Tilburg University
The service bullwhip effect
Akkermans, H.A.; Voss, C.
Published in:
International Journal of Operations and Production Management
DOI:
10.1108/ijopm-10-2012-0402
Publication date:
2013
Document Version
Peer reviewed version
Link to publication in Tilburg University Research Portal
Citation for published version (APA):
Akkermans, H. A., & Voss, C. (2013). The service bullwhip effect.
International Journal of Operations and
Production Management
,
33
(6), 765-788. https://doi.org/10.1108/ijopm-10-2012-0402
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Download date: 09. aug.. 2022
1
The service bullwhip effect
Henk Akkermans
Chris Voss
Tilburg University
Tilburg, The Netherlands
ha@uvt.nl
London Business School
London, United Kingdom
cvoss@london.edu
Abstract
Purpose
This paper sets out to explore whether and how the bullwhip effect, as found in
product supply chains, might also manifest itself in services, as well as what
policies can be successful for mitigating it.
Design/methodology/approach
A combination of analytic methods was used—inductive case analysis and analysis
of data from two service supply chains in the telecom industry.
Findings
Empirical evidence from two cases was examined and provides support for the
presence of a service bullwhip effect. Quantitative and qualitative case data were
used to explore how this effect manifests itself in services, the distinctive drivers of
the bullwhip effect in services, and the managerial actions that can either trigger or
mitigate these bullwhip effects. Eight propositions are developed. Three types of
characteristics that potentially make the bullwhip effect worse in services than in
manufacturing are identified: (1) the destabilizing effects of manual rework in
otherwise automated service processes, (2) the omission of accurate and timely data
on rework volumes upstream in the chain, pointing at future bullwhip effects
downstream, and (3) the lack of a supply-chain mindset within the various
departments jointly responsible for delivering the service, leading to longer delays
in reacting to service bullwhips as they develop over time.
Originality/value
This paper explores an area that has been well researched in manufacturing, but not
in services, and it contributes to both the theory and practice of service supply
chains.
Research limitations
The research is based on two cases within a single industry, limiting
generalizability. The propositions developed need testing in a wider set of contexts,
including hybrid service and product supply chains.
Practical implications
2
The implications of this research can help organizations prevent or reduce the
negative impact of planned and unplanned fluctuations in their service supply
chains.
Keywords: Service supply chain, Bullwhip effects, Service variability
Paper type: research paper
Introduction
As the importance of service supply chains is becoming recognized, there is a
need to understand whether phenomena identified in product supply chains also
exist in services. If they do, then there is a need to understand if they manifest
themselves in a similar or different way. One such phenomenon is the bullwhip
effect. The existence, and indeed, persistence of the bullwhip effect has been well
established in product supply chains (Forrester 1958; Lee, Padmanabhan & Whang
1997; Croson & Donohue 2006; Cachon, Randall & Schmidt 2007). Major demand
surges are not uncommon in services; however, these alone are not or may not
trigger bullwhip-type effects. In this paper we set out to explore whether the effect
also manifests itself in service supply chains, and if so, how. In addition, to what
extent are the managerial policies to address it different from those in
manufacturing settings?
In 2008, the Dutch cable company Ziggo suffered from unexplained escalation
of technical problems and workloads. There was a new, larger, call center, but
response lead time and customer complaints amplified over time. The company did
not know what was causing this. “The odd thing is: when we solve one
malfunctioning, another pops up; how is that possible? You tell us, and we will
solve it” (NRC Handelsblad, 2008). Such escalation from seemingly small
problems and the lack of understanding by the company regarding the causes and
how to avoid or address them is not uncommon. This is not a unique case. Time and
time again, organizations have let themselves get into similar situations. As a result,
problems that arise from inadequate planning or information get amplified further
down the service supply chain. When inadequately addressed, the amplification can
lead to a tipping point in the organization’s ability to handle problems, which then
leads to subsequent meltdown (Akkermans and Vos, 2003).
Theoretical Background
The bullwhip effect has been known to exist in manufacturing for a number of
years, being initially described and explained over half a century ago (Forrester,
1958), and receiving its present label in 1997 (Lee et al., 1997). Nowadays, it is
well recognized and there is considerable evidence that product organizations are
taking steps to minimize its effects (Cachon et al., 2007). There is considerable
knowledge of how it manifests itself, what its root causes are and what managerial
policies are available to mitigate its effects.
3
The bullwhip effect in manufacturing
In manufacturing, a bullwhip effect occurs when, in a chain of interlinked
process stages, the variation in the demand pattern coming out of a process stage is
greater than the variation of the demand that came into that process stage.
Moreover, this process of amplification is repeated from process stage to process
stage. This can be operationalized as follows: If the ingoing signal is mainly a
stationary one with considerable noise, it is the noise that is amplified, and hence,
one looks at variation:
Var
Demand from process stage n+1
> Var
Demand from process stage n.
