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DECISION
SUPPORT
SYSTEMS
PRESENTATION ON
NINTENDO GAMING
COMPANY
INTRODUCTION
PROBLEM
DSS TECHNIQUES
IMPLEMENTATION
CONCLUSION
REFERENCES
INTRODUCTION
 Nintendo Co.Ltd., one of the leading producers of video games in the
world, is facing severe competition from other competitors like Sony and
Microsoft.
 The game cube is the latest product released by Nintendo but this
product failed to make a mark in the market. The company is planning to
release a new product in order to regain its market share.
 This case mainly deals with the decision support system tools to analyse
the new product pricing based on the manufacturing costs and whether
Nintendo’s new product can sustain the success with new improvements
in the technology.
PROBLEM
 The game cube is the latest product released by Nintendo
but this product failed to make a mark in the market
 This case mainly deals with the decision support system
tools to analyse the new product pricing based on the
manufacturing costs and whether Nintendo’s new
product can sustain the success with new improvements
in the technology.
 Nintendo is the leading producers of video games in the
world
 Here success refers to customer satisfaction and profit
maximization
DSS TECHNIQUES
 Solver sample
 Decision tree
DSS is divided in to three fundamental components.
They are:
 Data base:
Data base is also a knowledge base where the data
is stored and retrieved when ever required
 Model:
It is the decision context and the criteria of the user
Which is represented in mathematical form or a model
 User Interface:
User is the important component and is the
communication between user and the machine
Decision Tree
 “A schematic tree-shaped diagram used to determine a course of action or
show a statistical probability. Each branch of the decision tree represents a
possible decision or occurrence. The tree structure shows how one choice
leads to the next, and the use of branches indicates that each option is
mutually exclusive”.
Decision trees provide an effective method of Decision Making because
they:
 Clearly lay out the problem so that all options can be challenged.
 Allow us to analyse fully the possible consequences of a decision.
 Provide a framework to quantify the values of outcomes and the
probabilities of achieving them.
 Help us to make the best decisions on the basis of existing information
and best guesses.
SOLVER IMPLEMENTATION
 Implementation of interactive applications of a
computer to organize and analyse the programs and it
has a built in tool called Solver
 Several other features namely goal seeking, What-if
analysis and data management.
 Most widely used and distributed and uses a general
optimization modelling system
What is a mathematical model?
 A Mathematical model is a system that represents a real
world situation in the form of symbols and expressions.
The mathematical model consists of different major
components such as
 Decision variables
 Functions
 Result variables
Implementation
 Now that the problem is clear and the reason for the problem, we
need to use the decision support systems and one’s own
intelligence to solve the problem.
 Now the problem, its cause, our goal and the relevant information
is in hand, the intelligence phase is completed and our next phase
is design phase.
 In the design phase, one should work on the models which can be
used to solve this problem. Generally the standard categories are
as follows
1. Optimization
2. Simulation
3. Heuristic models
4. Other models-What if analysis and Goal Seeking
 The best decision variables in this case that can be considered are as
follows
1. Heavy Users
2. Moderate Users
3. Normal Users
 Heavy Users: Heavy Users means are the people who made video
games as a part of their lives. According to the studies approximately
75% of Heavy Users are come under 16-30 age group such as
teenagers, youth and young adults. These Heavy Users require
maximum and best features such as processor speed, graphics,
storage, motion sensor, 3D etc. Gaming Company’s does generally keep
their focus on heavy users.
 Moderate Users: Moderate Users are the people who are interested in
console gaming but not addicted to these games. According to the
studies most of the Moderate Users come under age group of 10-16
and 45-60 such as children and adults. Moderate Users also want to
have good features but not best.
 Normal Users: Normal Users are the people who are in to these games
occasionally. According to the studies Adults are generally divided in
to Moderate and Normal users. Normal Users have more specification
about the cost and then the specifications and special features.
