An Analysis of Supply Chain Design in the Car Manufacturing Industry

This literature review examines the supply chain design within the car manufacturing industry, focusing on network models and quantitative methods used to address knowledge gaps. It begins by explaining the design and structure of car manufacturing supply chains, considering nodes, product flow, risks, decisions, authorities, disruptions, and stakeholders. The analysis identifies and reviews over 20 peer-reviewed articles employing mathematical and quantitative methods, with a focus on articles published after 2010, highlighting the importance of balancing inventories, managing the bullwhip effect, and integrating information flow. The review also addresses the role of agility in overcoming challenges, the impact of environmental concerns, and the potential of reverse supply chain logistics in reducing complexity and environmental damage. The study concludes by summarizing the key findings and providing a comprehensive reference list.

Running head: SUPPLY CHAIN DESIGN
SUPPLY CHAIN DESIGN
Supply Chain of Car Manufacturing
Name of the Student:
Name of the University:
Author Note:

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Introduction:
The aim of this study is to carry out a literature review of the supply chain design of
car manufacturing.
Step 1: Supply Chain Design
Car manufacturing supply chain:
Explanation of design and structure of car manufacturing supply chain and use of network
model in Car manufacturing supply chain in Australia:
Nodes:
Flow of product:
Risks:
Decisions:
Authorities:
Disruptions:
Stakeholders:
Step 2: Supply Chain Analysis
In the view point of Wang, Lai and Shi (2011), manufacturing systems and the
operation in supply chain and logistics are important economic activities as they remain a tool
for business activities and as a competitive means in the market where the company operates.
In order to remain competitive in the market, the companies are coming up with new plans
and ideas to spend the minimum time and expense for the inventory and the supply chain.

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The main objective of the manufacturers is to balance the inventories between the
requirements of the customers and the production capacity. As pointed out by Pishvaee, M.S.,
Rabbani and Torabi (2011), manufacturing management is a reconcile process that needs a
closed tied up with the inventory management. This definitely requires a swing in the
production rate as well as the stock and the inventory management. This level is commonly
known as the bullwhip effect. This indicates the fact that stock level variability in the supply
chain process can tend to higher in upstream rather than downstream. There has to be a
smooth transportation system so that maximum advantage can be gained in terms of
supporting the supply chain process. In the article, Elhedhli and Merrick (2012) discussed
that there can be fluctuation in the demand of the products and supply chain can never be
termed as the only reason behind this process. The demand volatility can be caused by several
external as well as internal forces. The supply chain process co-ordinates and holistically
integrates the information flow. The boom or lack of the demand of products will ultimately
generate a bull whip effect between the inbound, in house and the outbound logistics. The
mathematical design used in this research paper tried to focus on the fact that intra-
organisational supply chain produces the similar pattern as that of the bull whip effect in both
the time and the space considering a number of fundamental properties and characteristics of
bullwhip but under a single company.
In the paper, Pishvaee and Razmi (2012) discussed about the theory of swift, even
flow of productivity for the manufacturing process that falls with the increase in the
variability associated with the material flow. It should be understood that internal processes
are the own processes of a company and the most accessible areas to influence with the high
wealth of knowledge that would lead to an easy access to the real time data. As stated by
Javid and Azad (2010), demand amplification is self-induced by some internal mechanism.
There have been many studies that shows the real variability in demand from the customers