If the ingoing signal is greater, a one-time step increase or pulse, for example, as a
result of a sudden surge in demand, then variation is less appropriate and one looks
at the maximum degree of amplification between consecutive process stages
(Sterman, 2000):
Ampl
Demand from process stage n+1
> Ampl
Demand from process stage n.
There are a number of possible root causes of the bullwhip effect in product
supply chains. These complement rather than contradict each other. The first was
put forward by Forrester (1961), who found that, in supply chains, it takes managers
time to (1) observe that there is a change in demand and (2) react to that change.
Therefore, response delays are a fundamental driving factor of the bullwhip effect.
A second set of explanations focuses on bounded rationality (Simon, 1991).
Those in charge of production rates in these chains find making correct mental
calculations of how much demand already has accumulated, and how many orders
have been placed already, difficult. This has been observed in numerous
experimental studies of the well-known Beer Game (Croson & Donohue, 2006).
People tend to forget part of the orders they placed, or they do not correctly use the
information they have.
A third set of explanations, which can be seen as a special case of the second, is
industry practices that from a local and short-term perspective seem rational and
smart, but from a supply chain-wide and longer term perspective are not. These
include Lee et al.’s (1997) classic list of demand signaling, order batching,
promotion campaigns and shortage gaming.
The service supply chain
The bullwhip effect is seen as taking place in the context of a supply chain. The
concept of the product supply chain is very well established; however, that of the
service supply chain is still open to debate. Manufacturing supply chains have the
common link of managing the physical flow of goods; this obvious common link is
lacking among service supply chains (Ellram, Tate and Billington, 2004). A number
of authors have sought to frame the service supply chain by adapting established
product supply chain models, such as the SCOR model (Baltacioglu, Ada, Kaplan,
Yurt and Kaplan, 2007). So for example it has been defined as “the management of
information, processes, capacity, service performance and funds from the earliest
supplier to the ultimate customer” (Ellram et al., 2004) and “The service supply
chain is the network of suppliers, service providers, consumers and other supporting
units that performs the functions of transaction of resources required to produce
4
services; transformation of these resources into supporting and core services; and
the delivery of these services to customers” (Baltacioglu et al. 2007). More recently
scholars from service operations management, as opposed to supply chain
management, have begun to address the nature of service supply chains. Sampson
(2012) argues that models derived from product supply chains neglect some of the
important aspects of services. These aspects include that service supply chains are
frequently not linear, as in the traditional product supply chain, but are networks. In
addition, services are characterized by customer contact in service processes
(Chase, 1978) and by co-creation of value between supplier and customer (Vargo
and Lusch, 2010), and this may result in two-way flows between customers and
suppliers (Sampson, 2000). In this paper we chose to follow Sampson (2012), who
originally saw service supply chains as a dyadic phenomenon, but who now argues
that they are, in fact, extended networks. We define service as a producer–consumer
interactive process. As the definitions above do not explicitly see service as a
process, we build on Ellram et al., (2004) and define a service supply chain as a
network of interdependent service processes that span multiple process entities; a
process entity being “any entity that participates in a process such as a firm,
customers, agents of customers and so forth” (Sampson, 2012).There are a number
of important differences between products and services which may influence
service supply chains. The most important one is that building inventory to cope
with variability in demand is not possible; instead, other strategies are used such as
building backlogs, queues and the use of reservations systems (Sasser, 1976). In
addition, demand management is frequently used, often in a very sophisticated
manner such as yield management in the airline industry (Kimes, 1990). An
important distinguishing factor of services is the direct customer involvement in
many service processes. As Frei (2006) points out, the fact that they introduce
tremendous variability—but complain about any lack of consistency—is an
everyday reality. She argues that there are five types: arrival variability, request
variability, capability variability, and subjective preference variability. Recognizing
and dealing with customer-induced variability becomes an important task in service
supply chain management.
Bullwhip behavior in services
There is a growing body of literature on bullwhip-type behavior in service
supply chains. Akkermans and Vos (2003) reported amplification effects in a
service supply chain. Anderson, Morrice, and Lundeen (2005) examined the
dynamic behavior of service supply chains in the presence of varying demand and
information sharing: they “characterize the conditions under which a ‘bullwhip
effect’ (i.e., an increase in demand and backlog variability as one looks up the
supply chain) can occur.” They point out that in service supply chains, as compared
to product supply chains, the service firms must adjust their backlogs, as they have
no access to finished goods inventories. While Ellram et al. (2004) position the
service supply chain in the “service sector,” we argue that service supply chains can
occur in both what are considered service sectors, and in other sectors such as
manufacturing and health care. Clearly, many supply chains are hybrid, as they
manage both backlogs and inventories.
Akkermans and Vos (2003) propose that, in service supply chains, there are
both similarities and substantial differences to product supply chains in terms of the
nature and causes of amplification (bullwhip) effects. They also state that backlog,
