Measuring the ripple effect: a simulation based study of supply chains resilience using new metrics
| Autor | Laura Tobajas Machín/Roberto Domínguez Cañizares/Carla Talens Fayos/Víctor Fernandez-Viagas |
| Cargo del Autor | University of Seville/University of Seville/University of Seville/University of Seville |
| Páginas | 543-580 |
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CAPÍTULO 25
MEASURING THE RIPPLE EFFECT:
A SIMULATION BASED STUDY OF SUPPLY CHAINS
RESILIENCE USING NEW METRICS
LAURA TOBAJAS MACHÍN
University of Seville
ROBERTO DOMÍNGUEZ CAÑIZARES
University of Seville
CARLA TALENS FAYOS
University of Seville
VÍCTOR FERNANDEZ-VIAGAS
University of Seville
1. INTRODUCTION AND OBJECTIVES
The growth of supply chains (SCs) and their interconnectedness, cou-
pled with the competitive business environment and the pressure for
cost reduction, has led to the outsourcing and offshoring of many ma-
nufacturing activities, putting an enormous pressure on operations to
maintain undistracted and stable environments. This objective,
however, also increases their vulnerability to disruptions, which conse-
quently increases the operational and financial impact of such disrup-
tions (Zsidisin et al., 2005). Therefore, the study of the consequences
of disruptions, such as the well-known Ripple Effect, has gained pro-
minence in the recent years, and numerous authors have begun to deve-
lop research studies in order to understand and prevent these effects
(see e.g. Dolgui & Ivanov, 2021; Li et al., 2021; Katsaliaki et al., 2021;
Xu et al., 2020…).
SC disruptions have a major negative impact on the financial and ope-
rational performance of the different SC echelons as well as on the ove-
rall SC (Xu et al., 2020). For this reason, of the many possible
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classifications of disruptions, a very interesting one is that of Ivanov in
2017 (Ivanov et al., 2017). He classifies disruptions according to their
location impact in the chain, i.e., which part of the SC they affect. Thus,
disruptions can affect transportation, production echelons or supply
echelons. This classification facilitates their modelling.
This study focuses on disruptions affecting production echelons. In par-
ticular, the disruption studied is an alteration in the production of the
factory-echelon. This alteration is understood in the following sections
as a total interruption of its production capacity. Therefore, its conse-
quence is the factory-echelon being unable to supply the following
echelons in the SC. This phenomenon responds to possible real situa-
tions such as: fires in the factory, strikes, natural disasters in the vicinity
of the factory, blackouts… If the inability to supply caused by the ori-
ginal disruption affecting the factory propagates downstream and af-
fects other nodes in the chain, the Ripple Effect phenomenon takes
place (Dolgui et al., 2017).
The increase in the likelihood and magnitude of disruptions in the re-
cent years leads to scenarios with wide and serious consequences, and
with great study interest. The most recent scenario (not already finali-
zed in 2021) and biggest in terms of economics and involvement is the
one caused by COVID-19. The pandemic has been one of the biggest
challenges in the recent years leading to disruptions not only in the
health SC system but in a wide range of SCs with consequences that
continue to be noticed today. “COVID- pandemic is viewed as a new
type of disruption quite unlike any seen before” (Ivanov and Das, 2020).
This global pandemic has clearly demonstrated the key role of SCs in
the safe provision of goods and services to society (Ivanov and Dolgui,
2021).
Many of the disruptions derived from the pandemic caused Ripple Ef-
fect. The inactivity of some sectors of the worldwide industry led to
shortage of different products in the retailers all over the world. To offer
a recent example, a headline of the Washington Post reads: “The global
semiconductor shortage that has paralyzed automakers for nearly a
year shows signs of worsening, as new coronavirus infections halt chip
assembly lines in Southeast Asia, forcing more car companies and
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electronics manufacturers to suspend production” (The Washington
Post, 2021). There are also smaller examples of disruptions that caused
a Ripple Effect recently in many SCs, for instance, the exit of the United
Kingdom from the European Union. The days before the official exit
many trucks were trapped in the English Channel between France and
England, as the fear of not reaching a pact with the European Union
made the retailers and consumers increment their purchases and orders
causing traffic congestion. These phenomena led to delays in many SCs
as a cause of the inability of the truck drivers to reach the United King-
dom (the normal functioning of the SC had stopped) (DW, 2020). It is
also well known the case of the containership blocking the Suez Canal
causing shortage in numerous SCs and leading to enormous economic
losses (Business Insider, 2021).
Thus, the different events occurring lately, and their enormous conse-
quences have put the study of disruptions and the Ripple Effect in the
spotlight. One of the biggest challenges in the study of the Ripple Effect
is the definition of metrics to quantify this phenomenon. This study
aims to provide a new form of metrics in order to systematically analyse
and quantify the magnitude of the Ripple Effect over a wide variety of
SC configurations. The majority of the metrics used in previous works
are based on individual observations of the Ripple Effect, which is
hardly generalizable due to the uncertainty and stochasticity of some of
the processes involved in SCs. The methods and metrics described in
this study allow to automatically determine the impact of the disruption
along the chain over any number of observations for a generalization of
the results obtained. Therefore, statistical analysis can be employed to
determine the impact of different SC configurations on the magnitude
of the Ripple Effect. This study takes advantage of the proposed metrics
to perform a set of experiments using a simulation-based four-echelon
SC model. As a result, the impact of several factors such as disruption
period, demand´s standard deviation, lead times and demand forecast
method on the Ripple Effect is addressed using statistical analysis.
The rest of the document is organized as follows:
In Section 2 there is a brief literature review about the concepts of dis-
ruption and Ripple Effect and the different focus areas and
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