tulinayo fiona penlope cit masters report
TRANSCRIPT
A SYSTEM DYNAMICS MODEL FORSUPPLY CHAIN MANAGEMENT IN ARESOURCE CONSTRAINED SETTING
By
Tulinayo Fiona Penlope
2005/HD18/3620U
BIFA (Hons) Mak, PGDcs (Mak)
[email protected]/+256-772-304702
A Dissertation Submitted to the School of Graduate Studies
in Partial Fulfillment for the Award of Master of Science inComputer Science of Makerere University
OPTION : Computer Information Systems
April, 2007
Declaration
I Tulinayo Fiona Penlope do hereby declare that this Dissertation Report is original and hasnot been published and/or submitted for any other degree award to any other Universitybefore.
Signed.............................. Date......................................
Tulinayo Fiona PenlopeBIFA, PGD (Computer Science)Department of Information SystemsFaculty of Computing and Information Technology Makerere University
APPROVAL:
This Dissertation Report has been submitted for Examination with the approval of thefollowing supervisor.
Signed.............................. Date......................................
Dr. Ddembe Williams, PhdDepartment of Information SystemsFaculty of Computing and Information Technology Makerere University
Dedication
I dedicate this book to my Dear Parents, Sisters and Brothers.
ii
Acknowledgement
First of all I thank the Lord almighty for his endless love, care and blessings.
Secondly my parents, sisters and friends thats; Maureen, Sandra, Teddy, Mariam, Deogra-
tious, Noah, Rebecca and Jackie for there continuous love and support.
Special Thanks go to my supervisor Dr. Ddembe Williams who has been a key player in the
completion of this research I thank him for the support, time and efforts that he has put
into this Dissertation.
Lastly I thank the staffs of the faculty of computing and information Technology especially
the research office and Mrs. Agnes Rwashana Semwanga who have helped in one way or
another to accomplish this research.
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Contents
Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
List Of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Definition of key theoretical terms . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Sources of problems in Supply Chain Modeling: . . . . . . . . . . . . . . . . 6
1.4 Statement of the Problem: . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Aim and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5.1 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5.2 Specific Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6 Study scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.7 Significance of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Literature Review 12
2.1 State of the art in Supply Chain Management . . . . . . . . . . . . . . . . . 12
2.2 Modeling in Supply Chain Management . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Process Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
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2.2.2 Statistical Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.3 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.4 Discrete-Event Modeling . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.5 Mathematical Modeling . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.6 System Dynamics Modeling . . . . . . . . . . . . . . . . . . . . . . . 20
2.3 State of Practice in Supply Chain Management (Case Studies of SCM appli-
cation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3.1 The Beer game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.2 The Newspaper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4 Problems in SCM for constrained settings . . . . . . . . . . . . . . . . . . . 26
2.5 Characteristics of SCM decision support tool . . . . . . . . . . . . . . . . . . 27
2.5.1 Decision variables In supply Chain Modeling . . . . . . . . . . . . . . 28
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3 Methodology 30
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2 Field Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4 System dynamics modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.5 System Dynamics Model Building . . . . . . . . . . . . . . . . . . . . . . . . 32
3.6 Simulation Experiments: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.7 Evaluation and validation: . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4 Field Study 36
4.1 An illustrative real-world case study . . . . . . . . . . . . . . . . . . . . . . . 36
4.1.1 A Newspaper supply chain . . . . . . . . . . . . . . . . . . . . . . . . 36
4.2 The Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
v
4.2.1 Causal loop Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2.2 Stocks and Flows Analysis . . . . . . . . . . . . . . . . . . . . . . . . 42
4.3 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.4 Modeling Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.5 The interface Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.6 Simulation Output One . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.7 Simulation Output Two . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5 DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS 54
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.2.1 The supply chain market . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.3 The Simulation Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.4 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.6 Further Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
REFERENCES 58
APPENDICES 70
Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
APPENDICES 72
Appendix B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
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List of Figures
1.1 Dynamic Hypothesis for the Proposed SCM Model . . . . . . . . . . . . . . 8
2.1 The Forrester Supply Chain. . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2 The Beer Distribution Game. . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1 System Dynamics Modeling Process . . . . . . . . . . . . . . . . . . . . . . 32
3.2 Dynamic Synthesis Methodology Research Design. . . . . . . . . . . . . . . . 34
4.1 Causal loop diagram for the System Dynamics Supply Chain Model in resource
costrained settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.2 Stock and Flow Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.3 Tests of Between-Subjects Effects . . . . . . . . . . . . . . . . . . . . . . . . 44
4.4 Parameter Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.5 Time of the year Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.6 Newspaper Pairwise Comparisons . . . . . . . . . . . . . . . . . . . . . . . . 45
4.7 Time of the year Univeriate Tests . . . . . . . . . . . . . . . . . . . . . . . . 46
4.8 Newspaper Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.9 Newspaper Univeriate Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.10 Time of the year * Newspapers . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.11 Pairwise Comparisons between Newspapers and Time of the year . . . . . . 48
4.12 Estimated marginal means of measure . . . . . . . . . . . . . . . . . . . . . 49
4.13 User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
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4.14 Simulation Output: Sales, Orders, Disposal rate and Production rate . . . . 51
4.15 Simulation Output: Orders, Processed orders, Factory Orders and Inventory 52
5.1 Distribution code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5.2 Factory and market code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
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List Of Acronyms
ANN Artificial neural networks
B2B Business to Business
CW Warehouse
DC Distribution Centre
GPMS Global Production Management System
NN Neural Networks
OR Operations research
SC Supply Chain
SCM Supply Chain Management
SD System Dynamics
UNA United Nations Assembly
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Abstract
A case study approach at a media company (New Vision) in Uganda was used, where data
was collected and analyzed to give a better understanding of the problem under discussion.
The key research issue is to develop a system dynamic model for supply chain management
in a resource constrained setting. this will be used as a decision support tool for evaluation
and understanding of the problem that arise in a resource-constrained setting, to critically
identify the current practices in supply chain management and factors that affect supply
chains.
This research develops a new conceptual framework for supply chain management strategies
in resource-constrained settings and tested based on empirical evidence. The new schemes
proposed here provide a model to define and represent supply chain strategies, a contingency
model to support managers in designing supply chain strategies, and some hints for further
research. The articulation of supply networks, as an extension of supply chains, seeks to
accommodate and explain the commercial complexity associated with the creation and de-
livery of goods and services from the factory to their destination thats the customer.
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Chapter 1
Introduction
Supply chain management is a critically significant strategy that enterprises depend on in
meeting the challenges of today’s highly competitive and dynamic business environments.
An important aspect of supply chain management is how enterprises can detect the supply
chain behavioral changes due to endogenous and/or exogenous influences and to predict such
changes and their impacts in the short and long-term horizons (Luis et al.) [72].
The use of system dynamics (SD) simulation in supply chain management (SCM) started
with Jay Forrester’s Industrial Dynamics (Forrester, 1961) [41]. Forrester described a production-
distribution system that consisted of the flows of information, materials, orders, money,
manpower, and capital equipment across a supply chain (SC)(Luis et al.) [72].
1.1 Background
Supply Chain Management (SCM) represents crossroads where many academic disciplines
have converged. Interest in the field has steadily increased since the 1980s when the ben-
efits of collaborative, rather than adversarial, working relationships within and beyond the
organization were first identified (Ford 1980) [37]. Since then, a multitude of definitions
have been proposed concerning the concept of ”the supply chain”. These definitions can
be categorized as focused on either the internal supply chain, concerned with managing the
conversion process between departments of a single organization or the externalization of re-
lationships with customers and suppliers by the enterprise (Ellram and Cooper, 1993) [32];
(Porter, 1985) [92].
1
Ellram and Cooper (1993) [32] suggest that SCM is ”an integrating philosophy to manage
the total flow of a distribution channel from supplier to ultimate customer”. From the
various definitions proposed, it is possible to summarize that the concept of supply-chain
management is centered on organizational restructuring and extends to the development of
a company-wide collaborative culture but also embraces a strong sense of the integration of
all activities which control the timing and synchronization of material flows (Nick and Peter,
1997) [85].
SCM aims at integrating activities of an entire set of organizations from procurement of
material and product components to delivery of completed products to the final customer
which leads to improvements in channel performance among all channel members and not
solely with in one company (Kotzab 1999) [62] and is characterized by highly competitive
interactions among a large number of entities trying to achieve a multiplicity of objectives.
Consequently, a key factor in the success and viability of a trading agent is to be able to reason
about adversary agent strategies and adapt its own behavior. Thus, isolated evaluation of
agent designs and market protocols are limiting.
The central concept of system Dynamics is to understand how the system structure influence
emergent properties. The objects and people in a system interact through feedback loops,
where a change in one variable affects a change in other variables over time, which in turn
affects the original variable. System Dynamics as a method is important in identifying the
main feedback loops relevant to the problem interest. The key iterative procedures of this
method are: identify a problem, develop a dynamic hypothesis explaining the cause of the
system at the root of the problem, test the model to be certain that it reproduces the behavior
seen in the real situation and devise test in the model alternative policies that alleviate the
problem.
SD over time has developed as a method for modeling the behavior of complex social-
economic systems (Forrester 1961) [41], and it can enhance understanding the nature of an
organization’s soft, strategic issues (Brans et al 1998) [11]. The stock and flow notation
used in SD can be applied to build detailed conceptual models and facilitate identification
of information needs at different levels of managerial activity. System dynamics models are
used to redesign system structure and decision policies, which can then be implemented
2
(Flood and Jackson, 1993) [36].
Throughout history, new ideas and technologies have revolutionized supply chains and
changed the way we work. Since early 1970s, Japanese manufacturers like Toyota changed
the rules of production from mass to lean. Lean manufacturing focuses on flexibility and
quality more than on efficiency and quantity. Significant lean manufacturing ideas include
six-sigma quality control, just-in-time inventory and total quality management (Siems, 2005)
[99].
