Importance of Simulation Models in Supply Chain Management

The importance of simulation models in supply chain management is a critical aspect of modern business operations. This assignment provides an overview of the relevance of simulation models in supply chain management, highlighting their role in data collection, analysis, and decision-making. It also reviews various studies and research papers that have explored the application of simulation models in supply chain management, including collaborative transportation management, risk assessment, and supply chain redesign for resilience.

SIMULATION GAME

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
Interesting issues observed during the simulation game........................................................1
Things that will be done differently next time.......................................................................2
What is learned during simulation game?..............................................................................3
CONCLUSION ..............................................................................................................................7
REFERENCES................................................................................................................................9

INTRODUCTION
Supply chain refers to the process that is followed in production and distribution of commodity by the business firm. There are
number of entities that are involved in the supply chain. Because multiple entities are involved there are number of issues that are
faced by the firms in the supply chain. Some common issues that are faced in the supply chain are capacity availability, lack of talent,
complexity, compliance with rules and regulations tightly and cost as well as purchasing issues. These are the some of the issues that
are faced by the firms in the supply chain. In current time period firms are using analytics and simulation model to sort out issues of
supply chain and solving business problem. In the current report simulation is run on the relevant website and experience is gained
from same. Data is generated by playing a game on that website and on that basis entire report is prepared. In the report entire lessons
that are learned will be explained in detail and apart from this things that will be done next time while running simulation model is
also explain in detail. It can be said that current report is prepared in proper manner.
Interesting issues observed during the simulation game
There were several issues that we faced during the simulation game and one of them was that when we manually enter values
in the distributor dialogue box of game automatically website compute negative value of profit. It was hard to understand and identify
the fact due to which simulation generate negative results. Usually, in simulation model when we enter value of order in distributor
dialog box order is passed to the manufacturer and the latter entity supply relevant quantity to the business firm (Tako and Robinson,
2012). These quantities are passed to the retailer and then same is passed to the customers and then money is received from same. That
money is sent to retailer and from retailer same reached to the distributor. Finally, same amount reach to the customer level. It is the
interesting fact in the simulation model. Main challenge was to identify value that need to be enter in the distributor cell so that profit
can be earned in the business. There was no information available on real time basis about the inventory that is in stock of distributor
in warehouse. Hence, it was very hard task to make business decisions in respect to making purchase of products from the
manufacturers. There were some issues that were faced during simulation process. One of these issues is that while several iterations
are done loss happened then also it is very difficult to estimate the likely amount of order that must be placed to control the situation.
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Thus, overall it can be said that main issue that we faced is that it was very difficult task to prepare risk management strategy on time
to solve the issue. There is need to resolve these issues on time because if situation will not be control on time then in that case
situation will be out of control. If cost will not be controlled on time then in that situation will be out of control and it will be difficult
to make business decisions. Hence, it can be said that there is need to make prudent decisions in the simulation model. There is need
to do some experiments while running model. Only by doing so prudent use of stock can be done and stock can be kept in control. It
can be said that it is the prediction power which can give better results in the simulation model (Yoo, Cho and Yücesan, 2010). In this
regard there is need to analyse the data time to time and accordingly decisions must be made as a distributor in the model. In this
regard, strategy can be formulated under which data that is newly generated on placement of each order can be entered. On analysis of
data one can easily identify the strategy that must be followed to control expenses or loss in the business or simulation. Means that by
following appropriate strategy in the game good amount of profit can be earned and loss can be minimized. It can be said that
formulation of strategy is the only way that can be followed to solve the problem.
Things that will be done differently next time
In order to solve the problems that we are currently facing in simulation model some different approach will be followed.
Under this first of all game will be run for 52 weeks and data on same will be generated in the business. Like real world analytics will
be used to analyse the data. Simulation is used in the real life business problems. But for simulation there must be some inputs that
must be taken in to account (Carvalho and et.al., 2012). On that basis probability of happening or non-happening of certain event can
be identified and accordingly decisions can be taken. By accessing probability relationship between different variables can be
identified and problems can be sort out. In real business world supply chain analytics is used at large scale by the business firms.
