Impact of supply base structural complexity on financial performance: Roles of visible and not‐so‐visible characteristics

• Author: Guangzhi Shang,Guanyi Lu
• DOI: http://doi.org/10.1016/j.jom.2017.10.001
• Published date: 01 November 2017
• Date: 01 November 2017

Contents lists available at ScienceDirect

Journal of Operations Management

journal homepage: www.elsevier.com/locate/jom

Impact of supply base structural complexity on financial performance: Roles

of visible and not-so-visible characteristics

Guanyi Lu

a,∗

, Guangzhi Shang

b

a

Oregon State University, USA

b

Florida State University, USA

ARTICLE INFO

Accepted by: Mikko Ketokivi

Keywords:

Supply chain structure

Supply chain complexity

Supply base

Eliminative and cooperative structural links

ROA

Tobin's Q

ABSTRACT

Supply chains have become increasingly complex in the last decade, which makes their structural characteristics

important determinants of firm performance. Prior studies on supply chain structure have largely emphasized

network-level attributes but ignored supply-base level characteristics. However, in many cases it is the 1

st

tier

suppliers, not those “deep in the network,”that have most immediate influence on the buyer. In addition, some

structural characteristics, such as direct links between the buyer's suppliers and its customers, are not-so-visible

to the buyer, yet can impact its financial performance dramatically. The existing literature has overlooked these

not-so-visible structural links. Using objective supply chain data collected from Mergent Online and Compustat,we

map the supply base structure of 867 public firms. We construct three visible (horizontal, vertical and spatial)

and two not-so-visible (eliminative and cooperative) structural complexity metrics, and examine their impacts

on buyer firms' financial performance as measured by Return on Assets and Tobin's Q. Our empirical analysis

shows that the five dimensions have differential effects: some have negligible impacts while others appear to

strongly affect financial performance. Contrary to the common belief that complexity hurts performance, we find

that an individual complexity dimension may have both positive and negative effects, and the overall effect may

be non-linear.

1. Introduction

“Supply chains are growing increasingly complex, making them

harder to manage, operate, and change in response to customer,

competitive, and financial shifts.”–Wilson Perumal and Company,

2015.

Steinhilper et al. (2012) find that costs caused by supply chain

complexity account for up to 25% of manufacturing firms’total ex-

penditure. Supply chain complexity impedes decision-making (Manuj

and Sahin, 2011), fertilizes disruptions (Chopra and Sodhi, 2014) and

erodes plant level operational efficiency (Bozarth et al., 2009). Despite

these disadvantages, a general consensus among practitioners and

academics is that supply chains have become increasingly complex over

the last decades—with little relief in sight—owing to increasingly so-

phisticated customer requirements (Bode and Wagner, 2015; KPMG,

2011). As a result, the structural complexity characteristics of supply

chains have become important determinants of firm performance (Kim,

2014). A study by A.T. Kearney (2007) indicates that firms can increase

earnings by 3%–5% if they can make improvements based on supply

chain structure. Supplier management now involves more than just

building mutually beneficial, long-term relationships. It also requires an

in-depth understanding of the structural complexity of globally inter-

connected supply chains (Kim et al., 2015).

Supply chain structure has garnered much interest from Operations

Management (OM) scholars. Prior studies have largely adopted a social

network perspective to understand the network-level attributes of in-

terconnected firms and their influences (Bellamy et al., 2014; Kim et al.,

2011). While overall supply network structure is important (Kim et al.,

2015), a more nuanced understanding of the supply base structure is also

imperative. A supply base largely consists of 1

st

tier suppliers directly

connected to the focal buyer. Overall network structure emerges with

no single firm deliberately orchestrating its exact shape (Choi and

Hong, 2002). But while a supplier “deep in the network”may affect the

buyer, in many cases it is the supply base that more directly and

strongly influences performance (Wilhelm et al., 2016). As Sivadasan

et al. (2002, p.80) observed, “in a dynamic environment such as a

supply chain, even basic supplier-customer systems, with structurally

simple information and material flow formation, have a tendency to

exhibit operational complexity”and eventually impact buyers' financial

performance (Manuj and Sahin, 2011). A central challenge for

http://dx.doi.org/10.1016/j.jom.2017.10.001

Received 21 March 2016; Received in revised form 4 August 2017; Accepted 6 October 2017

∗

Corresponding author.