DECISION TREE
SOLVER
The decision variables used for maximizing the profit in
this case are as follows
 i. Variable 1=x1 will represent heavy gamers
 ii. Variable 2=x2 will represent moderate gamers
 iii. Variable 3=x3 will represent normal gamers
 The previous decision tree analysis provides the details
about the customer satisfaction.
 To analyse the case of profit maximization of a new
product of the Nintendo co.ltd some sample data is
being used according to the literature survey conducted
according to this case.
 The constraints equations necessary for the solver are as follows
• MOTION SENSOR CONTROLLERS:2(X1)+X2+X3<= total No of
individual constraints manufactured
• PROCESSOR : X1+X2+X3<= total No of individual constraints
manufactured
• GRAPHICS : X1+X2<= total No of individual constraints manufactured
• STORAGE : 4(X1)+2(X2)+X3<= total No of individual constraints
manufactured
• ENTERTAINMENT FEATURES : X1+X2+2(X3)<= total No of individual
constraints
manufactured
• ADD ON’S : X1<= total No of individual constraints manufactured
The total number of individual constraints, is obtained by:
 (The budget allocated to the constraint) divided by (The constraint
manufacturing cost)
SOLVER USING FIRST SET DATA
SOLVER USING SECOND SET OF DATA
CONCLUSION
 Depending on the information provided by the decision tree, an excel sheet has been
developed to maximize the profit using solver and to analyse the variation of profit
according to the budget distribution which eventually helps the organization to be
flexible about the selling price.
 Profit is calculated according to the equation:
 ∑ ((selling price-manufacture price) of a particular type)*(number of manufactured
consoles in that type)
 According to the two sets of data used in the solver, the sample budget allotted to the
constraints when allotted in the ratio of the manufacturing price showed a better profit
and production rate.
 The main disadvantage or the defect with this solution is that this data mining
technique doesn’t involve how my actual customers or gamers are present in the
market because of this sometimes to be number one in the market the maximum
category of gamers covers the market and the more profitable category of gamers w.r.t
the organization must be given priority which was not included in this solution.
 Other than the above defect the decision tree and solver sample will help the Nintendo
organization in all the possible ways to reclaim their 1st position in the market with
this new product.
ASSUMPTIONS
1) All satisfied customers will be purchasing the gaming
consoles of the company.
2) The company produces the data storage slots of 16 GB
each.
3) Heavy gamers require 64 GB storage, moderate gamers
requires 32 GB storage and 16 GB storage.
4) The total budget used in the case.
5) The manufacturing price of the constraints.
6) Multiple entertainment features mean 2 features.
7) Multiple controllers mean 2 controllers.
REFERENCES
 Nintendo APC Action Plan, VIEWED ON 08-05-2013,<
http://www.nintendo.com.au/pdfs/Nintendo_APC_Action_Plan.pdf>
GameCube, wiki, VIEWED ON 06-05-2013,<
https://en.wikipedia.org/wiki/GameCube
 TURBAN,SHARDA AND DELEN, DECISION SUPPORT SYSTEMS , CHAP-2.
Wii vs GameCube which is better, features, VIEWED ON 06-05-2013,<
http://www.officialnintendomagazine.co.uk/48229/features/vs-mode-wii-
vs-gamecube-which-is-better/>
 Video game industry statistics, about, VIEWED ON 05-05-2013,<
http://www.esrb.org/about/video-game-industry-statistics.jsp>
 Decision tree, wiki, VIEWED ON 05-05-2013,<
http://en.wikipedia.org/wiki/Decision_tree>
 MICHAEL MCWHERTOR, Nintendo sold over 15 million, VIEWED ON 09-
05-2013,< http://kotaku.com/5415661/nintendo-sold-over-15-million-
wiis-dss-last-week>
 Wii Costs Roughly 88 to Manufacture Analysts, news-multimedia, VIEWED
ON 07-05-2013,<
http://www.xbitlabs.com/news/multimedia/display/20090407124321_Ni
ntendo_Wii_Costs_Roughly_88_to_Manufacture__Analysts.html>
BY
DharmaTeja Chintala(17544632)
M S R Koushik Yanamandra(17514424)
Deepak Kataru( 17484255)

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DECISION SUPPORT SYSTEMS

  • 3. INTRODUCTION  Nintendo Co.Ltd., one of the leading producers of video games in the world, is facing severe competition from other competitors like Sony and Microsoft.  The game cube is the latest product released by Nintendo but this product failed to make a mark in the market. The company is planning to release a new product in order to regain its market share.  This case mainly deals with the decision support system tools to analyse the new product pricing based on the manufacturing costs and whether Nintendo’s new product can sustain the success with new improvements in the technology.