Under this supply chain related data can be analyzed and by using cluster analysis, decision tree and time series analysis as well as
market basket analysis different trends can be identified. On that basis it can be identified whether there is association between
multiple variables. On the basis of identified association with change in values of order, inventory and profit in distributor dialogue
box estimation can be made about the likely action that need to be taken to solve the problem. By using clustering method similarity in
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trends can be identified. Like it is possible that different clusters or groups can be formed under which similarity between negative
profit and orders that are in stock can be identified. On the basis of identified relationship by using clusters better decisions can be
made by the managers. There may be a large cluster where high degree of similarity can be observed between the number of inventory
that is in stock and profit. By making use of that association decisions about placement of order and in specific quantity can be taken
(Long, Lin and Sun, 2011). Thus, it can be said that there is huge importance of analytics for making prudent decisions in simulation
model. In the current time period simulation is used at large scale by the business firms. This is because by using same good decisions
are taken by the managers at the workplace. Thus, it can be said that there is huge significance of the simulation for the firms in terms
of making business decisions.
What is learned during simulation game?
Lots of things were learned during simulation game. As it can be observed that probability is widely used to identify values of
the variable during simulation model. One of the main thing that is learned during simulation game is that it is very difficult task to
make prediction about the happening of certain event especially when no data is available to make business decisions. I learned that
data analytics is the tool that can be used to run simulation in proper manner. Interrelationship can be identified among different
variables and on that basis prediction can be made about the relationship that exist among the variables (Tayur, Ganeshan and
Magazine, 2012). I also learn different techniques that can be used to analyse the data like descriptive analysis tools. Under this,
different variables excel sheet can be obtained from website and same can be analysed by using descriptive analysis tools. It must be
noted that by using descriptive statistics tools an overview of the variable can be identified. For example by using mean, median,
mode and standard deviation variable can be analysed in better manner. This reflects that there is huge significance of the descriptive
statistics for the simulation purpose. Linear regression model can be used to identify the relationship that exist between retailer and
wholesaler in terms of inventory (Zhao and et.al., 2011). Change that happened in one variable due to another can easily be identified
by using linear regression method and due to this reason it can be said that there is huge significance of these methods for the firms. I
learned that through simulation it is very difficult task to make prediction. This is because probability can be determined by an
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individual by making judgements. If judgement is wrong then in that case simulation can be done in wrong manner. Hence, it can be
said that there is huge importance of statistical tools for the business firms.
SC-1-1
Consumer Retailer Wholesaler Distributor
Week Order Inventory Shortage Order Inventory Shortage Order Inventory Shortage Order
1 9 7 0 10 6 0 10 8 2 8
2 11 0 0 15 0 5 18 0 6 24
3 9 1 0 15 0 7 20 0 12 12
4 8 3 0 0 13 0 5 7 0 10
5 7 3 0 10 11 0 5 10 0 0
6 9 0 6 10 6 0 10 10 0 10
7 10 0 0 10 2 1 10 0 0 10
8 13 0 3 14 0 2 15 0 5 15
9 9 0 0 14 2 6 10 0 0 5
10 7 5 0 5 7 0 7 8 0 3
11 6 7 0 7 10 0 5 8 0 2
12 8 4 0 8 9 0 6 5 0 6
Tota
l 106 30 9 118 66 21 121 56 25 105
4

Manufacturer
Inventory Shortage Order
8 0 8
0 12 48
0 4 0
38 0 0
38 0 0
28 0 5
18 0 5
8 0 15
8 0 5
20 0 5
23 0 0
22 0 0
211 16 91
SC-2-1
Consumer Retailer Wholesaler Distributor
Week Order Inventory Shortage Order Inventory Shortage Order Inventory Shortage Order
1 9 7 0 10 6 0 12 6 2 12
2 11 0 0 13 0 3 15 0 5 12
3 9 1 0 13 0 3 20 0 8 25
4 8 3 0 12 0 2 20 0 12 21
5 7 6 0 7 5 0 15 0 7 10
6 9 7 0 8 5 0 15 0 0 10
7 10 4 0 8 5 0 10 0 0 12
8 13 0 1 13 7 0 10 0 0 10
5

9 9 0 1 7 10 0 5 7 0 0
10 7 6 0 9 11 0 5 12 0 0
11 6 7 0 8 8 0 8 4 0 2
12 8 8 0 7 6 0 7 0 3 10
Total 106 49 2 115 63 8 142 29 37 124
Stan.
