Strategic Discrete-Event Simulation in Automotive Supply Chains

This report provides an overview of discrete-event simulation (DES) and its strategic application in supply chain management, particularly for a Tier-1 automotive supplier. It begins by introducing DES as a computer-based modeling technique that simulates complex systems through ordered sequences of events. The report then presents three case studies demonstrating the use of DES in various sectors: healthcare (optimizing surgical procedures and ICU management), vehicle manufacturing (modeling vehicle body production and assembly), and learning institutions (managing student flow and resource allocation). Key stages involved in simulation projects, including problem definition, project planning, system definition, model formulation, data collection and analysis, verification and validation, experimentation, and documentation, are also discussed. The report concludes by recommending the integration of DES to monitor the flow of goods in the supply chain, streamline activities, and enhance competitive advantage for the automotive supplier. Desklib provides a platform to access this and many other solved assignments for students.

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Running Head: SUPPLY CHAIN MODELING AND SIMULATION MODULE
Supply Chain Modeling and Simulation Module
Name
Institution

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SUPPLY CHAIN MODELING AND SIMULATION MODULE
Introduction
Automotive manufacturing and logistics is based on designing, planning and controlling
the flow of material and related information flows in relation to their supply chain. This calls for
strategic, tactical and operational task ranging from simple equipment all the way to international
supply chain thus calling for relating different entities to come up with a system. This paper is
going to give way that can be used to incorporate discrete event simulation
. technology to perform such complex tasks.
This report is intended to address the request placed by the line manager of Tier-1
automotive supplier. The paper will dwell majorly on how the company can incorporate strategic
use of discrete-event simulation techniques in the business to enhance supply chain. Over the
years, there has been rapid development in the technological sector, which greatly influences the
business supply chain and productivity. Supply chain management is one of most critical areas in
any organization thus requires to be handled with precaution in order to realize the goals and
target of the organization. In this case, the paper tends to come up with recommendation that can
be used by the firm to utilize this technology as discussed below
This paper is going to focus on discrete supply chain by giving three case studies in
which this technique has been used in addition to decoding the stages necessary in the simulation
project. Consequently, the focus of this report is to give the way forward that can be used by
Tier-1 automotive Supplier to facilitate its operation since it has never used this kind of
technology. Due to the large amount of movements of supplies taking place in the firm, the firm
wishes to integrate discrete-event simulation to monitor the flow of goods in the supply chain.
The active streamlining of activities to transform raw materials into products thus call for the

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utilization of this technology to reduce make management and monitoring much easier. The
reason for the use of this strategy is to increase competitive advantage in the market place
Discrete –event simulation
With reference to this kind of technology, it is used to simulate components that operate
at the highest level of abstraction than components simulated by continuous simulators on a
normal basis. DES is a computer-based modeling technique, which gives intuitive and less rigid
method with the ability to produce dynamic behavior of sophisticated systems and interaction
between characters or objects (Schmidtke, Heise & Hinrichsen 2014).. Within the same context
of discrete event simulation, an event is considered an incident responsible for causing changes
in some way in a given system. For instance, creation of a new event is as a result of simulation
component generates output. According to Prinz, Väätäinen, Laitila, Sikanen & Asikainen
(2019), the distinct feature of discrete simulator is that events here can only occur during a
distinct unit of time in the process of simulation. In this case, events cannot occur in between
time unit. This kind of simulation is usually faster and has the ability of producing accurate
approximation of system behavior.
According to Hoad & Kunc (2018), discrete event simulation can also be used to refer to
the codifying process of the behavior of complex or sophisticated systems as an ordered
sequence of events that are well defined. This event means changes that are specific in the state
of a system at specified point time. It is the method of simulating the behavior and performance
of facilities or life processes. In order to use this kind of technology in the supply chain
management, it requires the utilization of the high speed of computer and memory. Consider the
example presented below.

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Fig1.0 shows simple digital logic circuit.
Fig 1.2 shows graphical representation of discrete behavior.
Based on the assumption that NAND gate delays with two time unit and the NOT gate has a
delay of one time unit, the logic circuit tends to produce the output given in figure 1.2

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As asserted by Steinemann, Taiber, Fadel, Wegener & Kunz (2013), DES functions by
modeling systems as series of events. Discrete event simulation holds on the assumption that
there are no changes in the system between events. In this, characters or objects/patients are
modelled as independent entities through which each entity is assigned associated attribute
information. The information can be modified as time runs in the given Simulation. With
changes in the information, location of event changes depending on the status of the unit hence
modification of care given out by the system (Padhi, Wagner, Niranjan & Aggarwal 2013). DES
models are in apposition of allowing complex decision logic to be integrated and incorporated in
systems, which is not the case with other types of models. The most important aspect of discrete
event simulators is their ability to allow scenarios to be tested before being implemented. This is
important to decision makers as it gives them room to understand the alternative methods
through which a new policy or production may best be met (Navarra, Marambio, Oyarzún, Parra,
& Mucciardi 2017)..
Case study of the use of discrete event simulation
Discrete event simulation for over the last decades has taken over many different areas of
application. This is due to the wider use of this technology in different field fields of science and
the continuous use of available software programs by experts who are dedicated in this field. To
begin with, DES has in the resent years been used the health sector and medicine to help health
practitioners provide quality services to patient and manage diseases (Moghaddam, Sadrnia,
Aghel & Bannayan 2018).
Simulations have been applied to projects in hospitals that can be put into five categories
namely, surgeries, intensive care units (ICU) hospitals, specialties, outpatient clinics and
demographic health provision in addition to healthcare supply chain environment. The main

