Investigating the role of demand planning as a higher-order construct in mitigating disruptions in the European supply chains

Date23 March 2020
DOIhttps://doi.org/10.1108/IJLM-08-2019-0218
Published date23 March 2020
Pages665-696
AuthorArtur Swierczek
Subject MatterManagement science & operations,Logistics
Investigating the role of demand
planning as a higher-order
construct in mitigating disruptions
in the European supply chains
Artur Swierczek
Department of Business Logistics, University of Economic in Katowice,
Katowice, Poland
Abstract
Purpose The goal of the paper is twofold. First, it aims to empirically conceptualize whether a wide array of
fragmented demand planning activities, performed in supply chains, can be logically categorized into
actionable sets of practices, which then form a broader conceptualization of the demand planning process.
Second, regarding certain contextual factors, our research seeks to investigate the contribution of demand
planning, as a higher-order construct, to mitigating disruptions induced by operational risks in supply chains.
Design/methodology/approachIn t hiss tudy, PLS-SEM was used to estimate the reflective-formative nature
of themodel. The resultsof PLS-SEM were additionally complemented by theassessment of thepredictive power
of our model.Finally, to reveal possible contingency effects,the multigroup analysis(MGA) was conducted.
Findings The study suggests that demand planning process (DPP) is a second-order construct that is
composed of four sets of practices, including goal setting, data gathering, demand forecasting, communicating
the demand predictions and synchronizing supply with demand. The study also reveals that the demand
planning practices, only when considered together, as a higher-order factor, significantly contribute to
mitigating disruptions driven by operational risks. Finally, the research shows that the strength of the impact
of demand planning on disruptions is contextually dependent.
Research limitations/implications While the study makes some important contributions, the obtained
findings ought to be considered within the context of limitations. First, the study only investigates disruptions
driven by operational risks, ignoring the negative consequences of environmental risks (terrorist attacks,
natural disasters, etc.), which may have a far more negative impact on supply chains. Second, the sample is
mostly composed of medium and large companies, not necessarily representative of demand planning
performed by the entire spectrum of companies operating in the market.
Practical implications The study shows that to effectively mitigate disruptions induced by operational
risks, the demand planning practices should be integrated into a higher-order construct. Likewise, our research
demonstrates that the intensity of demand planning process is contingent upon a number of contextual factors,
including firm size, demand variability and demand volume.
Social implications The study indicates that to mitigate disruptions of operational risk, dema nd planning as a
higher-order dynamic capability can be referred to the concept of organizational learning, which contributes to
forming a critical common ground, ensuring the balance between formal and informal dynamic routines.
Originality/valueThe paper depicts thatto fully deal with disruptions, the demand planning practices need
to be integrated and categorized into the dedicated higher-order. This may lead to forming demand planning as
a higher-order dynamic capabilitythat provides a more rapid and efficient rebuttal to any disruptions triggered
by operational risks.
Keywords Demand planning, Risk, Disruptions, Supply chain
Paper type Research paper
1. Introduction
Although the usefulness of demand planning process in establishing customer-oriented
supply chains is relatively well-documented in the supply chain literature (Fisher, 1997;
Croxton et al., 2001;Croxton et al., 2008;Crum and Palmatier, 2003;Mentzer and Moon, 2005;
Investigating
the role of
demand
planning
665
The study was financed by the National Science Centre as a research project no. DEC-2012/05/E/HS4/
01598.
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 15 August 2019
Revised 1 December 2019
21 January 2020
30 January 2020
Accepted 30 January 2020
The International Journal of
Logistics Management
Vol. 31 No. 3, 2020
pp. 665-696
© Emerald Publishing Limited
0957-4093
DOI 10.1108/IJLM-08-2019-0218
Kaipia and Holmstr
om, 2007), the managerial process required to organize demand planning
for supply chains is yet to be empirically investigated. Correspondingly, demand planning is
often considered to represent one of the largest performance gaps, and as such, it is also
widely misunderstood by supply chain and business executives (Basson et al., 2019). Wallace
and Stahl (2008) alleged that demand planning can be the most challenging of all five steps
within executive sales and operations planning. In consequence, the demand planning
practices have only slightly improved over the past few decades, in spite of major advances
made in the sales forecasting methods (Armstrong et al., 2015). This issue appears to be even
more vital regarding the fact that several years ago, among the supply chain planning
processes, demand planning indicated the strongest relationship to supply chain
performance (Lockamy and McCormack, 2004). Demand planning is the supply chain
activity that uses sales forecast and other information inputs, and insights contributions from
different functions of an organization to draft a demand plan (Raza and Kilbourn, 2017). In
other words, demand planning offers a unique opportunity to develop forecasts that support
organizations to adapt and increase their influence (Armstrong, 2017). More specifically,
demand planning allows organizations to understand the nature of consumer demand and
ensures that the supply response is suited to this demand. A failure to understand the nature
of demand for the organizations products results in a mismatch between demand and supply,
which may result in an oversupply or a product shortage (Lawless, 2014). The process of
demand planning involves a number of practices, including goal setting, data gathering,
demand forecasting, communicating the demand predictions and synchronizing supply
with demand (Croxton et al., 2002). Likewise, to provide a robust conceptual background for
this study, we refer to the Resource Based View (RBV). This theory links the ability to derive
the competitive advantage to the possession of heterogeneous resources and capabilities
(Prahalad and Hamel, 1990). We argue that one of such capabilities is demand planning,
which, in the light of the dynamic approach to RBV, can be perceived as a dynamic capability
of supply chains. Following the study by Kaipia and Holmstrom (2007), to meet customer
demand, the contemporary supply chains need to increase their responsiveness through the
effective demand planning process. Thus, drawing upon the study by Eisenhardt and Martin
(2000), we consider demand planning as a type of organizational and strategic routine by
which the firms in supply chains obtain new resource configurations as markets emerge,
collide, split, evolve and die. In other words, demand planning as a dynamic capability should
react quickly to situational changes (Prahalad and Hamel, 1990). This makes demand
planning a valuable tool in dealing not only with demand uncertainty, but, more generally,
with widespread risk disruptions affecting supply chains. This is the issue of crucial
importance, as risk disruptions may be very costly for supply chains. There are numerous
examples of organizations whose operations were halted or disturbed in consequence of
specific risk factors (Hendricks and Singhal, 2003;Norrman and Jansson, 2004;Radjou et al.,
2002). One of the most significant risks is the operational risk, which may potentially or
actually disrupt the upstream and downstream operations performed in supply chains
(Menzter et al., 2004;Christopher and Peck, 2004;Tang, 2006). More specifically, the sources of
operational risk reside within or outside the firm, but inside a supply chain and may affect its
ability to operate in a proper way (Menzter et al., 2004). Consequently, operational risks are
referred to the potential and actual negative consequences in a supply, customer demand
(Tang, 2006) and adverse risk effects within the firm (Christopher and Peck, 2004).