SUPPLY CHAIN DESIGN 3
and thus, demand is definitely a great issue when it comes to the manufacturing and the
supply chain. In the research paper, Farahani et al. (2014) discussed about the internal supply
chain structure that shows the interaction of the system variables by flow of materials along
with information. The material flow is represented by the rate variables while the
accumulation of the flow of materials is represented by stock variables.
In this respect, Baghalian, Rezapour and Farahani (2013) mentioned that in the car
manufacturing companies, a low aggregated data generally leads to a higher variability
compared with the high aggregated data that of a multi company context. There can be a
possibility of very low incoming standard deviation rate but that would lead to tendency of
higher amplifications. In case, internal oscillation occurs, this will definitely result from the
non-linear and the delayed interactions of the internal process of supply chain. Dash is of the
opinion that the freight forwarder should be of also integrating the process into schedule and
provide with certain collective advices.
The manufacturing companies have identified agility as a crucial factor for their
survival and competitiveness. Agility is the way of overcoming the unexpected challenges
and opportunities. In the view point of Jabbarzadeh, Fahimnia and Seuring (2014), the
manufacturing companies should have the ability to understand the demand of the market and
depending on those market opportunities, the knowledge and relationship will speed. Agility
is definitely considered as the win win situation but the supply chain process should also
align with the network and the operations of the dynamic and turbulent nature of the market.
If there is not a systematic approach taken by the manufacturing companies, this will not
allow the necessary proficiency in change. Therefore, supply chain is always depended on

SUPPLY CHAIN DESIGN 4
how agility is measured by the company. The agile formula has many benefits if the
companies apply it. Nagurney (2010) is of the opinion that agility is the win-win formula for
any company because the number of cars to be manufactured depends on the agile formula
and that would reduce the extent of loss and the manufacturing companies will always take a
toll. This also helps in responsiveness, competencies, flexibility and the quickness of the
operational activities of the manufacturing organisations.
Elaborating on the concept of the network design and supply chain and its
effectiveness as an agile process, Nagurney and Nagurney (2010) pointed out that the process
of integration is based on the foundation of the supply chain and the sensitivity of the
mechanism of the supply chain. Contrary to the concept of agile manufacturing and supply
chain, Hasani, Zegordi and Nikbakhsh (2012) said that supply chain activities can depend on
a number of other factors as well and it is not always the demand of the particular good for a
company. In the article, Sadjady and Davoudpour (2012) proposed the CSCND model with a
multi-objective possibility mixed integer linear programming to solve the supply chain model
into an effective auxiliary crisp model. Same also stated the second phase of the model, and
proposed a fuzzy method for the same. According to this possibility model, there involve
imprecise co-efficient in the process of supply chain. This method is computationally
efficient than the fuzzy model and can be conveniently used for big manufacturing units. In
order to overcome the inequality constraints, it is important to get over such supply materials
and form a symmetrical and asymmetrical form of operational activity. This method is indeed
based on a strong mathematical model and it requires an expected interval to carry out the
operational activities. In the recent approach of solving the MOLP problem, Pishvaee, Razmi
and Torabi (2014) came up with a proven analytical method with a number of aggregation
functions to propose an interactive fuzzy solution. It helps to prepare the possible distribution
for the imprecise parameters that would directly calculate the numbers for the supply chain.

SUPPLY CHAIN DESIGN 5
The closely looked supply chain mathematical model holds the responsibility of the
reverse supply chain network as well that is tactical to the flow of decision. This will reverse
the decision of the same and save the time of all the possibilities. There has to be balanced
programmatic possibility for the supply chain process that would help the process of
manufacturing, buying and selling at a profitable stage for both the manufacturer as well as
the seller. In the article, Klibi, Martel and Guitouni, (2010) highlighted the concern related to
the environmental impact of the supply chain. There are a number of fuzzy mathematical
calculations that consciously acts as the minimization of the environmental impact on the
supply chain process. There is always a pressure on the manufacturers from the consumers as
well as from the governments to remind the firms about their contribution towards the
environmental sustainability. There has been a lot of debate on the proper method of supply
chain activities that has resulted in the framing of governmental legislations and
environmentally conscious consumers. Thus, the main focus of the supply chain activities
remains on the fact that the environment has to be saved. There are a number of factors that
triggers the driving forces of supply chain. The main purpose to reduce the environmental
degradation is by reducing the path of the supply chain process. Here lies the importance of
the network design of the supply chain. Reverse supply chain is another process that can help
in the process of reducing the complexity of supply chain and the damage caused to the
environment.
As commented by Lin and Wang (2011), reverse logistic network design addresses a
number of collection, recycling, recovery and disposal centres. This needs the location and
the capacity of the material flows. The significant impact on the environment has created the
need of creating a reverse logistic. The proposed mathematical model acted as the main
objective to overcome the situation. There were certain steps mentioned to define the