Today, businesses are improving their supply chains through better information engineering.
Since about 1995 around the time of the commercial application of the Internet there has
been a mass customization era. Now, manufacturers can mass-produce customized prod-
ucts. Firms are effectively using new information technologies like the Internet and wireless
telecommunications to improve service and delivery processes. Through secure intranet
systems and business-to-business (B2B) e-commerce platforms, the focus is on improving
information management: integrating internal systems with external partners like Amazon’s
practice of giving customers the ability to track the delivery location of their purchases
through Amazon’s website (Siems, 2005) [99].
1.2 Definition of key theoretical terms
To position this dissertation, this section defines key theoretical terms that have been used
including: Supply chain management, system dynamics modeling and constrained settings.
A Model: A model is an abstraction of a representation of a real or conceptual complex
system. A model is designed to display significant features and characteristics of the system
which one wishes to study, predict, modify or control (Law and Kelton, 2000) [66].Thus a
model includes some, but not all, aspects of the system being modeled. A model is valuable
to the extent that it provides useful insights, predictions and answers to the questions like
What if ? analysis it is used to address (Williams, 2003) [119].
System Dynamics Modeling: is the technique of constructing and running a model of
an abstract system in order to study its behavior without disrupting the environment of
3
the real system (Williams, 2004) [120]. The development of system dynamics models is an
iterative process. (Luna et al) [73] Understandings of the model and understandings of the
problem and the system are the key products that a system dynamics modeling effort should
accomplish (Richardson and Pugh, 1981) [93].
System dynamics model development is a system stage process that begins and ends with
understanding. (Richardson and Pugh, 1981) [93] identifies six key system dynamics model
building processes key and these include; (1) problem identification and definition, (2) sys-
tem conceptualization, (3) model formulation, (4) model testing and evaluation, (5) model
use, implementation and dissemination, and (6) design of learning strategy/infrastructure.
Simulations are generally employed when the complexity of the system being modelled is be-
yond what static models or other techniques can usefully represent (Kellner et al, 1995) [60].
Supply Chain Management: is an integrative approach to dealing with the planning
and control of materials and information from suppliers to end customers (Monczaka et al.,
1998); [82] (Jones and Riley, 1985) [59]. Research on supply chain management has ranged
from analytical definitions describing supply chains as networks of material processing cells
(Davis, 1993) [26]to investigations of supply chain partnerships (Spekman et al., (1998) [102]
and customer service (Maltz and Maltz, 1998) [74] Supply chain management often refers
either to a process-oriented management approach to sourcing, producing and delivering
goods and services to end consumers or, in a broader meaning, to the co-ordination of the
various actors belonging to the same supply chain (Harland, 1996) [50].
Resource Constrained Settings: represent the poorest and weakest segment of the in-
ternational community. These countries are characterized by their exposure to series of
vulnerabilities and constraints such as limited human institutional and productive capacity
acute susceptibility to external economic shocks, natural and man-made disasters and com-
municable diseases UNA (2001) [112]. Currently many of the central capital accumulation
and budgetary processes in the resource-constrained settings are highly dependent on inter-
national financial assistance. Sustained poverty reduction requires the efficient development
and utilization of productive capacities in a way in which the working-age population be-
4
comes more and more fully and productively employed LDR, (2004) [113]. There is a danger
that the fragile economic and social position which characterizes many of the resource con-
strained settings will deteriorate further unless major efforts are made by these countries,
supported by the international community, to adjust to the challenges of globalization LDR,
(1996) [113].
A System Dynamics Model for Supply Chain Management in a Resource Con-
strained Setting:
is a computer simulation model which is an abstract representation of the Supply Chain
Management in a Resource Constrained setting. Its characterized, by flow, feedback loops,
Stock and delays. It comprises of three main key words thats System Dynamics which
has developed over time as a method for modeling the behavior of complex socio-economic
systems (Forrester, 1961) [41], Supply chain management according to (Hung et al,2004) [54]
is a strategy where business partners jointly commit to work closely together, to bring greater
value to the consumer and/or their customers for the least possible overall supply cost. This
coordination includes that of order generation, order taking and order fulfillment/distribution
of products, services, or information and resource constrained which represents the poorest
and weakest segment of the international community UNA (2001) [112].
A SD model in a resource constrained setting begins with the importation of raw materials
and ends with the final consumer. There is importation of raw materials due to a number
of reasons; financial constraints, lack of expertise in terms of knowledge, insufficient raw
materials etc hence resource constrained.
There are studies in SCM like Forrester Model (Forrester 1961) [41] which encompasses ven-
dors, manufacturers/producers, distributors and retailers is characterized by a stock and flow
structure for the acquisition, storage, and conversion of inputs into outputs and the decision
rules governing these flows. The MIT Beer Game (Sterman, 1989) [106]which represents
a four-echelon supply chain including a retailer, a wholesaler, a distributor and a factory.
These models are not in a resource constrained setting but they give a clear understanding
of SCM in developed countries.
5
1.3 Sources of problems in Supply Chain Modeling:
Supply chain management strives for an optimal functioning of a supply chain. Generally,
optimal is seen as satisfying demand of ultimate consumers while minimizing inventories
at different positions in the chain, keeping order backlogs low (or even not having them at
all) and minimizing costs. In a real-life organizational context managing a supply chain is
difficult due to uncertainties in demand of consumers and the dependency of performance of
one chain member upon the decisions and actions of other supply chain members (Gwenny
et al.2005) [47].
The feedback on the effect of decisions such as ordering and transportation are usually de-
layed or indirect which might lead to the bullwhip effect which can be described as the
phenomenon where the variance in demand for wholesaler, distributor and manufacturer is
larger than for the retailer and amplifies upstream (e.g. Lee et al. (1997) [67] show this
effect in real-life settings (which is replicated by others) and address four operational causes
of the problem: errors in demand signal processing, inventory rationing, order batching, and
price variations. There is a still growing amount of research that further explores, mostly
using an analytical approach, the bullwhip effect and comes up with further analysis of the
problem and ways to solve it.
The optimal functioning of a supply chain seems to be often distorted by specific behavior of
individual decision makers in the chain. One of the distortions is the overestimation of de-
mand, resulting in the bullwhip effect. The bullwhip effect can be illustrated using the beer
game (Beer Distribution Game) originally developed and used at MIT (Sterman, 1989) [106].
Jacobs (2000) [58] developed an Internet version based on the original manual version of the
game that used boards and cards. The Internet version (and the original version) is ideal
for conducting experiments in a supply chain context.
Another aspect that complicates the modeling of supply chains is that they are heavily dis-
tributed. For the management of supply chains, there is not one central organization that
6
has the authority to make decisions on behalf of the entire supply chain. All actors take
their own decisions, based on limited information, as the other organizations do not share
all their data with others. This has a severe effect on the models that are created for sup-
ply chain decision making; many decisions have to be taken based on assumptions of other
organizations information or behavior (Alexander et al. 2005) [2].
On the contrary, Boyson et al. (2004) [10] state that modeling is more important and more
needed than ever before. Because of the nature of supply chain dynamics, managers often
do not have insight into the ripple effects of their decisions. Effects also can easily get lost in
the overwhelming flood of data that crosses the supply chain managers desk daily, weekly,
or monthly. A rapidly changing supply chain with a continuous change of partners leads
to different sets of decisions than a stable chain with long-term contracts (Alexander et al.
2005) [2].
1.4 Statement of the Problem:
The current growth in competition due to globalization of markets means more competition
for companies especially those in developing economies that have limited analytical tools for
business decisions. Market demand uncertainty affects the performance and behaviour of the
planning processes used by production to satisfy sales. This uncertainty and the resulting
instability in the production schedules affects the relationships with the suppliers from whom
the company purchases raw materials and component parts.
A number of Supply Chains have been developed over the years that is; Just-In-Time,
value chains, global sourcing, inventory and total quality management. Of which they have
strength and weakness causing preferences to users who apply them depending on the goods
being produced.In order to solve the problem, this research developed a SCM system dynam-
ics model that helps business managers and researchers in SCM make better and informed
decisions.
7
Figure 1.1: Dynamic Hypothesis for the Proposed SCM Model
Fig. 1.1 represents a dynamic hypothesis of SCM in a resource constrained setting. The
verbal descriptions coincide with the variables of the model. The arrows represent the
relationship between variables. The structure of the SC consists of variables internal and
external factors, perception by stakeholders of product effectiveness which add to the support
for product quality by stakeholders which also adds to improvement of the product and it
then adds to perceived quality of product creating a reinforcing loop as indicated above.
The second loop is a balancing loop, which consists of attractiveness to the firm where price
reduces on the firms attractiveness and perceived quality of the product adds to it, it then
adds to orders which add to factory production which also adds to factory inventory. From
factory inventory to attractiveness to the firm there is a delay and there is an opposite flow.
8
1.5 Aim and Objectives
1.5.1 Aim
The main aim of this research was to develop a system dynamics model for supply chain
management that will be used as a decision support tool for evaluation and understanding
of the problem that arise in a constrained SCM.
1.5.2 Specific Objectives
The specific objectives of this research project were as follows:
1. To critically identify current practices in supply chain management and factors that
affect supply chains.
2. To simulate and design a model for supply chain management and extensions for strate-
gic and risk analysis in a resource constrained setting.
3. To implement a system dynamics model.
4. To validate the developed model using a Case study.
1.6 Study scope
The research concentrated on distribution, transportation-capacity and the efficient ways to
dynamically determine the levels examined using a desk-top simulation tool due to limita-
tions of time, and cost. The project provides procedures that are implemented across all
subsystems over a six-month period.
