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2700+ words 18+ references.2 tables (attachment table 1 and table 2) and 1 structure fig (See reference 1 fig. 1) required, words in tables are counted in total words.More details see attachment.




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BST835 Risk Management in Supply Chains
Academic Year 2019-2020 (Spring Semester)
Essay Title (Choose a title that reflects your assignment)
Nowadays, managing supply chains in a competitive, high uncertainty and turbulent market is
very challenging. Hence, one of the biggest challenges in supply chains today is managing and
mitigating the risks that are inherent in every business situation. Supply chain risk management
(SCRM) consists of three main tasks: 1) risk identification 2) risk analysis 3) responses to risks.
Companies need to know and understand the category of the risks as well as the condition that
drives the risks, prioritise them based on importance and then propose alternative responses to
deal with them. In this coursework, after selecting a supply chain of the product of your interest,
you are asked i) to create a risk register for a supply chain of the product; ii) to suggest strategies
towards a robust and resilient supply chain and iii) to explore the potential of a supply chain
that may benefit from supply chain uncertainty. This coursework includes four parts. Once you
selected a product, you need to write an essay that contains the following parts:
Part 1: Supply network
Sketch and describe the supply network for the product of your choice. It must include a focus
organisation, downstream and upstream including at least 1-tier supplier and 1-tier customer.
Moreover, you need to describe the environment in which your supply chain is operating.
Focus organisation
Supply chain
Figure 1. an illustration of supply network
Part 2: Risk register development.
For the supply chain described in part one, you need to create a risk register which includes
risk identification, risk analysis and risk response strategies. The focus of this section is on
risks that cause damages and loss to the supply chain.
a) In the first phase of the risk register, you need to create a list of potential risky events
for the selected supply chain. It should include 2 events related to the focus
organisation, 4 events related to the supply chain and 4 events related to the
environment surrounding it.
b) The second phase is to calculate and analyse all the collected data from a, the main
point of this part is to obtain a Risk Priority Score (RPS)for each risk using the
probability of occurrence of a risk event and the impact of a risk event.
c) Once the risk priority score is obtained for each risk, the probability-impact matrix can
be developed. From the matrix, the risk profiling and its mitigation will be developed.
Part 3: Supply chain resilience
In section 2, you propose alternatives to deal with each risk identified. In parts 3, you need to
recommend two strategies that selected supply network can implement to improve its resilience
and consequently make it more robust1. Critically discuss the recommended strategies and how
they might improve the resilience in your selected supply chain.
Part 4: Events that benefits supply chain
Risky events may have both upside(benefits) and downside(harm) in supply chains. There are
many events that harm the supply chain, however it may sometimes benefit from disorder,
volatility or uncertainty. Identify one upside risk for the selected supply chain and critically
discuss how it might benefit your supply chain.
In completing Part 1-4, you need to provide supporting arguments for your choices of
risky events, their probability and consequences and responding strategies. Supporting
argument may come from technical report and /or research articles.
———————————————————————————————————–The essay should be NO MORE THAN 3,000 WORDS IN LENGTH including tables and
figures. References are excluded from world count. all sources should be acknowledged in the
appropriate place in the text. You are advised to use the Harvard referencing system.
Note: You are also advised to attach a cover sheet containing: the module code, module title,
lecturer’s name, scheme of study, student’s name and student number.
Ensure all sources of information are referenced correctly using the Cardiff Harvard Style of
Referencing – if unsure see the handout from the library.
Unfair Practice
This is an individual assignment and you are advised not to engage in any activity that might
lead to suspicions of Unfair Practice.
On the front page of the assignment you should include:
§ Name
§ Student number
Something that does not benefit or harm from disorder.
§ Title of coursework
§ Title of Module and module number
§ Name of lecturer
§ Date of submission
§ Word count
Students are advised to keep a second copy for themselves. Should there be special
circumstances that mean you are unable to meet the submission deadline, you must obtain an
extension from the Chair of the Board of Examiners. Forms are available from room A-04 or
Learning Central. If you are not in Cardiff then contact your Personal Tutor.
Coursework marking-criteria
For 90%+
An outstanding piece of work, showing mastery of the subject matter, with a highly developed ability to analyse,
synthesise and apply knowledge and concepts. All objectives of the assignment are covered and the work is free
of error with very high level of technical competence. There is evidence of critical reflection; and the work
demonstrates originality of thought, and the ability to tackle questions and issues not previously
encountered. Ideas are expressed with fluency. All coursework requirements are met and exceeded.
For 70% – 89%
An excellent piece of work, showing a high degree of mastery of the subject matter, with a well-developed ability
to analyse, synthesise and apply knowledge and concepts. All major objectives of the set work are covered, and
work is free of all but very minor errors, with a high level of technical competence. There is evidence of critical
reflection, and of ability to tackle questions and issues not previously encountered. Ideas are expressed clearly.