SUPPLY CHAIN DESIGN 6
complexity of such models and met heuristics approach was taken to show the benefits of the
algorithms proposed.
Step 3: Conclusion and summary:

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Reference list:
Amin, S.H. and Zhang, G., 2013. A multi-objective facility location model for closed-loop
supply chain network under uncertain demand and return. Applied Mathematical
Modelling, 37(6), pp.4165-4176.
Baghalian, A., Rezapour, S. and Farahani, R.Z., 2013. Robust supply chain network design
with service level against disruptions and demand uncertainties: A real-life case. European
Journal of Operational Research, 227(1), pp.199-215.
Coskun, S., Ozgur, L., Polat, O. and Gungor, A., 2016. A model proposal for green supply
chain network design based on consumer segmentation. Journal of Cleaner Production, 110,
pp.149-157.
Elhedhli, S. and Merrick, R., 2012. Green supply chain network design to reduce carbon
emissions. Transportation Research Part D: Transport and Environment, 17(5), pp.370-379.
Farahani, R.Z., Rezapour, S., Drezner, T. and Fallah, S., 2014. Competitive supply chain
network design: An overview of classifications, models, solution techniques and
applications. Omega, 45, pp.92-118.
Govindan, K., Fattahi, M. and Keyvanshokooh, E., 2017. Supply chain network design under
uncertainty: A comprehensive review and future research directions. European Journal of
Operational Research, 263(1), pp.108-141.
Hasani, A., Zegordi, S.H. and Nikbakhsh, E., 2012. Robust closed-loop supply chain network
design for perishable goods in agile manufacturing under uncertainty. International Journal
of Production Research, 50(16), pp.4649-4669.
Jabbarzadeh, A., Fahimnia, B. and Seuring, S., 2014. Dynamic supply chain network design
for the supply of blood in disasters: A robust model with real world

SUPPLY CHAIN DESIGN 9
application. Transportation Research Part E: Logistics and Transportation Review, 70,
pp.225-244.
Javid, A.A. and Azad, N., 2010. Incorporating location, routing and inventory decisions in
supply chain network design. Transportation Research Part E: Logistics and Transportation
Review, 46(5), pp.582-597.
Klibi, W., Martel, A. and Guitouni, A., 2010. The design of robust value-creating supply
chain networks: a critical review. European Journal of Operational Research, 203(2),
pp.283-293.
Lee, J.H., Moon, I.K. and Park, J.H., 2010. Multi-level supply chain network design with
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Lin, C.C. and Wang, T.H., 2011. Build-to-order supply chain network design under supply
and demand uncertainties. Transportation Research Part B: Methodological, 45(8), pp.1162-
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Nagurney, A. and Nagurney, L.S., 2010. Sustainable supply chain network design: A
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Nagurney, A., 2010. Optimal supply chain network design and redesign at minimal total cost
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Nagurney, A., Yu, M. and Qiang, Q., 2011. Supply chain network design for critical needs
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Pishvaee, M.S. and Razmi, J., 2012. Environmental supply chain network design using multi-
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Pishvaee, M.S., Rabbani, M. and Torabi, S.A., 2011. A robust optimization approach to
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Pishvaee, M.S., Razmi, J. and Torabi, S.A., 2014. An accelerated Benders decomposition
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medical needle and syringe supply chain. Transportation Research Part E: Logistics and
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Qi, L., Shen, Z.J.M. and Snyder, L.V., 2010. The effect of supply disruptions on supply chain
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Sadjady, H. and Davoudpour, H., 2012. Two-echelon, multi-commodity supply chain
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