1.7 Significance of the study
The motivation behind this research is to facilitate the decision-making process in resource
constrained settings for capacity planning of supply chains in such uncertain environments
by studying the long-term behavior of supply chains and to further offer a generic method-
9
ological framework that could address a wider spectrum of strategic SCM related problems.
Most of the standard methodologies for the analysis of supply chains study the steady
state of the system, i.e. they assume that all transient phenomena have been diminished.
This assumption may be valid in several supply chains, where product demand exhibits a
smooth pattern, i.e. demand has a low coefficient of variation (functional items, in (Fisher,
1997) [35]). However, there is an increa singly important family of products with shorter life
cycles and larger demand variability, for which the utilization of the traditional methodolo-
gies may lead to considerable errors (innovative items, in (Fisher, 1997) [35]). While focusing
on the latter, system dynamics (SD) methodology is employed, well known and proven in
strategic decision-making, as the major modeling and analysis tool in this research.
Seppala and Jan 1995 [97] state that a decision maker, who wants to understand the prob-
lem of a supply chain in its entirety and has limited time and resources, will find no models
suitable for his needs. Only spreadsheet, pen and paper are offered. In this research a model
was developed to give decision makers a better understanding of the flow of information,costs
incurred in the whole process on which they will base to examine and visualise the likely
effects to the trend of product prices and where they arise from. Hence, reducing on the
likely future fluctuations and bottlenecks that cause such fluctuations and by so doing they
give the most satisfactory results for better performances.
Each decision maker in this supply chain has a narrow view of the total chain. They see
only their own part of the chain and optimize it as best as they can. This causes problems
of sub-optimization in the whole chain: buffer stocks grow, information flow is delayed and
distorted, and throughput time grows. The supply chain system must be treated as a chain,
not examining every level (production, delivery, etc.) individually Seppala and Jan (1995)
[97].
The potential of system dynamics in evaluating the increasingly challenging market place
with growing field of competitors in resources constrained organizations was demonstrated
in this study and the developed System Dynamics model is the decision making tool that
is employed by supply chain management decision makers to generate an insight into the
10
available SCM alternatives and enhance their understanding before implementation.
This research made a contribution to the literature in terms of bringing together problems
of SCM in resource constrained settings. The resulting model Constitutes new knowledge
about SCM and will help managers and researchers in there decission making hence, cutting
costs, reducing information overload and help researchers evaluate related problems.
11
Chapter 2
Literature Review
2.1 State of the art in Supply Chain Management
The 1990s saw the development of supply management as an emerging academic discipline
Ellram and Carr (1994) [33]; Chen and Paulraj (2004) [15], firms began to see that effective
and efficient supply chain management could yield large direct (cost reduction) and indirect
(improvements in delivery performance, technology acquisition, etc.) improvements for the
firm. The economic environment in the late 1980s and early 1990s also forced firms to be-
come more competitive as markets become more globally competitive. Firms realized that,
in order to be able to compete, they must adopt best practice manufacturing techniques.
These techniques needed to focus on not only the internal production mechanism, but on
aligning what suppliers can deliver with what customers want. This leads to the lean revolu-
tion in the 1990s Womack et al., (1990) [124] and to mass customization and modularization
in the 2000s Pine et al., 1993 [90]; Duray et al., (2000) [30]. The current focus for firms
is on how they can better manage their supply chain and supply networks to reduce cost
and cycle times, and increase innovations and time to market for new products and services
Handfield et al.,(1999) [49].
Competition among companies is becoming keen and no longer between companies and
companies, but supply chains to supply chains Christopher, (1992) [18]. In order to enhance
their competitive edge, companies must continuously strive to seek defensive and offensive
approaches so as to increase their organizational effectiveness and better realization of or-
12
ganizational goals such as enhanced competitiveness, better customer care and increased
profitability. From the 1960s to the 1990s, companies placed their emphasis on customer
loyalty. Later, the focus was shifted to producing high quality products at reasonable cost.
Afterwards, developing a variety of products to meet different needs of customers became a
priority. In the 1990s, companies started discovering the impact of suppliers as of enormous
significance to customers delivering products to customers at the right time, at the right
place, and at the right price has become a new challenge rather than producing only high
quality products (Kwai-Sang Chin et al. (2004) [65]. Based on this evolution, both upstream
firms and downstream firms have to be managed directly or indirectly by companies in order
to satisfy their customers.
There are many definitions of the term ’supply chain’, of which the following is typical Marien
(2000) [77]: ”supply chain is that network of organizations that are involved, through up-
stream and down stream linkages, in the different processes and activities that produce value
in the form of products and services in the hands of the ultimate customer or consumers.”
emphasis is put on the following key characteristics of supply chains: - They are ’networks’,
supply chain linkages are upstream and down stream Upstream, Linkages, Processes, value
and the ultimate customer. This means that supply chain encompasses all organizations
and activities associated with the flow and transformation of goods from the raw materials
stage, through to the end user, as well as the associated information flows. Material and
information flows both up and down the supply chain Kenneth et al (2006) [61].
Supply chain management contains different relationships between suppliers and controls
supplies in the supply chain but does not form company organizations. Saunders (1997) [95],
Anderson et al (1997) [3] suggests that there are seven basic principles in managing a supply
chain. These include:
1. Segmenting the customers according to their demands and providing them with a
tailored set of products and services that will have maximum impact on them.
2. Customizing the logistics network through more robust logistics planning, enabled by
real time decision support tools that can handle flow-through distribution. More time
13
sensitive approaches to managing transportation will result in significant increase in
revenues and return on investment.
3. Listening to signals of market demand and planning the production according to them
helps the organizations to avoid situations like over stocking and out of stock during
peak seasons.
4. Differentiating products closer to the customer avoids product obsolescence and in-
creases the impact on the customers.
5. Sourcing strategically from suppliers who share the common goals improves the supply
chains efficiency as it reduces inventory and gives way to concepts like vendor-managed
inventory.
6. Developing supply chain wide common technology strategy improves interaction be-
tween the supply chain partners.
7. Adopting a common supply chain wide performance measure directs all the supply
chain partners to work towards a common goal and facilitates comparisons across
organizational boundaries.
The chain has to contain elements that guarantee a fast information flow between each of
the member elements. The whole supply chain must also be agile and flexible in order
to compete effectively and to respond quickly to changing customer demands Monczaka et
al. (1998) [82]. Supply Chain Management varies from one enterprise to another Kuglin
(1998) [64].
Lee (2000) [69] Points out that SCM involves the flows of material, information, and fi-
nance in a network consisting of customers, suppliers, manufacturers, and distributors. Co-
ordination and integration of these flows and their correlated activities within and across
companies through improved supply chain relationships to achieve a sustainable competitive
edge are critical for effective SCM. In fact, the SCM approach has been engaged by many
organizations to improve their organizational performance and enhance competitiveness in
the marketplace.
14
Christopher et al (2002) [16] Suggests that Supply chain managers strive to achieve the ide-
als of fully integrated efficient and effective supply chains, capable of creating and sustaining
competitive advantage. This is because there are a number of factors for managers to con-
sider in order to have a successful SCM of which the most important is to maximize sales and
minimize costs in terms of inputs. To this end they must balance downward cost pressures
and the need for efficiency, with effective means to manage the demands of market-driven
service requirements and the known risks of routine supply chain failures. Better manage-
ment and control of internal processes together with more open information flows within
and between organizations can do much to help. However, in a resource constrained setting
there are loopholes with the organization’s supply chain that are due to financial constrains
or lack of Technical knowledge.
In SCM no organization is an island and even the most carefully controlled processes are
still only as good as the links and nodes that support them. All are dependent on efficient
and reliable transportation and communication systems, an obvious point, but one that is
often overlooked Peck et al (2004) [88]. These issues are the subjects of the Centre for
Logistics and Supply Chain Management’s on-going Program of research into supply chain
risk and vulnerability. Modern commercial supply chains are in fact dynamic networks of
interconnected firms and industries Hakansson et al (1989) [48]. A supply chain is only as
strong as its weakest link Kenneth et al (2006) [61] in this statement its made to realize
that non of the subsystems can exist with out the other below or above it this implies that
each has a role to play though in the case of Just In time some processes are eliminated
hence reduction of costs. A network connected and interdependent organizations mutually
and cooperatively work together to control, manage and improve the flow of materials and
Information from suppliers to end user Atken (1998) [6]
2.2 Modeling in Supply Chain Management
Models are generally defined as explicit representations of some portions of reality as per-
ceived by some actor Wegner & Goldin (1999) [115]. Modeling in various forms is essential in
supporting complex human design activities. In the development of information systems, as
well as the re-engineering of work practices, the modeling of business processes or workflows
15
often plays a central part Hjalmarsson & Lind (2004) [52]. Models, particularly those that
offer good insight through visualization and graphs, can help companies to structure and
simplify their complex and dynamic supply chains Alexander et al (2005) [2].
2.2.1 Process Modeling
Process modeling refers to logically capturing and abstracting the systems components, re-
lationships and behavior, with respect to modeling objectives . Process models can be
descriptive, prescriptive, iconic or symbolic (Williams, 2003) [119].
Increasingly, researchers and practitioners are describing business organizations in terms of
processes rather than functional hierarchies. An organization may be viewed as a web of
interrelated processes that are designed to achieve certain organizational goals Wenhong and
Tung (1999) [116]. A business process, according to Davenport and Short (1990) [24], can
be defined as ”a set of related tasks performed to achieve a defined business outcome” and
they classified business processes in terms of three basic elements: Entities, objects and
activities. In studying process models for software engineering, Curtis et al. (1992) [23]
suggested that software process models have often presented software processes from one or
more different perspectives. They identified the four most common perspectives:
1. Functional;
2. Behavioral;
3. organizational; and
4. Informational.
The functional perspective depicts a process in terms of what activities are being performed
and which data (or information) flows are needed to link these activities. The behavioral
perspective represents a process in terms of when activities are being performed and how
they are performed using mechanisms such as feedback loops, iterations and triggers. The
organizational perspective depicts a process in terms of where and by whom activities are
being performed. Finally, the informational perspective represents a process in terms of
16
the entities (documents, data, or products) being produced or manipulated by the process.