  • 4. PROBLEM  The game cube is the latest product released by Nintendo but this product failed to make a mark in the market  This case mainly deals with the decision support system tools to analyse the new product pricing based on the manufacturing costs and whether Nintendo’s new product can sustain the success with new improvements in the technology.  Nintendo is the leading producers of video games in the world  Here success refers to customer satisfaction and profit maximization
  • 5. DSS TECHNIQUES  Solver sample  Decision tree
  • 6. DSS is divided in to three fundamental components. They are:  Data base: Data base is also a knowledge base where the data is stored and retrieved when ever required  Model: It is the decision context and the criteria of the user Which is represented in mathematical form or a model  User Interface: User is the important component and is the communication between user and the machine
  • 7. Decision Tree  “A schematic tree-shaped diagram used to determine a course of action or show a statistical probability. Each branch of the decision tree represents a possible decision or occurrence. The tree structure shows how one choice leads to the next, and the use of branches indicates that each option is mutually exclusive”. Decision trees provide an effective method of Decision Making because they:  Clearly lay out the problem so that all options can be challenged.  Allow us to analyse fully the possible consequences of a decision.  Provide a framework to quantify the values of outcomes and the probabilities of achieving them.  Help us to make the best decisions on the basis of existing information and best guesses.
  • 8. SOLVER IMPLEMENTATION  Implementation of interactive applications of a computer to organize and analyse the programs and it has a built in tool called Solver  Several other features namely goal seeking, What-if analysis and data management.  Most widely used and distributed and uses a general optimization modelling system
  • 9. What is a mathematical model?  A Mathematical model is a system that represents a real world situation in the form of symbols and expressions. The mathematical model consists of different major components such as  Decision variables  Functions  Result variables
  • 10. Implementation  Now that the problem is clear and the reason for the problem, we need to use the decision support systems and one’s own intelligence to solve the problem.  Now the problem, its cause, our goal and the relevant information is in hand, the intelligence phase is completed and our next phase is design phase.  In the design phase, one should work on the models which can be used to solve this problem. Generally the standard categories are as follows 1. Optimization 2. Simulation 3. Heuristic models 4. Other models-What if analysis and Goal Seeking
  • 11.  The best decision variables in this case that can be considered are as follows 1. Heavy Users 2. Moderate Users 3. Normal Users
  • 12.  Heavy Users: Heavy Users means are the people who made video games as a part of their lives. According to the studies approximately 75% of Heavy Users are come under 16-30 age group such as teenagers, youth and young adults. These Heavy Users require maximum and best features such as processor speed, graphics, storage, motion sensor, 3D etc. Gaming Company’s does generally keep their focus on heavy users.  Moderate Users: Moderate Users are the people who are interested in console gaming but not addicted to these games. According to the studies most of the Moderate Users come under age group of 10-16 and 45-60 such as children and adults. Moderate Users also want to have good features but not best.  Normal Users: Normal Users are the people who are in to these games occasionally. According to the studies Adults are generally divided in to Moderate and Normal users. Normal Users have more specification about the cost and then the specifications and special features.
  • 14.
