Structure and complexity in six supply chains of the Brazilian wind turbine industry

• DOI: https://doi.org/10.1108/IJLM-01-2020-0039
• Published date: 29 July 2020
• Date: 29 July 2020
• Pages: 23-39
• Subject Matter: Management science & operations,Logistics
• Author: Vivian Sebben Adami,Jorge Renato Verschoore,Miguel Afonso Sellitto

Structure and complexity in six

supply chains of the Brazilian wind

turbine industry

Vivian Sebben Adami

Production and Systems Graduate Program, Unisinos, S. Leopoldo, Brazil

Jorge Renato Verschoore

Business and Management Graduate Program, Unisinos, S. Leopoldo, Brazil, and

Miguel Afonso Sellitto

Production and Systems Graduate Program, Unisinos, S. Leopoldo, Brazil

Abstract

Purpose –The purpose of this article is to compare design choices and assess thestructural complexity of six

manufacturing supply chains (SCs) of the Brazilian wind turbine industry.

Design/methodology/approach –The research method is quantitative modeling. This study adopts the

social network perspective to provide a broad set of network metrics for comparative analysis and

characterization of the structural configuration and complexity of SCs. Transaction costs and the risk of

disruption supported the metrics employed in the study. Network size, network density, core-size and

centralization metrics stem from transaction costs, whereas constraint and betweenness centrality stem from

risk of disruption.

Findings –The main conclusion is that, in the Brazilian wind manufacturing industry, increasing the SC

structural complexity by adding redundant ties to minimize disruption risks, even implying higher transaction

costs, increases the capacity to win orders.

Research limitations/implications –Only the Brazilian wind turbine industry was studied. Therefore,

findings are not general, but specific, to the case.

Practical implications –Managers and practitioners of the Brazilian wind turbine industry should focus on

increasing the complexity of their SCs, even if it increases transaction costs, to ensure due dates compliance in

orders.

Originality/value –To the best of the available knowledge, there is no commonly accepted or shared

measurement for SC complexity, and this study proposed an alternative approach to bridge this research gap,

the structural perspective of social networks. Traditional measures were complemented by new metrics, and

the power of the application of social network analysis to SC investigations was empirically demonstrated in

different levels of analysis.

Keywords Logistics cost, Logistics strategy, Performance measurements, Supply chain competences, Supply

chain risk, Strategic management

Paper type Research paper

1. Introduction

Supply chains (SCs) are structured in a variety of ways, each reflecting the purchasing

strategy formulated by the focal company, ranging from vertically integrated to single

outsourcing or multi-sourcing with multiple tiers (Basole and Bellamy, 2014). Such a plethora

of possibilities in terms of structural configurations results in distinct degrees of complexity.

Many scholars recognize the structural configuration and complexity as mechanisms for

competitive advantage in SC (Reeve and Srinivasan, 2005;Reid and Sanders, 2007;Sezen,

2008;Badenhorst-Weiss and Nel, 2010). Scholars also recognize the effects of complexity in

terms of SC performance (Ramos et al., 2012;Sharma et al., 2019), productive efficiency (Kao

et al., 2017), logistics efficiency (Sellitto and Luchese, 2018), environmental impacts

Brazilian wind

turbine

industry

23

The research was supported by funds from CAPES and CNPq, the Brazilian agencies of scientific

research personnel. The authors declare that there is no conflict of interest involving the entire research.

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/0957-4093.htm

Received 28 January 2020

Revised 4 April 2020

Accepted 17 July 2020

The International Journal of

Logistics Management

Vol. 32 No. 1, 2021

pp. 23-39

© Emerald Publishing Limited

0957-4093

DOI 10.1108/IJLM-01-2020-0039

(Tachizawa and Wong, 2015;Sellitto, 2018), and resilience (Ledwoch et al., 2018;Yan et al.,

2015). Understanding how the SC structures are configured and assessing their degree of

complexity can help improving SC outputs (Lambert and Cooper, 2000).

SC has been studied mainly at the dyadic level (Borgatti and Li, 2009). However, restricting

the analysis to this level obscures much of their structural configuration and complexity

(Caridi et al., 2010). Regarding complexity assessment, some theoretical approaches and

techniques have been proposed by scholars, such as entropy-based studies (Isik, 2010;Cheng

et al., 2014;Ruiz-Hern

andez et al., 2019), assessment by multiple drivers (de Leeuw et al., 2013)

and supply-base level characterization (Lu and Shang, 2017). Nevertheless, to the best of our

knowledge, there is no commonly accepted or shared measurement for SC complexity

(de Leeuw et al., 2013). Our study focuses on an alternative approach to deal with these

questions, which is the structural perspective of social networks (SNs). This is the research

gap that this study aims to bridge.

Kim et al. (2011) made one of the earliest contributions based on the SN perspective.

They mapped the material flows of three SCs in the automotive industry and provided a

characterization of their complexity using size-type and density-type SN metrics. SN-based

approaches offer many other possibilities to investigate the underlying relationships that

support SC (Borgatti and Li, 2009). This potential, in general, has been little explored in

recent studies (Wichmann and Kauf mann, 2016). According to Wichmann and Kaufmann

(2016, p. 740), “scholars are not yet entirely aware of the many possibilities the SNA

approach offers to the SCM field.”There is room for the use of new metrics, exploration of

different levels of analysis and multiple network studies (Borgattiand Li, 2009;Wichmann

and Kaufmann, 2016). To broaden the analysis of SC structure and complexity, network

theories and concepts have been transferred to the supply chain management (SCM)

discipline, an approach motivated by the recognition of the method ological potential of SN

for analyzing several SC phenomena (Borgatti and Li, 2009;H



akansson and Persson, 2004).

From the SN perspective, SCs are networks of buyers and suppliers, which can be

characterized by a set of structural properties and their respective analytic network metrics

(Borgatti and Li, 2009). Buyers and suppliers are the SN nodes, and the SN ties are the

relationships or flows among them.

The purpose of our article is to compare design choices and assess the structural

complexity of six manufacturing SCs of the Brazilian wind turbine industry. The research

method is quantitative modeling. We adopted the SN perspective to reach a more complete

characterization of the structural configuration and complexity of the six SC, providing a

broader set of network metrics for comparative analysis and identification of key companies

and pinch points. Our study assumes that the main interest in studying the structural design

and complexity of an SC is its effects in terms of competitive performance and resilience. In

doing so, we aim at contributing to the advance of SN and SCM theories, showing empirically

the power of the application of SNA, in different levels of analyses, to the SC studies.

Concerning managerial matters, we sought to characterize the structural complexity of a

particular type of SC of the Brazilian wind energy industry, the wind turbine manufacturing

SC, where the strategic design is critical (Castell

oet al., 2017). General solutions for the entire

industry or other industries are outside the scope of our study and should be addressed by

further research in the same and other industries.

The article is divided into five sections. In the following section, we present the theoretical

framework, explaining the applicability of social network analysis (SNA) for the

characterization of SC and the possibilities in terms of network metrics for evaluating the

structural complexity. The classical trade-off between transaction costs and risk of

disruption is discussed, along with the rationales for choosing greater or lesser structural SC

complexity in the context of the wind turbine industry. Next, we present the research

methodology, detailing the techniques and procedures we used in our data collection and

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