These perspectives provide distinct and complementary representations of a software pro-
cess. However, each perspective by itself only gives a partial and incomplete view of the
process Wenhong and Tung (1999) [116].
The various approaches for business process modeling and the tool sets implementing them
have numerous features in common. They all try to capture which business tasks are going
to be automated, where the automating system is going to be deployed, who will use it, and
how it will integrate with other systems. In a nutshell, we find the following typical elements
in a business process modeling language Eriksson et al, (2000) [34]:
1. The organizational model describes the roles and areas of responsibilities within an
organization with respect to the activities of a business process. It presents a more
static view of a process.
2. The control flow describes the order of execution and the dependencies among the
various activities.
3. The data flow describes how the business entities (or artifacts) are manipulated by the
various activities.
4. Use cases describe the context of a business process and its externally visible behavior.
5. Collaboration diagrams can further document how business agents and artifacts work
together to perform a function.
All this information together provides an accurate semiformal specification of the business
process. In particular, the process requirements when an activity executes, how often it will
execute, and under what conditions it will end can usually only be described informally in
the form of use cases or textual descriptions in some natural language.
2.2.2 Statistical Modeling
Statistical analysis is a form of modeling that explicitly recognizes the existence of uncer-
tainty in a set of data. It is conventionally seen as having two possible roles-descriptive and
17
inferential. Descriptive statistics is simply concerned with summarizing the main character-
istics of a data set, particularly highlighting any patterns (and anomalies) that may not be
immediately obvious Mingers (2006) [81]. The implicit philosophy of statistical modeling
is inherently empiricist. That is, it largely restricts itself to analyzing empirically available
quantitative data rather than going beneath the surface to explain the mechanisms that give
rise to empirically observable events Mingers (2000) [80].
2.2.3 Neural Networks
Margarita (2002) [76] states that artificial neural networks are computational paradigms
based on mathematical models that unlike traditional computing have a structure and op-
eration that resembles that of the mammal brain. He futher says that Artificial neural
networks (ANN) or neural networks(NN) for short are also called connectionist systems,
parallel distributed systems or adaptive systems, because they are composed by a series of
interconnected processing elements that operate in parallel. Neural networks are typically
arranged in layers. Each layer in a layered network is an array of processing elements or neu-
rons. Information flows through each element in an input-output manner. In other words,
each element receives an input signal, manipulates it and forwards an output signal to the
other connected elements in the adjacent layer.
Lertpattarapong (2002) [71] points out that Neural network (NN) analysis is used to detect
changes in the SC behavior and map them into the future. NN, with their pattern recognition
capability, are effective mechanisms for that use. This can be very practical, as neural
networks can be encapsulated in a software agent that can in turn use the company’s ERP
records and business intelligence data to perform this task routinely in real-time in an actual
system. Using NN to detect changes in the SC will empower companies to detect any changes
occurring in the business environment that can affect their SC and hence give the company
enough time to adjust its business strategies in order to counteract the impact of these
changes.
Margarita (2002) [76] states that one of the original aims of artificial neural networks (ANN)
was to understand and shape the functional characteristics and computational properties of
the brain when it performs cognitive processes such as sensorial perception, concept catego-
18
rization, concept association and learning. However, today a great deal of effort is focused
on the development of neural networks for applications such as pattern recognition and
classification, data compression and optimization.
2.2.4 Discrete-Event Modeling
Discrete-Event simulation first emerged in the late 1950s and it has grown in popularity
steadily to be now recognized as the most frequently used of the classical Operational Re-
search techniques across a range of industries-manufacturing, travel, finance, health and
beyond Hollocks (2005) [53]. Kreutzer (2005) [63] gives a concise introduction and summary
of discrete event simulation’s foundations.
A discrete event simulation model Banks et al., (1996) [7] is built based on the actual
system.It usually focuses on productivity improvement; for instance, machine and human
resource utilization analysis, bottleneck analysis and throughput improvement.
In the area of discrete event simulation, there is a scarcity of research Leonardo Chwif et al.
(2006) [70] and according to Sevinc (1991) [98] ”No complete theories of model abstraction
exist, nor does any sufficiently general procedure”. While discrete event simulation has
long been a popular technique for studying industrial processes, it is also used widely for
planning e.g. for evaluating design alternatives in a production process; see Law and Kelton
(2000) [66] and Oakshott (1997) [86].
In many dynamic processes, particularly in industrial contexts like manufacturing, trans-
portation and inventory management, system states change at discrete points in time (i.e.
at events), rather than through continuous state fluctuations. In such discrete event simula-
tions it is often desirable or even necessary to treat many model components as individuals,
each with their own properties and processing history Wohlgemuth et al. (2006) [114].
Discrete-Event simulation permits the evaluation of operating performance prior to the im-
plementation of a system: It enables companies to perform powerful what-if analyses leading
them to better planning decisions; it permits the comparison of various operational alterna-
tives without interrupting the real system; it permits time compression so that timely policy
decisions can be made Chang et al. [12].
19
2.2.5 Mathematical Modeling
Neumaier (2003) [5] defines mathematical modeling as the art of translating problems from
an application area into tractable mathematical formulations whose theoretical and numer-
ical analysis provides insight, answers, and guidance useful for the originating application.
McLaughlin (1999) [79] states that a mathematical model is a statistical filter that not only
attempts the separation but also quantifies its success in that effort. To construct a model,
it is necessary to proceed from the known to the unknown or, at the very least, from the
better known to the less well known. McLaughlin (1999) [79] gives two ways to construct a
model and the choice depends upon whether it is the information or the error that is better
known, bearing in mind that ”known” and ”assumed” are not synonyms.
......In the first case, the model is designed that is to utilize the known properties of the
embedded information to extract the latter and leave the error behind. This approach is
commonly employed with stochastic data. Alternatively, if the error is the better known, the
model is designed to operate on the error, filtering it out and leaving the information behind.
This approach is nearly universal with deterministic data. In either case, the separation
will be imperfect and the information still a bit erroneous. The notion of deterministic
information is commonplace and requires no further elaboration
2.2.6 System Dynamics Modeling
System dynamics modeling is the technique of constructing and running a model of an ab-
stract system in order to study its behaviour without disrupting the environment of the real
system Williams, (2004) [120].
Due to the dynamic nature of supply chains, simulation is a natural and important instru-
ment for the analysis and design of supply chains and supply chain management Alexander
et al. (2005) [2]. The simulation models can help to structure, transform, condense and
visually display data in such a way that managers can quickly grasp a situation and act
upon the presented information Boyson et al. (2004) [10].
The application of system dynamics modeling to supply chain management has its roots in
industrial dynamics Forrester (1958, 1961) [39], [41]. System Dynamics is a methodology
for understanding the behavior of complex, dynamic social-technological-economic-political
20
(STEP) systems to show how system structures and the policies used in decision making
govern the behavior of the system. SD focuses on the structure and behavior of systems
composed of interacting feedback loops.
The objective of the SD approach is to capture the dynamic interaction of different system
variables and to analyze their impact on policy decisions over a long-term horizon and to
attain some desired goals through modifications of the system. For this, a system boundary is
defined and a model of the system is built. The systematic procedural steps in SD modeling
are as follows Roberts, (1978) [94]:
1. Define the problems to be solved and goals to be achieved;
2. Describe the system with a causal loop/influence diagram;
3. Formulate the structure of the model (i.e. develop the flow diagram for systematizing
symbols, arrow designator and the format of system dynamic modeling in the form of
Psuedo-code equations);
4. Collect the initial data/base data needed for model operation either from historical data
and/or from discussion with executives/planners who have knowledge and experience
of the system under study - these are the initial value of all the level variables, constants
and policy data;
5. Validate the model on some suitable criteria to establish sufficient confidence in the
model; and use the model to test various policy actions to find the best way to achieve
prescribed goals.
A supply chain being the ”extended enterprise” that encompasses vendors, manufactur-
ers/producers, distributors and retailers is characterized by a stock and flow structure for
the acquisition, storage, and conversion of inputs into outputs and the decision rules gov-
erning these flows Forrester, (1961) [41]; Sterman, (2000) [104]. The flows often create
important feedbacks among the partners of the extended chain, thus making SD a well-
suited modeling and analysis tool for strategic supply chain managementGeorgiadis et al
(2004) [44].
21
Morecroft (1988) [83] emphasized that model conceptualization begins with causal loops and
moves to rate/level flow diagrams and finally to explicit equations capturing the diagram
structure. Thus, the objective of the SD model is to capture the dynamic interaction of
different variables that the system has and to analyze the policy decision over a long-term
time horizon. Causal loop diagramming is an important tool, which helps the modeler to
conceptualize the real world system in terms of feedback loops.
Forrester (1961) [41] expands on his basic model through further and more detailed analysis,
and establishes a link between the use of the model and management education. Figure 2
shows the classic supply chain model that was used by Forrester in his simulation experi-
ments.
There is a downstream flow of material from the factory via the factory warehouse, the dis-
tributor and the retailer to the customer. Orders (information flow) flow upstream, and there
is a delay associated with each echelon in the chain, representing, for instance, the produc-
tion lead-time or delays for administrative tasks such as order processing. Researchers since
have coined the expression of the Forrester Model, which essentially is a simple four-level
supply chain (consisting of factory, a warehouse, a distributor and a retailer).