Nonetheless, decision-makers often do not realize the potential and actual cost of disruptions.
Therefore, there is a need to investigate managerial concepts such as demand planning,
which contribute to mitigating disruptions induced by certain risk factors. The prior studies
that investigate the contribution of demand planning to mitigating disruptions driven by
operational risks are scanty. Correspondingly, a limited number of the recent studies show
that demand planning might be very helpful for managers when dealing with the
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consequences of demand and control risks (Swierczek, 2019). Other studies demonstrate that
some demand planning practices tend to simultaneously mitigate or reinforce disruptions
driven by operational risk (Swierczek and Szozda, 2019). Likewise, there are several studies
that show the impact of certain demand practices on risk consequences (Jonsson and
Mattsson, 2013;Fiksel et al., 2015;Ambulkar et al., 2015). More specifically, the significance of
the link between risk and demand has been noticed by Juttner et al. (2003) who maintain that
supply chain risks refer to the possibility and effect of mismatch between supply and
demand. By the same token, Juttner and Maklan (2011) show that risk events in a supply
chain can have an immediate impact on the ability of a firm to meet customer demand.
Blackhurst et al. (2011) discussed that information sharing in a supply chain enabled to
identify disruptions and implement the risk mitigation strategy through the planning
initiatives. Likewise, the synchronization procedures of demand planning may facilitate to
deploy resources across the supply chain, which should be correctly aligned with disaster and
recovery planning. Other studies also underlined the relationship between data gathering
and risks (Bambacas and Patrickson, 2008;Blome and Schoenherr, 2011). Contrary to the
previous studies which, when elaborating on the link between demand planning and risk,
usually refer to certain demand planning practices, our research seeks to investigate the
contribution of demand planning, as a higher-order construct, to mitigating disruptions
induced by operational risks in supply chains.
This study makes three major contributions. First, in line with the RBV, we advance the
concept of demand planning, as a dynamic capability of supply chains, that can take the form
of higher-order factor, composed of four sets of underlying practices (first-order factors).
Further on, to the best of our knowledge, this is the first empirically-based study that aims to
empirically investigate the contribution of demand planning, as a higher-order construct, to
mitigating disruptions induced by operational risks in supply chains. Finally, through our
research we empirically assess the significance of contextual factors that may determine the
effectiveness of demand planning in mitigating the strength of disruptions driven by
operational risks in supply chains.
2. Literature review
Recent studies increasingly emphasize the dynamic nature of capabilities possessed by
supply chains. The concept of dynamic capabilities extends the resource-based view (RBV) to
the dimension of how well an organization can change over time (Grimm, 2008). Hence, from
the supply chain perspective, the dynamic capabilities can be depicted as unique sets of
inter-organizational routines, processes, relationships and special skills derived from
exchanges of information and knowledge among supply chain partners (Priem and Swink,
2012). Eisenhardt and Martin (2000) highlight that although the dynamic capabilities are
often described in vague terms, they actually consist of identifiable and specific routines.
Nonetheless, these routines have been often investigated outside the RBV research strand.
One of such routines is demand planning, a high-level capability that confers upon an
organizations management a set of decision options for producing significant outputs
(Winter, 2000). Given the dynamic nature of todays global business environment, the ability
to assess the environment and change rapidly is key for demand planning. Accordingly, the
insights from dynamic capability theory are also very applicable to demand planning. In this
study, we consider demand planning as a dynamic routine involving a structured and
measured set of practices, i.e. goal setting, data gathering, demand forecasting,
communicating the demand predictions and synchronizing supply with demand (Croxton
et al., 2008;Croxton et al., 2001;Bindra, 2014;Cassettari et al., 2017), designed to yield a specific
output for a particular customer and market (Davenport, 1993). The overarching aim of
demand planning is to keep the balance between supply and demand in a supply chain
(Metcalfe, 2012;Raza and Kilbourn, 2017). Nonetheless, from the perspective of demand
Investigating
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