However the originality required for a 90+ mark is absent. All coursework requirements are met and some are
For 60%-69%
A very good piece of work, showing a sound and thorough grasp of the subject-matter, though lacking the breadth
and depth required for a first class mark. A good attempt at analysis, synthesis and application of knowledge and
concepts, but more limited in scope than that required for a mark of 70+. Most objectives of the work set are
covered. Work is generally technically competent, but there may be a few gaps leading to some errors. Some
evidence of critical reflection, and the ability to make a reasonable attempt at tackling questions and issues not
previously encountered. Ideas are generally expressed with clarity, with some minor exceptions. All coursework
requirements are addressed adequately.
For 50%-59%
A fair piece of work, showing grasp of major elements of the subject-matter but possibly with some gaps or areas
of confusion. Only the basic requirements of the work are covered. The attempt at analysis, synthesis and
application of knowledge and concepts is superficial, with a heavy reliance on course materials. Work may
contain some errors, and technical competence is at a routine level only. Ability to tackle questions and issues not
previously encountered is limited. Little critical reflection. Some confusion and immaturity in expression of
ideas. Most coursework requirements are addressed.
For 40%-49%
A poor piece of work, showing some familiarity with the subject matter, but with major gaps and serious
misconceptions. Only some of the basic requirements of the work set are achieved. Little or no attempt at analysis,
synthesis or application of knowledge, and a low level of technical competence, with many errors. Difficulty in
beginning to address questions and issues not previously encountered. Some intended learning outcomes are
For 30%-39%
Work not of passable standard, with serious gaps in knowledge of the subject matter, and many areas of confusion.
Few or none of the basic requirements of the work set are achieved, and there is an inability to apply knowledge.
Technical competence is poor, with many serious errors. The student finds it difficult to begin to address questions
and issues not previously encountered. The level of expression and structure is very inadequate. Few intended
learning outcomes are achieved.
Below 30%
A very poor piece of work, showing that the student has failed to engage seriously with any of the subject matter
involved, and/or demonstrates total confusion over the requirements of the work set. Virtually none of the
intended learning outcomes are achieved.
Marking Grid
Complete and
developed and
Minor gaps
Good overall
Well developed
Minor gaps
Fair overall
Tackles new
applications with
Tackles most new
applications with
Makes a good
attempt at new
Makes some attempt
to tackle new
Some major
Partial grasp
Some evidence
of ability
Makes little attempt
to tackle new
Many major
Very little grasp
Little or no
Incapable of tackling
new applications
ability (where
mastery of all
High level of
Sound technical
with few gaps
Competent in
techniques only
Critical Ability
Expression of
Fluent and well
Clear and well
Generally well
Clear and well
Little or none
Low level of
Very low level
of technical
with many
major errors
Some lack of
clarity and
immaturity of
with some
Very confused
and lacking in
Computers in Industry 61 (2010) 250–259
Contents lists available at ScienceDirect
Computers in Industry
journal homepage:
Risk assessment and management for supply chain networks: A case study
Gonca Tuncel a,*, Gülgün Alpan b
Dokuz Eylül University, Department of Industrial Engineering, 35160, Buca – Izmir, Turkey
Laboratoire G-SCOP, CNRS, INPG, UJF, 46 avenue Félix Viallet, F38031, Grenoble, France
Article history:
Received 8 January 2009
Received in revised form 15 September 2009
Accepted 18 September 2009
Available online 7 November 2009
The aim of this study is to show how a timed Petri nets framework can be used to model and analyze a
supply chain (SC) network which is subject to various risks. The method is illustrated by an industrial
case study. We first investigate the disruption factors of the SC network by a failure mode, effects and
criticality analysis (FMECA) technique. We then integrate the risk management procedures into design,
planning, and performance evaluation process of supply chain networks through Petri net (PN) based
simulation. The developed PN model provides an efficient environment for defining uncertainties in the
system and evaluating the added value of the risk mitigation actions. The findings of the case study
shows that the system performance can be improved using risk management actions and the overall
system costs can be reduced by mitigation scenarios.
ß 2009 Elsevier B.V. All rights reserved.
Supply chain management
Risk management
High-level Petri nets
Performance evaluation
1. Introduction
Supply chain management (SCM) is defined as a set of methods
used to interconnect suppliers, manufacturers, warehouses and
clients so that the merchandise is produced and distributed at the
right quantities, to the right places at the right time with the
objective of minimizing global system costs and maximizing the
customer service levels [1]. There exist numerous quantitative
methods for the SCM [2]. The majority of these methods can be
regrouped into two classes: methods based on the discrete event
simulation and the methods based on the mathematical programming techniques. SCM based on discrete event simulation
generally deals with the tactical and operational level decisions
such as inventory control, material handling, layout design, and
vehicle routing/scheduling, while the mathematical programming
techniques are mostly used for long-term strategic decisionmaking. In today’s global marketplace, the supply chain networks
have become more complex with increased uncertainty [3]. The
literature on SCM is plentiful, however the proposed methods have
some limitations to handle the degree of complexity inherent to
real supply chain networks. In particular, most of the existing
models can only describe a restricted class of supply chains with
simplifications [4].
Petri nets (PN) are well-defined graphical technique for
specification and design of discrete event dynamic systems. They
offer a solid mathematical foundation for the analysis of the
* Corresponding author. Tel.: +90 232 412 76 17; fax: +90 232 412 76 08.