D 1.8181 3.0127
0.372
7
2.39
65 3.5385
1.178
5
5.01
39 3.8179
3.882
8
7.19
18
Manufacturer
Inventory Shortage Order
4 0 8
0 4 15
0 17 40
0 6 25
30 0 0
45 0 0
33 0 0
23 0 0
23 0 0
23 0 0
21 0 0
11 0 5
213 27 93
14.137
4.832
6
12.30
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There are varied issues that must be taken into account to sort out problem and performing simulation in better manner. In
simulation model there are lots of things that needs to be take care. By considering all factors that may affects results obtained on
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simulation model relevant results can be obtained. It can be observed that simulation model are highly complex in nature and it is
difficult to prepare it in systematic manner. It can be said that there is huge significance of the simulation model for the business firms.
During running simulation model I learn lots of things and one of them was that there is huge instability in market. As I observed that
we enter value of order as distributor in the game consistent loss was made in the business. It can be said that the main lesson that I
learned from simulation game is that market is highly volatile and demand keeps on changing consistently. It can be said that one need
to be proactive and need to take decisions on time (Bottani and Montanari, 2010). Apart from this one need to closely monitor changes
that comes in the output of game. For example, when for the first time order is placed it can be observed that what amount of change
comes in the profit amount. Then second time order will be placed and then in that case also change in profit will be observed. This
thing will be repeated two or three times more. One needs to keep in mind the scenario that is observed in case of ordered item and
each time change that comes in profit. This will help one who is playing game as distributor to keep track of changes that comes in the
variable. These things will help one in developing overall overview of the simulation game. On the basis of identified trends one can
easily identify the steps that it needs to take to improve its performance or obtaining better results in simulation game (Chan and
Zhang, 2011). Thus, it can be said that main thing that I learned from this game is that keeping track record of scenarios that originate
on placement of each order is the only key to transit from loss to profit.
But problem does not come to end here. This is because one cannot estimate better decisions only by considering changes in
scenarios. Every individual cannot keep changes in variables in mind and cannot use it as meaningful information for making business
decisions. The main thing that I learn is that in order to solve this problem some techniques can be used by using which data will be
recorded and by analysing same better decisions can be made. In this regard, some systematic approach can be followed in my opinion
first of all data must be gathered in relation to profit and number of orders placed (Ramanathan, 2014). Thereafter, by applying
regression method relationship can be identified between profit and number of orders in terms of percentage change that comes in
profit due to change in number of orders. Along with this, correlation can be identified by using regression method and under this
degree of relationship can be identified between profit and number of orders. Apart from this big variation comes in profit or not with
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change in stock can be identified by using regression analysis. It can be said that by identifying association between both variables
prediction can be made that in stock if one unit change is made what amount of change can come in earned profit amount.
This strategy will provide lots of inputs for making prudent decisions. The assumption that is made from my side can be
verified from the fact that in real practical world firms are using simulation model and supply chain analytics to make business
decisions. Data clustering is another approach that can be used to make decisions. Under this, different clusters can be formed and it
can be identified whether data points of order placed and earned profit are in proximity to each other (Azadeh and Alem, 2010). If
there is proximity then it can be said that equal change comes in profit and order placed when simulation is run. Through clustering
method grouping of variables is done that have similarity with each other. Thus, it can be said that clustering speak a lot about the data
and by using same better decision can be made by the individual. There are lots of many techniques that can be used to understand the
data and making business decisions. It is very important to use these methods because same help one in identifying multiple factors
that can be taken in to consideration to make order placement related decisions (Simulation models, 2017). In current time period
analytic solutions are used at large scale by the business firms and this help them to optimize their business operations. This lead to
decline in cost and elevation of profit in the business. It can be said that there is huge significance of use of analytical tools in the
simulation techniques. It can be said that firms that are operating in supply chain must use analytical tools to make business decisions.