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component of hospital activities are always surgical procedures that are applied on patients in
either ICU or being released to the recovery area (Prajapat & Tiwari 2017). Discrete event
simulation technology has been utilized in combination with other solutions to create optimal
environment in the surgical procedure and use of ICU. Instance, DES technology is being used in
the ICU to monitor neonates and babies born before the right period. Mixed integer programing
has been used to optimize the utilization of operating theatres .it has helped streamline surgery
sequences efficiently without disruption, which has been achieved through balancing the
scheduled blocks. This model has been used find the best utilization in surgical procedures
(Dunke & Nickel 2017).
Consequently, simulation models for determining the capacity of bed planning in the ICU
are being utilized. ICUs are using multiple server model that help to identify bottlenecks in
addition to improving the flow of patients and communication between surgeons with their
administrative staff
Case study 2 vehicle manufacturing
As denoted by Kotiadis & Tako (2018), Furthermore, DES software has been of
continuous use industry, the software has been used to breakdown each process in the
manufacturing of vehicle bodies into discrete parts. Vehicle manufacturing companies use this
technology in order to make it easier to analyze and give room for more factors to be considered
when modeling. The ability of DES to approximate continuous processes into defined non-
continuous event has enabled car manufactures to model he movement of a car part from
assembling until it reaches the paint shop as independently. It functions based on departure and
arrival time (Yong-Kuk, Philippe & Jong 2018).

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Consequently, vehicle-manufacturing companies are using DES technology to mold
complete vehicle bodies due to its powerful and accurate approximation of events (Johansson,
Skoogh, Andersson, Ahlberg & Hanson 2015). The entire vehicle manufacturing facility has
been modeled inform of sequence of operations being performed based on passive entities. The
attributes of the components passing through the processing sequence makes them to be handled
differently. Bokrantz, Skoogh, Lämkull, Hanna, , & Perera, (2018), denote that the use of the
DES in the vehicle manufacturing industry makes it easier in the production process because;
DES has the ability of predicting the results of the system being used over time; it also allows the
operators know how various parts of the system interact. Furthermore, vehicle companies like
TOYOTA, BMW and MISTUBISHI utilize the technology to help produced different models of
the vehicles (Ghani, Monfared & Harrison 2015.
Case study 3 use of DES in learning institutions
Universities, high schools and other institutions of learning have incorporated discrete event
simulation technologies in their schools to help create a conducive environment for learning.
DES soft wares are currently being used in schools to determine the student flow in academic
study periods (Bokrantz, Skoogh, Lämkull, Hanna & Perera 2018). In the event of seeking
optimal completion time of career and providing efficient services, DES programs are being
used. They have been used to model complex flow scenarios for each career in institutions of
higher learning to predict possible outcomes if the flow rules and polices of admission criteria
changes. Taking into consideration the number of students to be enrolled, it becomes possible to
estimate the amount of resources (teachers, sections, laboratories and councilors) to meet the
demand. Student programing is thus used to estimate and allocate sufficient resources to reduce

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bottleneck thus improving exit time for students in different academic career (Pedrielli, Matta,
Alfieri & Mengyi 2018).
Key stages involved in the simulation projects
Simulation application involves a set of specific steps so that the simulation may emerge
out successfully. The key steps to be undertaken in simulation projects are;
Problem definition
This step involves determining the goals of the study and defining what is to be solved by
the simulation. In order to understand the problem, further definition involving objective
observations of the process have to be studied. During this stage, it is important to determine
whether simulation is the best solution for the problem (Dode, Greig, Zolfaghari & Neumann
2016).
Project planning
At this step, the project tasks are broken down into packages of work and assigned to a
responsible party.it is necessary to indicate the milestones used for tacking progress. This is
necessary to determine whether time and resources will allow the accomplishment of the project.
 System definition-The main task of this step is the identification of system components
that are to be modelled and the measures of performance to be analyzed. The system is
usually complex thus to define it calls for experienced stimulator to find appropriate
levels of details and flexibility.

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 Model formulation- According to Bokrantz et al. (2018) this call for clear understanding
of how the actual system will function and determining the basic requirements of the
model that are necessary.
 Input data collection and analysis-The end of module formulation results to determination
of the type of data to be collected. This data is then fitted to theoretical distribution.
Consequently, the model is transplanted into programing language.
 Verification and analysis-This is the process of ensuring the model works as it was
planned. It is done through debugging or animation. The information can be verified but
not validated. Validation can only be achieved through statistical analysis.
 Experimentation and analysis-This is the second last step, which involves experimenting
and developing the alternative models, executing the simulation and comparing it
statistically with of real system.
 Documentation and analysis- It is made up of written reports or presentation. The report
usually discusses the results and implications of the study. Thus, the best course of action
is identified. Recommended and justified.
Fig 1.3 shows a sample representation of discrete event simulation model that can be
used in the supply chain management.

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The model above can be used to give
an overview of how incorporating
DES program can facilitate easy
management of unrelated tasks in an
organization ranging from acquisition
of raw materials to supply of finished products.
Conclusion and Recommendations
Following the nature of the Tier-1 automotive supply firm which involves complex and
variegated manufacturing contexts, with supply chains that are large and complex, it is
recommended for the firm to use DES software to help monitored the process of production
since it provides accurate approximation of events. Consequently, we do recommend that before
resolving into the use of simulation in the firm, the firm should conduct careful research to
determine the data and information needed in order to build a clear model of simulation. This
should only be done by experienced simulators to identify areas of flexibility.
To sum up, discrete event simulation technology is one of the most growing field
especially when it comes to application due to its accurate approximation of events thus resulting
quality products. This idea can be incorporated in various areas that need accuracy to increase
competitive advantage of the firm.

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References
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