E-mail addresses: (G. Tuncel),
(G. Alpan).
0166-3615/$ – see front matter ß 2009 Elsevier B.V. All rights reserved.
dynamic behavior of complex systems. Concurrent and asynchronous activities, multilayer resource sharing, routing flexibility,
limited buffers, and precedence constraints can be explicitly and
concisely modeled by PN. Several extensions to the basic PN
formalisms allow modeling the notion of time (e.g. time Petri nets),
stochastic behavior of the system (e.g. stochastic Petri nets),
complex structured data and algebraic expressions to annotate net
elements (e.g. high-level Petri nets such as colored Petri nets) (for
further reading see [5–10]).
Although the first applications of PN mostly focused on
computer systems and communication protocols, they have later
been extended and applied to a broader range of problems
including the modeling, analysis, and control of production
systems. Supply chain networks can be seen as a network of
several production systems. Hence extension of the utilization of
PN for the modeling and analysis of supply chains seems rather
natural. However, the literature of PN applications in the area of
SCM is rather rare. We will present here the most recent
applications which are reported in the last decade. In one of the
earlier studies which applied PN to SCM, Viswanadham and
Raghavan [11] employed generalized stochastic Petri nets (GSPN)
to describe dynamic behavior of supply chain networks. The
authors compared two different production planning and control
policies with respect to the total cost comprising of the inventory
carrying cost and delay cost. Van der Vorst et al. [12] considered SC
as a business process, and used colored Petri nets (CPN) to model
and evaluate alternative design scenarios through PN based
simulation. The proposed methodology was illustrated with a
case study in food industry. In Dong and Chen’s study [13], a
modular approach based on object-oriented predicate/transition
nets (OPTN) and computer integrated manufacturing open system
G. Tuncel, G. Alpan / Computers in Industry 61 (2010) 250–259
architecture (CIMOSA) based process behavior rules is used to
model and analyze the routing structures of a typical supply chain
process. The authors used p-invariants through system objects to
verify structural properties of the network such as deadlock
freeness and boundedness. Finally, the sequences of the operations
in supply chain process are analyzed by PN unfolding techniques. A
process management approach for SC based on XML-nets is
introduced by Von Mevius and Pibernik [14]. The advantages of the
proposed methodology is the capability of modeling supply chain
objects and the exchange of intra- and inter-organizational data in
a hierarchical structure. Demand fluctuations in SC, so-called the
Bullwhip Effect, was analyzed in PN based modeling framework in
Makajic-Nikolic et al. [15]. Supply chain modeling using CPN tools
and a beer game implementation is presented based on
hierarchical colored PN. Chen et al. [16] developed a new PN
model for modeling and performance evaluation of SC by
enhancing deterministic and stochastic Petri nets (DSPN) with
batch places and batch tokens. Structural analysis is performed
based on state equations and reachability analysis techniques.
Performance evaluation of an industrial SC which is composed of
three suppliers, three transporters for the suppliers, a manufacturer, and a transporter for the customer is also presented in this
study. Recently, Liu et al. [17] proposed a new approach for supply
chain event management (SCEM) through dependency graph
analysis and event rules. The authors presented seven basic
modeling patterns that arise commonly in SC and they used CPN
tools for the implementation of the proposed methodology under
different strategies.
The studies reviewed above demonstrate the advantages of PN
for SC modeling and analysis via state space representation and
mathematical foundation of PN theory. However, they do not
discuss the effects of disruptive factors on the supply chain
networks. We will refer to these disruptive factors as the risks in
the supply chain. In modern SC networks, in order to increase their
competitive edge, the firms employ new strategies such as recentring their activities by outsourcing some part of their
production, proposing increased diversity of products to capture
the market share and so on. Even though efficient in a stable
environment, these strategies augment the vulnerabilities of the
firms in an uncertain environment, thus resulting in operational
risks to take into account. Risk management in supply chain is a
rather new topic. In the literature, supply chain management,
performance analysis, and risk management (RM) have generally
been considered separately. The risks are mostly addressed from
the financial or economical perspective [18]. Although some recent
literature handles the risk management from the logistics point of
view, these studies often look at the single organizations’
vulnerabilities [19] and often focus on a single point of view such
as supply, demand, product or information management [20].
Supply chain management without considering risk issues in a
systemic perspective and their impact on the performance
measures eventually lead to suboptimal results and inconsistent
The main purpose of this study is to extend the utilization of
Petri nets for the risk management and real-time control of supply
chain networks. For this purpose, we propose a Petri net based
modeling framework to model the supply chain network and to
analyze the effects of various risks and mitigation actions on the
overall system’s performance. The advantages of PN based model is
twofold: first of all, this model provides us a computerized support
for decision-making through tracking of material and information
flow in the SC network by maintaining current status information
of the entire system, and properly generating the required data.
Hence, it can be used in the decision-making process of the SC
under the influence of disruptive factors. Secondly, unlike the
previous work reported [20], the PN model permits an easy
interface to model and analyze several disruptions (disruptions in
demand, transportation, quality, etc.) at the same time on the same
model. Another originality of this work is that we propose to
couple our Petri net model with a widely used risk analysis method
by product design and …
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