, we generate five propositions to advance knowledge and understanding of supply network stability. We discuss the implications of the propositions for the effective management, control, and development of supply networks. The GM approach enables fast screening to identify hidden vulnerabilities in extensive supply networks.

show abstract

The Structure of the Toyota Supply Network: An Empirical Analysis

Cited by 48 publications

References 55 publications

Modelling supply chain adaptation for disruptions: An empirically grounded complex adaptive systems approach

Modelling supply chain adaptation for disruptions: An empirically grounded complex adaptive systems approach

Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends

Identifying dynamical instabilities in supply networks using generalized modeling

Contact Info

Product

Resources

About