Optimal levels of (de)centralization for resilient supply chains

DOIhttps://doi.org/10.1108/IJLM-01-2017-0013
Pages435-455
Published date12 February 2018
Date12 February 2018
AuthorHorst Treiblmaier
Subject MatterManagement science & operations,Logistics
Optimal levels of
(de)centralization for resilient
supply chains
Horst Treiblmaier
Department of International Management, MODUL University Vienna,
Vienna, Austria
Abstract
Purpose The purpose of this paper is to illustrate how centralization and decentralization of supply chains
(SCs) play a major role in creating organizational resilience.
Design/methodology/approach Starting with the basic tenets of contingency theory and applying a
grounded theory approach, results from exploratory qualitative and quantitative studies are combined to
investigate the impact of (de)centralization on SC resilience capabilities.
Findings The findings from a comprehensive literature review combined with two empirical surveys
indicate that four important organizational capabilities are needed in order to cope with internal and external
disruptions: fast reactions to unforeseen disturbances, reducing the number of negative external forces,
reducing the impact of negative external forces and the quick return to normal operating processes.
Furthermore, it is illustrated how (de)centralization activities can support these capabilities and thus
maximize the SC resilience.
Originality/value This paper presents 12 measures for (de)centralization and shows how they can support
the four major capabilities of resilient companies. The results from qualitative and quantitative surveys allow
for a holistic understanding of the organization and provide a basis for future SC resilience research.
Keywords Grounded theory, Survey, Europe, Centralization, Decentralization, Supply chain resilience,
Empirical research, Contingency theory
Paper type Research paper
Introduction
Todays supply chains (SCs) are intricate networks, which, more often than not, connect
numerous companies located in geogr aphically dispersed regions. The in creasing
complexity of these networks and their overall vulnerability to external disruptions,
which is evidenced by past incidents ranging from natural disasters (e.g. earthquakes,
tsunamis, typhoons) to man-made events (e.g. environmental pollution, accidents, wars) has
directed the attention of practitioners and researchers to the necessity of creating resilient
SCs (Sáenz and Revilla, 2014). DAveni and Gunther (1994) use the term hypercompetition
to describe a situation with unpredictable external conditions and suggest that companies
formulate dynamic strategies to gain temporary competitive advantage. Accordingly,
Reeves et al. (2015) highlight the increased dynamics and diversity of todays business
environments and point out that it has never been more difficult for companies to choose the
right strategy.
Resilience, which is defined the ability to survive, adapt, and grow in the face of
turbulent change(Pettit et al., 2013, p. 46) is a multidimensional and highly complex
construct. Resilient SCs can help companies to manage volatile external environments and
to mitigate risk, which can be categorized as being either internal or external to the firm
(Christopher and Peck, 2004). Using 14 focus group discussions, Pettit et al. (2013) identified
The International Journal of
Logistics Management
Vol. 29 No. 1, 2018
pp. 435-455
© Emerald PublishingLimited
0957-4093
DOI 10.1108/IJLM-01-2017-0013
Received 23 January 2017
Revised 1 July 2017
30 August 2017
Accepted 8 October 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0957-4093.htm
This research was supported by the Grant No. 841333 ReSCUEwithin the BRIDGE program from
the Austrian Research Promotion Agency (FFG). The author wants to thank the team from
LOGISTIKUM Steyr (University of Applied Sciences Upper Austria) and especially Dr Markus
Gerschberger for his valuable input and support.
435
Optimal
levels of
(de)centralization
a total of 1,369 empirical items, which they summarized into a complex measurement tool.
In order to be of practical use, however, it is necessary to reduce the number of items and to
identify those capabilities which bear the potential to strengthen a companysresilience.
One of these capabilities is the flexibilitya company has to adapt its level of (de)centralization.
Centralization is a network-level metric (together with density and complexity) and can
be measured by the number of connections going through the single nodes. A star structure,
with a single node at the center being connected to all other nodes, has the highest level of
centralization, whereas the lowest level occurs when all nodes possess the same number of
connections (Kim et al., 2011). Dalton et al. (1980, p. 58) state that centralization involves the
locus of authority to make decisions in organizations.Tachizawa et al. (2015, p. 240)
provide a similar definition: the extent to which decisions are concentrated in a network
member.In the context of this paper, the term centralization refers to the distribution of
decision-making authority throughout the organizational hierarchy, including with respect
to activities such as research and development. Communication structures and the physical
proximity of customers and suppliers are taken into consideration, as these may also impact
the optimal level of centralization. The majority of centralization research deals with rather
specific issues such as finding the best inventory design or optimal pricing policies. Since
this necessarily involves a delegation of authority, problems such as these can be subsumed
in a more general framework that treats centralization as the locus of authority. The supply
chain management (SCM) literature generally refers to centralization as the extent to which
the power to make SCM decisions is concentrated in an organization(Kim, 2007).
A large stream of research has associated organizational structure and stability with the
degree of (de)centralization of authority (Van De Panne, 1991; Özen et al., 2012; Strebinger
and Treiblmaier, 2004). Previous research has extended the scope of interest from single
organizations toward complex SCs (Nagarajan and Bassok, 2008). Benefits and drawbacks
arising from centralization or decentralization of organizational units have been discussed
intensively in academic literature for several decades. Advocates of decentralization have
long argued that decentralized structures replacing rigid centralized bureaucracies make
companies more effective (Peters and Waterman, 1982). In different environmental
circumstances, however, centralization might be the better choice (Atherton, 1977). Political
changes such as Europes unification in 1992 were seen as an opportunity for the
centralization of distributed responsibilities, which, in turn, was expected to boost
performance (Ashayeri and Rongen, 1997). This discussion has endured for several decades
and incorporated contributions by authors from diverse research fields, as will be shown in
the following sections.
The idea that a fitbetween the degree of centralization of an organizations control
structures and its external context needs to be achieved in order to ensure organizational
stability resembles the basic tenets of contingency theory. It is noteworthy, however, that
different concepts of fit exist in social science research. In this paper, fit is conceptualized as
profile deviation, which is defined by Venkatraman (1989, p. 433) as the degree of
adherence to an externally specified profile.When it comes to finding the optimal level
of (de)centralization such a profile represents the best possible configuration of a companys
(de)centralization measures given a specific environment. Several forms of contingency
theories exist, all of which are rooted in behavioral science. The originalversion postulates
that no one best way of leadership exists and that environmental influences as well as
various circumstances determine the most appropriate way of governance (Fiedler, 1964).
Among others, contingency theory was successfully applied in the field of logistics
management in order to study the impact of various SC risks on strategic decision making
(Wagner and Bode, 2008), to investigate knowledge sources for logistics service providers
innovativeness (Bellingkrodt and Wallenburg, 2013), to assess the efficiency of risk
mitigation strategies in SCs (Talluri et al., 2013), to explain performance differences between
436
IJLM
29,1

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