Detecting disturbances in supply chains: the case of capacity constraints

DOIhttps://doi.org/10.1108/IJLM-12-2015-0223
Date08 May 2017
Published date08 May 2017
Pages398-416
AuthorVinaya Shukla,Mohamed Naim
Subject MatterManagement science & operations,Logistics
Detecting disturbances in
supply chains: the case of
capacity constraints
Vinaya Shukla
Department of International Management and Innovation,
Middlesex University, London, UK, and
Mohamed Naim
Department of Logistics and Operations Management, Cardiff Business School,
Cardiff University, Cardiff, UK
Abstract
Purpose The ability to detect disturbances quickly as they arise in a supply chain helps to manage them
efficiently and effectively. The purpose of this paper is to demonstrate the feasibility of automatically and
therefore quickly detecting a specific disturbance, which is constrained capacity at a supply chain echelon.
Design/methodology/approach Different supply chain echelons of a simulated four echelon supply
chain were individually capacity constrained to assess their impacts on the profiles of system variables, and
to develop a signature that related the profiles to the echelon location of the capacity constraint. A review of
disturbance detection techniques across various domains formed the basis for considering the signature-
based technique.
Findings The signature for detecting a capacity constrained echelon was found to be based on cluster
profiles of shipping and net inventory variables for that echelon as well as other echelons in a supply chain,
where the variables are represented as spectra.
Originality/value Detection of disturbances in a supply chain including that of constrained capacity at an
echelon has seen limited research where this study makes a contribution.
Keywords Supply chain risk, Clustering, Disturbance detection, Capacity constraint
Paper type Research paper
1. Introduction
Supply chains are vulnerable to disturbances. Described as unplanned and unanticipated
events that disrupt the normal flow of goods and materials in a supply chain and expose
associated firms to operational and financial risks (Craighead et al., 2007), disturbances
could be catastrophic such as earthquakes, fires, hurricanes and terrorist attacks, or
recurring, such as delays in material deliveries, process losses and inappropriate
information processing (Chopra and Sodhi, 2004). Together with risks which can be
characterised as potential occurrence of disturbances, disturbances in supply chains have
been a subject of intense research in recent times ( Juttner, 2005; Wagner and Bode, 2008;
Thun and Hoenig, 2011; Manuj et al., 2014). The important area of disturbance detection
however, appears to have been overlooked.
Detecting a disturbance, and doing so early/quickly, enables it to be more efficiently
and effectively mitigated as the greater reaction time causes more mitigation options to be
available. As an example, consider loss of production capacity at a supplier which could
delay incoming supplies at a production facility. An early detection of this disturbance
could mean mitigation options such as using alternative suppliers or rescheduling
production to produce alternative products or preparing customers for late delivery being
available; a delayed detection on the other hand could cause most or all of these options to
be unavailable resulting in idling of resources and/or delayed deliveries to end customers
with associated penalties, and thereby higher economic consequences (Bodendorf and
Zimmermann, 2005).
The International Journal of
Logistics Management
Vol. 28 No. 2, 2017
pp. 398-416
© Emerald PublishingLimited
0957-4093
DOI 10.1108/IJLM-12-2015-0223
Received 5 December 2015
Revised 12 March 2016
17 April 2016
Accepted 17 April 2016
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0957-4093.htm
398
IJLM
28,2
Though the advantages of early supply chain disturbance detection are well recognised
(Craighead et al., 2007; Blackhurst et al., 2011), actual interest in this subject as per the extant
literature has been limited. In fact, disturbance detection itself is discussed in only a few
studies; the discussion is also largely conceptual with the effectiveness of the detection
approaches discussed also being questionable. For example, the practice of physically
detecting and communicating disturbances as discussed in Svensson (2000) and Norrman
and Jansson (2004) is mostly between adjacent and not multiple echelons across supply
chains, and whose subjective nature leaves scope for errors in classifying a disturbance as
such or disturbances being not reported/misreported.
Other more objective and automated approaches such as track and trace that are used in
logistics contexts also have limitations, such as disturbances of only a specific kind like
delivery delays being detectable, the nature of detection being transactional, i.e. lacking in
intelligence, and the operational scope of detection being limited to a few echelons
(Karkkainen et al., 2004). Similarly, supply chain event management systems, which are an
adaptation of track and trace for supply chain contexts and detect discrepancies in ordering
and fulfilment processes are also transactional in nature and lack multi-echelon analysis
ability, i.e. the ability to consider the impact of disturbances in a cascaded structure
(Otto, 2003). A need therefore exists for:
(1) an information-centric disturbance detection approach (to avoid subjectivity/bias in
detection), which uses information from multiple echelons (to enable a supply
chain wide detection perspective);
(2) with the information being automatically processed through the use of protocols
(to enable speed, scale and intelligence in detection); and
(3) which could be applied for a variety of disturbances.
Such an approach has been successfully applied in other domains (Davenport and
Harris, 2005) but not pursued for supply chain disturbances despite calls to that effect
(Christopher and Lee, 2004; Blackhurst et al., 2005). Demonstrating the feasibility of this
approach and suggesting a framework for its practical use forms the focus of the
exploratory research that we present here. It is aimed at developing a protocol based on
system state information, i.e. time series data of variables such as orders, inventory and
shipping for automatic, and therefore quick, detection of disturbances in supply chains,
with the focus being on a specific disturbance, the capacity constraining disturbance.
In theoretical terms, the research contribution can be expla ined through sys tems theory
(Bertalanffy, 1951) which argues for systems to be treated holistically rather than in terms
of its constituent parts. Though this theory has been widely used in generic supply chain
work due to the interconnected nature (involving material and information flows) of
supply chain entities (Sterman, 2000; Frankel et al., 2008), here it is being used in the
context of supply chain disturbance detection and management which is not seen
previously. Specifically, negentropy or tendency of systems to reduce entropy, one of the
key aspects of systems theory is relevant here, with a disturbedsystem leaving a trail as
it transitions to a more stable/ordered state over time.
The rest of the paper is structured as follows. In the next section we highlight the
need for capacity constraint detection in supply chains. In Section 3, we explore
techniques being used in different domains to detect disturbances so that related
learnings could be applied here. In Section 4, we discuss findings from simulationof a four
echelon supply chain system incl. evolution of a protocol for detecting capacity
constraint at an echelon. Finally, the practical aspects of applying the protocol based
quick disturbance detection approach are discussed in Section 5, which is also the
concluding section.
399
Detecting
disturbances in
supply chains

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