22
Figure 2.1: The Forrester Supply Chain.Adapted from Forrester (1961) [41]
Using the Forrester Model as an example, Forrester (1961) [41] describes the modeling pro-
cess used in modeling continuous processes, whilst clearly emphasizing the importance of
information feedback to the System Dynamics method. Forrester (1958) [39] disapproves
of the approach taken by operations research (OR) in the 1950.s, where OR methods are
applied to isolated company problems. He suggests that the success of industrial companies
depends on the interaction between the flows of information, materials, orders, money, man-
power, and capital equipment Forrester (1961) [41], and states that the understanding and
control of these flows is the main task of management.The Forrester Model has received much
23
criticism over the years, which has served as a basis for applying and extending Forrester’s
research further. Despite its simplicity, the Forrester Model yielded important insights into
supply chain dynamics.
Demand amplification, a fundamental problem in supply chains, has only recently been
recognize to the full extend of the problem Towill (1996b) [109]. Forrester accidentally
established the ground rules for effective supply chain design, when he showed that medium
period demand amplification was a system dynamics phenomenon that could be tackled by
reducing, eliminating delays and the proper design of feedback loops. Towill (1996b) [109].
In order to manage the supply chain efficiently, a clear understanding of managing dynamics
in the supply chain is of high priority Sterman (1989a, b, 2000) [106], [107], [104]; Towill
(1989) [110], (1997) [111]; Disney et al., (1997) [28]; Lee et al., (1997a, b) [68] [69]. As
dynamics of the supply chain become a matter of great concern, a number of causes of
dynamics of the supply chain have been identified in terms of rational and irrational factors
(Lee et al., (1997a, b) [68], [69]; Sterman, (2000) [105]; Simchi-Levi et al., (2000) [101].
2.3 State of Practice in Supply Chain Management
(Case Studies of SCM application)
There are different practices in supply chain management as discussed: Sterman (1989) [106]
uses the beer distribution game to explore the behaviors of decision makers and demand
distortion. Although there are companies initiatives to mitigate this distortion affect, it was
not until second part of 1990s to give theoretical back round and underlying principles of
demand distortion. Lee (1997a) [68] analyzed the demand amplification along the supply
chain and named this amplification effect as bullwhip effect. Gavirneni et al. (1999) [43]
study the relationships between capacity, inventory, and information, as well as how they are
affected by the retailers policy and end-item demand distribution in a typical serial supply
chain with different information sharing scenarios. Lee et al. (2000) [69] differently study
the value of information sharing when demands are correlated over time in serial supply
chain. Their analysis suggest that the higher the demand correlation over time, the higher
the value of information sharing. Besides the information sharing, there are many studies
attempt to facilitate coordination in supply chain. Researchers use different aspects such
24
as contracts, game theory, hierarchical planning and recently multi-agent approach Ibrahim
and Ratna (2006) [56].
2.3.1 The Beer game
The MIT Beer Game Sterman, (1989) [106] represents a four-echelon supply chain including
a retailer, a wholesaler, a distributor and a factory. A flow of information (orders) goes from
the retailer to the factory and a flow of product returns. The game involves different delays:
two weeks delay for the order to reach the next echelon and two weeks transport delay from
the inventory of an echelon to the next.
Figure 2.2: The Beer Distribution Game.Adapted from Sterman (2000) [103]
The Beer Distribution Game has proven to be an important tool in the spread of System
Dynamics methodology, influencing many researchers and executives Sterman (2000) [105].
Especially for researchers, it provided an important experimental setup for understanding,
analyzing and simulating dynamic systems (Sterman (1989), Senge (1993)) [106], [96]. There
have been alternative application areas of the Beer Distribution Game where it has been used
as a complex problem in artificial intelligence Geyer-Schulz (1998) [45] and as a basis for
different experiments Croson and Donohue (2002) [22].
More recent enhancements include a computerized version of the basic game Simci-Levi,
(2000) [101] and an internet version Jacobs, (2000) [58]. These have made the game more
25
accessible and user friendly, but have done little to extend the learning potential of the game
Sparling, (2002) [25].
2.3.2 The Newspaper
Newspaper production operations are based on real world news, which may occur at odd
times, even well passed the closing edition time, determining how to operate the printing and
distribution of the newspaper is not a trivial task. Standard and static analysis tools cannot
capture the dynamics of such a changing environment Marelys et al. [75]. Research in this
area indicates that simulation modeling is a great tool for the modeling of the processes of
the newspaper industry. Some examples include the works by Annikka et al. (1994) [4] and
Fredick et al. (1997) [42]. Annikka et al.(1994) [4] used simulation as a tool for strategic
management. They used simulation to analyze the complex causalities of revenues from
the advertising and circulation market with the economic result of the newspaper. The
results of the model indicated how the newspaper market conditions were and described the
macro level economical measurements as well. Fredick et al. (1997) [42] used simulation to
test and validate a decision support system for the entire production process. The decision
support system was called GPMS (Global Production Management System). To test GPMS
without disturbing daily production, simulation was used to simulate the events from various
production subsystems and achieve the actual production state. These simulation results
were then fed to the GPMS for analysis. Thus, simulation has been used successfully to
support newspaper production.
2.4 Problems in SCM for constrained settings
In supply chain there are a number of issues that are receiving increasing attention, as ev-
idenced by their prevalent consideration in the work reviewed. These issues are: product
postponement which is the practice of delaying one or more operations to a later point in the
supply chain, using delaying the point of product differentiation, global vs. single-nation sup-
ply chain these are supply chains that operate in multiple nations and demand distortion and
variance amplification which is the phenomenon in which orders to the supplier have a large
variance than sales to the buyer and variance amplification occurs when the distortion of the
26
demand propagates upstream in amplified form Lee et al. (1997) [67]. There are additional
considerations affecting SC performances that are not present in supply chains operating in
a single nation that is; Import regulations, duty rates and exchange rates Beamon, (1998) [8].
Resource-constrained allocation problem is another issue affecting supply chain management.
Davis (1973) [27] describes resource allocation as ”the method of scheduling activities within
fixed amounts of resources available during each time period of project duration so as to min-
imize the increase in project duration.” This type of problem is characterized by a factorial
growth in the amount of computation required to consider all possible solutions as problem
size increases (Davis 1973) [27].
Scheduling problems these have a vital role in most manufacturing and production systems.
They concern allocation of scarce resources to tasks over a period of time Pinedo, (2002)
[91]. These problems are generally defined as decision-making problems with the aim of
optimizing one or more scheduling criteria. The diversity of scheduling problems, large-
scale dimensions and their dynamic nature make scheduling problems computationally very
complex and difficult to solve Petrovic et al. (2006) [29].
2.5 Characteristics of SCM decision support tool
A decision support tool is a computer-based system that brings together information from a
variety of sources, assists in the organization and analysis of information and facilitates the
evaluation of assumptions underlying the use of specific models Williams, (2002) [118].
Supply chain decisions have been classified based on their temporal and functional consid-
eration. Supply chain decisions can be broadly classified into three categories: Strategic
(long-term), tactical (medium-term), and operational (short-term and real-time) according
to the time horizon of the decisions. Functionally, there are four major decision areas in
supply chain management: Procurement, manufacturing, distribution, and logistics. In ad-
dition, there are also certain global decisions whose scope extends over multiple functions
Biswas and Narahari (2004) [9]. There are strategic, tactical, and operational questions in
27
each of these areas. These are described in detail by Shapiro (1999) [100].
Biswas and Narahari (2004) [9] state that supply chain decision making is a complex process
and Some of the important reasons for the complexity of the decision making process are:
1. Large scale nature of the supply chain networks,
2. Hierarchical structure of decisions,
3. Randomness of various inputs and operations,
4. Dynamic nature of interactions among supply chain elements.
2.5.1 Decision variables In supply Chain Modeling
In supply chains modeling the performance measures are expressed as functions of one or
more decision variables. These decision variables are then chosen in such a way as to optimize
one or more performance measures. The decision variables used in the reviewed models are
described below Beamon, (1998) [8]:
1. Production/distribution scheduling: scheduling the manufacturing and/or distri-
bution.
2. Inventory levels: Determining the amount and location of every raw material, sub-
assembly and final assemble storage.
3. Number of stages (echelons): Determining the number of stages or echelons that
will comprise the supply chain. This involves either increasing or decreasing the chains
level of vertical integration by combining stages or separating stages, respectively.
4. Distribution Center (DC) customer assignment: Determining which DC(s) will
serve which customer(s)
5. Plant product assignment: Determining which plant(s) will manufacture which
products
28
6. Buyer-Supplier relationships: Determining and developing critical aspects of the
buyer-supplier relationship
7. Product differentiation step specification: Determining the step with in the
process of product manufacturing at which the product should be differentiated.
8. Number of product types held in inventory: Determining the number of different
product types that will be held in finished goods inventory.
2.6 Conclusion
Modeling and analysis to gain a better understanding of the system complexity and to predict
system performance are critical in the system design stage, and often valuable for system
management Biswas and Narahari (2004) [9].
Simulation modeling can provide valuable insights into the operational characteristics of
supply chains. Variability and uncertainty are endemic in all systems, and certainly so in
supply chains Chatfield et al. (2006) [14]. Chatfield (2001) [13] found that uncertainty in
demands, production yields and rates, transportation times, and cost of goods over time are
common place in the actual operation of a supply chain, yet these operational factors are
often modeled deterministically. By accounting for uncertainty when modeling supply chains,
an insight into the impact of these factors can be gained. Thus, simulation modeling provides
an important tool for understanding supply chain behavior under changing conditions and
can give the information necessary to make informed decisions regarding supply chain design
and management.
29
Chapter 3
Methodology
3.1 Introduction
This section focuses on the methods used to collect and analyse data in this research. It
brings together two complementary methods using system dynamics modeling approach.
Simulation modeling and case study Law and Kelton, (2000) [66] which are powerful research
methods, for modeling and analysis whose added advantages can complement each other in
terms of theory building, testing and theory extension Williams, (2000) [117].