E-mail address: guanyi.lu@oregonstate.edu (G. Lu).

Journal of Operations Management 53–56 (2017) 23–44

Available online 29 October 2017

0272-6963/ © 2017 Elsevier B.V. All rights reserved.

T

advancing supply chain structure research, therefore, is to show how

and why supply base structural characteristics influence buyers’fi-

nancial performance.

In addition, some structural links are not-so-visible to the buyer, yet

impact its financial performance dramatically. For example, a supplier

can sell directly to the buyer's customers, with the potential to replace it

(Rossetti and Choi, 2005). Prior empirical studies have investigated the

visible structural links (i.e., the links that connect to the buyer). Yet the

neglect of not-so-visible structural links masks information critical to

supply chain management decisions. Furthermore, the visible and not-

so-visible links may be related. For instance, the potential elimination

threat posed by supplier-customer links (a not-so-visible factor) is likely

to be minimized when suppliers reside in geographically dispersed lo-

cations (a visible complexity measure). Thus, our motivation is to

construct a comprehensive set of supply base structural metrics and

answer the research question: how do the characteristics of supply base

structural complexity affect the buyer's financial performance?

Utilizing a proprietary objective dataset compiled from two data

sources—Mergent Online and Compustat, we map the supply base

structure of 867 public firms and construct two sets of structural

complexity metrics (specifically, visible and not-so-visible, see details in

Section 2). We empirically examine the individual effects of these

complexity dimensions on the buyer's financial performance. Contrary

to the literature, which states that supply chain complexity hurts firm

performance (Bozarth et al., 2009; Bode and Wagner, 2015), we pro-

pose that the effects of complexity dimensions at the supply base level

are complicated and mixed. An individual dimension may have both

positive and negative effects and the overall effect is contingent on the

magnitude of the complexity dimension itself. We find that some

complexity dimensions reveal a nonlinear (U-shaped or inverted-U)

relation with the buyer's financial performance. In addition, these

complexity dimensions exhibit differential effects; some wield con-

siderably stronger impacts than others.

This study makes two major theoretical contributions. First, it expands

our understanding of supply chain structure by channeling focus from the

broad network level to the more nuanced supply base level. The supply

base has stronger and more immediate performance impacts than the rest

of the supply network due to its “proximity”to the buyer (Wilhelm et al.,

2016). Second, by also emphasizing the not-so-visible structural links

where the buyer is generally not directly involved, our study extends the

conceptualization of supply base complexity and provides a more com-

prehensive set of structural dimensions. Understanding the impacts of

these dimensions is critical because the buyer is likely to influence only its

direct links (Bode and Wagner, 2015). As a result, this study addresses a

resonant theme within the supply chain structure research: showing how a

firm should manage structural characteristics separately, with the poten-

tial to mitigate the negative impacts of complexity dimensions it cannot

directly control. Our study also carries a methodological implication. Al-

most all studies on supply chain structure rely on information from the

buyer—primarily survey and qualitative data—to measure structural

complexity. However, the use of data solely from the buyer risks over-

looking the impact of structural links of which the focal firm is unaware.

We overcome this problem by constructing objective measures from

buyer-supplier links identified by independent third parties.

The rest of this article is organized as follows: Section 2reviews the

related literature. Section 3proposes the theoretical framework and de-

velops research hypotheses. Section 4discusses data source and variable

construction. Section 5depicts methods and reports results. Section 6

concludes the paper with a discussion on contributions and limitations.