  • 15. SOLVER The decision variables used for maximizing the profit in this case are as follows  i. Variable 1=x1 will represent heavy gamers  ii. Variable 2=x2 will represent moderate gamers  iii. Variable 3=x3 will represent normal gamers  The previous decision tree analysis provides the details about the customer satisfaction.  To analyse the case of profit maximization of a new product of the Nintendo co.ltd some sample data is being used according to the literature survey conducted according to this case.
  • 16.  The constraints equations necessary for the solver are as follows • MOTION SENSOR CONTROLLERS:2(X1)+X2+X3<= total No of individual constraints manufactured • PROCESSOR : X1+X2+X3<= total No of individual constraints manufactured • GRAPHICS : X1+X2<= total No of individual constraints manufactured • STORAGE : 4(X1)+2(X2)+X3<= total No of individual constraints manufactured • ENTERTAINMENT FEATURES : X1+X2+2(X3)<= total No of individual constraints manufactured • ADD ON’S : X1<= total No of individual constraints manufactured The total number of individual constraints, is obtained by:  (The budget allocated to the constraint) divided by (The constraint manufacturing cost)
  • 17. SOLVER USING FIRST SET DATA
  • 18. SOLVER USING SECOND SET OF DATA
  • 19. CONCLUSION  Depending on the information provided by the decision tree, an excel sheet has been developed to maximize the profit using solver and to analyse the variation of profit according to the budget distribution which eventually helps the organization to be flexible about the selling price.  Profit is calculated according to the equation:  ∑ ((selling price-manufacture price) of a particular type)*(number of manufactured consoles in that type)  According to the two sets of data used in the solver, the sample budget allotted to the constraints when allotted in the ratio of the manufacturing price showed a better profit and production rate.  The main disadvantage or the defect with this solution is that this data mining technique doesn’t involve how my actual customers or gamers are present in the market because of this sometimes to be number one in the market the maximum category of gamers covers the market and the more profitable category of gamers w.r.t the organization must be given priority which was not included in this solution.  Other than the above defect the decision tree and solver sample will help the Nintendo organization in all the possible ways to reclaim their 1st position in the market with this new product.
  • 20. ASSUMPTIONS 1) All satisfied customers will be purchasing the gaming consoles of the company. 2) The company produces the data storage slots of 16 GB each. 3) Heavy gamers require 64 GB storage, moderate gamers requires 32 GB storage and 16 GB storage. 4) The total budget used in the case. 5) The manufacturing price of the constraints. 6) Multiple entertainment features mean 2 features. 7) Multiple controllers mean 2 controllers.
  • 21. REFERENCES  Nintendo APC Action Plan, VIEWED ON 08-05-2013,< http://www.nintendo.com.au/pdfs/Nintendo_APC_Action_Plan.pdf> GameCube, wiki, VIEWED ON 06-05-2013,< https://en.wikipedia.org/wiki/GameCube  TURBAN,SHARDA AND DELEN, DECISION SUPPORT SYSTEMS , CHAP-2. Wii vs GameCube which is better, features, VIEWED ON 06-05-2013,< http://www.officialnintendomagazine.co.uk/48229/features/vs-mode-wii- vs-gamecube-which-is-better/>  Video game industry statistics, about, VIEWED ON 05-05-2013,< http://www.esrb.org/about/video-game-industry-statistics.jsp>  Decision tree, wiki, VIEWED ON 05-05-2013,< http://en.wikipedia.org/wiki/Decision_tree>  MICHAEL MCWHERTOR, Nintendo sold over 15 million, VIEWED ON 09- 05-2013,< http://kotaku.com/5415661/nintendo-sold-over-15-million- wiis-dss-last-week>  Wii Costs Roughly 88 to Manufacture Analysts, news-multimedia, VIEWED ON 07-05-2013,< http://www.xbitlabs.com/news/multimedia/display/20090407124321_Ni ntendo_Wii_Costs_Roughly_88_to_Manufacture__Analysts.html>
  • 22. BY DharmaTeja Chintala(17544632) M S R Koushik Yanamandra(17514424) Deepak Kataru( 17484255)