3.2 Field Study
Field studies and supporting data collection methods provide invaluable insights and discov-
eries during the System dynamics research. Field study is a term that applies to variety of
research methods, raging from low to high constraints. These methods share a focus on ob-
serving naturally occurring behavior under largely natural conditions Williams, (2000) [117].
Field studies were conducted from the Head office in industrial area where the distribution,
ordering and most of the processes of the newspapers take place. The resulting data was
used to simulate the generic Supply Chain Management system model.
3.3 Case Study
A Case Study is an exploratory (single in-depth study) or explanatory (cross-case analysis)
research strategy, which involves an empirical investigation of a particular contemporary phe-
30
nomenon within its real life context using multiple sources of evidence Williams, (2000) [117].
The case study methodology focuses on understanding the dynamics present within a single
setting Eisenhardt, (1989) [31], and to understand them within a particular context Yin,
(1994) [125].
3.4 System dynamics modeling
The field of system dynamics depends heavily upon the use of quantitative data to generate
feedback models (Luna et al.) [73] System dynamics is a method for analyzing the behavior
of any kind of system: biological, physical, sociological, economic, and others. It provides a
high level view of the system emphasizing the interactions between its constituent parts, as
well as the impact of time on its dynamic behavior Hustache et al.,(2001) [55].
SD is acknowledged Senge, (1993) [96]; Coyle, (1996) [21]; Richardson (1981) [93]; Wol-
stenhome (1990) [123]; Williams and Kennedy, (1997) [121] as an excellent medium for
exploring and identifying knowledge gaps, but it has not been utilized in the requirements
process engineering domain before Abdel-Hamid and Madnick, (1990) [1]; Williams and
Kennedy, (1997) [121]. The greatest strength of this approach is its ability to represent the
evolving state of a system through time. System dynamics methodology has been widely
applied to the study of the behavior of social and economic systems Forrester (1961, 1967)
[41], [40]; Graham, et al., (1990) [46], Sterman et al., (1997) [105]. Stocks, flows, delays, and
feedback loops, comprise the building blocks of the system dynamics methodology. Stocks
represent accumulations of an item within the system as of a given point in time.
Steps involved in system dynamics modeling are: (1) representing the hierarchy of system
structure with the help of causal loop diagrams, (2) defining the stocks, flows, and delays
within the system, (3) simulating the system behavior under various conditions by introduc-
ing external shocks to the system, and (4) using the simulation results to understand the
interrelationships of system components through the course of time. Modeling a dynamic
system involves capturing the interactions of its components through feedback loops, where
a change in one variable affects other variables over time, which in turn affects the original
31
variable. System dynamics demonstrates how most of our own decision-making policies are
the cause of the problems that we usually blame on others, and how to identify policies we
can follow to improve our situation (Morecroft, 1999) [84]
3.5 System Dynamics Model Building
System dynamics modeling is the technique of constructing and running a model of an
abstract system in order to study its behavior without disrupting the environment of the
real system. Simulation is the process of forming an abstract model from a real situation in
order to understand the impact of modification and the effect of introducing various strategies
on the situation (Williams, 2000) [117]. System dynamics model building process involves
six key activities as shown in Figure: 3 adapted from: (Richardson and Pugh, 1981) [93].
System dynamics model development is a system stage process that begins and ends with
understanding (Williams, 2000) [117].
Figure 3.1: System Dynamics Modeling ProcessAdapted from (Richardson and Pugh, 1981) [93]
Understandings of the model and understandings of the problem and the system are the
key products that a system dynamics modeling effort should accomplish (Richardson and
Pugh, 1981) [93]. Any system-dynamics modeling effort should have a goal to understand
32
better the problem under study and the system in which it is happening. An orientation
towards understanding and learning grants meaning to the definition-type activities and of-
fers context and meaning for the formalization-type activities of the process incrementing
the possibility of being successful at the insight-generation type of activities.
In this research a Dynamic Synthesis Methodology was used to allow the integration of
theoretical concepts and structuring of parts and elements of a process over time in such
a manner to form a formal functional entity, underpinned by synthesis as philosophy of
science (Williams, 2004) [120]. Dynamic Synthesis Methodology is a powerful empirical
research method that potentially makes useful contribution to body of System Dynamics
(Williams, 2004) [120]. The Dynamic Synthesis Methodology is grounded on well-tested and
developed theoretical anchors and builds on an existing epistemological philosophy of science
in the acquisition of knowledge (Churchman and Ackoff, 1961) [19].
The study used the following data collection techniques:-
1. Structured and Semi-structured Interviews of different stakeholders thats circulation
manager, inventory manager and data analyst of the organization under study were
conducted to collect primary data to verify the proposed operational model and the
conceptual model.
2. The questionnaire was developed based on the review of literature in Section (2) and
were distributed to different distribution centers and consumers to get more reliable
data, clear understanding of the system in question and to enable the researcher give
clear conclusions.
3. Participant observations of the System under study was done from the head office.
3.6 Simulation Experiments:
Simulation models are abstracts of the real world-view of a system or problem being solved.
simulation can be an effective, powerful and universal approach to problem solving in differ-
ent areas of application, to extend existing theories or identify new problems (Williams, et
33
Figure 3.2: Dynamic Synthesis Methodology Research Design.Adapted from Williams, (2000) [117].
al., 1999) [122]. The behaviour of the various system elements/components over time was
identified by computer simulation using STELLA Modeling software. This was done in a
trial and error method to demonstrate the likely effects of various decisions in the model.
3.7 Evaluation and validation:
Building valid and credible process models is an important aspect of a researcher’s repre-
sentation of the actual system being studied (Williams, 2000) [117]. Validation is a process
of establishing confidence in the soundness and usefulness of a model (Forrester and Senge
1980) [38]. Law and Kelton (2000) [66] suggest that if the model is ”valid”, then the de-
cisions made with the model should be similar to those that would be made by physically
experimenting with the system. A model is said to be credible when a simulation model and
its results are accepted by managers/customers as being valid, and used as an aid (tool) in
34
making decisions.
Given the reporting needs of the organisation that was used as a case study, data tools and
techniques used for analysis of the supply chain management model were EPI data, which
was used for data entry and SPSS for data analysis and Regression analysis. Vensim and
STELLA as tools were used to simulate the Causal loops, and supply chain management
model respectively and an algorithm was used for pattern evaluation.
35
Chapter 4
Field Study
4.1 An illustrative real-world case study
4.1.1 A Newspaper supply chain
A system dynamics model of the SC of an actual newspaper company was used in this re-
search to demonstrate the proposed methodology. This company was facing a problem of
persistent oscillations in its orders and processed orders. Even though the company has
maintained its market share, it has experienced serious competition and demand fluctua-
tions, which in turn impacted its work strategies. The company has been implementing
the following SC strategy: Utilization of supply relationship management to guarantee that
suppliers provide excellent product quality, meet due dates, and offer excellent prices.
The research approach and modeling methodology in the newspaper supply chain of a major
newspaper company in Uganda was applied. The supply chain of the company is comprised
of a central producer and warehouse (CW) located in Kampala, which then supplies directly
one hundred and six distribution centers around the country. The company divides these
distribution centers into four that’s Central, Kampala, Northern and Western, which supply
to different parts of the country
From the head office, this company supplies its products to various distribution centers
(regions) such as Central, Kampala, Northern, Eastern, and Western. The short life cycle
of the product have amplified the coordination problems, which in turn have caused excess
inventories and sometimes difficulties in consumer demand oscillations.
36
Another main factor is the intense competition from other companies. The competition
has forced the company to introduce more product varieties into the market to protect its
existing and potential market share.
Production capacity is another factor that adds to SC complexity because of its long delays,
huge investments, and new products with more complex manufacturing processes than pre-
vious generations. In addition, these complementary products are at the upstream of the
SC and their resulting fluctuations are higher.
4.2 The Simulation Model
Building the SD model of the company’s SC followed the steps of (Hines, 2000) [51]. The
first step was defining the problem, followed by understanding the formulations, developing
the causal loop diagrams, developing the stocks and flows diagrams, validation and testing.
First I developed the causal loop diagram of the entire chain, taking into consideration the
inventory control policies used by the company and the Distribution centre (DC). The entire
diagram, which includes all system variables and the regulating feedbacks, is exhibited in
Fig. 4.1 To develop the causal loop diagram, I used the following assumptions, the validity
of which was thoroughly checked with the CW and the DC:
1. No order is greater than the capacity of a single Vehicle.
2. Each Vehicle serves one Region at a time. This is generally true for centers that are in
the vicinity of the CW facilities. For the other ones each truck serves more than one
center at a route. Therefore, this assumption easily makes sense, assuming that the
lead-time is half of the loading-transportation- unloading time.
3. There are no emergency deliveries.
4. There is no collaboration (no lateral movements) among Centers.
The next step of the SD methodology involves the mapping of the causal loop diagram into
a dynamic simulation model using specialized software. I used the Vensim and STELLA
8.1 software for this purpose. The embedded mathematical equations are divided into two
37
main categories: the stock equations and the flow equations. Stock equations define the
accumulations within the system through the time integrals of the net flow rates. Another
typical form of stock equations is used to define the smoothed stock variables that are
expected values of specific variables usually obtained from their past values using exponential
smoothing (e.g. the smoothed stock variable Expected Demand
4.2.1 Causal loop Diagram
Causal loop diagrams are the basis on which the SD model is built. They depict, graphically,
the interactions and cause-and-effect relationships among the different system parameters
(Lertpattarapong 2002) [71]. During model development, Causal loop diagrams serve as
preliminary sketches of causal hypotheses and they can simplify the representation of a
model. The structure of a dynamic system model contains stock (state) and flow (rate)
variables. Stock variables are the accumulations (i.e. inventories), within the system, while
flow variables represent the flows in the system (i.e. order rate), which are the byproduct
of the decision-making process (Georgiadis et al 2004) [44]. The model structure and the
interrelationships among the variables are represented by stock-flow diagrams. A supply
chain being the total ‘extended enterprise ’that captures all partners including vendors,
manufacturers, producers, distributors and retailers, extends over multiple echelons. Each
partner of the chain typically manages his/her own inventory (operating as an autonomous
linkage of the chain), which is replenished from the upstream echelon, while using a control
policy to determine the frequency and magnitude of the orders(Georgiadis et al 2004) [44].