2. Literature review

2.1. Complexity in supply chains

The concept of complexity has triggered research in multiple aca-

demic disciplines. It generally pertains to system-level attributes about

connections among system constituents. In social science, Simon (1962,

p.468) offers an influential definition that a system is complex if it is

“made up of a large number of parts that interact in a non-simple way.”

This definition highlights two critical traits of complexity: structure and

behavior (Perrow, 1984; Senge, 2006). According to Bode and Wagner

(2015, p.216), the former “is often termed structural complexity (also

static or detail complexity) and refers to the number and variety of

elements defining the system.”The latter is often labeled “dynamic

complexity,”referring to the interactions of those elements. The two

traits are usually interrelated in practice. A large number of elements

implies a great number of possible interactions, which is especially true

when connected firms jointly assemble a final product (Bozarth et al.,

2009; Manuj and Sahin, 2011).

Prior studies on supply chain complexity have capitalized on both

traits and viewed complexity as a multi-dimensional concept. For in-

stance, Vachon and Klassen (2002) propose two dimensions: un-

certainty (which is associated with structure, i.e., the number of con-

stituents), and complicatedness (associated with behavior, i.e.,

interaction among constituents). Choi and Krause (2006) identify three

dimensions: the number of direct suppliers (structure), differentiation

among direct suppliers (structure), and the relationships among the

suppliers (behavior). Bozarth et al. (2009) also propose three: internal

manufacturing complexity, downstream complexity and upstream

complexity. They explicitly state that each of their complexity dimen-

sions can be characterized as both structural and behavioral. While

early studies provide insights into supply chain complexity, researchers

have failed to achieve a consensus about which dimensions best de-

scribe supply chain complexity, partly due to their different foci (Jacobs

and Swink, 2011; Manuj and Sahin, 2011). Our study focuses on

structural complexity, because it is explicitly measured by buyer-sup-

plier relationships. These relationships also reflect the interactions be-

tween firms. However, we note that interactions are difficult, if not

impossible, to capture fully and objectively. The scope of this study is

thus decidedly restricted to the structural complexity of a firm's supply

base with a particular focus on 1

st

tier suppliers.

Table 1 summarizes the most relevant studies. As we noted earlier,

the small number of studies examining supply chain structural attri-

butes largely emphasize network-level measures that link relational ties

to performance metrics such as firm innovation, social capital and re-

source access. For example, Bellamy et al. (2014) demonstrate that

supply network accessibility is significantly associated with innovation.

Kim et al. (2011) investigate the supply networks of three automobile

product lines (Honda Accord, Acura CL/TL and DaimlerChrysler Grand

Cherokee) and show how network centrality and density affect material

flow and contractual relationships. In network studies, the network

“position”matters. According to the social network theory, firms oc-

cupying a “central”network position (as manifested by measures such

as in-bound centrality, out-bound centrality, and network accessibility)

will outperform competitors due to superior access to resources. Note

that in a network, a buyer does not necessarily have “position”ad-

vantage over its suppliers because a supplier can have the same or even

higher level of centrality (or other network measures) than its buyer. In

contrast, at the supply base level, the buyer naturally occupies the

central position. In a supply network, position is a firm attribute such

that a number of firms may share similar position advantages. In a

supply base, what matters more is the link attribute, i.e., which parties

(e.g., two suppliers, or a supplier and a customer) are linked. Thus, how

the focal firm connects to its suppliers and customers and how they

connect with each other have strong performance implications. Com-

pared with existing network measures which reflect a firm's position

relative to others, our supply base measures capture how links are

“distributed”within a supply base. While the kernels of social network

theory can still be used in supply base research, its measures likely

cannot. Among the studies we reviewed, only Bode and Wagner (2015)

use “lower-than-network”level measures to study upstream supply

chain disruptions. The lack of research on complexity at the supply base

G. Lu, G. Shang Journal of Operations Management 53–56 (2017) 23–44

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