Thus, support of output that are generated by statistical methods can be used to run simulation in better manner.
CONCLUSION
On the basis of above discussion it is concluded that there is significant importance of simulation model for the business firms.
This is because by using same then can make better decisions. There are lots of things that one need to take care while doing a
simulation and by considering all these things simulation can be performed in better manner. While playing simulation game it is
difficult task to analyse trends deeply and in order to sort out this problem. This can be done by using statistical tools like regression
analysis, cluster analysis and decision tree. By using these tools different trends can be identified and by using same simulation can be
performed in proper manner. It can be concluded that there is significant importance of the simulation model for the entities that are
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operating in supply chain like distributor, retailer and other players. By making use of statistical tools relationship can be identified
between variables and by considering relevant output decisions can be taken in respect to simulation model. It can be said that
simulation model can be performed in better manner by using output that is generated by statistical tool. Thus, there is wide
application of statistical tool in respect to simulation. Firms must first collect data and then must analyse them by using statistical tool.
The output that is generated by using statistical tool can be used to make decision in respect to value that must be input in simulation
model. It can be said that there is huge significance of simulation model for players operating in supply chain.
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REFERENCES
Books and journals
Azadeh, A. and Alem, S.M., 2010. A flexible deterministic, stochastic and fuzzy Data Envelopment Analysis approach for supply
chain risk and vendor selection problem: Simulation analysis. Expert Systems with Applications. 37(12). pp.7438-7448.
Bottani, E. and Montanari, R., 2010. Supply chain design and cost analysis through simulation. International Journal of Production
Research. 48(10). pp.2859-2886.
Carvalho, H..and et.al., 2012. Supply chain redesign for resilience using simulation. Computers & Industrial Engineering, 62(1),
pp.329-341.
Chan, F.T. and Zhang, T., 2011. The impact of Collaborative Transportation Management on supply chain performance: A simulation
approach. Expert Systems with Applications. 38(3). pp.2319-2329.
Jacxsens, L. and et.al., 2010. Simulation modelling and risk assessment as tools to identify the impact of climate change on
microbiological food safety–The case study of fresh produce supply chain. Food Research International. 43(7). pp.1925-1935.
Long, Q., Lin, J. and Sun, Z., 2011. Modeling and distributed simulation of supply chain with a multi-agent platform. The
International Journal of Advanced Manufacturing Technology. 55(9). pp.1241-1252.
Ramanathan, U., 2014. Performance of supply chain collaboration–A simulation study. Expert Systems with Applications. 41(1).
pp.210-220.
Tako, A.A. and Robinson, S., 2012. The application of discrete event simulation and system dynamics in the logistics and supply
chain context. Decision support systems. 52(4). pp.802-815.
Tayur, S., Ganeshan, R. and Magazine, M., 2012. Quantitative models for supply chain management (Vol. 17). Springer Science &
Business Media.
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Thierry, C., Bel, G. and Thomas, A., 2010. The role of modeling and simulation in supply chain management. SCS M&S
Magazine. 1(4). pp.1-8.
Yoo, T., Cho, H. and Yücesan, E., 2010. Hybrid algorithm for discrete event simulation based supply chain optimization. Expert
Systems with Applications. 37(3). pp.2354-2361.
Zhao, C.C. and et.al., 2011. Agent-based modeling and simulation on multi-stage supply chain operation. In Advanced Materials
Research. Trans Tech Publications.
Online
Simulation models, 2017. [Online]. Available through :< http://www.solver.com/simulation-models>. [Accessed on 26th June 2017].
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