38
Figure 4.1: Causal loop diagram for the System Dynamics Supply Chain Model in resourcecostrained settings
The arrows in Fig. 4.1 represent the relations among variables. The direction of the influence
lines displays the direction of the effect. Signs ‘+’or ‘-’at the upper end of the influence lines
exhibit the sign of the effect. When the sign is ‘+’, the variables change in the same direction;
otherwise they change in the opposite one. The structure of the systems internal environment
consists of the stock variables Factory production and Inventory. Order Monitoring monitors
39
the accumulation of unfilled orders, i.e. orders that have been placed but not received yet,
while Inventory monitors the accumulation of products on hand. Orders increase Order
Monitoring. The rate of Order Fulfillment is determined by the Orders after a time delay
equal to Lead-time. Order Fulfillment reduces the stock of products in Order Monitoring
and increases Inventory. The variable Inventory is depleted by Sales. This process takes
time equal to the Response Time to Customer Demand.
The clear definition of the boundaries between the system under study and its external en-
vironment is an essential step of SD; thus, the model and its analysis are kept as simple as
possible while capturing all necessary elements for the analysis of the system under study.
The closed-loop structure of Fig. 4.1 restricts the endless accumulation of inventory (that
occurs in the model) whatever the demand level may be. This occurs due to two negative
feedback loops displayed in Loop #1 which is defined by the sequence of the variables Orders-
Order Fulfillment-Inventory-Inventory Position-Inventory Position Adjustment, while Loop
#2 is defined by the variables Orders-Order Monitoring-Inventory Position-Inventory Posi-
tion Adjustment. To explain the negative feedback mechanism, I follow the route around
Loop #1. An increase in Orders will increase the Order Fulfillment and thus, Inventory
and Inventory Position will also increase. This causes Inventory Position Adjustment to
decrease, since the Desired Inventory Position changes slowly and it can be assumed to be
constant for the next time step. Finally, the decrease in Inventory Position Adjustment
restricts Orders. Therefore, Orders will stabilize at a finite level and eventually the system
will reach an equilibrium (steady) state.
Capacity may refer to all operations of Order Monitoring, e.g. stock space, manpower,
production facilities, transportation means etc. Generally, capacity determination is quite
simple in a steady-state situation; however, in an evolving environment, as in the case under
study, it is important to consider a dynamic capacity planning policy.
It appears that a decision-maker could determine capacities for all these operations once
in the beginning of the planning horizon, and that this could be done using a standard
management technique that incorporates steady-state conditions. However, this is not the
case in the environment under study since product flows can change dramatically for several
reasons; for example promotion activities or price variation strategies of the competitors.
40
Although, such demand shifts take time to materialize, they have to be considered for the
development of efficient capacity planning policies. Thus, it is evident that an appropriate
modeling methodology needs to be able to capture the transient effects of flows in a Newspa-
per supply chain. SD has this capacity and moreover, it easily describes the diffusion effects
related to market behavior.
In addition, an operation may be performed using either owned capacity or additional leased
capacity. The problem of determining the optimal ratio of owned to leased capacity units
(”buy or lease” problem) is also typical in the New Vision operations. The causal loop
diagram in Fig. 4.1 illustrates the generic supply chain system embellished with a dynamic
loop that expresses a capacity planning decision-making mechanism. Specifically, I assume
that an operation may be performed using owned and leased (if needed) capacity units. This
control mechanism is modeled as a negative feedback loop.
More specifically, Capacity Needed is determined by a variable of the SC model. Capacity
needed is compared with the Actual Production Capacity. In case there is a capacity short-
age, Capacity is then leased to achieve the Desired Service Level. Capacity Expansion Rate
determines the rate of change of capacity towards the desired value. Capacity Expansion
Rate is modeled by pulse functions, the pulse magnitude is proportional to the Smoothed
Capacity Shortage (obtained from Capacity Shortage using first order exponential smooth-
ing to avoid unnecessary oscillations) multiplied by a control variable. Actual Capacity has
a useful lifetime (Capacity Life-Time), which regulates the Capacity Disposal Rate.
A decision-maker and/or regulator could further employ the developed model to capture the
impact of various policies using various levels of the above parameters; in other words, the
model can be used for the conduct of various ‘what-if’analyses. For example, the impact of
different leading strategies on the new or unexpected demand satisfaction and the capacity
utilization subject to a given capacity review period can be evaluated. On the other side, a
decision maker could investigate the impact of different values of capacity review periods for a
given capacity expansion policy. More advanced ‘what-if’analyses may be further conducted
to develop a long-term capacity planning strategy.
41
4.2.2 Stocks and Flows Analysis
The step that followed in building the SD model was converting the causal loop diagrams
into stocks and flows diagrams and defining the mathematical formulation. The basic SD
model in this thesis follows the generic models of Sterman (2000) [104].
Figure 4.2 shows the general structure of a system dynamics model used in dynamic anal-
ysis of the information system. The model consists of three interacting sectors: factory,
Distribution and Sales below are the interactions:
Figure 4.2: Stock and Flow Diagram
42
The model is composed of the three connected stocks and flows Sectors that are described
below.
1. The Factory Sector
This company runs a push-pull manufacturing process: a push process from the pre-
inserting processes to the inserting process and a pull process from the inserting process
to the packaging. Main state variables in the Model are the Factory production and
Inventories.
2. The Distribution Sector
This consists of the Inventory, Transportation Capacity and Sales. The Distribution
Sector mainly represents the causal relationship involving Inventory, orders filled and
Transportation Capacity. The inventory, distribution sub model represents the links
of inventories and distribution orders filled from the finished goods inventory to con-
sumers.
3. The Market Sector
This consists of the market sub model and the inventory. The market sub-model mainly
represents the causal relationship involving demand and customer orders .
4.3 Data Analysis
Raw data was collected, edited and analysed using Excel and SPSS 12.0, with the analysis
the following was carried out to give a clear understanding and complexity of the supply
chain; Newspaper and Time of the year Univariate Analysis of Variance (Tests of between
effects, parameter Estimates, Estimated Marginal means, pair wise comparisons, Univariate
tests) were carried out and below are the results.
The table in Figure 4.3 shows dependent variable measures of the different variables thats;
the newspaper, months, intercepts, corrected total, newspapers multiplied by months, Total
and corrected Total.
43
Figure 4.3: Tests of Between-Subjects Effects
Figure 4.4 is a table that represents the parameter estimates at a particular time of the
year that’s from January up to June. The dependent variable is measure representing three
variables that average supply, average sales and average returns.
Figure 4.4: Parameter Estimates
The table in Figure 4.5 below is based on time of the year which is from January to June
and the data has a dependent variable measure where by measure is equal to average sales,
44
average supply and average returns. In this table data is displayed across six-month duration,
indicating mean, Standard errors and the confidence interval for the different months.
Figure 4.5: Time of the year Estimates
Figure 4.6 below shows pairwise comparisons have measure as a dependent variable with
two different newspapers that’s Bukedde represented by two (2) and NewVision represented
by one (1). Based on the estimated marginal means the mean difference is significant at
the 0.05 level and there is adjustment for multiple comparisons: Least significant difference
(equivalent to no adjustment)
Figure 4.6: Newspaper Pairwise Comparisons
45
The Table below in Figure 4.7 represent data at the Time of the year (from January up
to June 2006) Univariate Tests. The F tests the effect of the Time of the year. This test
is based on the linearly independent pairwise comparisons among the estimated marginal
means.
Figure 4.7: Time of the year Univeriate Tests
The data below in figure 4.8 represent newspaper estimates indicating the different mean
standard error and a 95% confidence Interval. In this media company different newspapers
are produced in this case two papers were used that’s 1 representing NewVision and 2
representing Bukedde through out the seven days of the week.
Figure 4.8: Newspaper Estimates
46
Figure 4.9 represents newspaper univariate tests indicating the error and contrast. The F
tests the effect of the newspapers; this test is based on the linearly independent pairwise
comparisons among the estimated marginal means.
Figure 4.9: Newspaper Univeriate Tests
Figure 4.10 represents data across the different six months in the two newspapers. The
dependent variable is measure; the mean, standard error and a 95% Confidence interval are
clearly displayed in the table.
Figure 4.10: Time of the year * Newspapers
47
Figure 4.11 are pairwise comparisons of two different variables that’s time of the year it
gives the difference in means, standard errors, significance and a 95% confidence interval for
the difference. This is based on the estimated marginal means and there is adjustment for
multiple comparisons: Least significant difference is equivalent to adjustments.
Figure 4.11: Pairwise Comparisons between Newspapers and Time of the year
48
Figure 4.12 represents profile plots of estimated marginal means of measure of different
Newspapers that’s 1 for NewVision and 2 for Bukedde. With this graph February had the
least sales this is due to a number of reasons:
1. This month has fewer days as compared to other months.
2. The political season also affected sales as most people opted for the competitor news-
papers arguing that since The NewVision is a government paper it was believed to be
biased.
Figure 4.12: Estimated marginal means of measure
49
4.4 Modeling Decisions
In order to achieve the proposed objectives STELLA as a computer simulation program was
used to provide a framework and an easy-to-understand graphical interface for observing the
quantitative interaction of variables within the system. The graphical interface obtained can
be used to describe and analyze complex physical and social systems. During this simulation
a number of simulations were run until satisfactory results were obtained.
4.5 The interface Structure
Below is a general interface of the system which enables easy access to the model The
interface comprises of different buttons for quick and easy interactions with the model. It
also shows the general flow of information from the factory to the market.
Figure 4.13: User Interface
50
Figure 4.13 exhibits a high level structure model comprising of the Factory, distribution
center and market. This shows the flow of information and goods from the factory to the
final consumer, the blue arrow indicates the flow of both goods and information from the
factory to the distribution center and finally to the market and a feed back is indicated by
the red arrow. There is a direct arrow from the market to the factory this is only used
in case of emergency where an orders needs to be obtained immediately this is common in
the newspapers supply chain as News is instant. There are numeric displays that show the
Factory orders, Factory production, Orders and Sales. This enables the users make decisive
decisions on how to manage the supply chain.
4.6 Simulation Output One
Figure 4.14: Simulation Output: Sales, Orders, Disposal rate and Production rate
51
In Figure 4.14 variables that were used are orders [1], Sales [2], Disposal rate [3] and Pro-
duction rate [4]. This was done in order to compare the trend of the variables at different
times. The simulation results show that sales and Disposal rate initially fall to 0 in year one
and then rise steadily to ease off in year four. As illustrated the production rate is constant
from year one until the twelfth year but the orders are volatile.
4.7 Simulation Output Two
Figure 4.15: Simulation Output: Orders, Processed orders, Factory Orders and Inventory
52
Figure 4.15; shows that the inventory [4] is initially high at two hundred then reduces steadily
in the third year and its constant until the twelfth year. This means that with time there will
be minimum inventory, or what’s produced is all sold as per graph. The processed orders
[2] are initially at zero in the first two years from there they oscillates until the twelfth year
where it is highest at seven hundred. This means that there is an increase in the number of
processed orders as time goes by due to internal and external factors. The Factory orders
[3] take the same route as the processed orders though the oscillations are higher than the
processed orders. Finally the orders [1] initialize at forty and then increase to about fifty
four they oscillate having the highest at about fifty five and the lowest at twenty. In the
first five years there is a lot of instability but after the five years the system starts gaining
its stability.
53
Chapter 5
DISCUSSION, CONCLUSIONSAND RECOMMENDATIONS
5.1 Introduction
This chapter examines how the objectives of the research were achieved using System Dynam-
ics, modeling and simulation as well as validation using previous studies. The methodology
to detect changes in the SC behavior due to external and/or internal factors, recomendations,
outcomes and contribution of the research are also discussed in this charpter.
5.2 Discussion
The aim and objectives of this study were achieved using a number of techniques which
included a critical review of the literature, identifying of the issues and factors that af-
fect supply chain management. Critical variables of supply chain management in resource
costrained settings were identified for inclusion in a simulation-based tool.
The problem statement stage of the Systems Dynamics Methodology was accomplished by
constructing a dynamic hypothesis that helps to realise the interelationships of the critical
variables. Causal loop diagrams were constructed from the dynamic hypothesis depicting the
various variables that gave rise to the critical variables, plus the interelationships between
them.
The literature review discussed a number of areas evolving around the supply chain and sys-
tem dynamics; how its managed, steps involved, background and various definitions (views)
54
as per various authors.
This thesis introduces a methodology for detecting and predicting SC behavior changes based
on the dynamics of the supply chain in resource constrained setting. This methodology has
three phases: (1) capture the dynamics of the SC, (2) detect changes and predict the behavior
based on them, (3) make modifications to avoid the unwanted behavior in performance.
System dynamics is a good methodology to model the SC system and the impact of changes
in the business environment.
5.2.1 The supply chain market
The Newspaper market is dynamic in nature and due to internal and external changes for
example; change in management, Political Seasons, Weather and change is consumer taste
and preferences which are key components of many of today’s Newspaper leading companies
in resource constrained settings. Companies that look for new and better methods survive.
Process re-engineering is still a vital part of management’s daily struggle to maintain market
share. Increasingly, the ability of companies to operate at lower costs while delivering goods
and services to customers on time has become the focus of strategic planning meetings. One
of the most costly and difficult parts of a resource constrained business is to manage the
supply chain. The supply chain consists of a company’s network of suppliers, production
processes, warehousing and distribution methods, and customers. The goal is to manage all
aspects of the supply chain to meet customer demand without driving up costs or inventory.
5.3 The Simulation Tool
There are different variables in the Simulation tool . When the model is run it shows a
forecast into the future of how the sales, orders and disposal rate interact at various stages
in time. This helps decision makers come up with effective ways of how to handel the supply
chain at a point in time.
In the first experiment a graph showing Sales, Orders, Disposal rate and Production rate is
executed where the sales increase over time the disposal rate reduces and the production rate
is kept constant. The is amplification in the Orders this is true because the placed orders
55
vary from time to time
In the second experiment which comprises of Orders, Processed orders, Disposal rate and
Production rate mainly gives a comparison among different variables. The orders and Pro-
cessed orders are both amplified but the graph shows that orders are greater than processed
orders and they are both unstable thats they keep changing over a period of time while
others are kept constant.
5.4 Validation
Validation is the process of ensuring that the model is sufficiently accurate for the purpose
at hand Stewart (1997) [108].
The two key concepts in validation are; the ideas of sufficient accuracy and models that are
built for a specific purpose. There is no model that is 100% accurate, indeed, a model is not
meant to be completely accurate, but a simplified means for understanding and exploring
reality Pidd (2003).
One of the most difficult problems facing a simulation analyst is that of trying to determine
whether a simulation model is an accurate representation of the actual system being studied,
for the particular objectives. In this case, the following were done to validate the model:
1. A walk-thru using the loop diagram generated from the dynamic hypothesis was carried
out and showed that the logic was correct.
2. The stock and flow diagram generated no errors when run over a period of Time.
3. The tool was compared to the case study data that was collected and the model was
taken to the New Vision Circulation Manager at the head office to check whether the
system developed was valid depending on the procedures, processes and information
flow the company has in place. It was confirmed that the developed model derived
good results hence; the model is sufficiently accurate for a newspaper supply chain
management.
56
5.5 Conclusions
SD has been widely applied to SC applications to address various issues in developed areas,
this is unlike in resource-constrained settings where SD and SCM have been neglected and
have not been highly applied. One of the serious issues with SCM in resource-constrained
settings is the change in the SC behavior due to external market factors and/or internal
system and managerial factors. What makes it a significantly serious problem is that SC
behavior is dynamic and controlled by nonlinear interrelationships and interactions among
its components. Small variations in demand, for example, can simply cause disproportional
major fluctuations and oscillating reactions along the SC.
The simulation model was used as a tool to design a generic model for supply chain man-
agemnt in resource constrained settings derived from different steps(stages) in a madia com-
pany, which was used as a case study. Different variables like consumer demand, quantity
produced, competitor prodects, Markert Segmentation, Costs, Price, Revenue, Inventory,
product design and Sales were identified and used. This delivered interesting results as dis-
cussed above.
5.6 Further Research
With the completion of this research there is a lot of research that can be done in this
areas thats; Inventory management, Logistic Management, The supply chain bullwhip effect,
Capacity management etc. Simulation of various supply chain is also recommended to give
a better understanding of different supply chains in different settings.
During this research there was an issue of limited literature on supply chain management
in resource constrained setting which strained the researcher and no model in this setting
had been developed which required the research develop the model from scratch. Therefore
more research in this area is recommended at advanced stages
57
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APPENDICES
Appendix A: Code
Figure 5.1: Distribution code
70
Figure 5.2: Factory and market code
71
Appendix B:
Appendix B: Interview Questions
Distributor
1. What type of newspapers do you distribute?
2. How many newspapers do you receive from the head office and how soon are they
delivered?
3. Do you distribute all the news supplied to you from the head office?
4. If not what happens to the remaining newspapers?
5. On average how many newspapers are distributed daily?
6. How many are returned to the Distribution centre on a daily basis?
7. Please take me through the ordering and distribution process.
8. How often do you make/place orders?
9. When you place an order, how long do they take to be fulfilled?
10. Are all orders fulfilled? If not why?
72
Circulation Manager
1. Please take me through the distribution process?
2. How Many newspapers are printed every day on Average?
3. Do you distribute all the printed newspapers, If not what happens to the newspapers
that are not distributed?
4. How many distribution centers do you have?
5. How do you transport the newspapers to various centers and how soon are they re-
ceived?
a) Are there any delays or emergency cases?
6. How Many Newspapers are supplied to each distribution center everyday?
7. Please take me through the ordering process?
8. Approximately how many orders do you receive from distributors per day?
9. Are all orders placed fulfilled?
10. If not what happens to orders that are not fulfilled?
11. According to the data that I received from your company about returns and supplies,
the Month of February tremendously has low supplies why?
12. What affects your supplies?
73
Questionnaire
1. What newspaper do you read?
a) Bukedde
b) New Vision
2. How often do you read the selected newspaper above?
a) Every day b) Once a week c) 2 days a week
d) 3 days a week e) 4 days a week f) 5 days a week g) 6 days a week
3. How do you rate the content in the newspaper selected in (1) above?
a) Poor b) Fair c) Good d) Very good e) Excellent
4. What is your favorite Column in the newspaper selected in (1) above? And why?
...................................................................................................................
5. Do you receive Newspapers in your home area every day?
a) Yes b) No
6. If yes, how soon do you get the newspaper?
a) By 8:00 am b) by 10:00am c) by noon (12:00pm) d) after 12:00pm
7. Other than Bukedde and New Vision which other news paper do you read? and why?
.........................................................................................................
8. How do you rate the newspaper in (7) above?
a) Poor b) Fair c) Good d) Very good e) Excellent
9. Why do you choose to read the newspaper in (7) above?
.........................................................................................................
.........................................................................................................
10. Please note down any comments about Bukedde/New Vision newspapers in the space
below?
.....................................................................................................................
74